US20160026923A1 - System and method for determining a propensity of entity to take a specified action - Google Patents
System and method for determining a propensity of entity to take a specified action Download PDFInfo
- Publication number
- US20160026923A1 US20160026923A1 US14/562,524 US201414562524A US2016026923A1 US 20160026923 A1 US20160026923 A1 US 20160026923A1 US 201414562524 A US201414562524 A US 201414562524A US 2016026923 A1 US2016026923 A1 US 2016026923A1
- Authority
- US
- United States
- Prior art keywords
- entity
- propensity
- specified action
- take
- record
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
- G06N5/048—Fuzzy inferencing
-
- 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
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
-
- G06N7/005—
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/01—Customer relationship services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
Definitions
- FIG. 1 is a block diagram of an exemplary computer system, consistent with embodiments of the present disclosure
- FIG. 2 is a flowchart of an exemplary method for determining a propensity of an entity to take a specified action, consistent with embodiments of the present disclosure
- FIG. 3 is a flowchart of an exemplary method for creating a model to determine the propensity of an entity to take a specified action, consistent with embodiments of the present disclosure
- FIG. 4 provides an exemplary use case scenario for determining a propensity of an entity to take a specified action applied to an exemplary data structure, consistent with embodiments of the present disclosure.
- FIG. 5 illustrates an exemplary user interface, consistent with embodiments of the present disclosure.
- FIG. 6 illustrates another exemplary user interface, consistent with embodiments of the present disclosure.
- Embodiments disclosed herein are directed to, among other things, to systems and methods that can determine the propensity of an entity (e.g., a person, a household, or a company) to take a specified action.
- a specific action can involve determining the propensity that a customer will leave a supplier during a given time period (e.g., churn).
- Such factors that can affect the churn rate include customer dissatisfaction, cheaper and/or better offers from the competition, more successful sales and/or marketing by the competition, or reasons having to do with the customer life cycle.
- a supplier can receive an indication that a customer is likely to churn, the supplier can take one or more actions in order to keep the customer.
- the embodiments disclosed herein can assist with providing that indication.
- the systems and methods can access one or more data sources, the one or more data sources including information associated with the entity, form a record associated with the entity by integrating the information from the one or more data sources, generate, based on the record, one or more features associated with the entity, process the one or more features to determine the propensity of the entity to take the specified action, and output the propensity.
- the operations, techniques, and/or components described herein are implemented by a computer system, which can include one or more special-purpose computing devices.
- the special-purpose computing devices can be hard-wired to perform the operations, techniques, and/or components described herein.
- the special-purpose computing devices can include digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are persistently programmed to perform the operations, techniques, and/or components described herein.
- ASICs application-specific integrated circuits
- FPGAs field programmable gate arrays
- the special-purpose computing devices can include one or more hardware processors programmed to perform such features of the present disclosure pursuant to program instructions in firmware, memory, other storage, or a combination.
- Such special-purpose computing devices can combine custom hard-wired logic, ASICs, or FPGAs with custom programming to accomplish the techniques and other features of the present disclosure.
- the special-purpose computing devices can be desktop computer systems, portable computer systems, handheld devices, networking devices, or any other device that incorporates hard-wired and/or program logic to implement the techniques and other features of the present disclosure.
- the one or more special-purpose computing devices can be generally controlled and coordinated by operating system software, such as iOS, Android, Blackberry, Chrome OS, Windows XP, Windows Vista, Windows 7, Windows 8, Windows Server, Windows CE, Unix, Linux, SunOS, Solaris, VxWorks, or other compatible operating systems.
- operating system software such as iOS, Android, Blackberry, Chrome OS, Windows XP, Windows Vista, Windows 7, Windows 8, Windows Server, Windows CE, Unix, Linux, SunOS, Solaris, VxWorks, or other compatible operating systems.
- the computing device can be controlled by a proprietary operating system.
- Operating systems control and schedule computer processes for execution, perform memory management, provide file system, networking, I/O services, and provide a user interface functionality, such as a graphical user interface (“GUI”), among other things.
- GUI graphical user interface
- FIG. 1 is a block diagram that illustrates an implementation of a computer system 100 , which, as described above, can comprise one or more electronic devices.
- Computer system 100 includes a bus 102 or other communication mechanism for communicating information, and one or more hardware processors 104 (denoted as processor 104 for purposes of simplicity), coupled with bus 102 for processing information.
- One or more hardware processors 104 can be, for example, one or more microprocessors.
- Computer system 100 also includes a main memory 106 , such as a random access memory (RAM) or other dynamic storage device, coupled to bus 102 for storing information and instructions to be executed by one or more processors 104 .
- Main memory 106 also can be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 104 .
- Such instructions when stored in non-transitory storage media accessible to one or more processors 104 , render computer system 100 into a special-purpose machine that is customized to perform the operations specified in the instructions.
- Computer system 100 further includes a read only memory (ROM) 108 or other static storage device coupled to bus 102 for storing static information and instructions for processor 104 .
- ROM read only memory
- a storage device 110 such as a magnetic disk, optical disk, or USB thumb drive (Flash drive), etc., is provided and coupled to bus 102 for storing information and instructions.
- Computer system 100 can be coupled via bus 102 to a display 112 , such as a cathode ray tube (CRT), an LCD display, or a touchscreen, for displaying information to a computer user.
- a display 112 such as a cathode ray tube (CRT), an LCD display, or a touchscreen
- An input device 114 is coupled to bus 102 for communicating information and command selections to one or more processors 104 .
- cursor control 116 is Another type of user input device, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to one or more processors 104 and for controlling cursor movement on display 112 .
- the input device typically has two degrees of freedom in two axes, a first axis (for example, x) and a second axis (for example, y), that allows the device to specify positions in a plane.
- a first axis for example, x
- a second axis for example, y
- the same direction information and command selections as cursor control may be implemented via receiving touches on a touch screen without a cursor.
- Computer system 100 can include a user interface module to implement a GUI that may be stored in a mass storage device as executable software codes that are executed by the one or more computing devices.
- This and other modules may include, by way of example, components, such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables.
- module refers to logic embodied in hardware or firmware, or to a collection of software instructions, possibly having entry and exit points, written in a programming language, such as, for example, Java, Lua, C, and C++.
- a software module can be compiled and linked into an executable program, installed in a dynamic link library, or written in an interpreted programming language such as, for example, BASIC, Perl, Python, or Pig. It will be appreciated that software modules can be callable from other modules or from themselves, and/or can be invoked in response to detected events or interrupts.
- Software modules configured for execution on computing devices can be provided on a computer readable medium, such as a compact disc, digital video disc, flash drive, magnetic disc, or any other tangible medium, or as a digital download (and can be originally stored in a compressed or installable format that requires installation, decompression, or decryption prior to execution).
- a computer readable medium such as a compact disc, digital video disc, flash drive, magnetic disc, or any other tangible medium, or as a digital download (and can be originally stored in a compressed or installable format that requires installation, decompression, or decryption prior to execution).
- Such software code can be stored, partially or fully, on a memory device of the executing computing device, for execution by the computing device.
- Software instructions can be embedded in firmware, such as an EPROM.
- hardware modules can be comprised of connected logic units, such as gates and flip-flops, and/or can be comprised of programmable units, such as programmable gate arrays or processors.
- the modules or computing device functionality described herein are
- Computer system 100 can implement the techniques and other features described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the electronic device causes or programs computer system 100 to be a special-purpose machine. According to some embodiments, the techniques and other features described herein are performed by computer system 100 in response to one or more processors 104 executing one or more sequences of one or more instructions contained in main memory 106 . Such instructions can be read into main memory 106 from another storage medium, such as storage device 110 . Execution of the sequences of instructions contained in main memory 106 causes one or more processors 104 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry can be used in place of or in combination with software instructions.
- non-transitory media refers to any media storing data and/or instructions that cause a machine to operate in a specific fashion. Such non-transitory media can comprise non-volatile media and/or volatile media.
- Non-volatile media includes, for example, optical or magnetic disks, such as storage device 150 .
- Volatile media includes dynamic memory, such as main memory 106 .
- non-transitory media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge, a register memory, a processor cache, and networked versions of the same.
- Non-transitory media is distinct from, but can be used in conjunction with, transmission media.
- Transmission media participates in transferring information between storage media.
- transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 102 .
- transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.
- Various forms of media can be involved in carrying one or more sequences of one or more instructions to one or more processors 104 for execution.
- the instructions can initially be carried on a magnetic disk or solid state drive of a remote computer.
- the remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem.
- a modem local to computer system 100 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal.
- An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus 102 .
- Bus 102 carries the data to main memory 106 , from which processor 104 retrieves and executes the instructions.
- the instructions received by main memory 106 can optionally be stored on storage device 110 either before or after execution by one or more processors 104 .
- Computer system 100 can also include a communication interface 118 coupled to bus 102 .
- Communication interface 118 can provide a two-way data communication coupling to a network link 120 that is connected to a local network 122 .
- communication interface 118 can be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line.
- ISDN integrated services digital network
- communication interface 118 can be a local area network (LAN) card to provide a data communication connection to a compatible LAN.
- LAN local area network
- Wireless links can also be implemented.
- communication interface 118 can send and receive electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information.
- Network link 120 can typically provide data communication through one or more networks to other data devices.
- network link 120 can provide a connection through local network 122 to a host computer 124 or to data equipment operated by an Internet Service Provider (ISP) 126 .
- ISP 126 in turn provides data communication services through the world wide packet data communication network now commonly referred to as the “Internet” 128 .
- Internet 128 uses electrical, electromagnetic, or optical signals that carry digital data streams.
- the signals through the various networks and the signals on network link 120 and through communication interface 118 which carry the digital data to and from electronic device 110 , are example forms of transmission media.
- Computer system 100 can send messages and receive data, including program code, through the network(s), network link 120 and communication interface 118 .
- a server 130 might transmit a requested code for an application program through Internet 128 , ISP 126 , local network 122 and communication interface 118 .
- the received code can be executed by one or more processors 104 as it is received, and/or stored in storage device 110 , or other non-volatile storage for later execution.
- FIG. 2 is a flowchart representing an exemplary method 200 for determining the propensity of an entity to take a specified action. While the flowchart discloses the following steps in a particular order, it is appreciated that at least some of the steps can be moved, modified, or deleted where appropriate, consistent with embodiments of the present disclosure. In some embodiments, method 200 can be performed in full or in part by a computer system (e.g., computer system 100 ). It is appreciated that some of these steps can be performed in full or in part by other systems.
- a computer system e.g., computer system 100
- the computer system can access one or more data sources that include information associated with the entity.
- the one or more data sources can be stored locally at the computer system and/or at one or more remote servers (e.g., such as a remote database), or at one or more other remote devices.
- the information in the data sources can be stored in one or more multidimensional tables.
- information of a first type e.g., bill payment amount
- a second type e.g., automobile type
- a table can contain information associated with a single entity.
- a table can store information associated with a plurality of entities.
- each row in the table can correspond to a different entity (e.g., Household #1, Household #2, etc.) and each column in the table can correspond to a payment amount.
