CA2773220A1 - Providing a classification suggestion for concepts - Google Patents
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
- G06F16/287—Visualization; Browsing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/332—Query formulation
- G06F16/3322—Query formulation using system suggestions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
- G06F16/334—Query execution
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
- G06F16/353—Clustering; Classification into predefined classes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
- G06F16/355—Class or cluster creation or modification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
- G06F16/358—Browsing; Visualisation therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/93—Document management systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/954—Navigation, e.g. using categorised browsing
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
Abstract
A system (11) and method (40) for providing a classification (82) suggestion for concepts (13) is provided. A corpus of concepts (13) including reference concepts (14d) each associated with a classification (82) and uncoded concepts (14c) are maintained. A cluster (93) of uncoded concepts (14c) and reference concepts (14d) is provided. A neighborhood (70) of reference concepts (14c) in the cluster (93) is determined for at least one of the uncoded concepts (14c). A classification (82) of the neighborhood (70) is determined using a classifier. The classification (82) of the neighborhood is suggested as a classification for the at least one uncoded concept (14c).
Description
PROVIDING A CLASSIFICATION SUGGESTION FOR CONCEPTS
Il'-CI"lC.iI;lEl_:l) ---------------------------------------------------This application relates in general to irnforrilaation classi.ficatiou, in pa:rticula:r_ to a s\ stern and method for providing a classification sug~.jestaon for concepts.
'BACKGROUND ART
Historically, document review during the discovery phase of litigation and for other types of legal matters, such as due diligence and regulatory compliance, have been conducted n anually. During; document review. individual re ie ers, generally licensed attorneys, are typically assigned sets of documents for coding. A reviewer must c rre~fully study each document and categorize the document by assigning a code or other marker from a set of descriptive classifications, such as "privileged," "responsive." and "non responsive." The classifications can affect the disposition of each document, including admraissibill ty .into evidence. As well, during, discovery, document revie\.v can potentially affect the outcome of the legal underlying matter, and consistent and accurate results are crucial.
M nual document review is tedious and time-consuming. larking documents is performed at the sole discretion of each reviewer and inconsistent results can occur due to misunderstanding, time pressures. fatigue, or other factors. A large volume of documents reviewved,, often with only limited time, can create a loss of mental focus and a loss of purpose for the resultant classification. Each new reviewer also faces a steep learning curve to become familiar with the legal matter, coding categories., and review techniques.
Currently, with the increasingly widespread movement to electronically stored x? information (ES1), manual document review is becoming impracticable and outmoded. The often exponential growth of E S1. can exceed the Ixm ds reasonable ..for conventional .r manual human review and the sheer scale c .l tel:Ã:arty~ ESI review Underscores the need for computer-assisted ESI review tools.
Conventional ESI review tools have proven inadequate for providing efficient, accurate.
.
and consistent results. For example, DiscoverReady LU', a Delaware limited liabilitx company..
conducts semi-auton a:ted document review: ~ through multiple passe: over a document set in E S1.
form. During the first pass, documents are grouped by categoÃs and basic codes are assigned.
Subsec uent. I gasses refine farad aa:sig .f`urther earcc}diar s. Multiple pass ESI review also requires ra Priori project-specific knowledge engineering.. which is generally applicable to only a single ~l~
project, thereby losing the benefit of any inferred knoiyle.c e or experiential knot.-how for use in other review projects.
Thus. there remains a need for a system and method for increasing the efficiency of document reviews by providing classilicati.on suggestions based on reference, documents while > ultimately ensuring independent reviewer discretion..
DISCLOSURE OF THE INVENTION
Document rev ew elticiencv can be increased by identifying relationships between reference conccpts, which are concepts that have l eeia aass.i vied classification codes.. and wicoded concepts aid providing a suggestion for classification based on the classification relationships.
Uncoded concepts are foriried into conceptual clusters. The encoded concepts for a cluster are compared to a set of reference concepts. Those reference concepts most similar to the uncoded concepts are identified based on, for instance, semantic similarity and are used to form a classification suggestion. The classification suggestion can be provided with a confidence level that reflects the aanmou nt of similarity between the wicoded concepts and reference concepts in the nei~ hho:rhood. The classification suggestion. can then he accepted..
rejectedõ or ignored by a reviewer.
One embodiment provides a system and method for providing a classification suggestion for concepts. A corpus of electronically stored information includin ;
reference concepts each associated with ,a classification and uncoded concepts are maintained, A
cluster of encoded concepts and reference concepts is provided, A neighborhood of reference concepts in the cluster is determined for at least one of the iaracoded concepts. A
classification of the neighborhood is determined using a classifier. The classification of the neighborhood is suggested as a classification for the at least one uncoded concept..
Still other embodii-rients of the present invention will become readily apparent to those skilled in the art from the following detailed description, wherein are described embodiments by way of illustrating the best mode contemplated for carning out the invention.
As will be realized, the invention is capable of other and different embodiments and its several details are capable of modifications in r various obvious respects, all without departing from the spirit and the scope of the present invention. .Accordingly, the drawings acid detailed description are to be Ott re4carded as illustrative in nature and not as restrictive, DESCRIPTION OF TTIE DRAWINGS
FIGURE 1 is a block diagram showing a system f% r providing reference concepts as a suggestion for uncoded concepts, i.n accordance with one embodiment.
FIGURE 2 is a process flow diagram showing a method for providing a classification suggestion for uncoded concepts, in accordance k ith one embodiment, FIGURE 3 is a process floNv diagram showing a method for- providing a confidence level for a classification suoyestion for use in the method of FIGURE 'I.
> FIGURE 4 is a process flow diagram showing a. method for accepting orrqjecting a, classitical rrr slr~ #~estiori for use in the triefliod of FIGURE 2.
FIGURE 5 is a block diagram showing, by way of example. ways to generate a neighborhood of reference concepts for a clustered uncoded concept.to.r use in the method of FIGURE 2.
FIGURE 6 is a block diagram shr wwwing, 1w way of example, classifier routines for suggesting l classification for an 1#ncoded concept.th.r use in the method of FIGURE 2.
FIGURE 7 is a screenshot showing, by way of example, a visual display of reference concepts in relation to urncoded concepts.
RE 8 is a block diagram sho% ing, by %vay of example, a cluster with a combination of classified reference concepts- encoded concepts, and concepts given a.
classification.
FIGURE 9 is a table shownõ. by a ay of example. a matrix mapping of uncoded concepts and doculr eats.
BEST MODE FOR CARRYING OUT THE INVENTION, The ever.'-increasitng volume of ESI underlies the need for autonmating document review for improved consistency and throughput. Token clustering via injection utilizes reference, or previously classified tokens, which offer knowledge gle47.ned.from earlier i-ork in similar legal projects, as well as a reference point for classifying unc ded tokens, The tokens can inelude wwword-level. symbol-level, or character-lei el r1-grtlnrs, raw terms.
entities, or concepts. Other tokens, including, other atonic parse-level elements, are possible.
An n-gram is a. predetermined number of items selected from a. source. The items can include syllables, letters, or words- as well as other items. A raw term is a term that has not been processed or manipulated. Entities further refine nouns and noun phrases into people, places, and things, such as meetings, animals, relationships, and various other objects, Additionally, entities can represent other parts of gram.i-nar associ t:ted with semantic meanings to disambiguate 3{? different instances or occurrences of the Tammar'. Entities c in be extracted using entity extraction techniques kno\\i in the field.
Concepts" are Collections of nouns acid noun-phrases with common semantic meaning that can be extracted from ESI, including documents, through part-of-speech tagging, Each concept can represent one or more documents to be classified during a review.
Clustering of the concepts provides an. overall view of the document space. which allows users to easily ideaÃi.f : documents sharing a common theirte.
The clustering of tokens, for exrnxple, concepts, differs from document clustering, vvlrich groups related documents individually. In contrast. concept clustering groups related concepts., > which are, each representative of one or more related documents, Each concept can express an ideas or topic that n-nav~ .no be expressed by individual docun`tents. .A
concept rs analogous to a search query by identifying: documents associated with a particular idea or topic, A user can determine how particular concepts are related based on the concept clustering.
Further, users are able to intuittv'el identify documents by selecting one or more associated concepts in a cluster. For e.xanrple, a user n rav> wish to identify all documents :in r. particular corpus that are related to car manufacturing. The user can select the concept -'car, manufacturing' or "vehicle manufacture- within one of the clusters and subsequently, the associated documents are presented, l lowever, during document clustering, a user is first required to select a specific document from which other documents that are similarly related can then be identified.
Releren.ce tokens are previously classified based on the document content represented b that token and can be :injected :into clusters of uncoded, that is unclassified. tokens to :influence classification of the uncoded tokens. Specifically, relationships between an encoded token and the reference tokens, in terms of semantic similarity or distinction, can be used as are aid in providing sir Tgestions for classifying uncoded tokens. Once: classified, the newly-coded. or reference, tokens can be used to further classify the represented docuÃnents.
Although tokens., such as word-level or character-level n- :r{r.nrrr., rave terrIrs_ erit..ities, or concept s, can be clustered and displayed, the discussion below vvill focus on a concept as a particular token.
End-to end ESI review requires a computerized support environment within which classification can be performed. FIGURE I is a block diagram showing a system.
10 for providing reference concepts as a suggestion for uncoded concepts, in accordance with one embodiment. By way of illustration, the system 10 operates in a distributed computing environment, which includes a plurality, of heterogeneous sv:sten-is and iST
sources. Henceforth, a single item of ESI will be referenced as a "document,' although fiST can include other forms of non-do. currientt data, as described r frc . A backend server l l is coupled to a storage device 13.
which stores documents 144 in the form. of structured or unstructured data, a database 30 for maintaining information about the documents, and a. look up database 37 for storing rntny-to-many mappings 38 between documents and document features, such as concepts, and a concept document index 40, which maps documents to concepts. The storage device 13 also stores reference documents .4b, which provide a training set of trusted and Ionotn results for use in guiding ESI classification. The reference documents I4b can be hand-selected or automatically determined, Additionally. the set of reference documents c an be predetermined or can be generated dynamically- as the selected encoded docu-meats are classified and subsequently added > to the set of reference documents. Further, the backend server 11 can store uncoded concepts 14c ,and reference concepts I4d. Concepts are collections of nouns and noun phrases with conxaxnonn semantic meaning The not aas and noun--phrases can be extracted from one or more documents ina the corpus for review. Hereinafter, the terms `'classified." and `'coded' are Used interchangeably with the same intended meanitng, unless otherwise indicated.
The backend server 1 1 is coupled to an inntranetAv ork 21 and executes a workbench software suite 31 for providinu a user inEert aces framework for automated document m ana e,nment, processing, analysis, and cla: ;i fication. In a further embodiment, the backend server I I can be accessed via arl internet: wwork 22. The workbench suite 3I
includes a document mapper 32 that includes a clustering engine 33., sine. la ity searcher 34., classifier 35. and display ~ enera:Eor : 6. Other workbench suite anno?daales are possible.
The clustering engine 33 performs efficient concept scoring and clustering of uncoiled conccepts. such as described in common ly>-assigned U. S. Patent No. 7,610.3 3. U.S. Patent Application Serial \o. 12/844,810.. entitled Syste xn and Method for D1 spla-, Relationships Between Concepts to Provide Classification Suggestions via Inclusion."' filed July 27, 2110, pending, U.S. Patent Application Serial No. 12/ 844,792, entitled "Systenn and Method tor Displaying Relationships Between Concepts to Provide Classification Suggestions via laajection,"
filed July 2.7, 2010, pending. and t_U.5 Patent .App.lic ation serial No.
112/844,813., entitle d "System and Method for Displaying; Relationships Between Concepts to Provide Classification Suggestions via. Nearest Neighbor.." filed July 27, 0, pending, the disclosures of which is incorporated by reference.
Briefly. cl esters of uncoded concepts I 4c are l ornned and can be organized along vectors.
know n as spisnes, based oia to siarnilaarit of tine clusters, The similarity can be expressed in terms of distance. The uncoiled concepts 14c are Identified from a corpus of encoded documents for a.
document review project. In a further embodinment, the cluster set of encoded concepts can be predetermined based on a related document review project..
The similarity searcher 34 identities the reference concepts Wd that are, similar to selected uncoded concepts l4c, clusters, or spines. The classifier 35 provides a ntachine-generated suggestion and confidence level for classification of the selected encoded concepts 14c. clti<sters, or spines.- as further described below beginning with reference to F1 URE 2. The -display `venerator 36 arranges the clusters and spines in thematic or conceptual relationships in a tai o-dimensional visual displa space. Once generated,, the visual display space is transil-Ii tied to a to ork client 12 by the backend server I I via the doctrnlent mapper 32 for presenting to a rev iev.er. The revie\aer can include an individual person Nvh.o is assigned to review- and classify the concepts 14c by designating a code. Hereinafter, unless otherwise indicated, the terms "reviewer" and "custodian" are t; ed interchangeable i ith the same intended meat1ing. Other types of reviewers are possible, including machine>-implemented reviewers.
The documen mapper 32 operates on encoded concepts 14c.. , hich can he retrieved from the storage 13_ as well as a plurality of local and remote sources. The local and remote: sources can also store the reference concepts 14d., as we l as the encoded documents 14a and reference documents 14h. The local sources include aloc_urnettts and concepts 17 mai.ntai:ned.in a storage device .16 coupled to a local server 15 and documents 20 maintained in a storage device .19 coupled to a local client 18. The local server 15 and local client 18 are interconnected to the backend server 1.1 and the work client 1.2 over the intranetwork 2:1. In addition, the document mapper 32 can ident:if and retrieve documents frown .remote sources over he intemetA ork 22 _ including the Internet, through a gateway 23 interfaced to the intraneM- ork 21. =T he remote sources include documents 26 maintained in a storage device 25 coupled to a remote server 24a and documents 1-9 maintained in a storage device 28 coupled to a. remote client 24b. Other document and concept sources, either local or remote. are possible.
The ind.k idual documents 14a, 14h, 17.20- 1-6, 29 include all fore -ms and types of structured and unstructured 81 including electronic message stores, word processing documents, electronic mail (email) folders, Web pages.. and graphical or multimedia data.
Not,, itlrstanding. the documents could be in the form of structurally organized data, such as stored in spreadsheets or databases.
In one embodimenÃ, the individual documents 14a, 14b, IT, 2Ø. 26.. 29 can include electronic message folders storing email and attachments, such as maintained h the Outlook and Outlook Express products, licensed by Microsoft Corporation, Redmond, WA. The database can be on SAIL.-based relational database, such as the Oracle database managen-rent systen-r, Release 8n licensed by Oracle Corporation, Redwood Shores, CA.
Additionally, the individual documents 17. 'tl, 26, 29 include encoded documents, reference documents, and previously encoded documents that have been assigned a classification code. The number of uncoded documents may be too large for processing in a single pass.
Typically,- a subset of uncoded documents are selected for a document review assignment and stored as a doctanacrat corpus, which can also incl tide one or more reference documents, as discussed thf-a.
Moreover, the individual concepts 14c., l 4d, 17, 20.26.29 include encoded concepts and reference concepts. The encoded concepts, which are tanclassified, represent collections of nouns and noun-phrases that are semantically related and extracted from doctanien s in a document review project.
The reference concepts are initially encoded concepts that can be selected from the corpus or other source, of'uncoded concepts acrd stibseclaaently classified. \
'hen convbined w:ith uncoded concepts, such as described in conantonlv~assi~ttecl U. S, Patent Application Serial No.
1'21"844,810, entitled "Syste -a and Method for Displaying Relationships Between C OrICCpts to Provide Classification Suggestions vialnc.laasion..".filed July 27 2010, pending- C.S Patent Application. Serial No. 121844,792, entitled " Sy stein and Method for Displaying Relationships 13etvveen Concepts to Provide Classification Suggestions via Injection," filed July 27, 2010, pending.. and I J& Patent Application Serial No. 1/44.813, entitled ` SysÃem and Method for Displaying Relationships Between Concepts to Provide Classification Suggestions via Nearest Nei<ggh.bor.-' filed July 27, 2010, pending, the disclosures of t hich are incorporated b reference, the reference concepts can provide suggestions b r classification of the remaining tancoded concepts in the corpus based on visual relationships between the reference concepts and ancoded concepts, The reviewer can classify, one or more of the uncoded concepts by assigning a code to each concept, representing a classification, based on the suggestions. if desired. The suggestions can also be cased for c tlaer pcirfac ses,, such as elualià control. Concepts f. iven a ciaissificationl code by the :reviewer are then stored. .Additio.7 ally, the now-coded concepts can be used as reference concepts in related document review assignments. The assignment is completed once all uncoded concepts in the assignment have been assigned a classification code.
In a l irtlYer embodiment., the reference concepts can he used as a. training set to form machine-generated suggestions for cl<assit 'ing encoded concepts. The reference concepts are representative of the document corpLis fora review project, in which data organization or classification is desired. A set of reference concepts can be generated for each doewnent review project or alteniatively, the reference concepts can be representative of documents selected from a previously conducted document review protect that is related to the current document review project. Chided revo iewv assists a reviewer in. building a reference concept set representative of.
the corpus for use in classifying uncoded docuirments. Alternatively, the reference concept set can be selected from a previously conducted document review that is related to the cuarrent document review project..
1)urin F guided review, uncoded concepts that an dissimilar to each. other are identified based on a similarity threshold, rather methods for determining dissimilarity are possible.
Identifying a set of dissimilar concepts provides a group of concepts that is represerxtatiy,e of the corpus for r document re.v ievv project. F ach icientilied dissimilar concept i.s then classified by > assigning a particular code based on the content of the concept to generate a set of reference connceptt.for the document review project. Guided review can be performed by a reviewer, a machine. or a combination of the reviewer and machine.
Other methods for vfenerati.ng a reference concept set fora docurrment review project using guided review. are possible, including clustering A set of encoded concepts to be classified can be clustered, such as described in conurvonly-ast iLsnl U. Patent Application Serial No.
12/844,8 1 O, entitled "Sv stem and Method for Displaying Relationships Between Concepts to Provide Classification Suggestions via Inclusion," filed lull 27, 2010 pending-U-S, Patent Application Serial No, 1_I844,792, entitled "System and Method for Displaying Relation ships Between Concepts to Pro side Classification Suggestions via Injection,".lzled July 27, 2010.
pending. and I S. Patent Application Serial No. 1211'844,813, entitled "System and Method for Displaying Relationships Between Concepts to Provide Classification Suggestions via Nearest, Neighbor." filed July 27.. 2111 t), pending, the disclosures of which are incorporated by reference.
Briefly, a plurality of the clustered uncoded concepts is selected based on selection criteria, such as cluster centers or sample clusters. The cluster centers can be used to identify uncoded concepts in a cluster that are most similar or dissimilar to the cluster center. The identified encoded concepts are then selected for classification. After classification, the previously aancoded, concepts represent a reference set. In a further example, sample clusters can be used to generate a reference set by selecting one or more sample clusters based on cluster relation criteria, such as sire, content, similarity, or dissimilarity, The encoded concepts in the selected sample clusters are then selected for classification by assigning codes. The classified concepts represent a reference concept set for the document review project.
Other methods for selecting encoded concepts for use as a reference set are possible. Although.
the above process has been described with reference to concepts, other objects or tokens are possible.
For purposes of legal discovery. the codes used to classify encoded concepts can include "priv leged respc>rvsive," or `r .osv-respon=iv e,` Other codes are possible.
The assigned classification codes can be used as suggestions for classification of associated documents. For exarrrple, a document associated with three concepts, each assigned a "privileged" classification can also be considered privileged.'` Ã Ãlier types of stir gestions are possible. A "privileged"
document contains information that is protected by a privilege, n caning that the document should not he disclosed or "produced" to an opposing party. Disclosing a "prig ileged" docun em can result iri an unintentional ivaiver of the subject matter disclosed, A
"responsive" docurrieut contains information that is related to the legal niaÃter, while a "non-responsive- document includes inforrniation that is not related to the legal matter.
> Obtaining reference sets and cluster sets, and id.enÃiff'in r the most similar reference concepts can be perfbrnied by the system 10, which includes Individual computer t\'ste.i is, such as the backend server I I., work server 12. server 15, client 18. remote sere-'er 24a and remote client 27. The individual c01-tpinte:r systems are -entra1 purpose, progr<rtnr ed digital co.niptitin devices consisting of a central processing unit (CPU), random access memos {.AM)_ non-volatile secoridafy storage, such as a hard drive or CO ROM drive, network irrterf_aces.. and peripheral devices, including, user interfici.n i Deains, such 11, ,1 keyboard w id display >9. Die various implementations of the source code and object and byte codes can he hold on a cotriputer-readable storage medium, such as a floppy disk, hard drive, digital video disk (DV D).
random access memos (R' M), read-only niernory (ROM) and similar storage mediums. For esample- program code, including software programs, acid data a.re loaded into the RAM for execution and processing by the CPU and results are generated for display, outputt, transmittal. or stora;e.
C lassif:icatioii code suggestions associated with i confidence level can be provided to assist a reviewer in making classification decisions for uricoded concepts.
FIGURE 2 is a process flow diagram showing a method for providing a classification suggestion for uncoded concepts., in accordance with one embodiin'.ient. A set of encoded concepts is first identified.. then clustered, based on thematic or conceptual relationships (block 41). The clusters can be generated on.-demand or previously-generated and stored. as described in commonly-assigned U.S. Patent No. 7,610313, the disclosure of which is incorporated by reference.
Once obtained, an uncoded concept t ithin one of the clusters is selected (block 42)). A
neighborhood of reference concepts that is most relevant to the selected unicoded concept is identified (block 43). Determining the neighborhood of the selected encoded concept is further discussed below with reference to FIGURE 5. The neighborhood of reference concepts is determined separately for each cluster and can include one or i yore reference concepts within that cluster. The number of reference concepts in a neighborhood can be determined automatically or by an individual reviewer In a:further einbodiinent_ the neighborhood of reference concepts is defined for each available classification code or subset of class codes. A
classification for the selected encoded concept is suggested based on the classification of the similar coded reference concepts in the neighborhood (block 44). The suggested classification can then be accepted, rejected, or ignored by the acs ieaer as further described below \\rith reference to FIGURE 4. Optionally, a confidence level for the suggested classification can be provided (block 45). as further described below with reference to FIGURE 3.
f he machine-generated suggestion for classification and associated confidence leN.'el can > be determined by the d la fier as further discussed below with. reference to FIGURES 3 and 5.
Once generated, the reference concepts in the neighborhood and the selected Ãrrcotfed concept are analyzed to provide a classification suggestion. The analysis of the selected uncoded concept and neighborhood reference concepts can be based on one or more routines performed by the classifier, such as a nearest neighbor (NN') classifier, as further discussed below with reference to FIGURES. The classification suggestion is displayed to the rev iew:>er throu ;h visual display.
such as textually or LPraph:ically- or other was of display. For example, the suggestion can be displayed as part of a visual representation of the uncoded concept. as further discussed below with reference to FIGURES 7 and 8_ and as described in con.monly-assi; ned U.ts, Patent No.
7,27:1,804, the disclosure of which is incorporated by reference.
In r .tirrther embodiment. the classified concepts can be used to classify those documents represented b that concept. For example, in a product liability lawwwsuit, the plaintiff claims that a wood composite manufactured by the defendant induces and harbors mold growth. During dicoverv, all documents Within the corpus.for the la c stuit and relating to Ãa cold should be identified for reviekww. The concept for mold is clustered and includes a "responsive"
classification code. which indicates that the noun phrase mold is related to the legal natter.
1.) pon selection of the mold concept... all documents that include the noun phrase mold ml be identified using the mapping matrix, which is, described further below with reference to .FIGURE
9. The responsive classification code assigned to the concept can be used as a suggestiori for the document classification, However, if the document is represented by multiple concepts with different classification codes, each different code can be considered during, classification of the document.