- the information stored in the table can include entries associated with a temporal period.
- a table can store a bill payment date for each bill payment amount.
- the information can be stored as a continuous value (e.g., $800 as a bill payment amount), as a categorical value (e.g., “Sedan” or “Coupe” as an automobile type), as textual value, or as any other type of value.
- a table can be stored in either a row-oriented database or a column-oriented database.
- a row in a row-oriented table can contain information associated with an entity (e.g., Household #1) and data in the row can be stored serially such that information associated with the entity can be accessed in one operation.
- entity e.g., Household #1
- the computer system can access the one or more data sources periodically (e.g., once a week, once a month, etc.).
- the computer system can access the one or more data sources based on the one or more data sources being updated (e.g., a new entry, such as payment bill amount, is added to a table).
- the computer system can access the one or more data sources responsive to an input received from the user.
- the user input can identify the entity (e.g. Household #5) for which information is requested.
- the user input can identify a category or class of entities.
- the user input can identify a class of entities that are all consumers of a specified provisioning entity (e.g., insurance company), the user input can identify entities that are located within a specified geographic region (e.g., all households within the state of Illinois), or the user input can identify any other category of entities (e.g., all households with an income over $100,000).
- a specified provisioning entity e.g., insurance company
- the computer system can access the one or more data sources including information associated with the entities.
- method 200 can be performed periodically (e.g., once a week, once a month, etc.). In some embodiments, method 200 can be performed whenever the one or more data sources are accessed.
- the computer system can form a record including all information from the one or more data sources associated with the entity.
- the record can be formed by integrating the information that is associated with the entity from the one or more data sources.
- the record can contain a multitude of information related to the entity.
- the record can contain all information from the one or more data sources associated with a household (e.g., number of members in household, age of each member of the household, number of automobiles, income, monthly bill mounts for each automobile, types of automobiles, etc.).
- the record can be stored as a cogroup (e.g., the cogroup shown in FIG. 4 ).
- the record can be stored in either a row-oriented database or a column-oriented database.
- a row in a row-oriented record can be associated with a data source (e.g., bill payment amount) and data in the row can be stored serially such that data associated with that data source can be accessed in one operation.
- a data source e.g., bill payment amount
- the computer system can filter the record for information associated with the specified action.
- the specified action can be churn (e.g., cancellation of a subscription) and the computer system can filter the record for information related to churn.
- the computer system can provide context for the specified action.
- the computer system can determine whether the specified action will likely occur within a specified temporal period (e.g., one month).
- the computer system can filter out all information associated with a time that is outside (e.g., before or after) the specified temporal period.
- the computer system can determine the propensity for the specified action based on only recent events.
- the computer system can filter out information associated with a time before the specified time period (e.g., stale or less relevant information).
- each record can be filtered in a slightly different way.
- the record can be filtered according to a user input specifying an activity or temporal period.
- the record can be filtered automatically based on a presetting (e.g., the computer can be configured to filter out all information that is more than one year old).
- the computer system can generate, based on the record, one or more features associated with the entity.
- a feature can be any discernable way of sorting or classifying the record (e.g., average value, most recent value, most common value, etc.).
- the computer system can generate key value pairs, wherein each key value pair contains a feature and a value.
- the computer system can generate features such as “average bill payment amount”, “average income”, “average number of automobiles”, etc. and corresponding values such as “$670”, “$73K”, “2.3 cars”, etc.
- features can be associated with a time value.
- computer system can generate features for a specified temporal period (e.g., features can be based only on the most recent values).
- Feature values can be represented as a continuous value (e.g., $670), as a categorical value (e.g., “Sedan” or “Coupe”), as a textual value, or as any other type of value.
- feature values can be classified as weighted values. For example, a household income of $73,000 can be represented as weighted value of ⁇ 0.27 0 ⁇ , ⁇ 0.73 100000 ⁇ .
- the computer system can process the one or more features to determine the propensity of the entity to take the specified action.
- the propensity can be determined by applying a trained model, such as the model described in greater detail in FIG. 3 .
- the input to the model can be key value pairs of the one or more features associated with the entity and the specified actions and the output of the model can be the propensity of the entity to take the specified action.
- processing the one or more features associated with the entity can result in a multitude of useful insights regarding the features that influence the propensity of the entity to take the specified action.
- Such insights can include, for example, the features that are most influential on the propensity of the entity to take the specified action (e.g., change in income, etc.).
- the computer system can output the propensity.
- the computer system can output the propensity as a continuous value, such as a number or percentage (e.g., 80 or 80%) or as a categorical value (e.g., “low”, “medium”, or “high”).
- the computer system can generate a user interface, such as the user interfaces described in greater detail in FIGS. 5 and 6 for displaying the propensity.
- the computer system can output a plurality of propensities for a plurality of entities.
- the computer system can output the plurality of propensities as an a separate file (e.g., a text file or an Excel file) or as a table.
- FIG. 3 shows a flowchart representing an exemplary method 300 for creating a model to determine the propensity of an entity to take a specified action, consistent with embodiments of the present disclosure. While the flowchart discloses the following steps in a particular order, it is appreciated that at least some of the steps can be moved, modified, or deleted where appropriate, consistent with embodiments of the present disclosure. In some embodiments, method 300 can be performed in full or in part by a computer system (e.g., computer system 100 ). It is appreciated that some of these steps can be performed in full or in part by other systems.
- a computer system e.g., computer system 100
- the computer system can access one or more data sources that include information associated with the plurality of entities.
- the one or more data sources can be stored locally at the computer system and/or at one or more remote servers (e.g., such as a remote database), or at one or more other remote devices.
- the information in the data sources can be stored in one or more multidimensional tables.
- information of a first type e.g., bill payment amount
- a second type e.g., automobile type
- a plurality of table can contain information associated with the plurality of entities, wherein each table contains information associated with each entity.
- a table can store information associated with a plurality of entities. For example, each row in the table can correspond to a different entity (e.g., Household #1, Household #2, etc.) and each column in the table can correspond to a payment amount.
- the information stored in a table can include entries associated with a temporal period. For example, a table can store a bill payment date for each bill payment amount.
- the information can be stored as a continuous value (e.g., $800 as a bill payment amount), as a categorical value, (e.g., “Sedan” or “Coupe” as an automobile type), as textual value, or as any other type of value.
- a table can be stored in either a row-oriented database or a column-oriented database.
- a row in a row-oriented table can contain information associated with an entity (e.g., Household #1) and data in the row can be stored serially such that information associated with the entity can be accessed in one operation.
- the computer system can access the one or more data sources periodically (e.g., once a week, once a month, etc.). In other embodiments, the computer system can access the one or more data sources based on the one or more data sources being updated (e.g., a new entry, such as payment bill amount, is added to a table). In some embodiments, the computer system can access the one or more data sources responsive to an input received from the user. In some embodiments, the user input can specifically identify the plurality of entities (e.g., Household #1-#10,000) for use in generating the model. In some embodiments, the user input can identify a category or class of entities.
- the plurality of entities e.g., Household #1-#10,000
- the user input can identify a class of entities that are all consumers of a specified provisioning entity (e.g., insurance company), the user input can identify entities that are located within a specified geographic region (e.g., all households within the state of Illinois), or the user input can identify any other category of entities (e.g., all households with an income over $100,000).
- a specified provisioning entity e.g., insurance company
- the computer system can access the one or more data sources including information associated with the plurality of entities.
- the computer system can form a plurality of records including information from the one or more data sources associated with the plurality of entities, each record being associated with an entity.
- a record of the plurality of records can be formed by integrating information from the one or more data sources information that is associated with an entity of the plurality of entities.
- the record can contain a multitude of information related to the entity.
- the record can contain all information from the one or more data sources associated with a household (e.g., number of members in household, number of automobiles, income, monthly bill amounts for each automobile, etc.).
- the record can be stored as a cogroup (e.g., the cogroup shown in FIG. 4 ).
- the record can be stored in either a row-oriented database or a column-oriented database.
- a row in a record can be associated with a data source (e.g., bill payment amount) and data in the row can be stored serially such that data associated with that data source can be accessed in one operation.
- a data source e.g., bill payment amount
- the computer system can filter the plurality of records for information associated with the specified action.
- the specified action can be churn (e.g., cancellation or non-renewal of a subscription) and the computer system can filter the record for information related to churn.
- the computer system can provide context for (e.g., frame) the specified action.
- the computer system can determine whether the specified action will occur within a specified temporal period (e.g., one month).
- the computer system can filter out all information associated with a time that is outside (e.g., before or after) the specified temporal period.
- the computer system can determine the propensity for the specified action based on only recent information.
- the computer system can filter out information associated with a time before the specified temporal period (e.g., stale or less relevant information).
- each record can be filtered in a slightly different way.
- a record can be filtered according to a user input specifying an activity or temporal period.
- the record can be filtered automatically based on a presetting (e.g., the computer can be configured to filter out all information that is more than one year old).
- the computer system can frame the record by associating a label with the record.
- the label can represent whether the entity took the specified action within the specified temporal period.
- the computer system can associate a label of “1” or “true” if the entity took the specified action within the specified temporal period.
- the computer system can keep data from time period A to B (e.g., the specified temporal period) and determine whether the entity cancelled the subscription within a second time period, T. In this example, if the entity cancelled the subscription in time period T, the computer system can associate a label with the record indicating that the entity took the specified action.
- the computer system can create, for each record, a labelled example by generating one or more features associated with an entity of the plurality of entities.
- a feature can be any discernable way of sorting or classifying the record (e.g., average value, most recent value, most common value, etc.).
- the computer system 340 can generate key value pairs, wherein each key value pair contains a feature and a value.
- the computer system can generate features such as “average bill payment amount”, “average income”, “average number of automobiles”, etc. and corresponding values such as “$670”, “$73K”, “2.3 cars”, etc.
- features can be associated with a time value.
- computer system can generate features for a specified temporal period (e.g., features can be based only on the most recent values).
- Feature values can be represented as a continuous value (e.g., $670), as a categorical value (e.g., “Sedan” or “Coupe”), as a textual value, or as any other type of value.
- feature values can be classified as weighted values. For example, a household income of $73,000 can be represented as weighted value of ⁇ 0.27 0 ⁇ , ⁇ 0.73 100000 ⁇ .
- the labelled example can include the key value feature pairs and the record label (e.g., whether the entity took the specified action).
- the computer system can select a subset of the plurality of labelled examples to train a model.
- the subset can be created by randomly sampling the plurality of labelled examples. A random sample can allow for broader generalization of the model created at step 360 .
- the user can select the subset of labelled examples. For example, the user can select all entities with a particular feature (e.g., all households with at least 2 cars).
- the subset can be created by sampling labelled examples with a wide range of values for features that are known to be more important (e.g., change in income).
- the computer system can train a model using the subset of labelled examples.
- the model can be trained by generalizing a function that maps inputs (e.g., the one or more features) to outputs (e.g., the label, such as whether the specified action occurred).
- the model can perform regressions for each feature simultaneously.
- the model can be trained by a hyperparameter optimization algorithm.
- the hyperparameter optimization algorithm can perform a grid search through a hyperparameter space for the optimal hyperparameters.
- the hyperparameter algorithm can perform a random search through the hyperparameter space.
- the computer system can evaluate the hyperparameters against a holdout set of labelled examples.
- the computer system can apply the model trained by hyperparameter optimization to the holdout set.
- the computer system can retrain the model with different hyperparameters if a particular attribute (e.g., accuracy, area under the curve, log-likelihood, F1-score, Top N, etc.) of the model does not exceed a predetermined threshold.
- the computer system can continue to retrain the model until it obtains hyperparameters that exceed the threshold value.
- the computer system can train the model a predetermined number of times (e.g., 10).
- the computer system can evaluate the trained models against a holdout set and select the model with the most favorable attributes (e.g., accuracy, area under the curve, log-likelihood, F1-score, Top N, etc.).
- the computer system can output the model.
- the model can be outputted to a user for future use. For example, a user can use the model to determine the propensity of an entity to take a specified action.
- the computer system can output the model to be stored locally or to be transmitted to an external database.
- the computer system can output the model for use in another method, such as the method described in FIG. 2 , to determine the propensity of an entity to take a specified action.
- the computer system can output confidence levels for the model. For example, the computer system can output the particular attribute (e.g., accuracy, area under the curve, log-likelihood, F1-score, Top N, etc.) of the model with respect to the examples in the holdout set.
- the particular attribute e.g., accuracy, area under the curve, log-likelihood, F1-score, Top N, etc.
- FIG. 4 provides an exemplary use case scenario for determining a propensity of an entity to take a specified action applied to an exemplary data structure. While the flowchart discloses the following steps in a particular order, it is appreciated that at least some of the steps can be moved, modified, or deleted where appropriate, consistent with embodiments of the present disclosure.
- the use case scenario shown in FIG. 4 can be performed by a computer system (e.g., computer system 100 ). It is appreciated that some of these steps can be performed in full or in part by other systems.
- one or more data tables 410 acquired from one or more data sources can include information associated with the entity.
- the one or more data tables 410 can be stored locally at the computer system and/or at one or more remote servers (e.g., such as a remote database), or at one or more other remote devices.
- the information in the data tables can be stored in one or more multidimensional tables.
- information of a first type e.g., bill payment amount
- information of a second type e.g., income or number of cars
- a table can contain information associated with a single entity.
- Bill Amount table 410 shows the most recent bill payment amounts associated with the entity in this exemplary scenario.
- a table can store information associated with a plurality of entities. For example, each row in the table can correspond to a different entity (e.g., Household #1, Household #2, etc.) and each column in the table can correspond to a payment amount.
- the information stored in the table can include entries associated with a temporal period. For example, a table can store a bill payment date for each bill payment amount. As shown in FIG.
- Bill Payment Table 410 can store dates in the first column (e.g., 1/1/14, 2/1/14, and 3/1/14). Each bill payment date can be associated with the bill payment amount. For example, Bill Payment Table 410 shows that an amount of $800 was billed to the household on Jan. 1, 2014. The information can be stored as a continuous value (e.g., $800 as a bill payment amount), as a categorical value, (e.g., “Sedan” or “Coupe” as an automobile type), as textual value, or as any other type of value.
- a table can be stored in either a row-oriented database or a column-oriented database. For example, a row in a row-oriented table can contain information associated with an entity (e.g., Household #1) and data in the row can be stored serially such that information associated with the entity can be accessed in one operation.
- entity e.g., Household #1
- the computer system can form ( 420 ) a record 430 including some or all information from the one or more data sources associated with the entity.
- record 430 can be formed ( 420 ) by integrating the information from the one or more data sources that is associated with the entity.
- Record 430 can contain a multitude of information related to the entity.
- record 430 can contain all information from the one or more data sources associated with a household (e.g., number of members in household, number of automobiles, income, monthly bill mounts for each automobile, etc.).
- record 430 can be stored as a cogroup with each row of the cogroup associated with a different category of information.
- record 430 can be stored in either a row-oriented database or a column-oriented database.
- a row in a row-oriented record can be associated with a data source (e.g., bill payment amount) and data in the row can be stored serially such that data associated with that data source can be accessed in one operation.
- the “Bill Amount” is stored as row in record 430 .
- Bill amounts $800, $600, and $600 can be stored serially such that all of the payment amounts can be accessed in one operation.
- “Income” and “Number of Cars” are stored in separate rows in record 430 , and information from these sources (e.g. ⁇ $80K, $70K, $70K ⁇ and ⁇ 3, 2, 2 ⁇ ) can also be accessed in one operation.
- the computer system can filter record 430 for information associated with the specified action (not shown).
- the specified action can be churn (e.g., cancellation of a subscription) and the computer system can filter record 430 for information related to churn.
- the computer system can provide context for the specified action.
- the computer system can determine whether the specified action will occur within a specified temporal period (e.g., one month). The computer system can filter out all information associated with a time that is outside (e.g., before or after) the specified temporal period.
- the computer system can determine the propensity for the specified action based on only recent events.
- the computer system can filter out information associated with a time before the specified time period (e.g., stale or less relevant information).
- each record can be filtered in a slightly different way.
- Record 430 can be filtered according to a user input specifying an activity or temporal period.
- record 430 can be filtered automatically based on a presetting (e.g., the computer can be configured to filter out all information that is more than one year old).
- the computer system can determine the propensity of the entity to take the specified action based on only data from the previous month. In the example shown in FIG.
- the computer system can filter out the older entries of Bill Amount table 410 (e.g., Bill Amounts of $800 and $600 corresponding to bill dates in January and February).
- the computer system can also filter out similar entries in Income and Number of Cars tables 410 (e.g., incomes of $80K and $70K and 3 and 2 number of cars).
- the computer system can use only the most recent entries to determine the propensity of the household to take the specified action (e.g., $600 in Bill Amount table 410 , $70K in Income table 410 , and 2 in Number of Cars table 410 ).
- the computer system can generate ( 440 ), based on record 430 , one or more features 450 associated with the entity.
- a feature can be any discernable way of sorting or classifying the record (e.g., average value, most recent value, most common value, etc.).
- the computer system can generate key value pairs, wherein each key value pair contains a feature and a value.
- the computer system can generate one or more features 450 such as “average bill payment amount”, “average income”, “average number of automobiles”, etc. and corresponding values such as “$670”, “$73K”, “2.3 cars”, etc.
- the one or more features 450 can be associated with a time value.
- computer system can generate features for a specified temporal period (e.g., features can be based only on the most recent values).
- Feature values can be represented as a continuous value (e.g., $670), as a categorical value (e.g., “Sedan” or “Coupe”), as a textual value, or as any other type of value.
- the one or more feature 450 can be stored as classified as weighted values. For example, a household income of $73,000 can be represented as weighted value of ⁇ 0.27 0 ⁇ , ⁇ 0.73 100000 ⁇ .
- the one or more features can be extrapolated from the information contained in the record.
- a feature can be that the entity deactivated online payments (e.g. customer deactivated ETF payment on 2/20).
- the one or more features can be related to communications between the providing entity (e.g., insurance provider) and consuming entity (e.g., household).
- computer system 100 can analyze (e.g., tokenize) the transcript of a call between an agent and a household and assign a topical value to that call (e.g., “topic 5” corresponding to anger).
- Computer system 100 can store this information as a feature pair (not shown), such as the pair ⁇ “Service Call Topic” “5” ⁇ .
- the one or more features can be related to whether the household took a specified action (e.g., filed a claim or called to change policy).
- the computer system can process ( 460 ) the one or more features 450 to determine the propensity 470 of the entity to take the specified action.
- the propensity 470 can be determined by applying a trained model, such as the model described in greater detail in FIG. 3 .
- the input to the model can be key value pairs of the one or more features 450 associated with the entity and the specified actions and the output of the model can be the propensity 470 of the entity to take the specified action.
- processing the one or more features associated with the entity can result in a multitude of useful insights regarding the features that influence the propensity of the entity to take the specified action.
- Such insights can include, for example, the features that are most influential on the propensity of the entity to take the specified action (e.g., change in income, etc.).
- the computer system can output the propensity 470 .
- the computer system can output the propensity 470 as a continuous value, such as a number or percentage (e.g., 80 or 80%) or as a categorical value (e.g., “low”, “medium”, or “high”).
- the computer system can generate a user interface, such as the user interfaces described in greater detail in FIGS. 5 and 6 for displaying the propensity 470 .
- FIG. 5 illustrates an exemplary user interface 500 provided by a computer system (e.g., computer system 100 ) for display (e.g., display 122 ), in accordance with some embodiments.
- User interface 500 can include a plurality of tiles (e.g., tile 510 ), each tile representing an entity (e.g., a household).
- tiles can be arranged according to the propensity of the entity to take the specified action. For example, entities that are more likely to take the specified action can be located near the top of the display, whereas entities that are less likely to take the specified action can be lower on the display.
- the tiles can be arranged by date (e.g., date 520 ).
- entities with the most recent activities can be located near the top of the display.
- tile 510 with the most recent date 520 of Feb. 21, 2014 is located in the top left corner of the display.
- the tile to the right of tile 510 has the next most recent date (e.g., Feb. 20, 2014).
- Subsequent tiles have dates that are less recent.
- entities with the longest pending outstanding action can be located near the top of the screen.
- user interface 500 can be updated periodically (e.g., once a day, once a week, once a month, etc.). In other embodiments, user interface 500 can be updated when information associated with any of the entities stored in the one or more data sources is updated (e.g., a new entry, such as payment bill amount, is added to a table). In some embodiments, user interface 500 can update in response to an input received from the user.
- User interface 500 can automatically determine the entities for which to generate the display.
- user interface 500 can display entities associated with a particular user (e.g., John Smith, Triage Agent) once the user accesses user interface 500 .
- the user can specifically identify the entities for which to generate the display.
- the user can identity a category or class of entities for which to generate the display. For example, the user can identify a class of entities that are all consumers of a specified provisioning entity (e.g., insurance company), the user input can identify entities that are located within a specified geographic region (e.g., all households within the state of Illinois), or the user input can identify any other category of entities (e.g., all households with an income over $100,000).
- a specified provisioning entity e.g., insurance company
- the user input can identify entities that are located within a specified geographic region (e.g., all households within the state of Illinois), or the user input can identify any other category of entities (e.g., all households with an income over $100,000).
- user interface 500 can portray a date 520 (e.g., Feb. 21, 2014) associated with the entity in tile 510 .
- Date 520 can correspond to the current date, the date that method 200 was last performed for that entity, the date that information in the one or more data sources associated with that entity was last updated, or the date that the user last viewed the tile associated with the entity.
- user interface 500 can portray a propensity 540 of the entity to take the specified action (e.g., “Med”) in tile 510 .
- user interface 500 can portray the propensity as a categorical value, such as “Med” in tile 510 .
- user interface 500 can portray tile 510 in a color (e.g., green for “low”, red for “high”, etc.) representing the propensity. In some embodiments, user interface 500 can portray the propensity in tile 510 as numerical value or as a percentage.
- a color e.g., green for “low”, red for “high”, etc.