In afurtherembodiment, the concept clusters can be used with document clusters, which are described in commonly-owned in U.S. Patent Application Serial No, 12/833,860, entitled "S ster and Method for Displaying Relationships Between Electronically Stored Information to Provide Classification Suggestions via Inclusion. ' filed July 9. 2010, pes ding, and U.S. Patent Application Serial No. 12/833,872, entitled "System and Method for Displaying Relationships Between Electronically Stored Information to Preside Classification Suggestions via. Injection,"
filed July 9n 2010, pending, the disclosures of which is incorporated by reference. For example selecting a concept in the concept cluster display can identify one or more documents with a lo-ccsrnrtron idea or topic. Further selection of one of the documents represented by the selected cluster in the document concept display can identif documents that are similarly related to the content of the selected document, The identified documents can he the same or different rs the other documents represented by the concept.
> Similar documents can also he identified as described in ccsrnnresrtl -rrssi ned 1;.;'.S. Pale __nt Application Serial No. I"' 33,880. entitled Svc stem and Method for Displaying Relationships Bet een Electronically Stored Information to provide Classification Suggestions via Nearest Nei Phbor." filed July 9, 201.0, pending, the disclosure of which .is incorporated by reference.
In an even further embodiment, the documents identified from one of the concepts can be classified autoni a:tically as described in corramonl --assik; ned U.S. Patent A plication Serial" No.
12 833.769, entitled _Sv-stetn and Method for Providing a Classification Suggestion for Electronically Stored Information_" filed July }. 2010, pending, the, disclosure of which is incorporated by reference.
Once the suggested classification code is provided for the selected encoded concept. the classifier can provide a confidence, level for the suggested classification, which can he presented as an absolute z stirs or percerrta e. FIGURE 3 is a process flow diagram.
showing a method for providing a confidence level for a classification suggestion for use in the method of FIGURE. 2.
The confidence level is determined from a distance metric based on the amount of similarity of the encoded concept to the reference documents used for the classification suggestion (block, 4, 1), In one embodiment, the similarity between each reference concept in the neighborhood and the selected uncoded concept is determined as the cos of the score vectors for the concept and each reference concept being compared. The cos a provides a measure of relative similar or dissimilarity beta een tokens, including the concepts in the documents and it equivalent to the inner products between the score vectors for the uncoded document and the reference document.
In the described embodiment. the cos (3 is calculated in. accordance with the equation:
rr cos (T -Ãg 5=
where cos ;,, comprises the similarity metric between uncoded concept A and reference, concept B, S, comprises a score vector for the r:-ncoded concept -t and 5,, comprises a. score vector for the reference concept B. Other forms of determining similarity:
using a distance metric .30 are feasible- as would be reco4enized by one skilled in the art7 such as usin4e Euclidean distance.
.Practically. a reference concept in the neighborhood that is identical to the uncoded concept yyould result in a confidence level. of 100%, Nrh.ile a reference concept that is completely dissimilar would result in a. confidence level of 0%
Alternatively, the confidence level can take into account the classifications of reference concepts in the nei`-rh.borhood that are di t rent than the suggested classification and adjust the > confidence level accordingly (block 52). For example., the confidence level.
of the suggested classification can be reduced by subtractirrg the calculated simil,a ty>
metric of the insug`: e: ted classification from the si.milarit-N, metric of the reference concept of the suggested classification.
Other confidence level .measures are possible. The ref ie vve.r can consider confidence level when assigning a classification to a selected uncoded concept. Alternatively, the classifier can autoraratir tally assi rr the suggested clta sil_iccatitrrr uptirr dGterrrr oration. In one embodiment, t ae classifier only assigns an encoded concept with the suggested classification if the confidence level is above a threshold value (block 53), which can be set by the reviewer or the classifier.
For exarxmple_ a confidence level of more than 50% can be required for a classification to be suggested to t}re rei ie~~ er. Finally, once determined, the confidence level for the su gested classification is provided to the .reviewer (block 54).
The suggested classification can be accepted, rejected, or ignored by the reviewer.
FIGURE 4 is t process flow drag rani showing a method( for accept-in, or rejecting a class. fication suggestion .for use in the method of FIGURE 2. Once the classification bas been suggested (block 61 ),there reviewer can accept or reject. the suggestion (block 62), Ifaccepted, the previous) uncoded concept is coded with the suggested classification (block 6 ). Additionally.
the noww-coded concept can be stored as a coded concept. In a further embodiment., the suggested classification is a1.1to.natica.ll4 assigned to the uncodd d concept, as further described below with reference to FIGURE. 6. If rejected, the encoded concept remains uncoded and can be manuall classified by, the revfeNw-er under a different classification code (b oc'k 64). Once the selected encoded concept is assigned a classification code., either by the reviewer or automatically,, the newly classified concept can be added to the set of reference concepts for use in classifying further uncoded concepts. Subsequently, a further uncoded concept. can be selected for classification using similar reference concepts.
In a further embodiments if the manual classification is different from the suggested classification, a discordance is identified by the system (block 65).
Optionally, the discordance can be visually depicted to the reviewer (block 66). For example, the discordance can be displayed as part of a. visual representation of the discordant document,, as further discussed below with reference to FIGURE. 8. Additionally, the discordance is flagged if a discordance threshold value is exceeded, which can be set by the reviewer or the, classifier The discordance threshold is based on the confidence level. In one embodiment, the discordance value is identical to the confidence level of the suggested classification. In a further embodiment, the discordance value is the difference between the confidence level of the suggested classification and the confidence level of the rnanuall=-assigned classification..
> In a yet further embodiment. an entire cluster, or a cluster spine containing a aultiple clusters of uncoded documents can be selected and a classification for the entire chaster or cluster spine can be suggested. For instance, for cluster classification, a cluster is selected and a score vector for the center of the cluster is determined as described in commordy-.assigned Li S. Patent Application Serial No. 12/844,810. entitled System and Method for Displaying Relationships Between Concepts to Provide Classification Suggestions i.ia. inc]L ion" filed J l y 27, 2010..
pending, U ,.S. Patent Application Serial No. 1.2 844,792.. entitled, System and Me hod for Dispkn,ing Relationships Between Concepts to Provide Classification Suggestions via injection,""
fliled .halo' 2.7. 2010, pending, and U. S. Patent Application Serial No.
1_844,8 13, entitled "System and Method for Displaying Relationships Between Concepts to Provide Cktsslfin, lion Suggestions via Nearest Neighbor," filed,lu.alv 27.201.0, pending, the disclosures of which are incorporated by reference.
Briefly., a neighborhood o.r the selected cluster is determined based on a distance metric.
Each refs reance concept in the selected cluster is associated with a score.
vector and the distance is determined by comparing the score vector of the cluster center with the score vector for each of the reference concepts to determine a neighborhood of reference concepts that are closest to the cluster center. How ever, other methods for generating a neighborhood are possible. Once determined, one of the classification measures is applied to the nei4fhbo:rhood to determine a.
suggested classification for the selected chaster. as further discussed below with. reference to RE 6.
One or more reference concepts nearest to a selected uncoded concept are identified and provided as a neighborhood of reference concepts for the selected encoded concept. FIGURE 5 is a block diagram showing, by way of example. ways to generate a neighborhood 70 of reference concepts fora clustered uncoded concept for use in the method of FIGURE 2, rvpes of neiYehborhood Yceneration include inclusion 71, injection 72, and nearest neighbor 73. Other vays to generate the neighborhood are possible. Inclusion 71 includes using uncoded concepts and reference concepts to generate clusters, such as described in con a aonly,assigned U.S. Patent Application Serial No. 12/844,810, entitled System and Method for Displaying Relationships Between Concepts to Provide Classification Suggestions via :inclusion," filed bite 27 2010, pending, the disclosure of which is incorporated by reference. Briefly, a set of reference concepts is grouped v ith one, or more encoded concepts and are organized into clusters containing both uncoded and reference concepts, as discussed above. The reference concepts in the clustern or a subset thereof is then used as the neighborhood for an encoded concept.
injection 72 includes inserting reference concepts into clusters of encoded concepts based on similar dtv, such as described in coaa1monl\ -assi a ed US. Patent Application serial No.
12/844,792~ entitled "S\'steni and Method for Displ 'i..iig Relationships Between Concepts to esticaras t i;a lra ectic n " filed faal 27. 201(, pending the disclosure of Provide Classification SUI
which is incorporated by reference, Briefly . a set of clusters of imcoded concepts is obtained, as discussed above. Once obtained, a cluster center Is determined for each cluster, The cluster center is representative of all the concepts in that particular cluster. One or n-lore cluster centers can be compared with a set of reference concepts and those reference, concepts that satis.f ' a threshold of similarity to that cluster center are selected. The selected reference concepts are then inserted into the cluster associated with that cluster center. The selected reference concepts injected into the cluster can be the same or different as the selected reference concepts infected into another cluster. The reference, concepts in the cluster, or a subset thereof.. is then used as the neighborhood for an uricoded concept.
Nearest Neighbor 7? includes a comparison of uncoded concepts and reference concepts, such as dose ribecl .ire coraraxicaralv <assigrie,d C , .
P,a:tent.Applic,ation Serial No. 12/844.813, entitled "Sy stem and ;`lethod. for Displaying Relationships Between. Concepts to Provide Classification Suggestions via Nearest Neighbor," filed July 27, 2010, pending. the disclosure of e~hich Is incorporated h reference. Briefl)~., encoded concepts are identified and clustered, as discussed above. A reference set. of concepts is also identified, An uncoded concept is selected .from one of the clusters and compared against the reference set to identify one or more reference concepts that are sin lar to the selected uncoded concept. The similar reference concepts are identified based on a similarity measure calculated between the selected encoded concept and each reference document. Once identified- the similar reference concepts, or a subset thereof, is then used as the neighborhood.
An uncoded concept is compared to one or more reference concepts to determine a suggested classification code for the uncoded concept. FIGURE t is a block diagram showing, bs way of example. classifier routines $0 for suggesting a classification for an uncoded concept for use in the method of FIGURE 2. Types of classil er routines include minimum distance classification measure 82'. minimum average distance classification measure 83, maximum count classification measure 847 and distance weighted maximum count classification measure 8_5.
Other types of classification measures and classifiers are possible.
1 ,he miningÃtin. distance classification measure 8 2., also knovvn as closest neighbor, includes determining the closest reference concept neighbor in the neighborhood to the selected uncoded concept. Once determined_ the classification of the closest reference concept is used as the classification suggestion for the selected encoded concept. Score vectors for the selected encoded concept and for each of a number of reference concepts are, compared as the cos 0 to determine a distance metric. The distance metrics for the reference concepts are compared to ideitf the reference concept closest to the selected uncoded concept.
1iie rnininiu:in average distance classification distance measure 83 determines Ãhe distances of all reference concepts in the neighborhood, averages the dLter.mi.ned distances based ltt on classification, and uses the classification of the closest average distance reference concepts as the classification su gestion.. The maximum count classification measure 8$, also known as the voting classification measure, includes calculating the number of reference documents in the neighborhood and assigning a count, or "vote", to each reference concept. The classification that bras the most 'vvotes" is used as the classification suggestion for the encoded concept.
l> '.The distance weighted maximum count classification measure, 85 is a combination. ('11, tile minimum average distance 81. and maximum count classification measures 82, Each reference concept in the neighborhood is given a count, but the count is differentially weighted based on the distance that reference concept is from the selected uncoded concept. For example., a vote of a reference concept closer to the uncoded concept is weighted heavier than a reference concept ?0 further a.vv ax-, Tlie classification determined to have the l ighest vote count is suggested as the classification of the selected encoded concept.
A confidence level can be provided for the suggested classification code, as described above with reference to FIGURE 3. For example_ the neighborhood of a particular uncoded concept can contain a total of five reference concepts, with three classified as "responsive" and 25 Mo classified as "iron-responsive. Determining the classification suggestion using tile ma..imu rr count classification measure 84 results in a classification suggestion of "responsive"
for the uncodcdl concept, but the confidence level provided can be penalized for each of the non--suggested classification concepts in. the neighborhood. The penalty :reduces the confidence level of the classification. Other ways of determining the confidence level are possible.
30 'the clusters of uncoded concepts and reference concepts can be provided as a display to the reviewer. FIGURE 7 is a screenshot 943 shovvin .. by way of example, a visual display 91 of reference concepts in relation to uncoded concepts, Clusters 93 can be located along a spine, which is a vector. based on a similarit x of the encoded concepts in the clusters 93. Each cluster 93 is represented by a circle, however, other shapes, such as squares, rectangles, and triangles are t;
possible, as described in U.S. Patent No.. 6,88 &584-, the disclosure of which is incorporated b reference. The encoded concepts 94 are each represented by a. smaller circle yitlun the clusters 93, 'bile the reference concepts 95 are each represented by a circle with a diamond-shape \. ithin the boundaries of the circle. The reference concepts 95 can be fturiher represented by their-assigned c assifrcation code. C assilication codes can include "privi e. Fcc, respor sine, " and '`non-respor-asive," as \\\eli as other codes. Other class.it cation categc}saes are possible. For instance, privileged reference concepts can include a circle 3t.ith an "X.' in the center and non-responsive reference concepts can include a circle tikwitlr striped lines.
Other classification representations for the reference concepts and other classified concepts are possible, such as by color. Each cluster spine 96 is represented as a. vector along which the clusters are placed.
The display 91 can be manipulated by a. individual revieN e.r via. a compass 92, which enables the reviewer to navigate, explore. and search the clusters 93 and spines 96 appearing within the compass 92, as further described in commonly-assigned U.S. Patent No, 7356,777, the disclosure of which is incorporaÃed by reference, The compass 92 visually emphasizos clusters 93 located within the borders of the compass 2', while deenrphasizing clusters 93 appearing outside of the. compass 92.
Spine labels 99 appear outside of the compass 92 at an end of each cluster spine 96 to connect the outermost cluster of the cluster spine 96 to preferably the closest point along the periphery of the compass in one embodiment, the spine labels 99 are placed without overlap and circumferentially around the compass 92. Each spine label 99 corresponds to one or more concepts for the cluster that most closely describes a. cluster spine 96 appearing within the compass 92. Additionally, til cluster coIncepts for each of the spine .labels 99 can appear in a concepts list. (not shown) also provided in the display. Toolbar buttons 97 located at the top of the display 91 enable a user to execute specific commands for the composition of the spine groups displayed. A set of pull doi-vii menus 98 provide further control over the placement and manipulation of clusters 93 and cluster spines 96 within the display 9.1.
Other types of controls and functions are possible.
The toolbar buttons 97 and pull down menus 98 provide control to the reviewer to set parameters related to classification. For example, the confidence suggestion threshold and discordance threshold can be set. at a document, cluster, or cluster spine level AdditionalIN the reviewer can display the classification suugrostion as well as further details about the reference concepts used for the suggestion by clickin ; an encoded concept, cluster,, or spine. For example, a su estion guide 100 can be placed in the display 91 and can include a "Suggestion" field. a Confidence Level" field. The uggestion" field in the suggestion guide 100 provides the 1.6-classification suggestion for a selected document, cluster.- or spine. '11-le "Confidence Level"
field provides a confidence level of the suggested classification.
AIternatrveiy, the classification suggestion details can be revealed by hovering over the selection with the mouse.
In one embodianen.t. a garbage can 101 is provided to remove tokens, such as duster > concepts &orn consideration in the current set of clusters 93 Removed cluster concepts prevent those concepts Moan affec:tin ; future clusteriaa#Y_ as may occur \\vhen a re ie\\ver considers a concept irrelevant to the clusters 91 The display 91 provides a visual representation of the relationships between the:matically related concepts. including encoded concepts and sin .ilar reference concepts.
The encoded concepts and reference concepts located within t cluster or spine can be compared based on characteristics, such as a type of classification of the reference concepts, anu.mber of reference concepts for each classification code, and a number of classification category types in the cluster to identify relationships bet: wween the uncoded concepts and reference concepts. The reference concepts in the neighborhood of the uncoded concept can be used to provide a classification code suggestion for the trncoded concept. For example, .FIGURE 8 is a block diagram showvi:ng, by way of example, a cluster 110 with a combination of classified reference concepts. uncoded concepts. and concepts given a classification. The cluster 1.1() can include one "privileged"
reference concept 1 1.1, two ".non-.responsive" concepts 1122, seven uncoded concepts 113, one uncoded concept with a "privileged" code suggestion 114, one previously uncoded concept k~ itb to accepted "non-responsive" code suggestion 115. and one previously uncoded concept.
s aowvin ; a discordance 116 between the classification code suggested and the classification code manually assig nned by the reviewer.
The combination of "privileged,. I1 1 and "non-responsive" 112 reference concepts within the cluster can be used by a classifier to provide a classification suggestion to a reviewer for the encoded reference concepts 113, as further described above t ith reference to FIG U. 6, Uncoded concept 114 has been assigned a suggested classification code of "privileged" by the classier. The classification suggestion can be displayed textually or visually to the reviewer.
Other way s of displaying a suggested classification are possible. In one embodiment, encoded concepts are assigned a color and each classification code is assiYgned an individual color, Placing the color code of the suggestion on a portion 117 of the encoded concept 114 denotes the suggested classification code Similarly, the classification. suggestion for an. entire cluster can be displayed textually or visually, for example by assignin ; a color to the cluster circle matching the color of the suggested classification code.
A reviewer can choose to accept or reject the suggested classification, as described further above with reference to FIGURE 4, If accepted, the now-classified concept is given the color code of the suggested classification. For example, concept 115 previously assigned a suggestion of "no-responsive.`' ~vhich was subs equeritle. accepted b-,., the rovieNv'or, and given the visual depiction of"non-responsive.,.. In. a further embodiment, the suggested classification code is automatically assigned to the encoded docLinient i idiout the need of prior reviewer approval.
In a further ernbodirnents discordance hett =een the classification code suggested and the isÃual Ã.lassification c~.['tl~e c.c?rtce fl is noted kr th s stein. For example, discordant c_oincel [ -1 -16 is assigned a classification suggestion of "privileged" but coded as "non-responsive. With the discordant option selected, the classification suggested by the classifier is retained and displayed after the encoded concept is manually classified.
A corpus of documents for a review project can he divided into assignments using assignment criteria, such as custodian or source of the doctrtnents, content, document type, and date. Other criteria are possible. Each assignment is assi ;necl to ari indiv dual rei ie, er for analysis. . The assignments can be, separately analyzed or alternatively analyzed tooled-ler to determine concepts for the one or more assignments of documents. The content of each document within the corpus can be converted into a set of concepts. As described above., concepts typically include nouns and noun phrases obtained through part-of-speech tagging that have a common semantic meaning. The concepts, which are representative of the documents can be clustered to provide a classification suggestion of the: document content.
Clustering of the uncoded concepts provides ;roupings of related uncoded concepts and is based of a similarity metric using score voctors assigned to each uncoded concept, a:
described above and such as described in contrionly -assigned U.S. Patent No.
7.11 0.313, U. S.
Patent Application Serial No. 12/844,810, entitled "Sy,stem and Method for Displaying Relationships Bet Veen Concepts to Provide Classification Suggestions via Inclusion ,"filed Julti 27, 2010, pending, U.S. Patent Application Serial No. 12/844,792, entitled System and Method for Displaying Relationships Between Concepts to Provide Classification Suggestions via Injection." filed July 27, 2010, pending, and U.S. Patent Application Serial No. 121/844,813', entitled "Sstem and Method for Displaying Relationships Between Concepts to Provide Classification Suggestions via Nearest, Neiglhbor.:' filed July 27, 2010, pending, the. disclosures of which is incorporated by reference.
The score sectors can be generated using a matrix showing the itncoded concepts in relation to documents that contain the concepts. FIG RE 9 is a table show Ong_ by w <as of example, a i atrix mapping 1,20 of encoded concepts 124 and documents 123. The uncoded documents 1.2 3 are listed along a horizontal dimension 121 of the matr i r, while the concepts 121 are listed along a. vertical dimension 122. owwever, the placement of the uncoded documents 123 and concepts 1.24 can be reiversed. Each cell 125 within the matrix 120 includes a curriulative number of occurrences of each concept within a particular uncoiled document 123.
> Score vectors can be generated for each document by identifying the concepts and associated weights voitlun that document and ordering; the concepts along a vector with the associated concept weight. In the matrix 120, the score vector 126 for a document 123 can be identified as all the concepts included in that document and the associated zweights. which are based on the number of occurrences of each concept. Score vectors can also be generated for each concept by identifi ing the documents that contain that concept and determining a weight a. sociat:ed with each document. The documents and a. sociated wei hts are then ordered along a vector for each concept, as the concept score vector. In the rnatrix 120 _ the score rector 127 for a concept can be identified as all the documents that contain that concept and the associated weights. Classic cation of uncoded concepts then can be associated and applied to the uncoded documents associated with the concept.
In a further embodiment.. each document can be represented by more than one concept.
Accordingly. to determine a. classification code for the docun ent, the classification codes for each of the associated concepts can be analyzed and compared.. such as described above with reference to FIGURE 6. In one example, a classification code can be determined by counting the number of associated concepts for each classification code and then assigned the classification code with the most associated concepts. In a further exa miple.. one or more of the associated concepts can be weighted and the classification code associated with the hi4fhest weight of concepts is assigned. Other methods for determining a classification code for uncoded documents based on reference concepts are possible.
Although clustering, classil ration. and displaying relationships has been described above with reference to concepts, other tokens, such as word-level or character-level n-grams, raw terms, and entities, are possible.
While the invention has been particularly shown and described as referenced to the embodiments thereof, those skilled in the art will understand that the .i'oregoing and other changes in form and detail may be made therein without departing from the spirit and scope.
Il'-CI"lC.iI;lEl_:l) ---------------------------------------------------This application relates in general to irnforrilaation classi.ficatiou, in pa:rticula:r_ to a s\ stern and method for providing a classification sug~.jestaon for concepts.
'BACKGROUND ART
Historically, document review during the discovery phase of litigation and for other types of legal matters, such as due diligence and regulatory compliance, have been conducted n anually. During; document review. individual re ie ers, generally licensed attorneys, are typically assigned sets of documents for coding. A reviewer must c rre~fully study each document and categorize the document by assigning a code or other marker from a set of descriptive classifications, such as "privileged," "responsive." and "non responsive." The classifications can affect the disposition of each document, including admraissibill ty .into evidence. As well, during, discovery, document revie\.v can potentially affect the outcome of the legal underlying matter, and consistent and accurate results are crucial.
M nual document review is tedious and time-consuming. larking documents is performed at the sole discretion of each reviewer and inconsistent results can occur due to misunderstanding, time pressures. fatigue, or other factors. A large volume of documents reviewved,, often with only limited time, can create a loss of mental focus and a loss of purpose for the resultant classification. Each new reviewer also faces a steep learning curve to become familiar with the legal matter, coding categories., and review techniques.
Currently, with the increasingly widespread movement to electronically stored x? information (ES1), manual document review is becoming impracticable and outmoded. The often exponential growth of E S1. can exceed the Ixm ds reasonable ..for conventional .r manual human review and the sheer scale c .l tel:Ã:arty~ ESI review Underscores the need for computer-assisted ESI review tools.
Conventional ESI review tools have proven inadequate for providing efficient, accurate.
.
and consistent results. For example, DiscoverReady LU', a Delaware limited liabilitx company..
conducts semi-auton a:ted document review: ~ through multiple passe: over a document set in E S1.
form. During the first pass, documents are grouped by categoÃs and basic codes are assigned.
Subsec uent. I gasses refine farad aa:sig .f`urther earcc}diar s. Multiple pass ESI review also requires ra Priori project-specific knowledge engineering.. which is generally applicable to only a single ~l~
project, thereby losing the benefit of any inferred knoiyle.c e or experiential knot.-how for use in other review projects.
Thus. there remains a need for a system and method for increasing the efficiency of document reviews by providing classilicati.on suggestions based on reference, documents while > ultimately ensuring independent reviewer discretion..