- user interface 500 can portray the propensity in tile 510 as numerical value or as a percentage.
- User interface 500 can portray recent activity 530 in tile 510 .
- the recent activity 530 can be entered by a user.
- a recent activity could be that an “Agent called customer on 2/21 regarding discounts” as shown in tile 510 .
- user interface 500 can generate the recent activity based on the one or more features associated with the entity. For example, user interface 500 can display, “Customer registered an additional luxury vehicle on 2/18” in tile 510 responsive to this information being updated in the record associated with the entity.
- tile 510 can portray important features 540 associated with the entity. For example, as shown in tile 510 of FIG. 5 , these features can be “vehicle”, “discounts”, etc.
- user interface 500 can recommend an action for the user to take (e.g., service call). In some embodiments, this recommendation can relate to the recent activity 530 . A user can use this information to take preemptive action to prevent the entity from taking the specified action. By way of example, if the propensity of a household subscribing to an automobile insurance policy was high, the user could take remedial action (e.g., lower rate, contact customer to address customer concerns, etc.). In some embodiments user interface 500 can display a number uniquely identifying the entity (e.g., a policy number).
- user interface 500 can allow a user to click on tile 510 to access additional information associated with the entity. For example, a user can access user interface 600 shown in FIG. 6 below by clicking on one of the tiles shown in user interface 500 of FIG. 5 .
- user interface 600 can be inlaid over user interface 500 .
- user interface 600 can be a distinct user interface.
- User interface 500 can also allow access to additional user interfaces (not shown) through the “INBOX,” “FLAGGED,” and “STATS” links shown at the top of user interface 500 .
- the “INBOX” user interface can display messages between the user and other agents to track the remedial actions that were taken.
- the INBOX user interface can also be used to notify users of households with a higher likelihood of cancelling the subscription.
- the “FLAGGED” user interface can show customers (e.g., households) that the user believed were at risk for taking the specified action.
- the FLAGGED user interface can contain a list of the households most likely to cancel their insurance policy. In some embodiments, these households can be selected manually by the user.
- these households can be automatically populated if the propensity exceeds a predetermined threshold (e.g., the FLAGGED interface can be populated with all households with a “High” propensity).
- the FLAGGED user interface can allow the user to track remediation steps (e.g., contacting the household, changing policy, etc.). Households can remain in the FLAGGED user interface until their risk of taking the specified action has declined, the user has decided that the household is no longer at risk, or the specification action occurred (e.g., the household cancelled its subscription).
- the “STATS” interface can display metrics such as, for example, the rate at which the user was able to prevent the specified action from occurring categorized by action taken and the most common and/or trending issues.
- FIG. 6 illustrates another exemplary user interface 600 provided by the computer system (e.g., computer system 100 ) for display (e.g., display 112 ) in accordance with some embodiments.
- user interface 600 can be accessed by clicking on a tile (e.g., entity) in user interface 500 .
- User interface 600 can portray a date 610 (e.g., Feb. 18, 2014) associated with the entity. Date 610 can correspond to the current date, the date that method 200 was last performed for that entity, the date that information in the one or more data sources associated with that entity was last updated, or the date that the user last viewed the tile associated with the entity.
- user interface 600 can portray a propensity 620 of the entity to take the specified action.
- user interface 600 can portray propensity 620 as a categorical value, such as “Med.”.
- user interface 600 can convey propensity 620 by shading the top bar in a different color (e.g., green for “low”, red for “high”, etc.) representing propensity 620 .
- user interface 600 can portray propensity 620 as numerical value or as a percentage.
- user interface 600 can display the entity status 630 (e.g., “Active” if the household is currently subscribing to a policy).
- user interface 600 can display recent activities 640 associated with the entity. For example, as shown in FIG. 6 , user interface 600 can display that the “customer registered an additional luxury vehicle on 2/18”. User interface 600 can recommend an action 650 for the user to take (e.g., service call). In some embodiments, this recommendation 650 can relate to the recent activity.
- User interface 600 can provide the user with additional information associated with the entity. As shown in the bottom left panel of FIG. 6 , user interface 600 can display basic biographic information 660 for the entity. In the automobile insurance context, for example, user interface 600 can display the policy number, (e.g., 34726182), the entity name (e.g., household/owner of the policy, David Stark), the policy coverage start date (e.g., 12/12/2004), any secondary owners associated with the policy (e.g., James Watson), information associated with the insured automobile (e.g., 2013 Cadillac Escalade), and the type of insurance policy (e.g., Standard).
- the policy number e.g., 34726182
- the entity name e.g., household/owner of the policy, David Stark
- the policy coverage start date e.g., 12/12/2004
- any secondary owners associated with the policy e.g., James Watson
- information associated with the insured automobile e.g., 2013 Cadillac Escalade
- the type of insurance policy e
- user interface 600 can also display information for an agent 670 associated with the entity.
- the user interface 600 can display the name (e.g., Bruce Atherton) and contact information (e.g., 583 234-9172) of the agent.
- a user can use this information to take preemptive action to prevent the entity from taking the specified action.
- the user could contact the agent to take remedial action (e.g., lower rate, address customer concerns, etc.).
- the right panel of FIG. 6 can display recent events 680 associated with the entity.
- user interface 600 can display whether the entity status is active (e.g., whether the entity is currently subscribing to a policy) or whether the agent has taken any actions (e.g., called the household or subscriber).
- user interface 600 can also allow the user and agent to converse in the right panel. For example, the user can click on the “ADD AN UPDATE” button 690 to remind the agent to contact the entity.
- the user interface can display responsive comments 680 from the agent and the agent can add any actions taken 680 (e.g., calling the household).
Abstract
Systems and methods are disclosed for determining a propensity of an entity to take a specified action. In accordance with one implementation, a method is provided for determining the propensity. The method includes, for example, accessing one or more data sources, the one or more data sources including information associated with the entity, forming a record associated with the entity by integrating the information from the one or more data sources, generating, based on the record, one or more features associated with the entity, processing the one or more features to determine the propensity of the entity to take the specified action, and outputting the propensity.
Description
- This application claims priority to U.S. Provisional Patent Application No. 62/027,761, filed on Jul. 22, 2014, and U.S. Provisional Patent Application No. 62/039,305, filed on Aug. 19, 2014, the disclosures of which are expressly incorporated herein by reference in their entirety.
- The amount of information being processed and stored is rapidly increasing as technology advances present an ever-increasing ability to generate and store data. On the one hand, this vast amount of data allows entities to perform more detailed analyses than ever. But on the other hand, the vast amount of data makes it more difficult for entities to quickly sort through and determine the most relevant features of the data. Collecting, classifying, and analyzing large sets of data in an appropriate manner allows these entities to more quickly and efficiently identify patterns, thereby allowing them to predict future actions.
- Reference will now be made to the accompanying drawings, which illustrate exemplary embodiments of the present disclosure. In the drawings:
-
FIG. 1 is a block diagram of an exemplary computer system, consistent with embodiments of the present disclosure; -
FIG. 2 is a flowchart of an exemplary method for determining a propensity of an entity to take a specified action, consistent with embodiments of the present disclosure; -
FIG. 3 is a flowchart of an exemplary method for creating a model to determine the propensity of an entity to take a specified action, consistent with embodiments of the present disclosure; -
FIG. 4 provides an exemplary use case scenario for determining a propensity of an entity to take a specified action applied to an exemplary data structure, consistent with embodiments of the present disclosure. -
FIG. 5 illustrates an exemplary user interface, consistent with embodiments of the present disclosure; and -
FIG. 6 illustrates another exemplary user interface, consistent with embodiments of the present disclosure. - Reference will now be made in detail to several exemplary embodiments, including those illustrated in the accompanying drawings. Whenever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts.
- Embodiments disclosed herein are directed to, among other things, to systems and methods that can determine the propensity of an entity (e.g., a person, a household, or a company) to take a specified action. For example, a specific action can involve determining the propensity that a customer will leave a supplier during a given time period (e.g., churn). Such factors that can affect the churn rate include customer dissatisfaction, cheaper and/or better offers from the competition, more successful sales and/or marketing by the competition, or reasons having to do with the customer life cycle. If a supplier can receive an indication that a customer is likely to churn, the supplier can take one or more actions in order to keep the customer. The embodiments disclosed herein can assist with providing that indication.
- For example, the systems and methods can access one or more data sources, the one or more data sources including information associated with the entity, form a record associated with the entity by integrating the information from the one or more data sources, generate, based on the record, one or more features associated with the entity, process the one or more features to determine the propensity of the entity to take the specified action, and output the propensity.
- The operations, techniques, and/or components described herein are implemented by a computer system, which can include one or more special-purpose computing devices. The special-purpose computing devices can be hard-wired to perform the operations, techniques, and/or components described herein. The special-purpose computing devices can include digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are persistently programmed to perform the operations, techniques, and/or components described herein. The special-purpose computing devices can include one or more hardware processors programmed to perform such features of the present disclosure pursuant to program instructions in firmware, memory, other storage, or a combination. Such special-purpose computing devices can combine custom hard-wired logic, ASICs, or FPGAs with custom programming to accomplish the techniques and other features of the present disclosure. The special-purpose computing devices can be desktop computer systems, portable computer systems, handheld devices, networking devices, or any other device that incorporates hard-wired and/or program logic to implement the techniques and other features of the present disclosure.
- The one or more special-purpose computing devices can be generally controlled and coordinated by operating system software, such as iOS, Android, Blackberry, Chrome OS, Windows XP, Windows Vista, Windows 7, Windows 8, Windows Server, Windows CE, Unix, Linux, SunOS, Solaris, VxWorks, or other compatible operating systems. In other embodiments, the computing device can be controlled by a proprietary operating system. Operating systems control and schedule computer processes for execution, perform memory management, provide file system, networking, I/O services, and provide a user interface functionality, such as a graphical user interface (“GUI”), among other things.
- By way of example,
FIG. 1 is a block diagram that illustrates an implementation of acomputer system 100, which, as described above, can comprise one or more electronic devices.Computer system 100 includes abus 102 or other communication mechanism for communicating information, and one or more hardware processors 104 (denoted asprocessor 104 for purposes of simplicity), coupled withbus 102 for processing information. One ormore hardware processors 104 can be, for example, one or more microprocessors. -
Computer system 100 also includes a main memory 106, such as a random access memory (RAM) or other dynamic storage device, coupled tobus 102 for storing information and instructions to be executed by one ormore processors 104. Main memory 106 also can be used for storing temporary variables or other intermediate information during execution of instructions to be executed byprocessor 104. Such instructions, when stored in non-transitory storage media accessible to one ormore processors 104, rendercomputer system 100 into a special-purpose machine that is customized to perform the operations specified in the instructions. -
Computer system 100 further includes a read only memory (ROM) 108 or other static storage device coupled tobus 102 for storing static information and instructions forprocessor 104. Astorage device 110, such as a magnetic disk, optical disk, or USB thumb drive (Flash drive), etc., is provided and coupled tobus 102 for storing information and instructions. -
Computer system 100 can be coupled viabus 102 to adisplay 112, such as a cathode ray tube (CRT), an LCD display, or a touchscreen, for displaying information to a computer user. Aninput device 114, including alphanumeric and other keys, is coupled tobus 102 for communicating information and command selections to one ormore processors 104. Another type of user input device iscursor control 116, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to one ormore processors 104 and for controlling cursor movement ondisplay 112. The input device typically has two degrees of freedom in two axes, a first axis (for example, x) and a second axis (for example, y), that allows the device to specify positions in a plane. In some embodiments, the same direction information and command selections as cursor control may be implemented via receiving touches on a touch screen without a cursor. -
Computer system 100 can include a user interface module to implement a GUI that may be stored in a mass storage device as executable software codes that are executed by the one or more computing devices. This and other modules may include, by way of example, components, such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. - In general, the word “module,” as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, possibly having entry and exit points, written in a programming language, such as, for example, Java, Lua, C, and C++. A software module can be compiled and linked into an executable program, installed in a dynamic link library, or written in an interpreted programming language such as, for example, BASIC, Perl, Python, or Pig. It will be appreciated that software modules can be callable from other modules or from themselves, and/or can be invoked in response to detected events or interrupts. Software modules configured for execution on computing devices can be provided on a computer readable medium, such as a compact disc, digital video disc, flash drive, magnetic disc, or any other tangible medium, or as a digital download (and can be originally stored in a compressed or installable format that requires installation, decompression, or decryption prior to execution). Such software code can be stored, partially or fully, on a memory device of the executing computing device, for execution by the computing device. Software instructions can be embedded in firmware, such as an EPROM. It will be further appreciated that hardware modules can be comprised of connected logic units, such as gates and flip-flops, and/or can be comprised of programmable units, such as programmable gate arrays or processors. The modules or computing device functionality described herein are preferably implemented as software modules, but can be represented in hardware or firmware. Generally, the modules described herein refer to logical modules that may be combined with other modules or divided into sub-modules despite their physical organization or storage.
-
Computer system 100 can implement the techniques and other features described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the electronic device causes orprograms computer system 100 to be a special-purpose machine. According to some embodiments, the techniques and other features described herein are performed bycomputer system 100 in response to one ormore processors 104 executing one or more sequences of one or more instructions contained in main memory 106. Such instructions can be read into main memory 106 from another storage medium, such asstorage device 110. Execution of the sequences of instructions contained in main memory 106 causes one ormore processors 104 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry can be used in place of or in combination with software instructions. - The term “non-transitory media” as used herein refers to any media storing data and/or instructions that cause a machine to operate in a specific fashion. Such non-transitory media can comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device 150. Volatile media includes dynamic memory, such as main memory 106. Common forms of non-transitory media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge, a register memory, a processor cache, and networked versions of the same.
- Non-transitory media is distinct from, but can be used in conjunction with, transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise
bus 102. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications. - Various forms of media can be involved in carrying one or more sequences of one or more instructions to one or
more processors 104 for execution. For example, the instructions can initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local tocomputer system 100 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data onbus 102.Bus 102 carries the data to main memory 106, from whichprocessor 104 retrieves and executes the instructions. The instructions received by main memory 106 can optionally be stored onstorage device 110 either before or after execution by one ormore processors 104. -
Computer system 100 can also include acommunication interface 118 coupled tobus 102.Communication interface 118 can provide a two-way data communication coupling to anetwork link 120 that is connected to alocal network 122. For example,communication interface 118 can be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example,communication interface 118 can be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links can also be implemented. In any such implementation,communication interface 118 can send and receive electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information. - Network link 120 can typically provide data communication through one or more networks to other data devices. For example, network link 120 can provide a connection through
local network 122 to ahost computer 124 or to data equipment operated by an Internet Service Provider (ISP) 126.ISP 126 in turn provides data communication services through the world wide packet data communication network now commonly referred to as the “Internet” 128.Local network 122 andInternet 128 both use electrical, electromagnetic, or optical signals that carry digital data streams. The signals through the various networks and the signals onnetwork link 120 and throughcommunication interface 118, which carry the digital data to and fromelectronic device 110, are example forms of transmission media. -
Computer system 100 can send messages and receive data, including program code, through the network(s),network link 120 andcommunication interface 118. In the Internet example, aserver 130 might transmit a requested code for an application program throughInternet 128,ISP 126,local network 122 andcommunication interface 118. The received code can be executed by one ormore processors 104 as it is received, and/or stored instorage device 110, or other non-volatile storage for later execution. -
FIG. 2 is a flowchart representing anexemplary method 200 for determining the propensity of an entity to take a specified action. While the flowchart discloses the following steps in a particular order, it is appreciated that at least some of the steps can be moved, modified, or deleted where appropriate, consistent with embodiments of the present disclosure. In some embodiments,method 200 can be performed in full or in part by a computer system (e.g., computer system 100). It is appreciated that some of these steps can be performed in full or in part by other systems. - Referring to
FIG. 2 , atstep 210, the computer system can access one or more data sources that include information associated with the entity. The one or more data sources can be stored locally at the computer system and/or at one or more remote servers (e.g., such as a remote database), or at one or more other remote devices. In some embodiments, the information in the data sources can be stored in one or more multidimensional tables. By way of example, information of a first type (e.g., bill payment amount) associated with the entity, (e.g., a household), can be stored in a first multidimensional table and information of a second type (e.g., automobile type) associated with the entity can be stored in a second multidimensional table. In some embodiments a table can contain information associated with a single entity. In other embodiments, a table can store information associated with a plurality of entities. For example, each row in the table can correspond to a different entity (e.g., Household #1,Household # 2, etc.) and each column in the table can correspond to a payment amount. In some embodiments, the information stored in the table can include entries associated with a temporal period. For example, a table can store a bill payment date for each bill payment amount. The information can be stored as a continuous value (e.g., $800 as a bill payment amount), as a categorical value (e.g., “Sedan” or “Coupe” as an automobile type), as textual value, or as any other type of value. In some embodiments, a table can be stored in either a row-oriented database or a column-oriented database. For example, a row in a row-oriented table can contain information associated with an entity (e.g., Household #1) and data in the row can be stored serially such that information associated with the entity can be accessed in one operation. - In some embodiments the computer system can access the one or more data sources periodically (e.g., once a week, once a month, etc.). The computer system can access the one or more data sources based on the one or more data sources being updated (e.g., a new entry, such as payment bill amount, is added to a table). In some embodiments, the computer system can access the one or more data sources responsive to an input received from the user. The user input can identify the entity (e.g. Household #5) for which information is requested. In some embodiments, the user input can identify a category or class of entities. For example, the user input can identify a class of entities that are all consumers of a specified provisioning entity (e.g., insurance company), the user input can identify entities that are located within a specified geographic region (e.g., all households within the state of Illinois), or the user input can identify any other category of entities (e.g., all households with an income over $100,000). In response to the user input, the computer system can access the one or more data sources including information associated with the entities. In some embodiments,
method 200 can be performed periodically (e.g., once a week, once a month, etc.). In some embodiments,method 200 can be performed whenever the one or more data sources are accessed. - At
step 220, the computer system can form a record including all information from the one or more data sources associated with the entity. In some embodiments, the record can be formed by integrating the information that is associated with the entity from the one or more data sources. The record can contain a multitude of information related to the entity. For example, the record can contain all information from the one or more data sources associated with a household (e.g., number of members in household, age of each member of the household, number of automobiles, income, monthly bill mounts for each automobile, types of automobiles, etc.). In some embodiments, the record can be stored as a cogroup (e.g., the cogroup shown inFIG. 4 ). In some embodiments, the record can be stored in either a row-oriented database or a column-oriented database. For example, a row in a row-oriented record can be associated with a data source (e.g., bill payment amount) and data in the row can be stored serially such that data associated with that data source can be accessed in one operation. - At
step 230, the computer system can filter the record for information associated with the specified action. For example, the specified action can be churn (e.g., cancellation of a subscription) and the computer system can filter the record for information related to churn. In some embodiments, the computer system can provide context for the specified action. In some embodiments, the computer system can determine whether the specified action will likely occur within a specified temporal period (e.g., one month). The computer system can filter out all information associated with a time that is outside (e.g., before or after) the specified temporal period. In some embodiments, the computer system can determine the propensity for the specified action based on only recent events. For example, the computer system can filter out information associated with a time before the specified time period (e.g., stale or less relevant information). In some embodiments, each record can be filtered in a slightly different way. The record can be filtered according to a user input specifying an activity or temporal period. In some embodiments, the record can be filtered automatically based on a presetting (e.g., the computer can be configured to filter out all information that is more than one year old). - At
step 240, the computer system can generate, based on the record, one or more features associated with the entity. A feature can be any discernable way of sorting or classifying the record (e.g., average value, most recent value, most common value, etc.). In some embodiments, the computer system can generate key value pairs, wherein each key value pair contains a feature and a value. For example, the computer system can generate features such as “average bill payment amount”, “average income”, “average number of automobiles”, etc. and corresponding values such as “$670”, “$73K”, “2.3 cars”, etc. In some embodiments, features can be associated with a time value. For example, computer system can generate features for a specified temporal period (e.g., features can be based only on the most recent values). Feature values can be represented as a continuous value (e.g., $670), as a categorical value (e.g., “Sedan” or “Coupe”), as a textual value, or as any other type of value. In some embodiments, feature values can be classified as weighted values. For example, a household income of $73,000 can be represented as weighted value of {0.27 0}, {0.73 100000}. - At
step 250, the computer system can process the one or more features to determine the propensity of the entity to take the specified action. In some embodiments, the propensity can be determined by applying a trained model, such as the model described in greater detail inFIG. 3 . The input to the model can be key value pairs of the one or more features associated with the entity and the specified actions and the output of the model can be the propensity of the entity to take the specified action. In some embodiments, processing the one or more features associated with the entity can result in a multitude of useful insights regarding the features that influence the propensity of the entity to take the specified action. Such insights, can include, for example, the features that are most influential on the propensity of the entity to take the specified action (e.g., change in income, etc.). - At
step 260, the computer system can output the propensity. In some embodiments the computer system can output the propensity as a continuous value, such as a number or percentage (e.g., 80 or 80%) or as a categorical value (e.g., “low”, “medium”, or “high”). In some embodiments, the computer system can generate a user interface, such as the user interfaces described in greater detail inFIGS. 5 and 6 for displaying the propensity. In some embodiments, the computer system can output a plurality of propensities for a plurality of entities. The computer system can output the plurality of propensities as an a separate file (e.g., a text file or an Excel file) or as a table. -
FIG. 3 shows a flowchart representing anexemplary method 300 for creating a model to determine the propensity of an entity to take a specified action, consistent with embodiments of the present disclosure. While the flowchart discloses the following steps in a particular order, it is appreciated that at least some of the steps can be moved, modified, or deleted where appropriate, consistent with embodiments of the present disclosure. In some embodiments,method 300 can be performed in full or in part by a computer system (e.g., computer system 100). It is appreciated that some of these steps can be performed in full or in part by other systems. - Referring to
FIG. 3 , atstep 310, the computer system can access one or more data sources that include information associated with the plurality of entities. The one or more data sources can be stored locally at the computer system and/or at one or more remote servers (e.g., such as a remote database), or at one or more other remote devices. In some embodiments, the information in the data sources can be stored in one or more multidimensional tables. By way of example, information of a first type (e.g., bill payment amount) associated with the plurality of entities, (e.g., households), can be stored in a first multidimensional table and information of a second type (e.g., automobile type) associated with the entities can be stored in a second multidimensional table. In some embodiments a plurality of table can contain information associated with the plurality of entities, wherein each table contains information associated with each entity. In other embodiments, a table can store information associated with a plurality of entities. For example, each row in the table can correspond to a different entity (e.g., Household #1,Household # 2, etc.) and each column in the table can correspond to a payment amount. In some embodiments, the information stored in a table can include entries associated with a temporal period. For example, a table can store a bill payment date for each bill payment amount. The information can be stored as a continuous value (e.g., $800 as a bill payment amount), as a categorical value, (e.g., “Sedan” or “Coupe” as an automobile type), as textual value, or as any other type of value. In some embodiments, a table can be stored in either a row-oriented database or a column-oriented database. For example, a row in a row-oriented table can contain information associated with an entity (e.g., Household #1) and data in the row can be stored serially such that information associated with the entity can be accessed in one operation. - In some embodiments the computer system can access the one or more data sources periodically (e.g., once a week, once a month, etc.). In other embodiments, the computer system can access the one or more data sources based on the one or more data sources being updated (e.g., a new entry, such as payment bill amount, is added to a table). In some embodiments, the computer system can access the one or more data sources responsive to an input received from the user. In some embodiments, the user input can specifically identify the plurality of entities (e.g., Household #1-#10,000) for use in generating the model. In some embodiments, the user input can identify a category or class of entities. For example, the user input can identify a class of entities that are all consumers of a specified provisioning entity (e.g., insurance company), the user input can identify entities that are located within a specified geographic region (e.g., all households within the state of Illinois), or the user input can identify any other category of entities (e.g., all households with an income over $100,000). In response to a user input, the computer system can access the one or more data sources including information associated with the plurality of entities.