DISCLOSURE OF THE INVENTION
Document rev ew elticiencv can be increased by identifying relationships between reference conccpts, which are concepts that have l eeia aass.i vied classification codes.. and wicoded concepts aid providing a suggestion for classification based on the classification relationships.
Uncoded concepts are foriried into conceptual clusters. The encoded concepts for a cluster are compared to a set of reference concepts. Those reference concepts most similar to the uncoded concepts are identified based on, for instance, semantic similarity and are used to form a classification suggestion. The classification suggestion can be provided with a confidence level that reflects the aanmou nt of similarity between the wicoded concepts and reference concepts in the nei~ hho:rhood. The classification suggestion. can then he accepted..
rejectedõ or ignored by a reviewer.
One embodiment provides a system and method for providing a classification suggestion for concepts. A corpus of electronically stored information includin ;
reference concepts each associated with ,a classification and uncoded concepts are maintained, A
cluster of encoded concepts and reference concepts is provided, A neighborhood of reference concepts in the cluster is determined for at least one of the iaracoded concepts. A
classification of the neighborhood is determined using a classifier. The classification of the neighborhood is suggested as a classification for the at least one uncoded concept..
Still other embodii-rients of the present invention will become readily apparent to those skilled in the art from the following detailed description, wherein are described embodiments by way of illustrating the best mode contemplated for carning out the invention.
As will be realized, the invention is capable of other and different embodiments and its several details are capable of modifications in r various obvious respects, all without departing from the spirit and the scope of the present invention. .Accordingly, the drawings acid detailed description are to be Ott re4carded as illustrative in nature and not as restrictive, DESCRIPTION OF TTIE DRAWINGS
FIGURE 1 is a block diagram showing a system f% r providing reference concepts as a suggestion for uncoded concepts, i.n accordance with one embodiment.
FIGURE 2 is a process flow diagram showing a method for providing a classification suggestion for uncoded concepts, in accordance k ith one embodiment, FIGURE 3 is a process floNv diagram showing a method for- providing a confidence level for a classification suoyestion for use in the method of FIGURE 'I.
> FIGURE 4 is a process flow diagram showing a. method for accepting orrqjecting a, classitical rrr slr~ #~estiori for use in the triefliod of FIGURE 2.
FIGURE 5 is a block diagram showing, by way of example. ways to generate a neighborhood of reference concepts for a clustered uncoded concept.to.r use in the method of FIGURE 2.
FIGURE 6 is a block diagram shr wwwing, 1w way of example, classifier routines for suggesting l classification for an 1#ncoded concept.th.r use in the method of FIGURE 2.
FIGURE 7 is a screenshot showing, by way of example, a visual display of reference concepts in relation to urncoded concepts.
RE 8 is a block diagram sho% ing, by %vay of example, a cluster with a combination of classified reference concepts- encoded concepts, and concepts given a.
classification.
FIGURE 9 is a table shownõ. by a ay of example. a matrix mapping of uncoded concepts and doculr eats.
BEST MODE FOR CARRYING OUT THE INVENTION, The ever.'-increasitng volume of ESI underlies the need for autonmating document review for improved consistency and throughput. Token clustering via injection utilizes reference, or previously classified tokens, which offer knowledge gle47.ned.from earlier i-ork in similar legal projects, as well as a reference point for classifying unc ded tokens, The tokens can inelude wwword-level. symbol-level, or character-lei el r1-grtlnrs, raw terms.
entities, or concepts. Other tokens, including, other atonic parse-level elements, are possible.
An n-gram is a. predetermined number of items selected from a. source. The items can include syllables, letters, or words- as well as other items. A raw term is a term that has not been processed or manipulated. Entities further refine nouns and noun phrases into people, places, and things, such as meetings, animals, relationships, and various other objects, Additionally, entities can represent other parts of gram.i-nar associ t:ted with semantic meanings to disambiguate 3{? different instances or occurrences of the Tammar'. Entities c in be extracted using entity extraction techniques kno\\i in the field.
Concepts" are Collections of nouns acid noun-phrases with common semantic meaning that can be extracted from ESI, including documents, through part-of-speech tagging, Each concept can represent one or more documents to be classified during a review.
Clustering of the concepts provides an. overall view of the document space. which allows users to easily ideaÃi.f : documents sharing a common theirte.
The clustering of tokens, for exrnxple, concepts, differs from document clustering, vvlrich groups related documents individually. In contrast. concept clustering groups related concepts., > which are, each representative of one or more related documents, Each concept can express an ideas or topic that n-nav~ .no be expressed by individual docun`tents. .A
concept rs analogous to a search query by identifying: documents associated with a particular idea or topic, A user can determine how particular concepts are related based on the concept clustering.
Further, users are able to intuittv'el identify documents by selecting one or more associated concepts in a cluster. For e.xanrple, a user n rav> wish to identify all documents :in r. particular corpus that are related to car manufacturing. The user can select the concept -'car, manufacturing' or "vehicle manufacture- within one of the clusters and subsequently, the associated documents are presented, l lowever, during document clustering, a user is first required to select a specific document from which other documents that are similarly related can then be identified.
Releren.ce tokens are previously classified based on the document content represented b that token and can be :injected :into clusters of uncoded, that is unclassified. tokens to :influence classification of the uncoded tokens. Specifically, relationships between an encoded token and the reference tokens, in terms of semantic similarity or distinction, can be used as are aid in providing sir Tgestions for classifying uncoded tokens. Once: classified, the newly-coded. or reference, tokens can be used to further classify the represented docuÃnents.
Although tokens., such as word-level or character-level n- :r{r.nrrr., rave terrIrs_ erit..ities, or concept s, can be clustered and displayed, the discussion below vvill focus on a concept as a particular token.
End-to end ESI review requires a computerized support environment within which classification can be performed. FIGURE I is a block diagram showing a system.
10 for providing reference concepts as a suggestion for uncoded concepts, in accordance with one embodiment. By way of illustration, the system 10 operates in a distributed computing environment, which includes a plurality, of heterogeneous sv:sten-is and iST
sources. Henceforth, a single item of ESI will be referenced as a "document,' although fiST can include other forms of non-do. currientt data, as described r frc . A backend server l l is coupled to a storage device 13.
which stores documents 144 in the form. of structured or unstructured data, a database 30 for maintaining information about the documents, and a. look up database 37 for storing rntny-to-many mappings 38 between documents and document features, such as concepts, and a concept document index 40, which maps documents to concepts. The storage device 13 also stores reference documents .4b, which provide a training set of trusted and Ionotn results for use in guiding ESI classification. The reference documents I4b can be hand-selected or automatically determined, Additionally. the set of reference documents c an be predetermined or can be generated dynamically- as the selected encoded docu-meats are classified and subsequently added > to the set of reference documents. Further, the backend server 11 can store uncoded concepts 14c ,and reference concepts I4d. Concepts are collections of nouns and noun phrases with conxaxnonn semantic meaning The not aas and noun--phrases can be extracted from one or more documents ina the corpus for review. Hereinafter, the terms `'classified." and `'coded' are Used interchangeably with the same intended meanitng, unless otherwise indicated.
The backend server 1 1 is coupled to an inntranetAv ork 21 and executes a workbench software suite 31 for providinu a user inEert aces framework for automated document m ana e,nment, processing, analysis, and cla: ;i fication. In a further embodiment, the backend server I I can be accessed via arl internet: wwork 22. The workbench suite 3I
includes a document mapper 32 that includes a clustering engine 33., sine. la ity searcher 34., classifier 35. and display ~ enera:Eor : 6. Other workbench suite anno?daales are possible.
The clustering engine 33 performs efficient concept scoring and clustering of uncoiled conccepts. such as described in common ly>-assigned U. S. Patent No. 7,610.3 3. U.S. Patent Application Serial \o. 12/844,810.. entitled Syste xn and Method for D1 spla-, Relationships Between Concepts to Provide Classification Suggestions via Inclusion."' filed July 27, 2110, pending, U.S. Patent Application Serial No. 12/ 844,792, entitled "Systenn and Method tor Displaying Relationships Between Concepts to Provide Classification Suggestions via laajection,"
filed July 2.7, 2010, pending. and t_U.5 Patent .App.lic ation serial No.
112/844,813., entitle d "System and Method for Displaying; Relationships Between Concepts to Provide Classification Suggestions via. Nearest Neighbor.." filed July 27, 0, pending, the disclosures of which is incorporated by reference.
Briefly. cl esters of uncoded concepts I 4c are l ornned and can be organized along vectors.
know n as spisnes, based oia to siarnilaarit of tine clusters, The similarity can be expressed in terms of distance. The uncoiled concepts 14c are Identified from a corpus of encoded documents for a.
document review project. In a further embodinment, the cluster set of encoded concepts can be predetermined based on a related document review project..
The similarity searcher 34 identities the reference concepts Wd that are, similar to selected uncoded concepts l4c, clusters, or spines. The classifier 35 provides a ntachine-generated suggestion and confidence level for classification of the selected encoded concepts 14c. clti<sters, or spines.- as further described below beginning with reference to F1 URE 2. The -display `venerator 36 arranges the clusters and spines in thematic or conceptual relationships in a tai o-dimensional visual displa space. Once generated,, the visual display space is transil-Ii tied to a to ork client 12 by the backend server I I via the doctrnlent mapper 32 for presenting to a rev iev.er. The revie\aer can include an individual person Nvh.o is assigned to review- and classify the concepts 14c by designating a code. Hereinafter, unless otherwise indicated, the terms "reviewer" and "custodian" are t; ed interchangeable i ith the same intended meat1ing. Other types of reviewers are possible, including machine>-implemented reviewers.
The documen mapper 32 operates on encoded concepts 14c.. , hich can he retrieved from the storage 13_ as well as a plurality of local and remote sources. The local and remote: sources can also store the reference concepts 14d., as we l as the encoded documents 14a and reference documents 14h. The local sources include aloc_urnettts and concepts 17 mai.ntai:ned.in a storage device .16 coupled to a local server 15 and documents 20 maintained in a storage device .19 coupled to a local client 18. The local server 15 and local client 18 are interconnected to the backend server 1.1 and the work client 1.2 over the intranetwork 2:1. In addition, the document mapper 32 can ident:if and retrieve documents frown .remote sources over he intemetA ork 22 _ including the Internet, through a gateway 23 interfaced to the intraneM- ork 21. =T he remote sources include documents 26 maintained in a storage device 25 coupled to a remote server 24a and documents 1-9 maintained in a storage device 28 coupled to a. remote client 24b. Other document and concept sources, either local or remote. are possible.
The ind.k idual documents 14a, 14h, 17.20- 1-6, 29 include all fore -ms and types of structured and unstructured 81 including electronic message stores, word processing documents, electronic mail (email) folders, Web pages.. and graphical or multimedia data.
Not,, itlrstanding. the documents could be in the form of structurally organized data, such as stored in spreadsheets or databases.
In one embodimenÃ, the individual documents 14a, 14b, IT, 2Ø. 26.. 29 can include electronic message folders storing email and attachments, such as maintained h the Outlook and Outlook Express products, licensed by Microsoft Corporation, Redmond, WA. The database can be on SAIL.-based relational database, such as the Oracle database managen-rent systen-r, Release 8n licensed by Oracle Corporation, Redwood Shores, CA.
Additionally, the individual documents 17. 'tl, 26, 29 include encoded documents, reference documents, and previously encoded documents that have been assigned a classification code. The number of uncoded documents may be too large for processing in a single pass.
Typically,- a subset of uncoded documents are selected for a document review assignment and stored as a doctanacrat corpus, which can also incl tide one or more reference documents, as discussed thf-a.
Moreover, the individual concepts 14c., l 4d, 17, 20.26.29 include encoded concepts and reference concepts. The encoded concepts, which are tanclassified, represent collections of nouns and noun-phrases that are semantically related and extracted from doctanien s in a document review project.
The reference concepts are initially encoded concepts that can be selected from the corpus or other source, of'uncoded concepts acrd stibseclaaently classified. \
'hen convbined w:ith uncoded concepts, such as described in conantonlv~assi~ttecl U. S, Patent Application Serial No.
1'21"844,810, entitled "Syste -a and Method for Displaying Relationships Between C OrICCpts to Provide Classification Suggestions vialnc.laasion..".filed July 27 2010, pending- C.S Patent Application. Serial No. 121844,792, entitled " Sy stein and Method for Displaying Relationships 13etvveen Concepts to Provide Classification Suggestions via Injection," filed July 27, 2010, pending.. and I J& Patent Application Serial No. 1/44.813, entitled ` SysÃem and Method for Displaying Relationships Between Concepts to Provide Classification Suggestions via Nearest Nei<ggh.bor.-' filed July 27, 2010, pending, the disclosures of t hich are incorporated b reference, the reference concepts can provide suggestions b r classification of the remaining tancoded concepts in the corpus based on visual relationships between the reference concepts and ancoded concepts, The reviewer can classify, one or more of the uncoded concepts by assigning a code to each concept, representing a classification, based on the suggestions. if desired. The suggestions can also be cased for c tlaer pcirfac ses,, such as elualià control. Concepts f. iven a ciaissificationl code by the :reviewer are then stored. .Additio.7 ally, the now-coded concepts can be used as reference concepts in related document review assignments. The assignment is completed once all uncoded concepts in the assignment have been assigned a classification code.
In a l irtlYer embodiment., the reference concepts can he used as a. training set to form machine-generated suggestions for cl<assit 'ing encoded concepts. The reference concepts are representative of the document corpLis fora review project, in which data organization or classification is desired. A set of reference concepts can be generated for each doewnent review project or alteniatively, the reference concepts can be representative of documents selected from a previously conducted document review protect that is related to the current document review project. Chided revo iewv assists a reviewer in. building a reference concept set representative of.
the corpus for use in classifying uncoded docuirments. Alternatively, the reference concept set can be selected from a previously conducted document review that is related to the cuarrent document review project..
1)urin F guided review, uncoded concepts that an dissimilar to each. other are identified based on a similarity threshold, rather methods for determining dissimilarity are possible.
Identifying a set of dissimilar concepts provides a group of concepts that is represerxtatiy,e of the corpus for r document re.v ievv project. F ach icientilied dissimilar concept i.s then classified by > assigning a particular code based on the content of the concept to generate a set of reference connceptt.for the document review project. Guided review can be performed by a reviewer, a machine. or a combination of the reviewer and machine.
Other methods for vfenerati.ng a reference concept set fora docurrment review project using guided review. are possible, including clustering A set of encoded concepts to be classified can be clustered, such as described in conurvonly-ast iLsnl U. Patent Application Serial No.
12/844,8 1 O, entitled "Sv stem and Method for Displaying Relationships Between Concepts to Provide Classification Suggestions via Inclusion," filed lull 27, 2010 pending-U-S, Patent Application Serial No, 1_I844,792, entitled "System and Method for Displaying Relation ships Between Concepts to Pro side Classification Suggestions via Injection,".lzled July 27, 2010.
pending. and I S. Patent Application Serial No. 1211'844,813, entitled "System and Method for Displaying Relationships Between Concepts to Provide Classification Suggestions via Nearest, Neighbor." filed July 27.. 2111 t), pending, the disclosures of which are incorporated by reference.
Briefly, a plurality of the clustered uncoded concepts is selected based on selection criteria, such as cluster centers or sample clusters. The cluster centers can be used to identify uncoded concepts in a cluster that are most similar or dissimilar to the cluster center. The identified encoded concepts are then selected for classification. After classification, the previously aancoded, concepts represent a reference set. In a further example, sample clusters can be used to generate a reference set by selecting one or more sample clusters based on cluster relation criteria, such as sire, content, similarity, or dissimilarity, The encoded concepts in the selected sample clusters are then selected for classification by assigning codes. The classified concepts represent a reference concept set for the document review project.
Other methods for selecting encoded concepts for use as a reference set are possible. Although.
the above process has been described with reference to concepts, other objects or tokens are possible.
For purposes of legal discovery. the codes used to classify encoded concepts can include "priv leged respc>rvsive," or `r .osv-respon=iv e,` Other codes are possible.
The assigned classification codes can be used as suggestions for classification of associated documents. For exarrrple, a document associated with three concepts, each assigned a "privileged" classification can also be considered privileged.'` Ã Ãlier types of stir gestions are possible. A "privileged"
document contains information that is protected by a privilege, n caning that the document should not he disclosed or "produced" to an opposing party. Disclosing a "prig ileged" docun em can result iri an unintentional ivaiver of the subject matter disclosed, A
"responsive" docurrieut contains information that is related to the legal niaÃter, while a "non-responsive- document includes inforrniation that is not related to the legal matter.
> Obtaining reference sets and cluster sets, and id.enÃiff'in r the most similar reference concepts can be perfbrnied by the system 10, which includes Individual computer t\'ste.i is, such as the backend server I I., work server 12. server 15, client 18. remote sere-'er 24a and remote client 27. The individual c01-tpinte:r systems are -entra1 purpose, progr<rtnr ed digital co.niptitin devices consisting of a central processing unit (CPU), random access memos {.AM)_ non-volatile secoridafy storage, such as a hard drive or CO ROM drive, network irrterf_aces.. and peripheral devices, including, user interfici.n i Deains, such 11, ,1 keyboard w id display >9. Die various implementations of the source code and object and byte codes can he hold on a cotriputer-readable storage medium, such as a floppy disk, hard drive, digital video disk (DV D).
random access memos (R' M), read-only niernory (ROM) and similar storage mediums. For esample- program code, including software programs, acid data a.re loaded into the RAM for execution and processing by the CPU and results are generated for display, outputt, transmittal. or stora;e.
C lassif:icatioii code suggestions associated with i confidence level can be provided to assist a reviewer in making classification decisions for uricoded concepts.
FIGURE 2 is a process flow diagram showing a method for providing a classification suggestion for uncoded concepts., in accordance with one embodiin'.ient. A set of encoded concepts is first identified.. then clustered, based on thematic or conceptual relationships (block 41). The clusters can be generated on.-demand or previously-generated and stored. as described in commonly-assigned U.S. Patent No. 7,610313, the disclosure of which is incorporated by reference.
Once obtained, an uncoded concept t ithin one of the clusters is selected (block 42)). A
neighborhood of reference concepts that is most relevant to the selected unicoded concept is identified (block 43). Determining the neighborhood of the selected encoded concept is further discussed below with reference to FIGURE 5. The neighborhood of reference concepts is determined separately for each cluster and can include one or i yore reference concepts within that cluster. The number of reference concepts in a neighborhood can be determined automatically or by an individual reviewer In a:further einbodiinent_ the neighborhood of reference concepts is defined for each available classification code or subset of class codes. A
classification for the selected encoded concept is suggested based on the classification of the similar coded reference concepts in the neighborhood (block 44). The suggested classification can then be accepted, rejected, or ignored by the acs ieaer as further described below \\rith reference to FIGURE 4. Optionally, a confidence level for the suggested classification can be provided (block 45). as further described below with reference to FIGURE 3.
f he machine-generated suggestion for classification and associated confidence leN.'el can > be determined by the d la fier as further discussed below with. reference to FIGURES 3 and 5.
Once generated, the reference concepts in the neighborhood and the selected Ãrrcotfed concept are analyzed to provide a classification suggestion. The analysis of the selected uncoded concept and neighborhood reference concepts can be based on one or more routines performed by the classifier, such as a nearest neighbor (NN') classifier, as further discussed below with reference to FIGURES. The classification suggestion is displayed to the rev iew:>er throu ;h visual display.
such as textually or LPraph:ically- or other was of display. For example, the suggestion can be displayed as part of a visual representation of the uncoded concept. as further discussed below with reference to FIGURES 7 and 8_ and as described in con.monly-assi; ned U.ts, Patent No.
7,27:1,804, the disclosure of which is incorporated by reference.
In r .tirrther embodiment. the classified concepts can be used to classify those documents represented b that concept. For example, in a product liability lawwwsuit, the plaintiff claims that a wood composite manufactured by the defendant induces and harbors mold growth. During dicoverv, all documents Within the corpus.for the la c stuit and relating to Ãa cold should be identified for reviekww. The concept for mold is clustered and includes a "responsive"
classification code. which indicates that the noun phrase mold is related to the legal natter.
1.) pon selection of the mold concept... all documents that include the noun phrase mold ml be identified using the mapping matrix, which is, described further below with reference to .FIGURE
9. The responsive classification code assigned to the concept can be used as a suggestiori for the document classification, However, if the document is represented by multiple concepts with different classification codes, each different code can be considered during, classification of the document.
In afurtherembodiment, the concept clusters can be used with document clusters, which are described in commonly-owned in U.S. Patent Application Serial No, 12/833,860, entitled "S ster and Method for Displaying Relationships Between Electronically Stored Information to Provide Classification Suggestions via Inclusion. ' filed July 9. 2010, pes ding, and U.S. Patent Application Serial No. 12/833,872, entitled "System and Method for Displaying Relationships Between Electronically Stored Information to Preside Classification Suggestions via. Injection,"
filed July 9n 2010, pending, the disclosures of which is incorporated by reference. For example selecting a concept in the concept cluster display can identify one or more documents with a lo-ccsrnrtron idea or topic. Further selection of one of the documents represented by the selected cluster in the document concept display can identif documents that are similarly related to the content of the selected document, The identified documents can he the same or different rs the other documents represented by the concept.
> Similar documents can also he identified as described in ccsrnnresrtl -rrssi ned 1;.;'.S. Pale __nt Application Serial No. I"' 33,880. entitled Svc stem and Method for Displaying Relationships Bet een Electronically Stored Information to provide Classification Suggestions via Nearest Nei Phbor." filed July 9, 201.0, pending, the disclosure of which .is incorporated by reference.
In an even further embodiment, the documents identified from one of the concepts can be classified autoni a:tically as described in corramonl --assik; ned U.S. Patent A plication Serial" No.
12 833.769, entitled _Sv-stetn and Method for Providing a Classification Suggestion for Electronically Stored Information_" filed July }. 2010, pending, the, disclosure of which is incorporated by reference.
Once the suggested classification code is provided for the selected encoded concept. the classifier can provide a confidence, level for the suggested classification, which can he presented as an absolute z stirs or percerrta e. FIGURE 3 is a process flow diagram.
showing a method for providing a confidence level for a classification suggestion for use in the method of FIGURE. 2.
The confidence level is determined from a distance metric based on the amount of similarity of the encoded concept to the reference documents used for the classification suggestion (block, 4, 1), In one embodiment, the similarity between each reference concept in the neighborhood and the selected uncoded concept is determined as the cos of the score vectors for the concept and each reference concept being compared. The cos a provides a measure of relative similar or dissimilarity beta een tokens, including the concepts in the documents and it equivalent to the inner products between the score vectors for the uncoded document and the reference document.
In the described embodiment. the cos (3 is calculated in. accordance with the equation:
rr cos (T -Ãg 5=
where cos ;,, comprises the similarity metric between uncoded concept A and reference, concept B, S, comprises a score vector for the r:-ncoded concept -t and 5,, comprises a. score vector for the reference concept B. Other forms of determining similarity:
using a distance metric .30 are feasible- as would be reco4enized by one skilled in the art7 such as usin4e Euclidean distance.
.Practically. a reference concept in the neighborhood that is identical to the uncoded concept yyould result in a confidence level. of 100%, Nrh.ile a reference concept that is completely dissimilar would result in a. confidence level of 0%
Alternatively, the confidence level can take into account the classifications of reference concepts in the nei`-rh.borhood that are di t rent than the suggested classification and adjust the > confidence level accordingly (block 52). For example., the confidence level.
of the suggested classification can be reduced by subtractirrg the calculated simil,a ty>
metric of the insug`: e: ted classification from the si.milarit-N, metric of the reference concept of the suggested classification.
Other confidence level .measures are possible. The ref ie vve.r can consider confidence level when assigning a classification to a selected uncoded concept. Alternatively, the classifier can autoraratir tally assi rr the suggested clta sil_iccatitrrr uptirr dGterrrr oration. In one embodiment, t ae classifier only assigns an encoded concept with the suggested classification if the confidence level is above a threshold value (block 53), which can be set by the reviewer or the classifier.
For exarxmple_ a confidence level of more than 50% can be required for a classification to be suggested to t}re rei ie~~ er. Finally, once determined, the confidence level for the su gested classification is provided to the .reviewer (block 54).
The suggested classification can be accepted, rejected, or ignored by the reviewer.
FIGURE 4 is t process flow drag rani showing a method( for accept-in, or rejecting a class. fication suggestion .for use in the method of FIGURE 2. Once the classification bas been suggested (block 61 ),there reviewer can accept or reject. the suggestion (block 62), Ifaccepted, the previous) uncoded concept is coded with the suggested classification (block 6 ). Additionally.
the noww-coded concept can be stored as a coded concept. In a further embodiment., the suggested classification is a1.1to.natica.ll4 assigned to the uncodd d concept, as further described below with reference to FIGURE. 6. If rejected, the encoded concept remains uncoded and can be manuall classified by, the revfeNw-er under a different classification code (b oc'k 64). Once the selected encoded concept is assigned a classification code., either by the reviewer or automatically,, the newly classified concept can be added to the set of reference concepts for use in classifying further uncoded concepts. Subsequently, a further uncoded concept. can be selected for classification using similar reference concepts.
In a further embodiments if the manual classification is different from the suggested classification, a discordance is identified by the system (block 65).
Optionally, the discordance can be visually depicted to the reviewer (block 66). For example, the discordance can be displayed as part of a. visual representation of the discordant document,, as further discussed below with reference to FIGURE. 8. Additionally, the discordance is flagged if a discordance threshold value is exceeded, which can be set by the reviewer or the, classifier The discordance threshold is based on the confidence level. In one embodiment, the discordance value is identical to the confidence level of the suggested classification. In a further embodiment, the discordance value is the difference between the confidence level of the suggested classification and the confidence level of the rnanuall=-assigned classification..
> In a yet further embodiment. an entire cluster, or a cluster spine containing a aultiple clusters of uncoded documents can be selected and a classification for the entire chaster or cluster spine can be suggested. For instance, for cluster classification, a cluster is selected and a score vector for the center of the cluster is determined as described in commordy-.assigned Li S. Patent Application Serial No. 12/844,810. entitled System and Method for Displaying Relationships Between Concepts to Provide Classification Suggestions i.ia. inc]L ion" filed J l y 27, 2010..
pending, U ,.S. Patent Application Serial No. 1.2 844,792.. entitled, System and Me hod for Dispkn,ing Relationships Between Concepts to Provide Classification Suggestions via injection,""
fliled .halo' 2.7. 2010, pending, and U. S. Patent Application Serial No.
1_844,8 13, entitled "System and Method for Displaying Relationships Between Concepts to Provide Cktsslfin, lion Suggestions via Nearest Neighbor," filed,lu.alv 27.201.0, pending, the disclosures of which are incorporated by reference.
Briefly., a neighborhood o.r the selected cluster is determined based on a distance metric.
Each refs reance concept in the selected cluster is associated with a score.
vector and the distance is determined by comparing the score vector of the cluster center with the score vector for each of the reference concepts to determine a neighborhood of reference concepts that are closest to the cluster center. How ever, other methods for generating a neighborhood are possible. Once determined, one of the classification measures is applied to the nei4fhbo:rhood to determine a.
suggested classification for the selected chaster. as further discussed below with. reference to RE 6.
One or more reference concepts nearest to a selected uncoded concept are identified and provided as a neighborhood of reference concepts for the selected encoded concept. FIGURE 5 is a block diagram showing, by way of example. ways to generate a neighborhood 70 of reference concepts fora clustered uncoded concept for use in the method of FIGURE 2, rvpes of neiYehborhood Yceneration include inclusion 71, injection 72, and nearest neighbor 73. Other vays to generate the neighborhood are possible. Inclusion 71 includes using uncoded concepts and reference concepts to generate clusters, such as described in con a aonly,assigned U.S. Patent Application Serial No. 12/844,810, entitled System and Method for Displaying Relationships Between Concepts to Provide Classification Suggestions via :inclusion," filed bite 27 2010, pending, the disclosure of which is incorporated by reference. Briefly, a set of reference concepts is grouped v ith one, or more encoded concepts and are organized into clusters containing both uncoded and reference concepts, as discussed above. The reference concepts in the clustern or a subset thereof is then used as the neighborhood for an encoded concept.
injection 72 includes inserting reference concepts into clusters of encoded concepts based on similar dtv, such as described in coaa1monl\ -assi a ed US. Patent Application serial No.
12/844,792~ entitled "S\'steni and Method for Displ 'i..iig Relationships Between Concepts to esticaras t i;a lra ectic n " filed faal 27. 201(, pending the disclosure of Provide Classification SUI
which is incorporated by reference, Briefly . a set of clusters of imcoded concepts is obtained, as discussed above. Once obtained, a cluster center Is determined for each cluster, The cluster center is representative of all the concepts in that particular cluster. One or n-lore cluster centers can be compared with a set of reference concepts and those reference, concepts that satis.f ' a threshold of similarity to that cluster center are selected. The selected reference concepts are then inserted into the cluster associated with that cluster center. The selected reference concepts injected into the cluster can be the same or different as the selected reference concepts infected into another cluster. The reference, concepts in the cluster, or a subset thereof.. is then used as the neighborhood for an uricoded concept.
Nearest Neighbor 7? includes a comparison of uncoded concepts and reference concepts, such as dose ribecl .ire coraraxicaralv <assigrie,d C , .
P,a:tent.Applic,ation Serial No. 12/844.813, entitled "Sy stem and ;`lethod. for Displaying Relationships Between. Concepts to Provide Classification Suggestions via Nearest Neighbor," filed July 27, 2010, pending. the disclosure of e~hich Is incorporated h reference. Briefl)~., encoded concepts are identified and clustered, as discussed above. A reference set. of concepts is also identified, An uncoded concept is selected .from one of the clusters and compared against the reference set to identify one or more reference concepts that are sin lar to the selected uncoded concept. The similar reference concepts are identified based on a similarity measure calculated between the selected encoded concept and each reference document. Once identified- the similar reference concepts, or a subset thereof, is then used as the neighborhood.
An uncoded concept is compared to one or more reference concepts to determine a suggested classification code for the uncoded concept. FIGURE t is a block diagram showing, bs way of example. classifier routines $0 for suggesting a classification for an uncoded concept for use in the method of FIGURE 2. Types of classil er routines include minimum distance classification measure 82'. minimum average distance classification measure 83, maximum count classification measure 847 and distance weighted maximum count classification measure 8_5.
Other types of classification measures and classifiers are possible.
1 ,he miningÃtin. distance classification measure 8 2., also knovvn as closest neighbor, includes determining the closest reference concept neighbor in the neighborhood to the selected uncoded concept. Once determined_ the classification of the closest reference concept is used as the classification suggestion for the selected encoded concept. Score vectors for the selected encoded concept and for each of a number of reference concepts are, compared as the cos 0 to determine a distance metric. The distance metrics for the reference concepts are compared to ideitf the reference concept closest to the selected uncoded concept.
1iie rnininiu:in average distance classification distance measure 83 determines Ãhe distances of all reference concepts in the neighborhood, averages the dLter.mi.ned distances based ltt on classification, and uses the classification of the closest average distance reference concepts as the classification su gestion.. The maximum count classification measure 8$, also known as the voting classification measure, includes calculating the number of reference documents in the neighborhood and assigning a count, or "vote", to each reference concept. The classification that bras the most 'vvotes" is used as the classification suggestion for the encoded concept.
l> '.The distance weighted maximum count classification measure, 85 is a combination. ('11, tile minimum average distance 81. and maximum count classification measures 82, Each reference concept in the neighborhood is given a count, but the count is differentially weighted based on the distance that reference concept is from the selected uncoded concept. For example., a vote of a reference concept closer to the uncoded concept is weighted heavier than a reference concept ?0 further a.vv ax-, Tlie classification determined to have the l ighest vote count is suggested as the classification of the selected encoded concept.
A confidence level can be provided for the suggested classification code, as described above with reference to FIGURE 3. For example_ the neighborhood of a particular uncoded concept can contain a total of five reference concepts, with three classified as "responsive" and 25 Mo classified as "iron-responsive. Determining the classification suggestion using tile ma..imu rr count classification measure 84 results in a classification suggestion of "responsive"
for the uncodcdl concept, but the confidence level provided can be penalized for each of the non--suggested classification concepts in. the neighborhood. The penalty :reduces the confidence level of the classification. Other ways of determining the confidence level are possible.
30 'the clusters of uncoded concepts and reference concepts can be provided as a display to the reviewer. FIGURE 7 is a screenshot 943 shovvin .. by way of example, a visual display 91 of reference concepts in relation to uncoded concepts, Clusters 93 can be located along a spine, which is a vector. based on a similarit x of the encoded concepts in the clusters 93. Each cluster 93 is represented by a circle, however, other shapes, such as squares, rectangles, and triangles are t;
possible, as described in U.S. Patent No.. 6,88 &584-, the disclosure of which is incorporated b reference. The encoded concepts 94 are each represented by a. smaller circle yitlun the clusters 93, 'bile the reference concepts 95 are each represented by a circle with a diamond-shape \. ithin the boundaries of the circle. The reference concepts 95 can be fturiher represented by their-assigned c assifrcation code. C assilication codes can include "privi e. Fcc, respor sine, " and '`non-respor-asive," as \\\eli as other codes. Other class.it cation categc}saes are possible. For instance, privileged reference concepts can include a circle 3t.ith an "X.' in the center and non-responsive reference concepts can include a circle tikwitlr striped lines.
Other classification representations for the reference concepts and other classified concepts are possible, such as by color. Each cluster spine 96 is represented as a. vector along which the clusters are placed.
The display 91 can be manipulated by a. individual revieN e.r via. a compass 92, which enables the reviewer to navigate, explore. and search the clusters 93 and spines 96 appearing within the compass 92, as further described in commonly-assigned U.S. Patent No, 7356,777, the disclosure of which is incorporaÃed by reference, The compass 92 visually emphasizos clusters 93 located within the borders of the compass 2', while deenrphasizing clusters 93 appearing outside of the. compass 92.
Spine labels 99 appear outside of the compass 92 at an end of each cluster spine 96 to connect the outermost cluster of the cluster spine 96 to preferably the closest point along the periphery of the compass in one embodiment, the spine labels 99 are placed without overlap and circumferentially around the compass 92. Each spine label 99 corresponds to one or more concepts for the cluster that most closely describes a. cluster spine 96 appearing within the compass 92. Additionally, til cluster coIncepts for each of the spine .labels 99 can appear in a concepts list. (not shown) also provided in the display. Toolbar buttons 97 located at the top of the display 91 enable a user to execute specific commands for the composition of the spine groups displayed. A set of pull doi-vii menus 98 provide further control over the placement and manipulation of clusters 93 and cluster spines 96 within the display 9.1.
Other types of controls and functions are possible.
The toolbar buttons 97 and pull down menus 98 provide control to the reviewer to set parameters related to classification. For example, the confidence suggestion threshold and discordance threshold can be set. at a document, cluster, or cluster spine level AdditionalIN the reviewer can display the classification suugrostion as well as further details about the reference concepts used for the suggestion by clickin ; an encoded concept, cluster,, or spine. For example, a su estion guide 100 can be placed in the display 91 and can include a "Suggestion" field. a Confidence Level" field. The uggestion" field in the suggestion guide 100 provides the 1.6-classification suggestion for a selected document, cluster.- or spine. '11-le "Confidence Level"
field provides a confidence level of the suggested classification.
AIternatrveiy, the classification suggestion details can be revealed by hovering over the selection with the mouse.
In one embodianen.t. a garbage can 101 is provided to remove tokens, such as duster > concepts &orn consideration in the current set of clusters 93 Removed cluster concepts prevent those concepts Moan affec:tin ; future clusteriaa#Y_ as may occur \\vhen a re ie\\ver considers a concept irrelevant to the clusters 91 The display 91 provides a visual representation of the relationships between the:matically related concepts. including encoded concepts and sin .ilar reference concepts.
The encoded concepts and reference concepts located within t cluster or spine can be compared based on characteristics, such as a type of classification of the reference concepts, anu.mber of reference concepts for each classification code, and a number of classification category types in the cluster to identify relationships bet: wween the uncoded concepts and reference concepts. The reference concepts in the neighborhood of the uncoded concept can be used to provide a classification code suggestion for the trncoded concept. For example, .FIGURE 8 is a block diagram showvi:ng, by way of example, a cluster 110 with a combination of classified reference concepts. uncoded concepts. and concepts given a classification. The cluster 1.1() can include one "privileged"
reference concept 1 1.1, two ".non-.responsive" concepts 1122, seven uncoded concepts 113, one uncoded concept with a "privileged" code suggestion 114, one previously uncoded concept k~ itb to accepted "non-responsive" code suggestion 115. and one previously uncoded concept.
s aowvin ; a discordance 116 between the classification code suggested and the classification code manually assig nned by the reviewer.
The combination of "privileged,. I1 1 and "non-responsive" 112 reference concepts within the cluster can be used by a classifier to provide a classification suggestion to a reviewer for the encoded reference concepts 113, as further described above t ith reference to FIG U. 6, Uncoded concept 114 has been assigned a suggested classification code of "privileged" by the classier. The classification suggestion can be displayed textually or visually to the reviewer.
Other way s of displaying a suggested classification are possible. In one embodiment, encoded concepts are assigned a color and each classification code is assiYgned an individual color, Placing the color code of the suggestion on a portion 117 of the encoded concept 114 denotes the suggested classification code Similarly, the classification. suggestion for an. entire cluster can be displayed textually or visually, for example by assignin ; a color to the cluster circle matching the color of the suggested classification code.
A reviewer can choose to accept or reject the suggested classification, as described further above with reference to FIGURE 4, If accepted, the now-classified concept is given the color code of the suggested classification. For example, concept 115 previously assigned a suggestion of "no-responsive.`' ~vhich was subs equeritle. accepted b-,., the rovieNv'or, and given the visual depiction of"non-responsive.,.. In. a further embodiment, the suggested classification code is automatically assigned to the encoded docLinient i idiout the need of prior reviewer approval.
In a further ernbodirnents discordance hett =een the classification code suggested and the isÃual Ã.lassification c~.['tl~e c.c?rtce fl is noted kr th s stein. For example, discordant c_oincel [ -1 -16 is assigned a classification suggestion of "privileged" but coded as "non-responsive. With the discordant option selected, the classification suggested by the classifier is retained and displayed after the encoded concept is manually classified.
A corpus of documents for a review project can he divided into assignments using assignment criteria, such as custodian or source of the doctrtnents, content, document type, and date. Other criteria are possible. Each assignment is assi ;necl to ari indiv dual rei ie, er for analysis. . The assignments can be, separately analyzed or alternatively analyzed tooled-ler to determine concepts for the one or more assignments of documents. The content of each document within the corpus can be converted into a set of concepts. As described above., concepts typically include nouns and noun phrases obtained through part-of-speech tagging that have a common semantic meaning. The concepts, which are representative of the documents can be clustered to provide a classification suggestion of the: document content.
Clustering of the uncoded concepts provides ;roupings of related uncoded concepts and is based of a similarity metric using score voctors assigned to each uncoded concept, a:
described above and such as described in contrionly -assigned U.S. Patent No.
7.11 0.313, U. S.
Patent Application Serial No. 12/844,810, entitled "Sy,stem and Method for Displaying Relationships Bet Veen Concepts to Provide Classification Suggestions via Inclusion ,"filed Julti 27, 2010, pending, U.S. Patent Application Serial No. 12/844,792, entitled System and Method for Displaying Relationships Between Concepts to Provide Classification Suggestions via Injection." filed July 27, 2010, pending, and U.S. Patent Application Serial No. 121/844,813', entitled "Sstem and Method for Displaying Relationships Between Concepts to Provide Classification Suggestions via Nearest, Neiglhbor.:' filed July 27, 2010, pending, the. disclosures of which is incorporated by reference.
The score sectors can be generated using a matrix showing the itncoded concepts in relation to documents that contain the concepts. FIG RE 9 is a table show Ong_ by w <as of example, a i atrix mapping 1,20 of encoded concepts 124 and documents 123. The uncoded documents 1.2 3 are listed along a horizontal dimension 121 of the matr i r, while the concepts 121 are listed along a. vertical dimension 122. owwever, the placement of the uncoded documents 123 and concepts 1.24 can be reiversed. Each cell 125 within the matrix 120 includes a curriulative number of occurrences of each concept within a particular uncoiled document 123.
> Score vectors can be generated for each document by identifying the concepts and associated weights voitlun that document and ordering; the concepts along a vector with the associated concept weight. In the matrix 120, the score vector 126 for a document 123 can be identified as all the concepts included in that document and the associated zweights. which are based on the number of occurrences of each concept. Score vectors can also be generated for each concept by identifi ing the documents that contain that concept and determining a weight a. sociat:ed with each document. The documents and a. sociated wei hts are then ordered along a vector for each concept, as the concept score vector. In the rnatrix 120 _ the score rector 127 for a concept can be identified as all the documents that contain that concept and the associated weights. Classic cation of uncoded concepts then can be associated and applied to the uncoded documents associated with the concept.
In a further embodiment.. each document can be represented by more than one concept.
Accordingly. to determine a. classification code for the docun ent, the classification codes for each of the associated concepts can be analyzed and compared.. such as described above with reference to FIGURE 6. In one example, a classification code can be determined by counting the number of associated concepts for each classification code and then assigned the classification code with the most associated concepts. In a further exa miple.. one or more of the associated concepts can be weighted and the classification code associated with the hi4fhest weight of concepts is assigned. Other methods for determining a classification code for uncoded documents based on reference concepts are possible.
Although clustering, classil ration. and displaying relationships has been described above with reference to concepts, other tokens, such as word-level or character-level n-grams, raw terms, and entities, are possible.
While the invention has been particularly shown and described as referenced to the embodiments thereof, those skilled in the art will understand that the .i'oregoing and other changes in form and detail may be made therein without departing from the spirit and scope.
Claims (20)
1. A method (40) for providing a classification suggestion for concepts (13), comprising the steps of:
maintaining a corpus of reference concepts (14d), each associated with a classification, wherein each concept comprises nouns and nous phrases with common semantic meaning that are extracted from a set of documents;
obtaining uncoded concepts (14c), each extracted from one or more other documents;
generating a cluster of the uncoded concepts (14c);
determining a neighborhood of reference concepts (14d) in the cluster for at least one of the uncoded concepts (14c);
determining a classification of the neighborhood using a classifier; and suggesting the classification of the neighborhood as a classification for the at least one uncoded concept (14c), wherein the steps are performed by a suitably programmed computer.
maintaining a corpus of reference concepts (14d), each associated with a classification, wherein each concept comprises nouns and nous phrases with common semantic meaning that are extracted from a set of documents;
obtaining uncoded concepts (14c), each extracted from one or more other documents;
generating a cluster of the uncoded concepts (14c);
determining a neighborhood of reference concepts (14d) in the cluster for at least one of the uncoded concepts (14c);
determining a classification of the neighborhood using a classifier; and suggesting the classification of the neighborhood as a classification for the at least one uncoded concept (14c), wherein the steps are performed by a suitably programmed computer.
2. A method (40) according to Claim 1, further comprising:
applying the suggested classification to the one or more documents associated with the at least one uncoded concept (14c)
applying the suggested classification to the one or more documents associated with the at least one uncoded concept (14c)
3. A method (40) according to Claim 1, further comprising:
determining a distance metric based on the similarity of each reference concept (14d) in the neighborhood to the at least one uncoded concept (14c);
and assigning the classification of the reference concept (14d) in the neighborhood with the closest distance metric as the classification of the neighborhood.
determining a distance metric based on the similarity of each reference concept (14d) in the neighborhood to the at least one uncoded concept (14c);
and assigning the classification of the reference concept (14d) in the neighborhood with the closest distance metric as the classification of the neighborhood.
4. A method (40) according to Claim 1, further comprising:
determining a distance metric based on the similarity of each reference concept (14d) in the neighborhood to the at least one uncoded concept (14c);
summing the distance metrics of the reference concepts (14d) associated with the same classification;
averaging the sums of the distance metrics in each classification; and assigning the classification of the reference concepts (14d) in the neighborhood with the closest average distance metric as the classification of the neighborhood.
determining a distance metric based on the similarity of each reference concept (14d) in the neighborhood to the at least one uncoded concept (14c);
summing the distance metrics of the reference concepts (14d) associated with the same classification;
averaging the sums of the distance metrics in each classification; and assigning the classification of the reference concepts (14d) in the neighborhood with the closest average distance metric as the classification of the neighborhood.
5. A method (40) according to Claim 1, further comprising:
calculating a vote for each reference concept (14d) in the neighborhood; and assigning the classification of the reference concepts (14d) in the neighborhood with the highest calculated vote total as the classification of the neighborhood.
calculating a vote for each reference concept (14d) in the neighborhood; and assigning the classification of the reference concepts (14d) in the neighborhood with the highest calculated vote total as the classification of the neighborhood.
6. A method (40) according to Claim 1, further comprising:
calculating a vote for each reference concept (14d) in the neighborhood;
determining a distance metric based on the similarity of each reference concept (14d) in the neighborhood to the at least one uncoded concept (14c);
differentially weighing the votes based on the distance metric; and assigning the classification of the reference concepts (14d) in the neighborhood with the highest differentially weighted vote total as the classification of the neighborhood.
calculating a vote for each reference concept (14d) in the neighborhood;
determining a distance metric based on the similarity of each reference concept (14d) in the neighborhood to the at least one uncoded concept (14c);
differentially weighing the votes based on the distance metric; and assigning the classification of the reference concepts (14d) in the neighborhood with the highest differentially weighted vote total as the classification of the neighborhood.
7. A method (40) according to Claim 1, further comprising:
providing a confidence level of the suggested classification.
providing a confidence level of the suggested classification.
8. A method (40) according to Claim 7, further comprising displaying the confidence level only when above a confidence level threshold.
9. A method (40) according to Claim 1, wherein the neighborhood is determined based on one of inclusion (71), injection (72), and nearest neighbor (73).
10. A method (40) according to Claim 1, wherein the classifier is one of minimum distance (84), minimum average distance (81), maximum counts (82), and distance weighted maximum count (83).
11. A system for providing a classification suggestion for concepts, comprising:
a database to store a corpus of reference concepts (14d), each associated with a classification and uncoded concepts (14c), each extracted from one or more documents;
a clustering engine to generate a cluster of uncoded concepts (14c) and reference concepts (14d);
a neighborhood module to determine a neighborhood of reference concepts (14d) in the cluster for at least one of the uncoded concepts (14c);
a classification module to determine a classification of the neighborhood using a classifier; and a suggestion module to suggest the classification of the neighborhood as a classification for the at least one uncoded concept (14c).
a database to store a corpus of reference concepts (14d), each associated with a classification and uncoded concepts (14c), each extracted from one or more documents;
a clustering engine to generate a cluster of uncoded concepts (14c) and reference concepts (14d);
a neighborhood module to determine a neighborhood of reference concepts (14d) in the cluster for at least one of the uncoded concepts (14c);
a classification module to determine a classification of the neighborhood using a classifier; and a suggestion module to suggest the classification of the neighborhood as a classification for the at least one uncoded concept (14c).
12. A system according to Claim 11, further comprising:
a marking module to apply the suggested classification to the one or more documents associated with the at least one uncoded concept (14c).
a marking module to apply the suggested classification to the one or more documents associated with the at least one uncoded concept (14c).