- At
step 320, the computer system can form a plurality of records including information from the one or more data sources associated with the plurality of entities, each record being associated with an entity. In some embodiments, a record of the plurality of records can be formed by integrating information from the one or more data sources information that is associated with an entity of the plurality of entities. The record can contain a multitude of information related to the entity. For example, the record can contain all information from the one or more data sources associated with a household (e.g., number of members in household, number of automobiles, income, monthly bill amounts for each automobile, etc.). In some embodiments, the record can be stored as a cogroup (e.g., the cogroup shown inFIG. 4 ). In some embodiments, the record can be stored in either a row-oriented database or a column-oriented database. For example, a row in a record can be associated with a data source (e.g., bill payment amount) and data in the row can be stored serially such that data associated with that data source can be accessed in one operation. - At
step 330, the computer system can filter the plurality of records for information associated with the specified action. For example, the specified action can be churn (e.g., cancellation or non-renewal of a subscription) and the computer system can filter the record for information related to churn. In some embodiments, the computer system can provide context for (e.g., frame) the specified action. In some embodiments, the computer system can determine whether the specified action will occur within a specified temporal period (e.g., one month). The computer system can filter out all information associated with a time that is outside (e.g., before or after) the specified temporal period. In some embodiments, the computer system can determine the propensity for the specified action based on only recent information. For example, the computer system can filter out information associated with a time before the specified temporal period (e.g., stale or less relevant information). In some embodiments, each record can be filtered in a slightly different way. A record can be filtered according to a user input specifying an activity or temporal period. In some embodiments, the record can be filtered automatically based on a presetting (e.g., the computer can be configured to filter out all information that is more than one year old). - The computer system can frame the record by associating a label with the record. In some embodiments, the label can represent whether the entity took the specified action within the specified temporal period. For example, the computer system can associate a label of “1” or “true” if the entity took the specified action within the specified temporal period. By way of example, in the context of the cancellation of a subscription, the computer system can keep data from time period A to B (e.g., the specified temporal period) and determine whether the entity cancelled the subscription within a second time period, T. In this example, if the entity cancelled the subscription in time period T, the computer system can associate a label with the record indicating that the entity took the specified action.
- At
step 340, the computer system can create, for each record, a labelled example by generating one or more features associated with an entity of the plurality of entities. A feature can be any discernable way of sorting or classifying the record (e.g., average value, most recent value, most common value, etc.). In some embodiments, thecomputer system 340 can generate key value pairs, wherein each key value pair contains a feature and a value. For example, the computer system can generate features such as “average bill payment amount”, “average income”, “average number of automobiles”, etc. and corresponding values such as “$670”, “$73K”, “2.3 cars”, etc. In some embodiments, features can be associated with a time value. For example, computer system can generate features for a specified temporal period (e.g., features can be based only on the most recent values). Feature values can be represented as a continuous value (e.g., $670), as a categorical value (e.g., “Sedan” or “Coupe”), as a textual value, or as any other type of value. In some embodiments, feature values can be classified as weighted values. For example, a household income of $73,000 can be represented as weighted value of {0.27 0}, {0.73 100000}. In some embodiments, the labelled example can include the key value feature pairs and the record label (e.g., whether the entity took the specified action). - At
step 350, the computer system can select a subset of the plurality of labelled examples to train a model. In some embodiments, the subset can be created by randomly sampling the plurality of labelled examples. A random sample can allow for broader generalization of the model created at step 360. In some embodiments, the user can select the subset of labelled examples. For example, the user can select all entities with a particular feature (e.g., all households with at least 2 cars). In some embodiments, the subset can be created by sampling labelled examples with a wide range of values for features that are known to be more important (e.g., change in income). - At step 360, the computer system can train a model using the subset of labelled examples. For example, the model can be trained by generalizing a function that maps inputs (e.g., the one or more features) to outputs (e.g., the label, such as whether the specified action occurred). In some embodiments, the model can perform regressions for each feature simultaneously. In some embodiments, the model can be trained by a hyperparameter optimization algorithm. In some embodiments, the hyperparameter optimization algorithm can perform a grid search through a hyperparameter space for the optimal hyperparameters. In some embodiments, the hyperparameter algorithm can perform a random search through the hyperparameter space. The computer system can evaluate the hyperparameters against a holdout set of labelled examples. For example, the computer system can apply the model trained by hyperparameter optimization to the holdout set. In some embodiments, the computer system can retrain the model with different hyperparameters if a particular attribute (e.g., accuracy, area under the curve, log-likelihood, F1-score, Top N, etc.) of the model does not exceed a predetermined threshold. In some embodiments, the computer system can continue to retrain the model until it obtains hyperparameters that exceed the threshold value. In some embodiments, the computer system can train the model a predetermined number of times (e.g., 10). The computer system can evaluate the trained models against a holdout set and select the model with the most favorable attributes (e.g., accuracy, area under the curve, log-likelihood, F1-score, Top N, etc.).
- At
step 370, the computer system can output the model. In some embodiments, the model can be outputted to a user for future use. For example, a user can use the model to determine the propensity of an entity to take a specified action. In other embodiments, the computer system can output the model to be stored locally or to be transmitted to an external database. In some embodiments, the computer system can output the model for use in another method, such as the method described inFIG. 2 , to determine the propensity of an entity to take a specified action. In some embodiments, the computer system can output confidence levels for the model. For example, the computer system can output the particular attribute (e.g., accuracy, area under the curve, log-likelihood, F1-score, Top N, etc.) of the model with respect to the examples in the holdout set. -
FIG. 4 provides an exemplary use case scenario for determining a propensity of an entity to take a specified action applied to an exemplary data structure. While the flowchart discloses the following steps in a particular order, it is appreciated that at least some of the steps can be moved, modified, or deleted where appropriate, consistent with embodiments of the present disclosure. In some embodiments, the use case scenario shown inFIG. 4 can be performed by a computer system (e.g., computer system 100). It is appreciated that some of these steps can be performed in full or in part by other systems. - Referring to
FIG. 4 , one or more data tables 410 acquired from one or more data sources can include information associated with the entity. The one or more data tables 410 can be stored locally at the computer system and/or at one or more remote servers (e.g., such as a remote database), or at one or more other remote devices. In some embodiments, the information in the data tables can be stored in one or more multidimensional tables. By way of example, as shown inFIG. 4 , information of a first type (e.g., bill payment amount) associated with the entity, (e.g., a household), can be stored in a first multidimensional table 410 and information of a second type (e.g., income or number of cars) associated with the entity can be stored in a second multidimensional table 410. In some embodiments a table can contain information associated with a single entity. For example, Bill Amount table 410 shows the most recent bill payment amounts associated with the entity in this exemplary scenario. In other embodiments (not shown), a table can store information associated with a plurality of entities. For example, each row in the table can correspond to a different entity (e.g., Household #1,Household # 2, etc.) and each column in the table can correspond to a payment amount. In some embodiments, the information stored in the table can include entries associated with a temporal period. For example, a table can store a bill payment date for each bill payment amount. As shown inFIG. 4 , Bill Payment Table 410 can store dates in the first column (e.g., 1/1/14, 2/1/14, and 3/1/14). Each bill payment date can be associated with the bill payment amount. For example, Bill Payment Table 410 shows that an amount of $800 was billed to the household on Jan. 1, 2014. The information can be stored as a continuous value (e.g., $800 as a bill payment amount), as a categorical value, (e.g., “Sedan” or “Coupe” as an automobile type), as textual value, or as any other type of value. In some embodiments, a table can be stored in either a row-oriented database or a column-oriented database. For example, a row in a row-oriented table can contain information associated with an entity (e.g., Household #1) and data in the row can be stored serially such that information associated with the entity can be accessed in one operation. - The computer system can form (420) a
record 430 including some or all information from the one or more data sources associated with the entity. In some embodiments,record 430 can be formed (420) by integrating the information from the one or more data sources that is associated with the entity.Record 430 can contain a multitude of information related to the entity. For example,record 430 can contain all information from the one or more data sources associated with a household (e.g., number of members in household, number of automobiles, income, monthly bill mounts for each automobile, etc.). In some embodiments,record 430 can be stored as a cogroup with each row of the cogroup associated with a different category of information. In some embodiments,record 430 can be stored in either a row-oriented database or a column-oriented database. For example, a row in a row-oriented record can be associated with a data source (e.g., bill payment amount) and data in the row can be stored serially such that data associated with that data source can be accessed in one operation. As shown inFIG. 4 , the “Bill Amount” is stored as row inrecord 430. Bill amounts $800, $600, and $600 can be stored serially such that all of the payment amounts can be accessed in one operation. Similarly, “Income” and “Number of Cars” are stored in separate rows inrecord 430, and information from these sources (e.g. {$80K, $70K, $70K} and {3, 2, 2}) can also be accessed in one operation. - In some embodiments, the computer system can filter
record 430 for information associated with the specified action (not shown). For example, the specified action can be churn (e.g., cancellation of a subscription) and the computer system can filterrecord 430 for information related to churn. In some embodiments, the computer system can provide context for the specified action. In some embodiments, the computer system can determine whether the specified action will occur within a specified temporal period (e.g., one month). The computer system can filter out all information associated with a time that is outside (e.g., before or after) the specified temporal period. In some embodiments, the computer system can determine the propensity for the specified action based on only recent events. For example, the computer system can filter out information associated with a time before the specified time period (e.g., stale or less relevant information). In some embodiments, each record can be filtered in a slightly different way.Record 430 can be filtered according to a user input specifying an activity or temporal period. In some embodiments,record 430 can be filtered automatically based on a presetting (e.g., the computer can be configured to filter out all information that is more than one year old). For example, the computer system can determine the propensity of the entity to take the specified action based on only data from the previous month. In the example shown inFIG. 4 , the computer system can filter out the older entries of Bill Amount table 410 (e.g., Bill Amounts of $800 and $600 corresponding to bill dates in January and February). The computer system can also filter out similar entries in Income and Number of Cars tables 410 (e.g., incomes of $80K and $70K and 3 and 2 number of cars). Thus, the computer system can use only the most recent entries to determine the propensity of the household to take the specified action (e.g., $600 in Bill Amount table 410, $70K in Income table 410, and 2 in Number of Cars table 410). - The computer system can generate (440), based on
record 430, one ormore features 450 associated with the entity. A feature can be any discernable way of sorting or classifying the record (e.g., average value, most recent value, most common value, etc.). In some embodiments, the computer system can generate key value pairs, wherein each key value pair contains a feature and a value. For example, the computer system can generate one ormore features 450 such as “average bill payment amount”, “average income”, “average number of automobiles”, etc. and corresponding values such as “$670”, “$73K”, “2.3 cars”, etc. In some embodiments, the one ormore features 450 can be associated with a time value. For example, computer system can generate features for a specified temporal period (e.g., features can be based only on the most recent values). Feature values can be represented as a continuous value (e.g., $670), as a categorical value (e.g., “Sedan” or “Coupe”), as a textual value, or as any other type of value. In some embodiments, the one ormore feature 450 can be stored as classified as weighted values. For example, a household income of $73,000 can be represented as weighted value of {0.27 0}, {0.73 100000}. - In some embodiments, the one or more features can be extrapolated from the information contained in the record. For example, a feature can be that the entity deactivated online payments (e.g. customer deactivated ETF payment on 2/20). In some embodiments, the one or more features can be related to communications between the providing entity (e.g., insurance provider) and consuming entity (e.g., household). For example,
computer system 100 can analyze (e.g., tokenize) the transcript of a call between an agent and a household and assign a topical value to that call (e.g., “topic 5” corresponding to anger).Computer system 100 can store this information as a feature pair (not shown), such as the pair {“Service Call Topic” “5”}. In some embodiments, the one or more features can be related to whether the household took a specified action (e.g., filed a claim or called to change policy). - In some embodiments, the computer system can process (460) the one or
more features 450 to determine thepropensity 470 of the entity to take the specified action. In some embodiments, thepropensity 470 can be determined by applying a trained model, such as the model described in greater detail inFIG. 3 . The input to the model can be key value pairs of the one ormore features 450 associated with the entity and the specified actions and the output of the model can be thepropensity 470 of the entity to take the specified action. In some embodiments, processing the one or more features associated with the entity can result in a multitude of useful insights regarding the features that influence the propensity of the entity to take the specified action. Such insights, can include, for example, the features that are most influential on the propensity of the entity to take the specified action (e.g., change in income, etc.). - In some embodiments, the computer system can output the
propensity 470. In some embodiments, the computer system can output thepropensity 470 as a continuous value, such as a number or percentage (e.g., 80 or 80%) or as a categorical value (e.g., “low”, “medium”, or “high”). In some embodiments, the computer system can generate a user interface, such as the user interfaces described in greater detail inFIGS. 5 and 6 for displaying thepropensity 470. -
FIG. 5 illustrates anexemplary user interface 500 provided by a computer system (e.g., computer system 100) for display (e.g., display 122), in accordance with some embodiments.User interface 500 can include a plurality of tiles (e.g., tile 510), each tile representing an entity (e.g., a household). In some embodiments, tiles can be arranged according to the propensity of the entity to take the specified action. For example, entities that are more likely to take the specified action can be located near the top of the display, whereas entities that are less likely to take the specified action can be lower on the display. As shown inFIG. 5 , in some embodiments, the tiles can be arranged by date (e.g., date 520). For example, entities with the most recent activities can be located near the top of the display. By way of example, tile 510 with the mostrecent date 520 of Feb. 21, 2014 is located in the top left corner of the display. The tile to the right oftile 510 has the next most recent date (e.g., Feb. 20, 2014). Subsequent tiles have dates that are less recent. In other embodiments, entities with the longest pending outstanding action can be located near the top of the screen. - In some embodiments,
user interface 500 can be updated periodically (e.g., once a day, once a week, once a month, etc.). In other embodiments,user interface 500 can be updated when information associated with any of the entities stored in the one or more data sources is updated (e.g., a new entry, such as payment bill amount, is added to a table). In some embodiments,user interface 500 can update in response to an input received from the user. -
User interface 500 can automatically determine the entities for which to generate the display. In some embodiments,user interface 500 can display entities associated with a particular user (e.g., John Smith, Triage Agent) once the user accessesuser interface 500. In some embodiments, the user can specifically identify the entities for which to generate the display. In some embodiments, the user can identity a category or class of entities for which to generate the display. For example, the user can identify a class of entities that are all consumers of a specified provisioning entity (e.g., insurance company), the user input can identify entities that are located within a specified geographic region (e.g., all households within the state of Illinois), or the user input can identify any other category of entities (e.g., all households with an income over $100,000). - In some embodiments,
user interface 500 can portray a date 520 (e.g., Feb. 21, 2014) associated with the entity intile 510.Date 520 can correspond to the current date, the date thatmethod 200 was last performed for that entity, the date that information in the one or more data sources associated with that entity was last updated, or the date that the user last viewed the tile associated with the entity. In some embodiments,user interface 500 can portray apropensity 540 of the entity to take the specified action (e.g., “Med”) intile 510. For example, as shown inFIG. 5 ,user interface 500 can portray the propensity as a categorical value, such as “Med” intile 510. In some embodiments,user interface 500 can portraytile 510 in a color (e.g., green for “low”, red for “high”, etc.) representing the propensity. In some embodiments,user interface 500 can portray the propensity intile 510 as numerical value or as a percentage. -
User interface 500 can portrayrecent activity 530 intile 510. In some embodiments, therecent activity 530 can be entered by a user. By way of example, a recent activity could be that an “Agent called customer on 2/21 regarding discounts” as shown intile 510. In some embodiments,user interface 500 can generate the recent activity based on the one or more features associated with the entity. For example,user interface 500 can display, “Customer registered an additional luxury vehicle on 2/18” intile 510 responsive to this information being updated in the record associated with the entity. In some embodiments,tile 510 can portrayimportant features 540 associated with the entity. For example, as shown intile 510 ofFIG. 5 , these features can be “vehicle”, “discounts”, etc. In some embodiments,user interface 500 can recommend an action for the user to take (e.g., service call). In some embodiments, this recommendation can relate to therecent activity 530. A user can use this information to take preemptive action to prevent the entity from taking the specified action. By way of example, if the propensity of a household subscribing to an automobile insurance policy was high, the user could take remedial action (e.g., lower rate, contact customer to address customer concerns, etc.). In someembodiments user interface 500 can display a number uniquely identifying the entity (e.g., a policy number). - In some embodiments,
user interface 500 can allow a user to click ontile 510 to access additional information associated with the entity. For example, a user can accessuser interface 600 shown inFIG. 6 below by clicking on one of the tiles shown inuser interface 500 ofFIG. 5 . In some embodiments,user interface 600 can be inlaid overuser interface 500. In some embodiments,user interface 600 can be a distinct user interface. -
User interface 500 can also allow access to additional user interfaces (not shown) through the “INBOX,” “FLAGGED,” and “STATS” links shown at the top ofuser interface 500. The “INBOX” user interface can display messages between the user and other agents to track the remedial actions that were taken. The INBOX user interface can also be used to notify users of households with a higher likelihood of cancelling the subscription. The “FLAGGED” user interface can show customers (e.g., households) that the user believed were at risk for taking the specified action. For example, the FLAGGED user interface can contain a list of the households most likely to cancel their insurance policy. In some embodiments, these households can be selected manually by the user. In some embodiments, these households can be automatically populated if the propensity exceeds a predetermined threshold (e.g., the FLAGGED interface can be populated with all households with a “High” propensity). The FLAGGED user interface can allow the user to track remediation steps (e.g., contacting the household, changing policy, etc.). Households can remain in the FLAGGED user interface until their risk of taking the specified action has declined, the user has decided that the household is no longer at risk, or the specification action occurred (e.g., the household cancelled its subscription). The “STATS” interface can display metrics such as, for example, the rate at which the user was able to prevent the specified action from occurring categorized by action taken and the most common and/or trending issues. -
FIG. 6 illustrates anotherexemplary user interface 600 provided by the computer system (e.g., computer system 100) for display (e.g., display 112) in accordance with some embodiments. In some embodiments,user interface 600 can be accessed by clicking on a tile (e.g., entity) inuser interface 500.User interface 600 can portray a date 610 (e.g., Feb. 18, 2014) associated with the entity.Date 610 can correspond to the current date, the date thatmethod 200 was last performed for that entity, the date that information in the one or more data sources associated with that entity was last updated, or the date that the user last viewed the tile associated with the entity. In some embodiments,user interface 600 can portray apropensity 620 of the entity to take the specified action. For example, as shown inFIG. 6 ,user interface 600 can portraypropensity 620 as a categorical value, such as “Med.”. In some embodiments,user interface 600 can conveypropensity 620 by shading the top bar in a different color (e.g., green for “low”, red for “high”, etc.) representingpropensity 620. In some embodiments,user interface 600 can portraypropensity 620 as numerical value or as a percentage. In someembodiments user interface 600 can display the entity status 630 (e.g., “Active” if the household is currently subscribing to a policy). - In some embodiments,
user interface 600 can displayrecent activities 640 associated with the entity. For example, as shown inFIG. 6 ,user interface 600 can display that the “customer registered an additional luxury vehicle on 2/18”.User interface 600 can recommend anaction 650 for the user to take (e.g., service call). In some embodiments, thisrecommendation 650 can relate to the recent activity. -
User interface 600 can provide the user with additional information associated with the entity. As shown in the bottom left panel ofFIG. 6 ,user interface 600 can display basicbiographic information 660 for the entity. In the automobile insurance context, for example,user interface 600 can display the policy number, (e.g., 34726182), the entity name (e.g., household/owner of the policy, David Stark), the policy coverage start date (e.g., 12/12/2004), any secondary owners associated with the policy (e.g., James Watson), information associated with the insured automobile (e.g., 2013 Cadillac Escalade), and the type of insurance policy (e.g., Standard). - In some embodiments,
user interface 600 can also display information for anagent 670 associated with the entity. For example, theuser interface 600 can display the name (e.g., Bruce Atherton) and contact information (e.g., 583 234-9172) of the agent. A user can use this information to take preemptive action to prevent the entity from taking the specified action. By way of example, if the propensity of churning for a household subscribing to an automobile insurance policy was high, the user could contact the agent to take remedial action (e.g., lower rate, address customer concerns, etc.). - In some embodiments, the right panel of
FIG. 6 , can displayrecent events 680 associated with the entity. For example,user interface 600 can display whether the entity status is active (e.g., whether the entity is currently subscribing to a policy) or whether the agent has taken any actions (e.g., called the household or subscriber). In some embodiments,user interface 600 can also allow the user and agent to converse in the right panel. For example, the user can click on the “ADD AN UPDATE”button 690 to remind the agent to contact the entity. The user interface can displayresponsive comments 680 from the agent and the agent can add any actions taken 680 (e.g., calling the household). - Embodiments of the present disclosure have been described herein with reference to numerous specific details that can vary from implementation to implementation. Certain adaptations and modifications of the described embodiments can be made. Other embodiments can be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the present disclosure being indicated by the following claims. It is also intended that the sequence of steps shown in figures are only for illustrative purposes and are not intended to be limited to any particular sequence of steps. As such, it is appreciated that these steps can be performed in a different order while implementing the exemplary methods or processes disclosed herein.