13. A system according to Claim 11, further comprising:
a distance module to determine a distance metric based on the similarity of each reference concept (14d) in the neighborhood to the at least one uncoded concept (14c); and an assign module to assign the classification of the reference concept (14d) in the neighborhood with the closest distance metric as the classification of the neighborhood
a distance module to determine a distance metric based on the similarity of each reference concept (14d) in the neighborhood to the at least one uncoded concept (14c); and an assign module to assign the classification of the reference concept (14d) in the neighborhood with the closest distance metric as the classification of the neighborhood
14. A system according to Claim 11, further comprising:
a distance module to determine a distance metric based on the similarity of each reference concept (14d) in the neighborhood to the at least one uncoded concept (14c);
a calculation module to sum the distance metrics of the reference concepts (14d) associated with the same classification and to average the sums of the distance metrics in each classification; and an assign module to assign the classification of the reference concepts (14d) in the neighborhood with the closest average distance metric as the classification of the neighborhood.
a distance module to determine a distance metric based on the similarity of each reference concept (14d) in the neighborhood to the at least one uncoded concept (14c);
a calculation module to sum the distance metrics of the reference concepts (14d) associated with the same classification and to average the sums of the distance metrics in each classification; and an assign module to assign the classification of the reference concepts (14d) in the neighborhood with the closest average distance metric as the classification of the neighborhood.
15. A system according to Claim 11, further comprising:
a vote module to calculate a vote for each reference concept (14d) in the neighborhood; and an assign module to assign the classification of the reference concepts (14d) in the neighborhood with the highest calculated vote total as the classification of the neighborhood.
a vote module to calculate a vote for each reference concept (14d) in the neighborhood; and an assign module to assign the classification of the reference concepts (14d) in the neighborhood with the highest calculated vote total as the classification of the neighborhood.
16. A system according to Claim 11, further comprising:
a vote module to calculate a vote for each reference concept (14d) in the neighborhood;
a distance module to determine a distance metric based on the similarity of each reference concept (14d) in the neighborhood to the at least one uncoded concept (14c);
a weight module to differentially weigh the votes based on the distance metric; and an assign module to assign the classification of the reference concepts (14d) in the neighborhood with the highest differentially weighted vote total as the classification of the neighborhood.
a vote module to calculate a vote for each reference concept (14d) in the neighborhood;
a distance module to determine a distance metric based on the similarity of each reference concept (14d) in the neighborhood to the at least one uncoded concept (14c);
a weight module to differentially weigh the votes based on the distance metric; and an assign module to assign the classification of the reference concepts (14d) in the neighborhood with the highest differentially weighted vote total as the classification of the neighborhood.
17. A system according to Claim 11, further comprising:
a confidence module to provide a confidence level of the suggested classification.
a confidence module to provide a confidence level of the suggested classification.
18. A system according to Claim 17, further comprising a display to display the confidence level only when above a confidence level threshold.
19. A system according to Claim 11, wherein the neighborhood is determined based on one of inclusion (71), injection (72), and nearest neighbor (73).
20. A system according to Claim 11, wherein the classifier is one of minimum distance (84), minimum average distance (81), maximum counts (82), and distance weighted maximum count (83).
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Families Citing this family (240)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20050171948A1 (en) * | 2002-12-11 | 2005-08-04 | Knight William C. | System and method for identifying critical features in an ordered scale space within a multi-dimensional feature space |
US7191175B2 (en) * | 2004-02-13 | 2007-03-13 | Attenex Corporation | System and method for arranging concept clusters in thematic neighborhood relationships in a two-dimensional visual display space |
WO2006128183A2 (en) | 2005-05-27 | 2006-11-30 | Schwegman, Lundberg, Woessner & Kluth, P.A. | Method and apparatus for cross-referencing important ip relationships |
US8862591B2 (en) * | 2006-08-22 | 2014-10-14 | Twitter, Inc. | System and method for evaluating sentiment |
US8930331B2 (en) | 2007-02-21 | 2015-01-06 | Palantir Technologies | Providing unique views of data based on changes or rules |
US8935249B2 (en) | 2007-06-26 | 2015-01-13 | Oracle Otc Subsidiary Llc | Visualization of concepts within a collection of information |
US8775441B2 (en) | 2008-01-16 | 2014-07-08 | Ab Initio Technology Llc | Managing an archive for approximate string matching |
US9348499B2 (en) | 2008-09-15 | 2016-05-24 | Palantir Technologies, Inc. | Sharing objects that rely on local resources with outside servers |
US8676904B2 (en) | 2008-10-02 | 2014-03-18 | Apple Inc. | Electronic devices with voice command and contextual data processing capabilities |
CA3014839C (en) | 2008-10-23 | 2019-01-08 | Arlen Anderson | Fuzzy data operations |
US8339680B2 (en) | 2009-04-02 | 2012-12-25 | Xerox Corporation | Printer image log system for document gathering and retention |
US20130179386A1 (en) * | 2009-04-09 | 2013-07-11 | Sigram Schindler | Innovation expert system, ies, and its ptr data structure, ptr-ds |
US20130132320A1 (en) * | 2009-04-09 | 2013-05-23 | Sigram Schindler Beteiligungsgesellschaft Mbh | Innovation expert system, ies, and its ptr data structure, ptr-ds |
US20140258145A1 (en) * | 2009-04-09 | 2014-09-11 | Sigram Schindler | Semi-automatic generation / customization of (all) confirmative legal argument chains (lacs) in a claimed invention's spl test, as enabled by its "inventive concepts" |
US20140012766A1 (en) * | 2009-04-09 | 2014-01-09 | Sigram Schindler | Inventive concepts enabled semi-automatic tests of patents |
US8713018B2 (en) * | 2009-07-28 | 2014-04-29 | Fti Consulting, Inc. | System and method for displaying relationships between electronically stored information to provide classification suggestions via inclusion |
US8799326B2 (en) * | 2010-03-31 | 2014-08-05 | Thomson Reuters Global Resources | System for managing electronically stored information |
US8799791B2 (en) | 2010-03-31 | 2014-08-05 | Thomson Reuters Global Resources | System for use in editorial review of stored information |
US7933859B1 (en) | 2010-05-25 | 2011-04-26 | Recommind, Inc. | Systems and methods for predictive coding |
US9342569B2 (en) * | 2010-12-15 | 2016-05-17 | Sap Se | System and method of adding user interface element groups |
US9589053B1 (en) * | 2010-12-17 | 2017-03-07 | The Boeing Company | Method and apparatus for constructing a query based upon concepts associated with one or more search terms |
EP4120101A1 (en) * | 2011-01-07 | 2023-01-18 | Ixreveal, Inc. | Concepts and link discovery system |
US8826439B1 (en) * | 2011-01-26 | 2014-09-02 | Symantec Corporation | Encoding machine code instructions for static feature based malware clustering |
US8769708B2 (en) * | 2011-01-26 | 2014-07-01 | DiscoverReady LLC | Privileged document identification and classification system |
US8527516B1 (en) * | 2011-02-25 | 2013-09-03 | Google Inc. | Identifying similar digital text volumes |
US9904726B2 (en) * | 2011-05-04 | 2018-02-27 | Black Hills IP Holdings, LLC. | Apparatus and method for automated and assisted patent claim mapping and expense planning |
US9785634B2 (en) | 2011-06-04 | 2017-10-10 | Recommind, Inc. | Integration and combination of random sampling and document batching |
US8799240B2 (en) | 2011-06-23 | 2014-08-05 | Palantir Technologies, Inc. | System and method for investigating large amounts of data |
US9092482B2 (en) | 2013-03-14 | 2015-07-28 | Palantir Technologies, Inc. | Fair scheduling for mixed-query loads |
US9547693B1 (en) | 2011-06-23 | 2017-01-17 | Palantir Technologies Inc. | Periodic database search manager for multiple data sources |
US8732574B2 (en) | 2011-08-25 | 2014-05-20 | Palantir Technologies, Inc. | System and method for parameterizing documents for automatic workflow generation |
US8504542B2 (en) | 2011-09-02 | 2013-08-06 | Palantir Technologies, Inc. | Multi-row transactions |
US10402484B2 (en) * | 2011-10-27 | 2019-09-03 | Entit Software Llc | Aligning annotation of fields of documents |
CN108388632B (en) | 2011-11-15 | 2021-11-19 | 起元科技有限公司 | Data clustering, segmentation, and parallelization |
WO2013126311A2 (en) * | 2012-02-20 | 2013-08-29 | Apixio, Inc. | Clinical knowledge extraction, provider-guided educational material and top of mind concepts for patient |
US20140013242A1 (en) * | 2012-07-06 | 2014-01-09 | The Nasdaq Omx Group, Inc. | Collaborative due diligence review system |
CN103577301B (en) | 2012-07-20 | 2017-12-05 | 腾讯科技(深圳)有限公司 | A kind of method and terminal of show process information |
US9280520B2 (en) | 2012-08-02 | 2016-03-08 | American Express Travel Related Services Company, Inc. | Systems and methods for semantic information retrieval |
AU2013296278B2 (en) * | 2012-08-03 | 2018-06-14 | Stryker Corporation | Systems and methods for robotic surgery |
US20140067394A1 (en) * | 2012-08-28 | 2014-03-06 | King Abdulaziz City For Science And Technology | System and method for decoding speech |
US9141882B1 (en) | 2012-10-19 | 2015-09-22 | Networked Insights, Llc | Clustering of text units using dimensionality reduction of multi-dimensional arrays |
US9348677B2 (en) | 2012-10-22 | 2016-05-24 | Palantir Technologies Inc. | System and method for batch evaluation programs |
US9659087B2 (en) * | 2012-11-19 | 2017-05-23 | Amplero, Inc. | Unsupervised prioritization and visualization of clusters |
US20140214835A1 (en) * | 2013-01-29 | 2014-07-31 | Richard Thomas Oehrle | System and method for automatically classifying documents |
US9380431B1 (en) | 2013-01-31 | 2016-06-28 | Palantir Technologies, Inc. | Use of teams in a mobile application |
KR20230137475A (en) | 2013-02-07 | 2023-10-04 | 애플 인크. | Voice trigger for a digital assistant |
DE102013101871A1 (en) * | 2013-02-26 | 2014-08-28 | PSYWARE GmbH | Word-based speech analysis and speech analysis facility |
US10037314B2 (en) | 2013-03-14 | 2018-07-31 | Palantir Technologies, Inc. | Mobile reports |
US8635225B1 (en) * | 2013-03-14 | 2014-01-21 | Purediscovery Corporation | Representative document selection |
US10275778B1 (en) | 2013-03-15 | 2019-04-30 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive investigation based on automatic malfeasance clustering of related data in various data structures |
US9122681B2 (en) | 2013-03-15 | 2015-09-01 | Gordon Villy Cormack | Systems and methods for classifying electronic information using advanced active learning techniques |
US8937619B2 (en) | 2013-03-15 | 2015-01-20 | Palantir Technologies Inc. | Generating an object time series from data objects |
US9965937B2 (en) | 2013-03-15 | 2018-05-08 | Palantir Technologies Inc. | External malware data item clustering and analysis |
US8868486B2 (en) | 2013-03-15 | 2014-10-21 | Palantir Technologies Inc. | Time-sensitive cube |
US8818892B1 (en) | 2013-03-15 | 2014-08-26 | Palantir Technologies, Inc. | Prioritizing data clusters with customizable scoring strategies |
US8909656B2 (en) | 2013-03-15 | 2014-12-09 | Palantir Technologies Inc. | Filter chains with associated multipath views for exploring large data sets |
US8917274B2 (en) | 2013-03-15 | 2014-12-23 | Palantir Technologies Inc. | Event matrix based on integrated data |
US8799799B1 (en) | 2013-05-07 | 2014-08-05 | Palantir Technologies Inc. | Interactive geospatial map |
US20140365206A1 (en) * | 2013-06-06 | 2014-12-11 | Xerox Corporation | Method and system for idea spotting in idea-generating social media platforms |
US9230015B2 (en) * | 2013-07-02 | 2016-01-05 | Hewlett-Packard Development Company, L.P. | Deriving an interestingness measure for a cluster |
US8713467B1 (en) | 2013-08-09 | 2014-04-29 | Palantir Technologies, Inc. | Context-sensitive views |
US9639818B2 (en) * | 2013-08-30 | 2017-05-02 | Sap Se | Creation of event types for news mining for enterprise resource planning |
JP5669904B1 (en) * | 2013-09-06 | 2015-02-18 | 株式会社Ubic | Document search system, document search method, and document search program for providing prior information |
US8938686B1 (en) | 2013-10-03 | 2015-01-20 | Palantir Technologies Inc. | Systems and methods for analyzing performance of an entity |
US9116975B2 (en) | 2013-10-18 | 2015-08-25 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive simultaneous querying of multiple data stores |
US8924872B1 (en) | 2013-10-18 | 2014-12-30 | Palantir Technologies Inc. | Overview user interface of emergency call data of a law enforcement agency |
US9021384B1 (en) | 2013-11-04 | 2015-04-28 | Palantir Technologies Inc. | Interactive vehicle information map |
US8868537B1 (en) | 2013-11-11 | 2014-10-21 | Palantir Technologies, Inc. | Simple web search |
US9105000B1 (en) | 2013-12-10 | 2015-08-11 | Palantir Technologies Inc. | Aggregating data from a plurality of data sources |
US10025834B2 (en) | 2013-12-16 | 2018-07-17 | Palantir Technologies Inc. | Methods and systems for analyzing entity performance |
US10356032B2 (en) | 2013-12-26 | 2019-07-16 | Palantir Technologies Inc. | System and method for detecting confidential information emails |
US9619521B1 (en) * | 2013-12-30 | 2017-04-11 | Google Inc. | Classification using concept ranking according to negative exemplars |
US8832832B1 (en) | 2014-01-03 | 2014-09-09 | Palantir Technologies Inc. | IP reputation |
KR20150081981A (en) * | 2014-01-07 | 2015-07-15 | 삼성전자주식회사 | Apparatus and Method for structuring contents of meeting |
WO2015118616A1 (en) * | 2014-02-04 | 2015-08-13 | 株式会社Ubic | Document analysis system, document analysis method, and document analysis program |
US9836520B2 (en) | 2014-02-12 | 2017-12-05 | International Business Machines Corporation | System and method for automatically validating classified data objects |
US9483162B2 (en) | 2014-02-20 | 2016-11-01 | Palantir Technologies Inc. | Relationship visualizations |
US9727376B1 (en) | 2014-03-04 | 2017-08-08 | Palantir Technologies, Inc. | Mobile tasks |
US20150254791A1 (en) * | 2014-03-10 | 2015-09-10 | Fmr Llc | Quality control calculator for document review |
US9684705B1 (en) * | 2014-03-14 | 2017-06-20 | Symantec Corporation | Systems and methods for clustering data |
US8924429B1 (en) | 2014-03-18 | 2014-12-30 | Palantir Technologies Inc. | Determining and extracting changed data from a data source |
US9857958B2 (en) | 2014-04-28 | 2018-01-02 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive access of, investigation of, and analysis of data objects stored in one or more databases |
US9009171B1 (en) | 2014-05-02 | 2015-04-14 | Palantir Technologies Inc. | Systems and methods for active column filtering |
US10102277B2 (en) | 2014-05-15 | 2018-10-16 | Sentient Technologies (Barbados) Limited | Bayesian visual interactive search |
US10606883B2 (en) | 2014-05-15 | 2020-03-31 | Evolv Technology Solutions, Inc. | Selection of initial document collection for visual interactive search |
US20150331908A1 (en) | 2014-05-15 | 2015-11-19 | Genetic Finance (Barbados) Limited | Visual interactive search |
US9535910B2 (en) | 2014-05-31 | 2017-01-03 | International Business Machines Corporation | Corpus generation based upon document attributes |
US9338493B2 (en) | 2014-06-30 | 2016-05-10 | Apple Inc. | Intelligent automated assistant for TV user interactions |
US9535974B1 (en) | 2014-06-30 | 2017-01-03 | Palantir Technologies Inc. | Systems and methods for identifying key phrase clusters within documents |
US9619557B2 (en) | 2014-06-30 | 2017-04-11 | Palantir Technologies, Inc. | Systems and methods for key phrase characterization of documents |
US9256664B2 (en) | 2014-07-03 | 2016-02-09 | Palantir Technologies Inc. | System and method for news events detection and visualization |
US9202249B1 (en) | 2014-07-03 | 2015-12-01 | Palantir Technologies Inc. | Data item clustering and analysis |
US9779171B2 (en) * | 2014-08-29 | 2017-10-03 | Linkedin Corporation | Faceting search results |
US9454281B2 (en) | 2014-09-03 | 2016-09-27 | Palantir Technologies Inc. | System for providing dynamic linked panels in user interface |
US9501851B2 (en) | 2014-10-03 | 2016-11-22 | Palantir Technologies Inc. | Time-series analysis system |
US9767172B2 (en) | 2014-10-03 | 2017-09-19 | Palantir Technologies Inc. | Data aggregation and analysis system |
US9785328B2 (en) | 2014-10-06 | 2017-10-10 | Palantir Technologies Inc. | Presentation of multivariate data on a graphical user interface of a computing system |
US9984133B2 (en) | 2014-10-16 | 2018-05-29 | Palantir Technologies Inc. | Schematic and database linking system |
US10002543B2 (en) * | 2014-11-04 | 2018-06-19 | Knotbird LLC | System and methods for transforming language into interactive elements |
US9229952B1 (en) | 2014-11-05 | 2016-01-05 | Palantir Technologies, Inc. | History preserving data pipeline system and method |
US9043894B1 (en) | 2014-11-06 | 2015-05-26 | Palantir Technologies Inc. | Malicious software detection in a computing system |
US20160162458A1 (en) * | 2014-12-09 | 2016-06-09 | Idibon, Inc. | Graphical systems and methods for human-in-the-loop machine intelligence |
US10552994B2 (en) | 2014-12-22 | 2020-02-04 | Palantir Technologies Inc. | Systems and interactive user interfaces for dynamic retrieval, analysis, and triage of data items |
US10362133B1 (en) | 2014-12-22 | 2019-07-23 | Palantir Technologies Inc. | Communication data processing architecture |
US9367872B1 (en) | 2014-12-22 | 2016-06-14 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive investigation of bad actor behavior based on automatic clustering of related data in various data structures |
US9348920B1 (en) | 2014-12-22 | 2016-05-24 | Palantir Technologies Inc. | Concept indexing among database of documents using machine learning techniques |
US9870205B1 (en) | 2014-12-29 | 2018-01-16 | Palantir Technologies Inc. | Storing logical units of program code generated using a dynamic programming notebook user interface |
US9335911B1 (en) | 2014-12-29 | 2016-05-10 | Palantir Technologies Inc. | Interactive user interface for dynamic data analysis exploration and query processing |
US9817563B1 (en) | 2014-12-29 | 2017-11-14 | Palantir Technologies Inc. | System and method of generating data points from one or more data stores of data items for chart creation and manipulation |
US10372879B2 (en) | 2014-12-31 | 2019-08-06 | Palantir Technologies Inc. | Medical claims lead summary report generation |
US9727560B2 (en) | 2015-02-25 | 2017-08-08 | Palantir Technologies Inc. | Systems and methods for organizing and identifying documents via hierarchies and dimensions of tags |
EP3611632A1 (en) | 2015-03-16 | 2020-02-19 | Palantir Technologies Inc. | Displaying attribute and event data along paths |
US9886467B2 (en) | 2015-03-19 | 2018-02-06 | Plantir Technologies Inc. | System and method for comparing and visualizing data entities and data entity series |
EP3309686B1 (en) * | 2015-04-10 | 2020-08-19 | Soliton Systems K.K. | Electronic mail transmission error determination device, electronic mail transmission system, and recording medium |
US11010768B2 (en) * | 2015-04-30 | 2021-05-18 | Oracle International Corporation | Character-based attribute value extraction system |
US10671675B2 (en) | 2015-06-19 | 2020-06-02 | Gordon V. Cormack | Systems and methods for a scalable continuous active learning approach to information classification |
US9760564B2 (en) * | 2015-07-09 | 2017-09-12 | International Business Machines Corporation | Extracting veiled meaning in natural language content |
US9392008B1 (en) | 2015-07-23 | 2016-07-12 | Palantir Technologies Inc. | Systems and methods for identifying information related to payment card breaches |
US9454785B1 (en) | 2015-07-30 | 2016-09-27 | Palantir Technologies Inc. | Systems and user interfaces for holistic, data-driven investigation of bad actor behavior based on clustering and scoring of related data |
US9996595B2 (en) | 2015-08-03 | 2018-06-12 | Palantir Technologies, Inc. | Providing full data provenance visualization for versioned datasets |
US9456000B1 (en) | 2015-08-06 | 2016-09-27 | Palantir Technologies Inc. | Systems, methods, user interfaces, and computer-readable media for investigating potential malicious communications |
US10489391B1 (en) | 2015-08-17 | 2019-11-26 | Palantir Technologies Inc. | Systems and methods for grouping and enriching data items accessed from one or more databases for presentation in a user interface |
US9600146B2 (en) | 2015-08-17 | 2017-03-21 | Palantir Technologies Inc. | Interactive geospatial map |
US10853378B1 (en) | 2015-08-25 | 2020-12-01 | Palantir Technologies Inc. | Electronic note management via a connected entity graph |
US11150917B2 (en) | 2015-08-26 | 2021-10-19 | Palantir Technologies Inc. | System for data aggregation and analysis of data from a plurality of data sources |
US9485265B1 (en) | 2015-08-28 | 2016-11-01 | Palantir Technologies Inc. | Malicious activity detection system capable of efficiently processing data accessed from databases and generating alerts for display in interactive user interfaces |
US10706434B1 (en) | 2015-09-01 | 2020-07-07 | Palantir Technologies Inc. | Methods and systems for determining location information |
US9576015B1 (en) | 2015-09-09 | 2017-02-21 | Palantir Technologies, Inc. | Domain-specific language for dataset transformations |
US10896377B2 (en) | 2015-09-10 | 2021-01-19 | International Business Machines Corporation | Categorizing concept terms for game-based training in cognitive computing systems |
US11188844B2 (en) | 2015-09-10 | 2021-11-30 | International Business Machines Corporation | Game-based training for cognitive computing systems |
US10296617B1 (en) | 2015-10-05 | 2019-05-21 | Palantir Technologies Inc. | Searches of highly structured data |
US9424669B1 (en) | 2015-10-21 | 2016-08-23 | Palantir Technologies Inc. | Generating graphical representations of event participation flow |
US10613722B1 (en) | 2015-10-27 | 2020-04-07 | Palantir Technologies Inc. | Distorting a graph on a computer display to improve the computer's ability to display the graph to, and interact with, a user |
US10691473B2 (en) | 2015-11-06 | 2020-06-23 | Apple Inc. | Intelligent automated assistant in a messaging environment |
US9665628B1 (en) | 2015-12-06 | 2017-05-30 | Xeeva, Inc. | Systems and/or methods for automatically classifying and enriching data records imported from big data and/or other sources to help ensure data integrity and consistency |
WO2017098475A1 (en) * | 2015-12-09 | 2017-06-15 | Sentient Technologies (Barbados) Limited | Bayesian visual interactive search |
US9542446B1 (en) | 2015-12-17 | 2017-01-10 | Palantir Technologies, Inc. | Automatic generation of composite datasets based on hierarchical fields |
EP3395019B1 (en) | 2015-12-21 | 2022-03-30 | Google LLC | Automatic suggestions and other content for messaging applications |
WO2017112786A1 (en) | 2015-12-21 | 2017-06-29 | Google Inc. | Automatic suggestions for message exchange threads |
US10268735B1 (en) | 2015-12-29 | 2019-04-23 | Palantir Technologies Inc. | Graph based resolution of matching items in data sources |
US9823818B1 (en) | 2015-12-29 | 2017-11-21 | Palantir Technologies Inc. | Systems and interactive user interfaces for automatic generation of temporal representation of data objects |
US9612723B1 (en) * | 2015-12-30 | 2017-04-04 | Palantir Technologies Inc. | Composite graphical interface with shareable data-objects |
US11086640B2 (en) * | 2015-12-30 | 2021-08-10 | Palantir Technologies Inc. | Composite graphical interface with shareable data-objects |
CN108701118B (en) * | 2016-02-11 | 2022-06-24 | 电子湾有限公司 | Semantic category classification |
US10094674B2 (en) * | 2016-02-16 | 2018-10-09 | Ford Global Technologies, Llc | Predictive vehicle task scheduling |
CA3016598A1 (en) * | 2016-03-04 | 2017-09-08 | Findmine | Methods, systems, and media for providing information based on grouping information |
US10698938B2 (en) | 2016-03-18 | 2020-06-30 | Palantir Technologies Inc. | Systems and methods for organizing and identifying documents via hierarchies and dimensions of tags |
US10235395B2 (en) * | 2016-03-28 | 2019-03-19 | International Business Machines Corporation | Keyword identification for an enterprise resource planning manager |
US10650558B2 (en) | 2016-04-04 | 2020-05-12 | Palantir Technologies Inc. | Techniques for displaying stack graphs |
AU2017274558B2 (en) * | 2016-06-02 | 2021-11-11 | Nuix North America Inc. | Analyzing clusters of coded documents |
US10095747B1 (en) * | 2016-06-06 | 2018-10-09 | @Legal Discovery LLC | Similar document identification using artificial intelligence |
US10776399B1 (en) | 2016-06-06 | 2020-09-15 | Casepoint LLC | Document classification prediction and content analytics using artificial intelligence |
US10909459B2 (en) | 2016-06-09 | 2021-02-02 | Cognizant Technology Solutions U.S. Corporation | Content embedding using deep metric learning algorithms |
US10007674B2 (en) | 2016-06-13 | 2018-06-26 | Palantir Technologies Inc. | Data revision control in large-scale data analytic systems |
US20170357909A1 (en) * | 2016-06-14 | 2017-12-14 | Xerox Corporation | System and method to efficiently label documents alternating machine and human labelling steps |
US10324609B2 (en) | 2016-07-21 | 2019-06-18 | Palantir Technologies Inc. | System for providing dynamic linked panels in user interface |
US10719188B2 (en) | 2016-07-21 | 2020-07-21 | Palantir Technologies Inc. | Cached database and synchronization system for providing dynamic linked panels in user interface |
US9740368B1 (en) * | 2016-08-10 | 2017-08-22 | Quid, Inc. | Positioning labels on graphical visualizations of graphs |
US10635727B2 (en) | 2016-08-16 | 2020-04-28 | Ebay Inc. | Semantic forward search indexing of publication corpus |
US10387461B2 (en) | 2016-08-16 | 2019-08-20 | Google Llc | Techniques for suggesting electronic messages based on user activity and other context |
US10437840B1 (en) | 2016-08-19 | 2019-10-08 | Palantir Technologies Inc. | Focused probabilistic entity resolution from multiple data sources |
US9881066B1 (en) | 2016-08-31 | 2018-01-30 | Palantir Technologies, Inc. | Systems, methods, user interfaces and algorithms for performing database analysis and search of information involving structured and/or semi-structured data |
CN109716727B (en) | 2016-09-20 | 2021-10-15 | 谷歌有限责任公司 | Method and system for obtaining permission to access data associated with a user |
US10015124B2 (en) * | 2016-09-20 | 2018-07-03 | Google Llc | Automatic response suggestions based on images received in messaging applications |
US10547574B2 (en) | 2016-09-20 | 2020-01-28 | Google Llc | Suggested responses based on message stickers |
US20180101773A1 (en) * | 2016-10-07 | 2018-04-12 | Futurewei Technologies, Inc. | Apparatus and method for spatial processing of concepts |
US10127268B2 (en) * | 2016-10-07 | 2018-11-13 | Microsoft Technology Licensing, Llc | Repairing data through domain knowledge |
CN107092618A (en) * | 2016-10-27 | 2017-08-25 | 北京小度信息科技有限公司 | A kind of information processing method and device |
US11681942B2 (en) | 2016-10-27 | 2023-06-20 | Dropbox, Inc. | Providing intelligent file name suggestions |
US9852377B1 (en) * | 2016-11-10 | 2017-12-26 | Dropbox, Inc. | Providing intelligent storage location suggestions |
US10416846B2 (en) | 2016-11-12 | 2019-09-17 | Google Llc | Determining graphical element(s) for inclusion in an electronic communication |
US10318630B1 (en) | 2016-11-21 | 2019-06-11 | Palantir Technologies Inc. | Analysis of large bodies of textual data |
US11205103B2 (en) | 2016-12-09 | 2021-12-21 | The Research Foundation for the State University | Semisupervised autoencoder for sentiment analysis |
USD837257S1 (en) * | 2016-12-12 | 2019-01-01 | Caterpillar Inc. | Display screen or portion thereof with graphical user interface set |
US9715495B1 (en) * | 2016-12-15 | 2017-07-25 | Quid, Inc. | Topic-influenced document relationship graphs |
US10552436B2 (en) | 2016-12-28 | 2020-02-04 | Palantir Technologies Inc. | Systems and methods for retrieving and processing data for display |
US10460602B1 (en) | 2016-12-28 | 2019-10-29 | Palantir Technologies Inc. | Interactive vehicle information mapping system |
US10972297B2 (en) | 2017-01-23 | 2021-04-06 | Bank Of America Corporation | Data processing system with machine learning engine to provide automated collaboration assistance functions |
US10297255B2 (en) | 2017-01-23 | 2019-05-21 | Bank Of America Corporation | Data processing system with machine learning engine to provide automated collaboration assistance functions |
US10146768B2 (en) | 2017-01-25 | 2018-12-04 | Google Llc | Automatic suggested responses to images received in messages using language model |
RU2647643C1 (en) * | 2017-01-25 | 2018-03-16 | Акционерное общество "Кросс технолоджис" | System for establishing a confidentiality mark in an electronic document, accounting and control of work with confidential electronic documents |
US10733224B2 (en) * | 2017-02-07 | 2020-08-04 | International Business Machines Corporation | Automatic corpus selection and halting condition detection for semantic asset expansion |
US10740557B1 (en) | 2017-02-14 | 2020-08-11 | Casepoint LLC | Technology platform for data discovery |
US11158012B1 (en) | 2017-02-14 | 2021-10-26 | Casepoint LLC | Customizing a data discovery user interface based on artificial intelligence |
US11275794B1 (en) * | 2017-02-14 | 2022-03-15 | Casepoint LLC | CaseAssist story designer |
US10475219B1 (en) | 2017-03-30 | 2019-11-12 | Palantir Technologies Inc. | Multidimensional arc chart for visual comparison |
DK180048B1 (en) | 2017-05-11 | 2020-02-04 | Apple Inc. | MAINTAINING THE DATA PROTECTION OF PERSONAL INFORMATION |
DK179496B1 (en) | 2017-05-12 | 2019-01-15 | Apple Inc. | USER-SPECIFIC Acoustic Models |
DK201770429A1 (en) | 2017-05-12 | 2018-12-14 | Apple Inc. | Low-latency intelligent automated assistant |
US10891485B2 (en) | 2017-05-16 | 2021-01-12 | Google Llc | Image archival based on image categories |
US10776566B2 (en) * | 2017-05-24 | 2020-09-15 | Nathan J. DeVries | System and method of document generation |
US10956406B2 (en) | 2017-06-12 | 2021-03-23 | Palantir Technologies Inc. | Propagated deletion of database records and derived data |
US10348658B2 (en) | 2017-06-15 | 2019-07-09 | Google Llc | Suggested items for use with embedded applications in chat conversations |
US10404636B2 (en) | 2017-06-15 | 2019-09-03 | Google Llc | Embedded programs and interfaces for chat conversations |
US11244765B2 (en) * | 2017-06-28 | 2022-02-08 | Ayasdi Ai Llc | Systems and methods for topological data analysis using nearest neighbors |
US10403011B1 (en) | 2017-07-18 | 2019-09-03 | Palantir Technologies Inc. | Passing system with an interactive user interface |
US10755142B2 (en) | 2017-09-05 | 2020-08-25 | Cognizant Technology Solutions U.S. Corporation | Automated and unsupervised generation of real-world training data |
US10755144B2 (en) | 2017-09-05 | 2020-08-25 | Cognizant Technology Solutions U.S. Corporation | Automated and unsupervised generation of real-world training data |
US20190182197A1 (en) * | 2017-10-10 | 2019-06-13 | Soliton Systems K.K. | Warning apparatus for preventing electronic mail wrong transmission, electronic mail transmission system, and program |
US10616280B2 (en) | 2017-10-25 | 2020-04-07 | Bank Of America Corporation | Network security system with cognitive engine for dynamic automation |
US10437984B2 (en) | 2017-10-26 | 2019-10-08 | Bank Of America Corporation | Authentication protocol elevation triggering system |
US10686684B2 (en) | 2017-11-02 | 2020-06-16 | Bank Of America Corporation | Individual application flow isotope tagging within a network infrastructure |
US11615129B2 (en) * | 2017-11-28 | 2023-03-28 | International Business Machines Corporation | Electronic message text classification framework selection |
US10929476B2 (en) | 2017-12-14 | 2021-02-23 | Palantir Technologies Inc. | Systems and methods for visualizing and analyzing multi-dimensional data |
US10891526B2 (en) | 2017-12-22 | 2021-01-12 | Google Llc | Functional image archiving |
US11599369B1 (en) | 2018-03-08 | 2023-03-07 | Palantir Technologies Inc. | Graphical user interface configuration system |
CN108520740B (en) * | 2018-04-13 | 2022-04-19 | 国家计算机网络与信息安全管理中心 | Audio content consistency analysis method and analysis system based on multiple characteristics |
US10754822B1 (en) | 2018-04-18 | 2020-08-25 | Palantir Technologies Inc. | Systems and methods for ontology migration |
US10885021B1 (en) | 2018-05-02 | 2021-01-05 | Palantir Technologies Inc. | Interactive interpreter and graphical user interface |
US10928918B2 (en) | 2018-05-07 | 2021-02-23 | Apple Inc. | Raise to speak |
CN108733791B (en) * | 2018-05-11 | 2020-11-20 | 北京科技大学 | Network event detection method |
DK180639B1 (en) | 2018-06-01 | 2021-11-04 | Apple Inc | DISABILITY OF ATTENTION-ATTENTIVE VIRTUAL ASSISTANT |
CN108897832B (en) * | 2018-06-22 | 2021-09-03 | 申报家(广州)智能科技发展有限公司 | Method and device for automatically analyzing value information |
US10902066B2 (en) | 2018-07-23 | 2021-01-26 | Open Text Holdings, Inc. | Electronic discovery using predictive filtering |
RU2699234C1 (en) * | 2018-08-08 | 2019-09-05 | Общество с ограниченной ответственностью "Инновационные технологии" | Method of safe use of an electronic document |
US11698921B2 (en) | 2018-09-17 | 2023-07-11 | Ebay Inc. | Search system for providing search results using query understanding and semantic binary signatures |
US11462215B2 (en) | 2018-09-28 | 2022-10-04 | Apple Inc. | Multi-modal inputs for voice commands |
US11144185B1 (en) * | 2018-09-28 | 2021-10-12 | Splunk Inc. | Generating and providing concurrent journey visualizations associated with different journey definitions |
US11762869B1 (en) | 2018-09-28 | 2023-09-19 | Splunk Inc. | Generating journey flow visualization with node placement based on shortest distance to journey start |
JP7322438B2 (en) * | 2019-03-14 | 2023-08-08 | 富士フイルムビジネスイノベーション株式会社 | Information processing device and information processing program |
US11348573B2 (en) | 2019-03-18 | 2022-05-31 | Apple Inc. | Multimodality in digital assistant systems |
US11307752B2 (en) | 2019-05-06 | 2022-04-19 | Apple Inc. | User configurable task triggers |
US11269812B2 (en) | 2019-05-10 | 2022-03-08 | International Business Machines Corporation | Derived relationship for collaboration documents |
EP3882786A4 (en) * | 2019-05-17 | 2022-03-23 | Aixs, Inc. | Cluster analysis method, cluster analysis system, and cluster analysis program |
US20200380389A1 (en) * | 2019-05-31 | 2020-12-03 | Apple Inc. | Sentiment and intent analysis for customizing suggestions using user-specific information |
US11227599B2 (en) | 2019-06-01 | 2022-01-18 | Apple Inc. | Methods and user interfaces for voice-based control of electronic devices |
US11556845B2 (en) * | 2019-08-29 | 2023-01-17 | International Business Machines Corporation | System for identifying duplicate parties using entity resolution |
US11334716B2 (en) | 2019-09-09 | 2022-05-17 | International Business Machines Corporation | Document anonymization including selective token modification |
US11551006B2 (en) * | 2019-09-09 | 2023-01-10 | International Business Machines Corporation | Removal of personality signatures |
US11361025B2 (en) * | 2020-02-03 | 2022-06-14 | ZenPayroll, Inc. | Automated field placement for uploaded documents |
KR102425770B1 (en) * | 2020-04-13 | 2022-07-28 | 네이버 주식회사 | Method and system for providing search terms whose popularity increases rapidly |
US11061543B1 (en) | 2020-05-11 | 2021-07-13 | Apple Inc. | Providing relevant data items based on context |
US11490204B2 (en) | 2020-07-20 | 2022-11-01 | Apple Inc. | Multi-device audio adjustment coordination |
US11438683B2 (en) | 2020-07-21 | 2022-09-06 | Apple Inc. | User identification using headphones |
WO2022035942A1 (en) * | 2020-08-11 | 2022-02-17 | Nationstar Mortgage LLC, d/b/a/ Mr. Cooper | Systems and methods for machine learning-based document classification |
US11669704B2 (en) | 2020-09-02 | 2023-06-06 | Kyocera Document Solutions Inc. | Document classification neural network and OCR-to-barcode conversion |
US11874200B2 (en) * | 2020-09-08 | 2024-01-16 | International Business Machines Corporation | Digital twin enabled equipment diagnostics based on acoustic modeling |
US11809694B2 (en) * | 2020-09-30 | 2023-11-07 | Aon Risk Services, Inc. Of Maryland | Intellectual-property landscaping platform with interactive graphical element |
US11600362B2 (en) | 2020-09-30 | 2023-03-07 | International Business Machines Corporation | Visually representing concepts and relationships on an electronic interface for delivered content |
JP2022059725A (en) * | 2020-10-02 | 2022-04-14 | 富士フイルムビジネスイノベーション株式会社 | Information processor, information processing system, and program |
Family Cites Families (371)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US3416150A (en) | 1965-08-02 | 1968-12-10 | Data Disc Inc | Magnetic recording disc cartridge |
US3426210A (en) | 1965-12-22 | 1969-02-04 | Rca Corp | Control circuit for automatically quantizing signals at desired levels |
BE757866A (en) | 1969-12-22 | 1971-04-01 | Ibm | MAGNETIC DISC MEMORY ELEMENT AND RECORDING APPARATUS IN USE |
JPS6283787A (en) * | 1985-10-09 | 1987-04-17 | 株式会社日立製作所 | Output control system for display screen |
US4930077A (en) * | 1987-04-06 | 1990-05-29 | Fan David P | Information processing expert system for text analysis and predicting public opinion based information available to the public |
GB8719572D0 (en) | 1987-08-19 | 1987-09-23 | Krebs M S | Sigscan text retrieval system |
US5121338A (en) | 1988-03-10 | 1992-06-09 | Indiana University Foundation | Method for detecting subpopulations in spectral analysis |
US4893253A (en) * | 1988-03-10 | 1990-01-09 | Indiana University Foundation | Method for analyzing intact capsules and tablets by near-infrared reflectance spectrometry |
US5056021A (en) | 1989-06-08 | 1991-10-08 | Carolyn Ausborn | Method and apparatus for abstracting concepts from natural language |
US5860136A (en) * | 1989-06-16 | 1999-01-12 | Fenner; Peter R. | Method and apparatus for use of associated memory with large key spaces |
US6089742A (en) | 1989-11-01 | 2000-07-18 | Warmerdam; Thomas P. H. | Method and apparatus for controlling robots and the like using a bubble data hierarchy placed along a medial axis |
US5276789A (en) | 1990-05-14 | 1994-01-04 | Hewlett-Packard Co. | Graphic display of network topology |
US5182773A (en) | 1991-03-22 | 1993-01-26 | International Business Machines Corporation | Speaker-independent label coding apparatus |
US5477451A (en) | 1991-07-25 | 1995-12-19 | International Business Machines Corp. | Method and system for natural language translation |
US5278980A (en) | 1991-08-16 | 1994-01-11 | Xerox Corporation | Iterative technique for phrase query formation and an information retrieval system employing same |
US5488725A (en) * | 1991-10-08 | 1996-01-30 | West Publishing Company | System of document representation retrieval by successive iterated probability sampling |
US5442778A (en) * | 1991-11-12 | 1995-08-15 | Xerox Corporation | Scatter-gather: a cluster-based method and apparatus for browsing large document collections |
US5371807A (en) | 1992-03-20 | 1994-12-06 | Digital Equipment Corporation | Method and apparatus for text classification |
US5359724A (en) | 1992-03-30 | 1994-10-25 | Arbor Software Corporation | Method and apparatus for storing and retrieving multi-dimensional data in computer memory |
JP3566720B2 (en) * | 1992-04-30 | 2004-09-15 | アプル・コンピュータ・インコーポレーテッド | Method and apparatus for organizing information in a computer system |
JP3364242B2 (en) | 1992-07-03 | 2003-01-08 | 株式会社東芝 | Link learning device for artificial neural networks |
EP0615201B1 (en) | 1993-03-12 | 2001-01-10 | Kabushiki Kaisha Toshiba | Document detection system using detection result presentation for facilitating user's comprehension |
US5528735A (en) * | 1993-03-23 | 1996-06-18 | Silicon Graphics Inc. | Method and apparatus for displaying data within a three-dimensional information landscape |
JPH0756933A (en) | 1993-06-24 | 1995-03-03 | Xerox Corp | Method for retrieval of document |
US6173275B1 (en) | 1993-09-20 | 2001-01-09 | Hnc Software, Inc. | Representation and retrieval of images using context vectors derived from image information elements |
US5619709A (en) | 1993-09-20 | 1997-04-08 | Hnc, Inc. | System and method of context vector generation and retrieval |
US7251637B1 (en) | 1993-09-20 | 2007-07-31 | Fair Isaac Corporation | Context vector generation and retrieval |
US5450535A (en) | 1993-09-24 | 1995-09-12 | At&T Corp. | Graphs employing clusters |
US5675819A (en) | 1994-06-16 | 1997-10-07 | Xerox Corporation | Document information retrieval using global word co-occurrence patterns |
US5959699A (en) * | 1994-06-28 | 1999-09-28 | Samsung Electronics Co., Ltd. | Reception mode control in radio receivers for receiving both VSB and QAM digital television signals |
US5706497A (en) | 1994-08-15 | 1998-01-06 | Nec Research Institute, Inc. | Document retrieval using fuzzy-logic inference |
US5619632A (en) * | 1994-09-14 | 1997-04-08 | Xerox Corporation | Displaying node-link structure with region of greater spacings and peripheral branches |
US5758257A (en) | 1994-11-29 | 1998-05-26 | Herz; Frederick | System and method for scheduling broadcast of and access to video programs and other data using customer profiles |
US5635929A (en) | 1995-02-13 | 1997-06-03 | Hughes Aircraft Company | Low bit rate video encoder and decoder |
US5724571A (en) | 1995-07-07 | 1998-03-03 | Sun Microsystems, Inc. | Method and apparatus for generating query responses in a computer-based document retrieval system |
US5844991A (en) | 1995-08-07 | 1998-12-01 | The Regents Of The University Of California | Script identification from images using cluster-based templates |
US6006221A (en) * | 1995-08-16 | 1999-12-21 | Syracuse University | Multilingual document retrieval system and method using semantic vector matching |
US5737734A (en) * | 1995-09-15 | 1998-04-07 | Infonautics Corporation | Query word relevance adjustment in a search of an information retrieval system |
US5799276A (en) * | 1995-11-07 | 1998-08-25 | Accent Incorporated | Knowledge-based speech recognition system and methods having frame length computed based upon estimated pitch period of vocalic intervals |
US5842203A (en) * | 1995-12-01 | 1998-11-24 | International Business Machines Corporation | Method and system for performing non-boolean search queries in a graphical user interface |
US5787422A (en) | 1996-01-11 | 1998-07-28 | Xerox Corporation | Method and apparatus for information accesss employing overlapping clusters |
US5819260A (en) | 1996-01-22 | 1998-10-06 | Lexis-Nexis | Phrase recognition method and apparatus |
US5862325A (en) | 1996-02-29 | 1999-01-19 | Intermind Corporation | Computer-based communication system and method using metadata defining a control structure |
US5867799A (en) * | 1996-04-04 | 1999-02-02 | Lang; Andrew K. | Information system and method for filtering a massive flow of information entities to meet user information classification needs |
US6026397A (en) * | 1996-05-22 | 2000-02-15 | Electronic Data Systems Corporation | Data analysis system and method |
US5794236A (en) * | 1996-05-29 | 1998-08-11 | Lexis-Nexis | Computer-based system for classifying documents into a hierarchy and linking the classifications to the hierarchy |
US5864871A (en) | 1996-06-04 | 1999-01-26 | Multex Systems | Information delivery system and method including on-line entitlements |
US6453327B1 (en) | 1996-06-10 | 2002-09-17 | Sun Microsystems, Inc. | Method and apparatus for identifying and discarding junk electronic mail |
US5915249A (en) | 1996-06-14 | 1999-06-22 | Excite, Inc. | System and method for accelerated query evaluation of very large full-text databases |
JP3540511B2 (en) | 1996-06-18 | 2004-07-07 | 株式会社東芝 | Electronic signature verification device |
US5909677A (en) * | 1996-06-18 | 1999-06-01 | Digital Equipment Corporation | Method for determining the resemblance of documents |
US5864846A (en) * | 1996-06-28 | 1999-01-26 | Siemens Corporate Research, Inc. | Method for facilitating world wide web searches utilizing a document distribution fusion strategy |
US5920854A (en) | 1996-08-14 | 1999-07-06 | Infoseek Corporation | Real-time document collection search engine with phrase indexing |
US6108637A (en) | 1996-09-03 | 2000-08-22 | Nielsen Media Research, Inc. | Content display monitor |
US5857179A (en) * | 1996-09-09 | 1999-01-05 | Digital Equipment Corporation | Computer method and apparatus for clustering documents and automatic generation of cluster keywords |
US6484168B1 (en) | 1996-09-13 | 2002-11-19 | Battelle Memorial Institute | System for information discovery |
US6092091A (en) | 1996-09-13 | 2000-07-18 | Kabushiki Kaisha Toshiba | Device and method for filtering information, device and method for monitoring updated document information and information storage medium used in same devices |
US5870740A (en) | 1996-09-30 | 1999-02-09 | Apple Computer, Inc. | System and method for improving the ranking of information retrieval results for short queries |
US5950146A (en) | 1996-10-04 | 1999-09-07 | At & T Corp. | Support vector method for function estimation |
US5987446A (en) * | 1996-11-12 | 1999-11-16 | U.S. West, Inc. | Searching large collections of text using multiple search engines concurrently |
JP3598742B2 (en) | 1996-11-25 | 2004-12-08 | 富士ゼロックス株式会社 | Document search device and document search method |
US5966126A (en) | 1996-12-23 | 1999-10-12 | Szabo; Andrew J. | Graphic user interface for database system |
US6253218B1 (en) | 1996-12-26 | 2001-06-26 | Atsushi Aoki | Three dimensional data display method utilizing view point tracing and reduced document images |
US6092059A (en) | 1996-12-27 | 2000-07-18 | Cognex Corporation | Automatic classifier for real time inspection and classification |
US5950189A (en) * | 1997-01-02 | 1999-09-07 | At&T Corp | Retrieval system and method |
US5924105A (en) * | 1997-01-27 | 1999-07-13 | Michigan State University | Method and product for determining salient features for use in information searching |
JP3550929B2 (en) | 1997-01-28 | 2004-08-04 | 富士通株式会社 | Reference frequency counting apparatus and method in interactive hypertext information reference system |
US6108446A (en) | 1997-02-18 | 2000-08-22 | Hoshen; Joseph | Method and apparatus for extracting cluster shape features from digital images |
US5819258A (en) * | 1997-03-07 | 1998-10-06 | Digital Equipment Corporation | Method and apparatus for automatically generating hierarchical categories from large document collections |
US6137499A (en) * | 1997-03-07 | 2000-10-24 | Silicon Graphics, Inc. | Method, system, and computer program product for visualizing data using partial hierarchies |
US5895470A (en) | 1997-04-09 | 1999-04-20 | Xerox Corporation | System for categorizing documents in a linked collection of documents |
US5835905A (en) | 1997-04-09 | 1998-11-10 | Xerox Corporation | System for predicting documents relevant to focus documents by spreading activation through network representations of a linked collection of documents |
US6460034B1 (en) | 1997-05-21 | 2002-10-01 | Oracle Corporation | Document knowledge base research and retrieval system |
US5940821A (en) | 1997-05-21 | 1999-08-17 | Oracle Corporation | Information presentation in a knowledge base search and retrieval system |
US6148102A (en) | 1997-05-29 | 2000-11-14 | Adobe Systems Incorporated | Recognizing text in a multicolor image |
US6154213A (en) | 1997-05-30 | 2000-11-28 | Rennison; Earl F. | Immersive movement-based interaction with large complex information structures |
JPH1115759A (en) | 1997-06-16 | 1999-01-22 | Digital Equip Corp <Dec> | Full text index type mail preserving device |
US6137911A (en) | 1997-06-16 | 2000-10-24 | The Dialog Corporation Plc | Test classification system and method |
GB9713019D0 (en) | 1997-06-20 | 1997-08-27 | Xerox Corp | Linguistic search system |
US6470307B1 (en) | 1997-06-23 | 2002-10-22 | National Research Council Of Canada | Method and apparatus for automatically identifying keywords within a document |
US6012053A (en) | 1997-06-23 | 2000-01-04 | Lycos, Inc. | Computer system with user-controlled relevance ranking of search results |
US6070133A (en) | 1997-07-21 | 2000-05-30 | Battelle Memorial Institute | Information retrieval system utilizing wavelet transform |
US5974412A (en) | 1997-09-24 | 1999-10-26 | Sapient Health Network | Intelligent query system for automatically indexing information in a database and automatically categorizing users |
JP3584458B2 (en) * | 1997-10-31 | 2004-11-04 | ソニー株式会社 | Pattern recognition device and pattern recognition method |
US6134541A (en) | 1997-10-31 | 2000-10-17 | International Business Machines Corporation | Searching multidimensional indexes using associated clustering and dimension reduction information |
US6122628A (en) | 1997-10-31 | 2000-09-19 | International Business Machines Corporation | Multidimensional data clustering and dimension reduction for indexing and searching |
US5987457A (en) | 1997-11-25 | 1999-11-16 | Acceleration Software International Corporation | Query refinement method for searching documents |
US6154219A (en) | 1997-12-01 | 2000-11-28 | Microsoft Corporation | System and method for optimally placing labels on a map |
US6389436B1 (en) * | 1997-12-15 | 2002-05-14 | International Business Machines Corporation | Enhanced hypertext categorization using hyperlinks |
AU1907899A (en) | 1997-12-22 | 1999-07-12 | Accepted Marketing, Inc. | E-mail filter and method thereof |
US6094649A (en) | 1997-12-22 | 2000-07-25 | Partnet, Inc. | Keyword searches of structured databases |
US6449612B1 (en) | 1998-03-17 | 2002-09-10 | Microsoft Corporation | Varying cluster number in a scalable clustering system for use with large databases |
US6038574A (en) | 1998-03-18 | 2000-03-14 | Xerox Corporation | Method and apparatus for clustering a collection of linked documents using co-citation analysis |
US6484196B1 (en) | 1998-03-20 | 2002-11-19 | Advanced Web Solutions | Internet messaging system and method for use in computer networks |
US6119124A (en) | 1998-03-26 | 2000-09-12 | Digital Equipment Corporation | Method for clustering closely resembling data objects |
US6418431B1 (en) | 1998-03-30 | 2002-07-09 | Microsoft Corporation | Information retrieval and speech recognition based on language models |
JPH11316779A (en) | 1998-04-30 | 1999-11-16 | Fujitsu Ltd | Witness system |
US6345243B1 (en) * | 1998-05-27 | 2002-02-05 | Lionbridge Technologies, Inc. | System, method, and product for dynamically propagating translations in a translation-memory system |
US7209949B2 (en) | 1998-05-29 | 2007-04-24 | Research In Motion Limited | System and method for synchronizing information between a host system and a mobile data communication device |
US7266365B2 (en) | 1998-05-29 | 2007-09-04 | Research In Motion Limited | System and method for delayed transmission of bundled command messages |
US6438564B1 (en) | 1998-06-17 | 2002-08-20 | Microsoft Corporation | Method for associating a discussion with a document |
US6100901A (en) | 1998-06-22 | 2000-08-08 | International Business Machines Corporation | Method and apparatus for cluster exploration and visualization |
US6216123B1 (en) | 1998-06-24 | 2001-04-10 | Novell, Inc. | Method and system for rapid retrieval in a full text indexing system |
US6742003B2 (en) * | 2001-04-30 | 2004-05-25 | Microsoft Corporation | Apparatus and accompanying methods for visualizing clusters of data and hierarchical cluster classifications |
JP2000019556A (en) | 1998-06-29 | 2000-01-21 | Hitachi Ltd | Liquid crystal display device |
US6300947B1 (en) | 1998-07-06 | 2001-10-09 | International Business Machines Corporation | Display screen and window size related web page adaptation system |
US6446061B1 (en) | 1998-07-31 | 2002-09-03 | International Business Machines Corporation | Taxonomy generation for document collections |
US6167368A (en) * | 1998-08-14 | 2000-12-26 | The Trustees Of Columbia University In The City Of New York | Method and system for indentifying significant topics of a document |
WO2000011540A1 (en) | 1998-08-24 | 2000-03-02 | Hitachi, Ltd. | Method for displaying multimedia information processing method, processing device, and processing system |
US6243713B1 (en) | 1998-08-24 | 2001-06-05 | Excalibur Technologies Corp. | Multimedia document retrieval by application of multimedia queries to a unified index of multimedia data for a plurality of multimedia data types |
US6414677B1 (en) | 1998-09-14 | 2002-07-02 | Microsoft Corporation | Methods, apparatus and data structures for providing a user interface, which exploits spatial memory in three-dimensions, to objects and which visually groups proximally located objects |
WO2000016209A1 (en) | 1998-09-15 | 2000-03-23 | Local2Me.Com, Inc. | Dynamic matchingtm of users for group communication |
JP3903610B2 (en) | 1998-09-28 | 2007-04-11 | 富士ゼロックス株式会社 | Search device, search method, and computer-readable recording medium storing search program |
US7039856B2 (en) * | 1998-09-30 | 2006-05-02 | Ricoh Co., Ltd. | Automatic document classification using text and images |
GB9821787D0 (en) | 1998-10-06 | 1998-12-02 | Data Limited | Apparatus for classifying or processing data |
US6789230B2 (en) | 1998-10-09 | 2004-09-07 | Microsoft Corporation | Creating a summary having sentences with the highest weight, and lowest length |
US6415283B1 (en) | 1998-10-13 | 2002-07-02 | Orack Corporation | Methods and apparatus for determining focal points of clusters in a tree structure |
US6480843B2 (en) | 1998-11-03 | 2002-11-12 | Nec Usa, Inc. | Supporting web-query expansion efficiently using multi-granularity indexing and query processing |
US6678705B1 (en) | 1998-11-16 | 2004-01-13 | At&T Corp. | System for archiving electronic documents using messaging groupware |
US6628304B2 (en) | 1998-12-09 | 2003-09-30 | Cisco Technology, Inc. | Method and apparatus providing a graphical user interface for representing and navigating hierarchical networks |
US6442592B1 (en) | 1998-12-11 | 2002-08-27 | Micro Computer Systems, Inc. | Message center system |
US6816175B1 (en) | 1998-12-19 | 2004-11-09 | International Business Machines Corporation | Orthogonal browsing in object hierarchies |
JP2000187668A (en) | 1998-12-22 | 2000-07-04 | Hitachi Ltd | Grouping method and overlap excluding method |
US6549957B1 (en) | 1998-12-22 | 2003-04-15 | International Business Machines Corporation | Apparatus for preventing automatic generation of a chain reaction of messages if a prior extracted message is similar to current processed message |
JP2000285140A (en) | 1998-12-24 | 2000-10-13 | Ricoh Co Ltd | Device and method for processing document, device and method for classifying document, and computer readable recording medium recorded with program for allowing computer to execute these methods |
US6349307B1 (en) | 1998-12-28 | 2002-02-19 | U.S. Philips Corporation | Cooperative topical servers with automatic prefiltering and routing |
US6363374B1 (en) | 1998-12-31 | 2002-03-26 | Microsoft Corporation | Text proximity filtering in search systems using same sentence restrictions |
DE60044220D1 (en) | 1999-01-26 | 2010-06-02 | Xerox Corp | Multimodal information access |
US6564202B1 (en) | 1999-01-26 | 2003-05-13 | Xerox Corporation | System and method for visually representing the contents of a multiple data object cluster |
US6922699B2 (en) | 1999-01-26 | 2005-07-26 | Xerox Corporation | System and method for quantitatively representing data objects in vector space |
US6598054B2 (en) | 1999-01-26 | 2003-07-22 | Xerox Corporation | System and method for clustering data objects in a collection |
US6360227B1 (en) * | 1999-01-29 | 2002-03-19 | International Business Machines Corporation | System and method for generating taxonomies with applications to content-based recommendations |
US6941325B1 (en) | 1999-02-01 | 2005-09-06 | The Trustees Of Columbia University | Multimedia archive description scheme |
US8095581B2 (en) * | 1999-02-05 | 2012-01-10 | Gregory A Stobbs | Computer-implemented patent portfolio analysis method and apparatus |
WO2000046701A1 (en) | 1999-02-08 | 2000-08-10 | Huntsman Ici Chemicals Llc | Method for retrieving semantically distant analogies |
US7277919B1 (en) | 1999-03-19 | 2007-10-02 | Bigfix, Inc. | Relevance clause for computed relevance messaging |
US6510406B1 (en) | 1999-03-23 | 2003-01-21 | Mathsoft, Inc. | Inverse inference engine for high performance web search |
US6862710B1 (en) | 1999-03-23 | 2005-03-01 | Insightful Corporation | Internet navigation using soft hyperlinks |
US6408294B1 (en) | 1999-03-31 | 2002-06-18 | Verizon Laboratories Inc. | Common term optimization |
US6647341B1 (en) | 1999-04-09 | 2003-11-11 | Whitehead Institute For Biomedical Research | Methods for classifying samples and ascertaining previously unknown classes |
US6496822B2 (en) | 1999-04-12 | 2002-12-17 | Micron Technology, Inc. | Methods of providing computer systems with bundled access to restricted-access databases |
US6377287B1 (en) | 1999-04-19 | 2002-04-23 | Hewlett-Packard Company | Technique for visualizing large web-based hierarchical hyperbolic space with multi-paths |
US6629097B1 (en) | 1999-04-28 | 2003-09-30 | Douglas K. Keith | Displaying implicit associations among items in loosely-structured data sets |
EP1049030A1 (en) | 1999-04-28 | 2000-11-02 | SER Systeme AG Produkte und Anwendungen der Datenverarbeitung | Classification method and apparatus |
NZ515293A (en) * | 1999-05-05 | 2004-04-30 | West Publishing Company D | Document-classification system, method and software |
US6493703B1 (en) | 1999-05-11 | 2002-12-10 | Prophet Financial Systems | System and method for implementing intelligent online community message board |
US6606625B1 (en) | 1999-06-03 | 2003-08-12 | University Of Southern California | Wrapper induction by hierarchical data analysis |
US6701305B1 (en) | 1999-06-09 | 2004-03-02 | The Boeing Company | Methods, apparatus and computer program products for information retrieval and document classification utilizing a multidimensional subspace |
US6611825B1 (en) | 1999-06-09 | 2003-08-26 | The Boeing Company | Method and system for text mining using multidimensional subspaces |
US8065155B1 (en) | 1999-06-10 | 2011-11-22 | Gazdzinski Robert F | Adaptive advertising apparatus and methods |
US6711585B1 (en) | 1999-06-15 | 2004-03-23 | Kanisa Inc. | System and method for implementing a knowledge management system |
US6438537B1 (en) | 1999-06-22 | 2002-08-20 | Microsoft Corporation | Usage based aggregation optimization |
US6415171B1 (en) | 1999-07-16 | 2002-07-02 | International Business Machines Corporation | System and method for fusing three-dimensional shape data on distorted images without correcting for distortion |
US6389433B1 (en) | 1999-07-16 | 2002-05-14 | Microsoft Corporation | Method and system for automatically merging files into a single instance store |
US7240199B2 (en) | 2000-12-06 | 2007-07-03 | Rpost International Limited | System and method for verifying delivery and integrity of electronic messages |
CA2381460A1 (en) | 1999-08-06 | 2001-02-15 | James S. Wiltshire, Jr. | System and method for classifying legal concepts using legal topic scheme |
US6523063B1 (en) | 1999-08-30 | 2003-02-18 | Zaplet, Inc. | Method system and program product for accessing a file using values from a redirect message string for each change of the link identifier |
US6651057B1 (en) | 1999-09-03 | 2003-11-18 | Bbnt Solutions Llc | Method and apparatus for score normalization for information retrieval applications |
US6542635B1 (en) | 1999-09-08 | 2003-04-01 | Lucent Technologies Inc. | Method for document comparison and classification using document image layout |
US6260038B1 (en) | 1999-09-13 | 2001-07-10 | International Businemss Machines Corporation | Clustering mixed attribute patterns |
US6990238B1 (en) | 1999-09-30 | 2006-01-24 | Battelle Memorial Institute | Data processing, analysis, and visualization system for use with disparate data types |
US6544123B1 (en) | 1999-10-29 | 2003-04-08 | Square Co., Ltd. | Game apparatus, command input method for video game and computer-readable recording medium recording programs for realizing the same |
US8032409B1 (en) | 1999-11-22 | 2011-10-04 | Accenture Global Services Limited | Enhanced visibility during installation management in a network-based supply chain environment |
US7130807B1 (en) | 1999-11-22 | 2006-10-31 | Accenture Llp | Technology sharing during demand and supply planning in a network-based supply chain environment |
US6507847B1 (en) | 1999-12-17 | 2003-01-14 | Openwave Systems Inc. | History database structure for Usenet |
US6751621B1 (en) | 2000-01-27 | 2004-06-15 | Manning & Napier Information Services, Llc. | Construction of trainable semantic vectors and clustering, classification, and searching using trainable semantic vectors |
US6542889B1 (en) | 2000-01-28 | 2003-04-01 | International Business Machines Corporation | Methods and apparatus for similarity text search based on conceptual indexing |
US6654739B1 (en) | 2000-01-31 | 2003-11-25 | International Business Machines Corporation | Lightweight document clustering |
US6571225B1 (en) | 2000-02-11 | 2003-05-27 | International Business Machines Corporation | Text categorizers based on regularizing adaptations of the problem of computing linear separators |
US7412462B2 (en) | 2000-02-18 | 2008-08-12 | Burnside Acquisition, Llc | Data repository and method for promoting network storage of data |
US7117246B2 (en) | 2000-02-22 | 2006-10-03 | Sendmail, Inc. | Electronic mail system with methodology providing distributed message store |
EP1281115A2 (en) | 2000-03-17 | 2003-02-05 | Vizible.com Inc. | A three dimensional spatial user interface |
US6560597B1 (en) | 2000-03-21 | 2003-05-06 | International Business Machines Corporation | Concept decomposition using clustering |
US6757646B2 (en) | 2000-03-22 | 2004-06-29 | Insightful Corporation | Extended functionality for an inverse inference engine based web search |
US6785679B1 (en) | 2000-03-29 | 2004-08-31 | Brassring, Llc | Method and apparatus for sending and tracking resume data sent via URL |
US20020055919A1 (en) | 2000-03-31 | 2002-05-09 | Harlequin Limited | Method and system for gathering, organizing, and displaying information from data searches |
US6970931B1 (en) | 2000-04-03 | 2005-11-29 | International Business Machines Corporation | Method for translucent online interaction |
AU2001249805A1 (en) | 2000-04-03 | 2001-10-15 | 3-Dimensional Pharmaceuticals, Inc. | Method, system, and computer program product for representing object relationships in a multidimensional space |
US6915308B1 (en) | 2000-04-06 | 2005-07-05 | Claritech Corporation | Method and apparatus for information mining and filtering |
US7478403B1 (en) | 2000-04-21 | 2009-01-13 | Sun Microsystems, Inc. | Secure access to managed network objects using a configurable platform-independent gateway providing individual object-level access control |
US6584564B2 (en) | 2000-04-25 | 2003-06-24 | Sigaba Corporation | Secure e-mail system |
US7325127B2 (en) | 2000-04-25 | 2008-01-29 | Secure Data In Motion, Inc. | Security server system |
US7698167B2 (en) | 2000-04-28 | 2010-04-13 | Computer Pundits, Inc. | Catalog building method and system |
US6879332B2 (en) | 2000-05-16 | 2005-04-12 | Groxis, Inc. | User interface for displaying and exploring hierarchical information |
EP1156430A2 (en) | 2000-05-17 | 2001-11-21 | Matsushita Electric Industrial Co., Ltd. | Information retrieval system |
US6883001B2 (en) | 2000-05-26 | 2005-04-19 | Fujitsu Limited | Document information search apparatus and method and recording medium storing document information search program therein |
US6519580B1 (en) | 2000-06-08 | 2003-02-11 | International Business Machines Corporation | Decision-tree-based symbolic rule induction system for text categorization |
US6697998B1 (en) | 2000-06-12 | 2004-02-24 | International Business Machines Corporation | Automatic labeling of unlabeled text data |
US20020078090A1 (en) | 2000-06-30 | 2002-06-20 | Hwang Chung Hee | Ontological concept-based, user-centric text summarization |
US7490092B2 (en) | 2000-07-06 | 2009-02-10 | Streamsage, Inc. | Method and system for indexing and searching timed media information based upon relevance intervals |
US6738759B1 (en) | 2000-07-07 | 2004-05-18 | Infoglide Corporation, Inc. | System and method for performing similarity searching using pointer optimization |
US7376635B1 (en) | 2000-07-21 | 2008-05-20 | Ford Global Technologies, Llc | Theme-based system and method for classifying documents |
JP2002041544A (en) | 2000-07-25 | 2002-02-08 | Toshiba Corp | Text information analyzing device |
US6675159B1 (en) | 2000-07-27 | 2004-01-06 | Science Applic Int Corp | Concept-based search and retrieval system |
US20020032735A1 (en) | 2000-08-25 | 2002-03-14 | Daniel Burnstein | Apparatus, means and methods for automatic community formation for phones and computer networks |
NZ524894A (en) * | 2000-09-02 | 2005-03-24 | Gruenenthal Chemie | Antisense oligonucleotides against VR1 |
JP2002082745A (en) | 2000-09-07 | 2002-03-22 | Sony Corp | Device and method for information processing, and program storage medium |
AUPR033800A0 (en) | 2000-09-25 | 2000-10-19 | Telstra R & D Management Pty Ltd | A document categorisation system |
US7430717B1 (en) | 2000-09-26 | 2008-09-30 | International Business Machines Corporation | Method for adapting a K-means text clustering to emerging data |
US7131060B1 (en) | 2000-09-29 | 2006-10-31 | Raytheon Company | System and method for automatic placement of labels for interactive graphics applications |
US7457948B1 (en) | 2000-09-29 | 2008-11-25 | Lucent Technologies Inc. | Automated authentication handling system |
US7197470B1 (en) | 2000-10-11 | 2007-03-27 | Buzzmetrics, Ltd. | System and method for collection analysis of electronic discussion methods |
US6684205B1 (en) | 2000-10-18 | 2004-01-27 | International Business Machines Corporation | Clustering hypertext with applications to web searching |
CA2323883C (en) | 2000-10-19 | 2016-02-16 | Patrick Ryan Morin | Method and device for classifying internet objects and objects stored oncomputer-readable media |
US7233940B2 (en) | 2000-11-06 | 2007-06-19 | Answers Corporation | System for processing at least partially structured data |
US7107266B1 (en) | 2000-11-09 | 2006-09-12 | Inxight Software, Inc. | Method and apparatus for auditing training supersets |
AU2002220172A1 (en) | 2000-11-15 | 2002-05-27 | David M. Holbrook | Apparatus and method for organizing and/or presenting data |
US6978419B1 (en) | 2000-11-15 | 2005-12-20 | Justsystem Corporation | Method and apparatus for efficient identification of duplicate and near-duplicate documents and text spans using high-discriminability text fragments |
US7379913B2 (en) | 2000-11-27 | 2008-05-27 | Nextworth, Inc. | Anonymous transaction system |
AU2002235147A1 (en) | 2000-11-30 | 2002-06-11 | Webtone Technologies, Inc. | Web session collaboration |
US7003551B2 (en) | 2000-11-30 | 2006-02-21 | Bellsouth Intellectual Property Corp. | Method and apparatus for minimizing storage of common attachment files in an e-mail communications server |
US7607083B2 (en) | 2000-12-12 | 2009-10-20 | Nec Corporation | Test summarization using relevance measures and latent semantic analysis |
KR20020049164A (en) | 2000-12-19 | 2002-06-26 | 오길록 | The System and Method for Auto - Document - classification by Learning Category using Genetic algorithm and Term cluster |
US6751628B2 (en) | 2001-01-11 | 2004-06-15 | Dolphin Search | Process and system for sparse vector and matrix representation of document indexing and retrieval |
US7562110B2 (en) | 2001-01-11 | 2009-07-14 | F5 Networks, Inc. | File switch and switched file system |
US7233886B2 (en) | 2001-01-19 | 2007-06-19 | Smartsignal Corporation | Adaptive modeling of changed states in predictive condition monitoring |
US6658423B1 (en) | 2001-01-24 | 2003-12-02 | Google, Inc. | Detecting duplicate and near-duplicate files |
JP4022374B2 (en) | 2001-01-26 | 2007-12-19 | 株式会社ルネサステクノロジ | Semiconductor device manufacturing method and system |
JP3768105B2 (en) | 2001-01-29 | 2006-04-19 | 株式会社東芝 | Translation apparatus, translation method, and translation program |
US6640009B2 (en) | 2001-02-06 | 2003-10-28 | International Business Machines Corporation | Identification, separation and compression of multiple forms with mutants |
WO2002063493A1 (en) | 2001-02-08 | 2002-08-15 | 2028, Inc. | Methods and systems for automated semantic knowledge leveraging graph theoretic analysis and the inherent structure of communication |
CA2433525A1 (en) | 2001-02-12 | 2002-08-22 | Legato Systems, Inc. | System and method of indexing unique electronic mail messages and uses for the same |
US7366759B2 (en) | 2001-02-22 | 2008-04-29 | Parity Communications, Inc. | Method and system for characterizing relationships in social networks |
US6823333B2 (en) | 2001-03-02 | 2004-11-23 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | System, method and apparatus for conducting a keyterm search |
US6819344B2 (en) | 2001-03-12 | 2004-11-16 | Microsoft Corporation | Visualization of multi-dimensional data having an unbounded dimension |
US20030130991A1 (en) | 2001-03-28 | 2003-07-10 | Fidel Reijerse | Knowledge discovery from data sets |
US7353204B2 (en) | 2001-04-03 | 2008-04-01 | Zix Corporation | Certified transmission system |
US6714929B1 (en) | 2001-04-13 | 2004-03-30 | Auguri Corporation | Weighted preference data search system and method |
US6804665B2 (en) | 2001-04-18 | 2004-10-12 | International Business Machines Corporation | Method and apparatus for discovering knowledge gaps between problems and solutions in text databases |
US6601621B2 (en) * | 2001-04-18 | 2003-08-05 | Black & Decker Inc. | Portable Power Planer |
US7020645B2 (en) | 2001-04-19 | 2006-03-28 | Eoriginal, Inc. | Systems and methods for state-less authentication |
US7155668B2 (en) | 2001-04-19 | 2006-12-26 | International Business Machines Corporation | Method and system for identifying relationships between text documents and structured variables pertaining to the text documents |
US6970881B1 (en) | 2001-05-07 | 2005-11-29 | Intelligenxia, Inc. | Concept-based method and system for dynamically analyzing unstructured information |
US7194483B1 (en) | 2001-05-07 | 2007-03-20 | Intelligenxia, Inc. | Method, system, and computer program product for concept-based multi-dimensional analysis of unstructured information |
US6735578B2 (en) | 2001-05-10 | 2004-05-11 | Honeywell International Inc. | Indexing of knowledge base in multilayer self-organizing maps with hessian and perturbation induced fast learning |
US20020184193A1 (en) | 2001-05-30 | 2002-12-05 | Meir Cohen | Method and system for performing a similarity search using a dissimilarity based indexing structure |
US6675164B2 (en) | 2001-06-08 | 2004-01-06 | The Regents Of The University Of California | Parallel object-oriented data mining system |
US6993535B2 (en) | 2001-06-18 | 2006-01-31 | International Business Machines Corporation | Business method and apparatus for employing induced multimedia classifiers based on unified representation of features reflecting disparate modalities |
WO2002103578A1 (en) | 2001-06-19 | 2002-12-27 | Biozak, Inc. | Dynamic search engine and database |
US20070043774A1 (en) | 2001-06-27 | 2007-02-22 | Inxight Software, Inc. | Method and Apparatus for Incremental Computation of the Accuracy of a Categorization-by-Example System |
US7266545B2 (en) | 2001-08-07 | 2007-09-04 | International Business Machines Corporation | Methods and apparatus for indexing in a database and for retrieving data from a database in accordance with queries using example sets |
US7117432B1 (en) | 2001-08-13 | 2006-10-03 | Xerox Corporation | Meta-document management system with transit triggered enrichment |
JP4701564B2 (en) | 2001-08-31 | 2011-06-15 | ソニー株式会社 | Menu display device and menu display method |
US6778995B1 (en) * | 2001-08-31 | 2004-08-17 | Attenex Corporation | System and method for efficiently generating cluster groupings in a multi-dimensional concept space |
US6888548B1 (en) | 2001-08-31 | 2005-05-03 | Attenex Corporation | System and method for generating a visualized data representation preserving independent variable geometric relationships |
US20030084066A1 (en) * | 2001-10-31 | 2003-05-01 | Waterman Scott A. | Device and method for assisting knowledge engineer in associating intelligence with content |
US7062498B2 (en) * | 2001-11-02 | 2006-06-13 | Thomson Legal Regulatory Global Ag | Systems, methods, and software for classifying text from judicial opinions and other documents |
AUPR958901A0 (en) | 2001-12-18 | 2002-01-24 | Telstra New Wave Pty Ltd | Information resource taxonomy |
US6826568B2 (en) * | 2001-12-20 | 2004-11-30 | Microsoft Corporation | Methods and system for model matching |
ATE466345T1 (en) | 2002-01-16 | 2010-05-15 | Elucidon Group Ltd | RETRIEVAL OF INFORMATION DATA WHERE DATA IS ORGANIZED INTO TERMS, DOCUMENTS AND DOCUMENT CORPORAS |
US20040205482A1 (en) | 2002-01-24 | 2004-10-14 | International Business Machines Corporation | Method and apparatus for active annotation of multimedia content |
US7271804B2 (en) | 2002-02-25 | 2007-09-18 | Attenex Corporation | System and method for arranging concept clusters in thematic relationships in a two-dimensional visual display area |
US7188107B2 (en) | 2002-03-06 | 2007-03-06 | Infoglide Software Corporation | System and method for classification of documents |
US20030172048A1 (en) | 2002-03-06 | 2003-09-11 | Business Machines Corporation | Text search system for complex queries |
US6968511B1 (en) | 2002-03-07 | 2005-11-22 | Microsoft Corporation | Graphical user interface, data structure and associated method for cluster-based document management |
US20030174179A1 (en) | 2002-03-12 | 2003-09-18 | Suermondt Henri Jacques | Tool for visualizing data patterns of a hierarchical classification structure |
US6847966B1 (en) | 2002-04-24 | 2005-01-25 | Engenium Corporation | Method and system for optimally searching a document database using a representative semantic space |
US20040205578A1 (en) | 2002-04-25 | 2004-10-14 | Wolff Alan S. | System and method for converting document to reusable learning object |
US7188117B2 (en) | 2002-05-17 | 2007-03-06 | Xerox Corporation | Systems and methods for authoritativeness grading, estimation and sorting of documents in large heterogeneous document collections |
US6996575B2 (en) | 2002-05-31 | 2006-02-07 | Sas Institute Inc. | Computer-implemented system and method for text-based document processing |
JP3870862B2 (en) | 2002-07-12 | 2007-01-24 | ソニー株式会社 | Liquid crystal display device, control method thereof, and portable terminal |
US20040034633A1 (en) | 2002-08-05 | 2004-02-19 | Rickard John Terrell | Data search system and method using mutual subsethood measures |
US20040024755A1 (en) | 2002-08-05 | 2004-02-05 | Rickard John Terrell | System and method for indexing non-textual data |
US20050226511A1 (en) | 2002-08-26 | 2005-10-13 | Short Gordon K | Apparatus and method for organizing and presenting content |
US6886010B2 (en) | 2002-09-30 | 2005-04-26 | The United States Of America As Represented By The Secretary Of The Navy | Method for data and text mining and literature-based discovery |
US7246113B2 (en) | 2002-10-02 | 2007-07-17 | General Electric Company | Systems and methods for selecting a material that best matches a desired set of properties |
US20040163034A1 (en) | 2002-10-17 | 2004-08-19 | Sean Colbath | Systems and methods for labeling clusters of documents |
US7373612B2 (en) | 2002-10-21 | 2008-05-13 | Battelle Memorial Institute | Multidimensional structured data visualization method and apparatus, text visualization method and apparatus, method and apparatus for visualizing and graphically navigating the world wide web, method and apparatus for visualizing hierarchies |
US7231384B2 (en) | 2002-10-25 | 2007-06-12 | Sap Aktiengesellschaft | Navigation tool for exploring a knowledge base |
US7158957B2 (en) | 2002-11-21 | 2007-01-02 | Honeywell International Inc. | Supervised self organizing maps with fuzzy error correction |
US7233938B2 (en) * | 2002-12-27 | 2007-06-19 | Dictaphone Corporation | Systems and methods for coding information |
EP1590742A2 (en) | 2003-01-10 | 2005-11-02 | Cohesive Knowledge Solutions, Inc. | Universal knowledge information and data storage system |
US7472110B2 (en) | 2003-01-29 | 2008-12-30 | Microsoft Corporation | System and method for employing social networks for information discovery |
JP4828091B2 (en) | 2003-03-05 | 2011-11-30 | ヒューレット・パッカード・カンパニー | Clustering method program and apparatus |
US7197497B2 (en) | 2003-04-25 | 2007-03-27 | Overture Services, Inc. | Method and apparatus for machine learning a document relevance function |
US20040215608A1 (en) | 2003-04-25 | 2004-10-28 | Alastair Gourlay | Search engine supplemented with URL's that provide access to the search results from predefined search queries |
US7146361B2 (en) | 2003-05-30 | 2006-12-05 | International Business Machines Corporation | System, method and computer program product for performing unstructured information management and automatic text analysis, including a search operator functioning as a Weighted AND (WAND) |
US20040243556A1 (en) | 2003-05-30 | 2004-12-02 | International Business Machines Corporation | System, method and computer program product for performing unstructured information management and automatic text analysis, and including a document common analysis system (CAS) |
US7496228B2 (en) * | 2003-06-13 | 2009-02-24 | Landwehr Val R | Method and system for detecting and classifying objects in images, such as insects and other arthropods |
US7639868B1 (en) | 2003-06-16 | 2009-12-29 | Drexel University | Automated learning of model classifications |
GB2403558A (en) | 2003-07-02 | 2005-01-05 | Sony Uk Ltd | Document searching and method for presenting the results |
US20070020642A1 (en) | 2003-07-03 | 2007-01-25 | Zhan Deng | Structural interaction fingerprint |
US7610313B2 (en) * | 2003-07-25 | 2009-10-27 | Attenex Corporation | System and method for performing efficient document scoring and clustering |
US7233843B2 (en) | 2003-08-08 | 2007-06-19 | Electric Power Group, Llc | Real-time performance monitoring and management system |
US7519565B2 (en) | 2003-11-03 | 2009-04-14 | Cloudmark, Inc. | Methods and apparatuses for classifying electronic documents |
EP1536315A1 (en) | 2003-11-27 | 2005-06-01 | Sony NetServices GmbH | Information retrieval device |
US7885901B2 (en) | 2004-01-29 | 2011-02-08 | Yahoo! Inc. | Method and system for seeding online social network contacts |
US7191175B2 (en) * | 2004-02-13 | 2007-03-13 | Attenex Corporation | System and method for arranging concept clusters in thematic neighborhood relationships in a two-dimensional visual display space |
US7433893B2 (en) | 2004-03-08 | 2008-10-07 | Marpex Inc. | Method and system for compression indexing and efficient proximity search of text data |
US20050203924A1 (en) | 2004-03-13 | 2005-09-15 | Rosenberg Gerald B. | System and methods for analytic research and literate reporting of authoritative document collections |
US7584221B2 (en) | 2004-03-18 | 2009-09-01 | Microsoft Corporation | Field weighting in text searching |
US7716223B2 (en) | 2004-03-29 | 2010-05-11 | Google Inc. | Variable personalization of search results in a search engine |
US7761447B2 (en) | 2004-04-08 | 2010-07-20 | Microsoft Corporation | Systems and methods that rank search results |
EP2487599A1 (en) | 2004-05-04 | 2012-08-15 | Boston Consulting Group, Inc. | Method and apparatus for selecting, analyzing and visualizing related database records as a network |
US20050283473A1 (en) | 2004-06-17 | 2005-12-22 | Armand Rousso | Apparatus, method and system of artificial intelligence for data searching applications |
US20070214133A1 (en) * | 2004-06-23 | 2007-09-13 | Edo Liberty | Methods for filtering data and filling in missing data using nonlinear inference |
US7359894B1 (en) | 2004-06-30 | 2008-04-15 | Google Inc. | Methods and systems for requesting and providing information in a social network |
US7668376B2 (en) | 2004-06-30 | 2010-02-23 | National Instruments Corporation | Shape feature extraction and classification |
US7562069B1 (en) | 2004-07-01 | 2009-07-14 | Aol Llc | Query disambiguation |
US7233106B2 (en) | 2004-07-14 | 2007-06-19 | Taiwan Oasis Technology Co., Ltd. | LED chip capping construction |
US8015124B2 (en) | 2004-07-21 | 2011-09-06 | Equivio Ltd | Method for determining near duplicate data objects |
US8302164B2 (en) | 2004-07-22 | 2012-10-30 | Facebook, Inc. | Authorization and authentication based on an individual's social network |
US20060053382A1 (en) | 2004-09-03 | 2006-03-09 | Biowisdom Limited | System and method for facilitating user interaction with multi-relational ontologies |
JP4491308B2 (en) | 2004-09-24 | 2010-06-30 | 富士通株式会社 | Network monitoring method and apparatus |
US7712049B2 (en) | 2004-09-30 | 2010-05-04 | Microsoft Corporation | Two-dimensional radial user interface for computer software applications |
US7805446B2 (en) | 2004-10-12 | 2010-09-28 | Ut-Battelle Llc | Agent-based method for distributed clustering of textual information |
US7292244B2 (en) | 2004-10-18 | 2007-11-06 | Microsoft Corporation | System and method for automatic label placement on charts |
US7574409B2 (en) * | 2004-11-04 | 2009-08-11 | Vericept Corporation | Method, apparatus, and system for clustering and classification |
KR100703690B1 (en) | 2004-11-19 | 2007-04-05 | 삼성전자주식회사 | User interface and method for managing icon by grouping using skin image |
CA2500573A1 (en) | 2005-03-14 | 2006-09-14 | Oculus Info Inc. | Advances in nspace - system and method for information analysis |
US20060122997A1 (en) | 2004-12-02 | 2006-06-08 | Dah-Chih Lin | System and method for text searching using weighted keywords |
US20060122974A1 (en) | 2004-12-03 | 2006-06-08 | Igor Perisic | System and method for a dynamic content driven rendering of social networks |
US7404151B2 (en) * | 2005-01-26 | 2008-07-22 | Attenex Corporation | System and method for providing a dynamic user interface for a dense three-dimensional scene |
US7356777B2 (en) | 2005-01-26 | 2008-04-08 | Attenex Corporation | System and method for providing a dynamic user interface for a dense three-dimensional scene |
US8819569B2 (en) | 2005-02-18 | 2014-08-26 | Zumobi, Inc | Single-handed approach for navigation of application tiles using panning and zooming |
US8468048B2 (en) * | 2005-04-22 | 2013-06-18 | Google Inc. | Suggesting targeting information for ads, such as websites and/or categories of websites for example |
US7801841B2 (en) * | 2005-06-20 | 2010-09-21 | New York University | Method, system and software arrangement for reconstructing formal descriptive models of processes from functional/modal data using suitable ontology |
US8914726B2 (en) | 2005-08-20 | 2014-12-16 | Riverbed Technology, Inc. | Visualizing a complex network based on a set of objects of interest |
FR2891058B1 (en) | 2005-09-20 | 2007-11-02 | Draka Comteq France | COMPOSITION FIBER OF CHROMATIC DISPERSION AND CUMULATED DISPERSION SLOPE. |
US7558769B2 (en) * | 2005-09-30 | 2009-07-07 | Google Inc. | Identifying clusters of similar reviews and displaying representative reviews from multiple clusters |
US20070112758A1 (en) | 2005-11-14 | 2007-05-17 | Aol Llc | Displaying User Feedback for Search Results From People Related to a User |
US7730425B2 (en) | 2005-11-30 | 2010-06-01 | De Los Reyes Isabelo | Function-oriented user interface |
US8726144B2 (en) | 2005-12-23 | 2014-05-13 | Xerox Corporation | Interactive learning-based document annotation |
US7668789B1 (en) * | 2006-03-30 | 2010-02-23 | Hewlett-Packard Development Company, L.P. | Comparing distributions of cases over groups of categories |
US8150827B2 (en) | 2006-06-07 | 2012-04-03 | Renew Data Corp. | Methods for enhancing efficiency and cost effectiveness of first pass review of documents |
US20080189273A1 (en) | 2006-06-07 | 2008-08-07 | Digital Mandate, Llc | System and method for utilizing advanced search and highlighting techniques for isolating subsets of relevant content data |
US20100198802A1 (en) | 2006-06-07 | 2010-08-05 | Renew Data Corp. | System and method for optimizing search objects submitted to a data resource |
US7831928B1 (en) | 2006-06-22 | 2010-11-09 | Digg, Inc. | Content visualization |
US20080005081A1 (en) | 2006-06-28 | 2008-01-03 | Sun Microsystems, Inc. | Method and apparatus for searching and resource discovery in a distributed enterprise system |
US7640219B2 (en) | 2006-08-04 | 2009-12-29 | NDSU - Research Foundation | Parameter optimized nearest neighbor vote and boundary based classification |
US7523349B2 (en) | 2006-08-25 | 2009-04-21 | Accenture Global Services Gmbh | Data visualization for diagnosing computing systems |
US8010534B2 (en) | 2006-08-31 | 2011-08-30 | Orcatec Llc | Identifying related objects using quantum clustering |
JP4836739B2 (en) | 2006-10-04 | 2011-12-14 | 株式会社日立製作所 | Product information providing system and product information providing method |
US8442972B2 (en) | 2006-10-11 | 2013-05-14 | Collarity, Inc. | Negative associations for search results ranking and refinement |
WO2008046098A2 (en) | 2006-10-13 | 2008-04-17 | Move, Inc. | Multi-tiered cascading crawling system |
JP2008102594A (en) | 2006-10-17 | 2008-05-01 | Fujitsu Ltd | Content retrieval method and retrieval device |
US20080109762A1 (en) | 2006-11-03 | 2008-05-08 | Microsoft Corporation | Visual document user interface system |
US20080140817A1 (en) | 2006-12-06 | 2008-06-12 | Agarwal Manoj K | System and method for performance problem localization |
US8065307B2 (en) * | 2006-12-20 | 2011-11-22 | Microsoft Corporation | Parsing, analysis and scoring of document content |
WO2008079305A1 (en) | 2006-12-20 | 2008-07-03 | Artificial Medical Intelligence, Inc. | Delphi method for medical coding |
US7743059B2 (en) * | 2007-03-30 | 2010-06-22 | Amazon Technologies, Inc. | Cluster-based management of collections of items |
US8275773B2 (en) | 2007-03-30 | 2012-09-25 | Stuart Donnelly | Method of searching text to find relevant content |
US7711747B2 (en) * | 2007-04-06 | 2010-05-04 | Xerox Corporation | Interactive cleaning for automatic document clustering and categorization |
US8381122B2 (en) | 2007-06-08 | 2013-02-19 | Apple Inc. | Multi-dimensional application environment |
US8239379B2 (en) * | 2007-07-13 | 2012-08-07 | Xerox Corporation | Semi-supervised visual clustering |
WO2009018223A1 (en) | 2007-07-27 | 2009-02-05 | Sparkip, Inc. | System and methods for clustering large database of documents |
US7936913B2 (en) | 2007-08-07 | 2011-05-03 | Nextslide Imaging Llc | Network image review in clinical hematology |
US10762080B2 (en) * | 2007-08-14 | 2020-09-01 | John Nicholas and Kristin Gross Trust | Temporal document sorter and method |
US7853432B2 (en) * | 2007-10-02 | 2010-12-14 | The Regents Of The University Of Michigan | Method and apparatus for clustering and visualization of multicolor cytometry data |
WO2009052265A1 (en) | 2007-10-19 | 2009-04-23 | Huron Consulting Group, Inc. | Document review system and method |
US9015633B2 (en) | 2007-10-22 | 2015-04-21 | Sony Corporation | Data management apparatus and method for organizing data elements into multiple categories for display |
US8326823B2 (en) | 2007-11-01 | 2012-12-04 | Ebay Inc. | Navigation for large scale graphs |
US8862608B2 (en) | 2007-11-13 | 2014-10-14 | Wal-Mart Stores, Inc. | Information retrieval using category as a consideration |
US8311344B2 (en) | 2008-02-15 | 2012-11-13 | Digitalsmiths, Inc. | Systems and methods for semantically classifying shots in video |
US8515946B2 (en) | 2008-02-28 | 2013-08-20 | Microsoft Corporation | Location description for federation and discoverability |
US9082080B2 (en) | 2008-03-05 | 2015-07-14 | Kofax, Inc. | Systems and methods for organizing data sets |
US20090228811A1 (en) | 2008-03-10 | 2009-09-10 | Randy Adams | Systems and methods for processing a plurality of documents |
US20090259622A1 (en) | 2008-04-11 | 2009-10-15 | Kolz Daniel P | Classification of Data Based on Previously Classified Data |
US20090265631A1 (en) | 2008-04-18 | 2009-10-22 | Yahoo! Inc. | System and method for a user interface to navigate a collection of tags labeling content |
US8676815B2 (en) | 2008-05-07 | 2014-03-18 | City University Of Hong Kong | Suffix tree similarity measure for document clustering |
US11461785B2 (en) * | 2008-07-10 | 2022-10-04 | Ron M. Redlich | System and method to identify, classify and monetize information as an intangible asset and a production model based thereon |
US8086480B2 (en) * | 2008-09-25 | 2011-12-27 | Ebay Inc. | Methods and systems for activity-based recommendations |
US8250481B2 (en) | 2008-10-16 | 2012-08-21 | The Curators Of The University Of Missouri | Visualizing geographic-area change detected from high-resolution, remotely sensed imagery |
US8572376B2 (en) | 2009-03-27 | 2013-10-29 | Bank Of America Corporation | Decryption of electronic communication in an electronic discovery enterprise system |
US20100250477A1 (en) | 2009-03-31 | 2010-09-30 | Shekhar Yadav | Systems and methods for optimizing a campaign |
US20100268661A1 (en) | 2009-04-20 | 2010-10-21 | 4-Tell, Inc | Recommendation Systems |
CN102483745B (en) | 2009-06-03 | 2014-05-14 | 谷歌公司 | Co-selected image classification |
US8165974B2 (en) * | 2009-06-08 | 2012-04-24 | Xerox Corporation | System and method for assisted document review |
US8478749B2 (en) * | 2009-07-20 | 2013-07-02 | Lexisnexis, A Division Of Reed Elsevier Inc. | Method and apparatus for determining relevant search results using a matrix framework |
US8713018B2 (en) * | 2009-07-28 | 2014-04-29 | Fti Consulting, Inc. | System and method for displaying relationships between electronically stored information to provide classification suggestions via inclusion |
WO2013019879A2 (en) * | 2011-08-01 | 2013-02-07 | Hewlett-Packard Development Company, L.P. | Asset model import connector |
-
2010
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- 2010-07-27 EP EP10739781A patent/EP2460097A1/en not_active Withdrawn
- 2010-07-27 CA CA2773219A patent/CA2773219C/en not_active Expired - Fee Related
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- 2010-07-27 WO PCT/US2010/043453 patent/WO2011017134A1/en active Application Filing
- 2010-07-27 WO PCT/US2010/043331 patent/WO2011017080A1/en active Application Filing
- 2010-07-27 US US12/844,792 patent/US9477751B2/en not_active Expired - Fee Related
- 2010-07-27 EP EP10739788A patent/EP2460099A1/en not_active Withdrawn
- 2010-07-27 EP EP10739780A patent/EP2460096A2/en not_active Withdrawn
- 2010-07-27 EP EP10742351A patent/EP2460102A2/en not_active Withdrawn
- 2010-07-27 US US12/844,810 patent/US8700627B2/en active Active
- 2010-07-27 EP EP10740799A patent/EP2460100A1/en not_active Withdrawn
- 2010-07-28 CA CA2773263A patent/CA2773263C/en not_active Expired - Fee Related
- 2010-07-28 WO PCT/US2010/043516 patent/WO2011017155A1/en active Application Filing
- 2010-07-28 WO PCT/US2010/043506 patent/WO2011017152A2/en active Application Filing
- 2010-07-28 EP EP10740803A patent/EP2460101A2/en not_active Withdrawn
- 2010-07-28 EP EP10747718A patent/EP2460103A1/en not_active Withdrawn
- 2010-07-28 CA CA2773319A patent/CA2773319C/en not_active Expired - Fee Related
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2013
- 2013-08-19 US US13/970,560 patent/US8909647B2/en not_active Expired - Fee Related
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2014
- 2014-01-17 US US14/158,743 patent/US9165062B2/en active Active
- 2014-02-03 US US14/171,741 patent/US20140156564A1/en not_active Abandoned
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- 2014-04-28 US US14/263,934 patent/US9542483B2/en active Active
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2015
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2016
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2017
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2018
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