Claims (20)
1. A system for determining a propensity of an entity to take a specified action, the system comprising:
one or more computer-readable storage media configured to store instructions; and
one or more processors configured to execute the instructions to:
acquire information associated with the entity from one or more data sources;
form a record associated with the entity by integrating the information from the one or more data sources;
generate, based on the record, one or more features associated with the entity;
process the one or more features to determine the propensity of the entity to take the specified action; and
output the propensity.
2. The system of claim 1 , wherein the one or more processors are further configured to filter the record for information associated with the specified action.
3. The system of claim 1 , wherein the one or more processors are further configured to train a model to predict the propensity of the entity to take the specified action.
4. The system of claim 3 , wherein the one are more processors are further configured to determine, based on the trained model and the record, the relative importance of the one or more features.
5. The system of claim 1 , wherein the one or more processors are further configured to:
acquire a temporal period; and
determine the propensity of the entity to take the specified action within the temporal period.
6. The system of claim 1 , wherein the one or more processors are further configured to generate a user interface to display the propensity of the entity to take the specified action.
7. The system of claim 1 , wherein the entity is a household and the specified action is churn.
8. A method for determining a propensity of an entity to take a specified action, the method being performed by one or more processors and comprising:
acquiring information associated with the entity from one or more data sources;
forming a record associated with the entity by integrating the information from the one or more data sources;
generating, based on the record, one or more features associated with the entity;
processing the one or more features to determine the propensity of the entity to take the specified action; and
outputting the propensity.
9. The method of claim 8 , further comprising filtering the record for information associated with the specified action.
10. The method of claim 8 , further comprising training a model to predict the propensity of the entity to take the specified action.
11. The method of claim 10 , further comprising determining, based on the trained model and the record, the relative importance of the one or more features.
12. The method of claim 8 , further comprising:
acquiring a temporal period; and
determining the propensity of the entity to take the specified action within the temporal period.
13. The method of claim 8 , further comprising generating a user interface to display the propensity of the entity to take the specified action.
14. The method of claim 8 , wherein the entity is a household and the specified action is churn.
15. A non-transitory computer-readable medium storing a set of instructions that are executable by one or more processors to cause the one or more processors to perform a method for determining a propensity of an entity to take a specified action, the method comprising:
acquiring information associated with the entity one or more data sources;
forming a record associated with the entity by integrating the information from the one or more data sources;
generating, based on the record, one or more features associated with the entity;
processing the one or more features to determine the propensity of the entity to take the specified action; and
outputting the propensity.
16. The non-transitory computer-readable medium of claim 15 , further comprising instructions executable by the one or more processors to cause the one or more processors to perform:
training a model to predict the propensity of the entity to take the specified action.
17. The non-transitory computer-readable medium of claim 16 , further comprising instructions executable by the one or more processors to cause the one or more processors to perform:
determining, based on the trained model and the record, the relative importance of the one or more features.
18. The non-transitory computer-readable medium of claim 15 , further comprising instructions executable by the one or more processors to cause the one or more processors to perform:
acquiring a temporal period; and
determining the propensity of the entity to take the specified action within the temporal period.
19. The non-transitory computer-readable medium of claim 15 , further comprising instructions executable by the one or more processors to cause the one or more processors to perform:
generating a user interface to display the propensity of the entity to take the specified action.
20. The non-transitory computer-readable medium of claim 15 , wherein the entity is a household and the specified action is churn.
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US14/562,524 US20160026923A1 (en) | 2014-07-22 | 2014-12-05 | System and method for determining a propensity of entity to take a specified action |
US15/689,757 US11521096B2 (en) | 2014-07-22 | 2017-08-29 | System and method for determining a propensity of entity to take a specified action |
US17/961,822 US11861515B2 (en) | 2014-07-22 | 2022-10-07 | System and method for determining a propensity of entity to take a specified action |
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201462027761P | 2014-07-22 | 2014-07-22 | |
US201462039305P | 2014-08-19 | 2014-08-19 | |
US14/562,524 US20160026923A1 (en) | 2014-07-22 | 2014-12-05 | System and method for determining a propensity of entity to take a specified action |
Related Child Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/689,757 Continuation US11521096B2 (en) | 2014-07-22 | 2017-08-29 | System and method for determining a propensity of entity to take a specified action |
Publications (1)
Publication Number | Publication Date |
---|---|
US20160026923A1 true US20160026923A1 (en) | 2016-01-28 |
Family
ID=55166992
Family Applications (3)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US14/562,524 Abandoned US20160026923A1 (en) | 2014-07-22 | 2014-12-05 | System and method for determining a propensity of entity to take a specified action |
US15/689,757 Active 2038-12-08 US11521096B2 (en) | 2014-07-22 | 2017-08-29 | System and method for determining a propensity of entity to take a specified action |
US17/961,822 Active US11861515B2 (en) | 2014-07-22 | 2022-10-07 | System and method for determining a propensity of entity to take a specified action |
Family Applications After (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/689,757 Active 2038-12-08 US11521096B2 (en) | 2014-07-22 | 2017-08-29 | System and method for determining a propensity of entity to take a specified action |
US17/961,822 Active US11861515B2 (en) | 2014-07-22 | 2022-10-07 | System and method for determining a propensity of entity to take a specified action |
Country Status (1)
Country | Link |
---|---|
US (3) | US20160026923A1 (en) |
Cited By (60)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9449074B1 (en) | 2014-03-18 | 2016-09-20 | Palantir Technologies Inc. | Determining and extracting changed data from a data source |
US9471370B2 (en) | 2012-10-22 | 2016-10-18 | Palantir Technologies, Inc. | System and method for stack-based batch evaluation of program instructions |
US9514205B1 (en) | 2015-09-04 | 2016-12-06 | Palantir Technologies Inc. | Systems and methods for importing data from electronic data files |
US9621676B2 (en) | 2012-03-02 | 2017-04-11 | Palantir Technologies, Inc. | System and method for accessing data objects via remote references |
US9652291B2 (en) | 2013-03-14 | 2017-05-16 | Palantir Technologies, Inc. | System and method utilizing a shared cache to provide zero copy memory mapped database |
US9652510B1 (en) | 2015-12-29 | 2017-05-16 | Palantir Technologies Inc. | Systems and user interfaces for data analysis including artificial intelligence algorithms for generating optimized packages of data items |
US9727622B2 (en) | 2013-12-16 | 2017-08-08 | Palantir Technologies, Inc. | Methods and systems for analyzing entity performance |
US9740369B2 (en) | 2013-03-15 | 2017-08-22 | Palantir Technologies Inc. | Systems and methods for providing a tagging interface for external content |
US9852205B2 (en) | 2013-03-15 | 2017-12-26 | Palantir Technologies Inc. | Time-sensitive cube |
US9880987B2 (en) | 2011-08-25 | 2018-01-30 | Palantir Technologies, Inc. | System and method for parameterizing documents for automatic workflow generation |
US9891808B2 (en) | 2015-03-16 | 2018-02-13 | Palantir Technologies Inc. | Interactive user interfaces for location-based data analysis |
US9898335B1 (en) | 2012-10-22 | 2018-02-20 | Palantir Technologies Inc. | System and method for batch evaluation programs |
US9898167B2 (en) | 2013-03-15 | 2018-02-20 | Palantir Technologies Inc. | Systems and methods for providing a tagging interface for external content |
US9953445B2 (en) | 2013-05-07 | 2018-04-24 | Palantir Technologies Inc. | Interactive data object map |
US9996229B2 (en) | 2013-10-03 | 2018-06-12 | Palantir Technologies Inc. | Systems and methods for analyzing performance of an entity |
US10103953B1 (en) | 2015-05-12 | 2018-10-16 | Palantir Technologies Inc. | Methods and systems for analyzing entity performance |
US10114884B1 (en) | 2015-12-16 | 2018-10-30 | Palantir Technologies Inc. | Systems and methods for attribute analysis of one or more databases |
US10152306B2 (en) | 2016-11-07 | 2018-12-11 | Palantir Technologies Inc. | Framework for developing and deploying applications |
US10163061B2 (en) * | 2015-06-18 | 2018-12-25 | International Business Machines Corporation | Quality-directed adaptive analytic retraining |
US10180934B2 (en) | 2017-03-02 | 2019-01-15 | Palantir Technologies Inc. | Automatic translation of spreadsheets into scripts |
US10198515B1 (en) | 2013-12-10 | 2019-02-05 | Palantir Technologies Inc. | System and method for aggregating data from a plurality of data sources |
US10204119B1 (en) | 2017-07-20 | 2019-02-12 | Palantir Technologies, Inc. | Inferring a dataset schema from input files |
US10242072B2 (en) | 2014-12-15 | 2019-03-26 | Palantir Technologies Inc. | System and method for associating related records to common entities across multiple lists |
US10261763B2 (en) | 2016-12-13 | 2019-04-16 | Palantir Technologies Inc. | Extensible data transformation authoring and validation system |
US10331797B2 (en) | 2011-09-02 | 2019-06-25 | Palantir Technologies Inc. | Transaction protocol for reading database values |
US10360252B1 (en) | 2017-12-08 | 2019-07-23 | Palantir Technologies Inc. | Detection and enrichment of missing data or metadata for large data sets |
US10373078B1 (en) | 2016-08-15 | 2019-08-06 | Palantir Technologies Inc. | Vector generation for distributed data sets |
US10373099B1 (en) | 2015-12-18 | 2019-08-06 | Palantir Technologies Inc. | Misalignment detection system for efficiently processing database-stored data and automatically generating misalignment information for display in interactive user interfaces |
US10444941B2 (en) | 2015-08-17 | 2019-10-15 | Palantir Technologies Inc. | Interactive geospatial map |
US10452678B2 (en) | 2013-03-15 | 2019-10-22 | Palantir Technologies Inc. | Filter chains for exploring large data sets |
US10509844B1 (en) | 2017-01-19 | 2019-12-17 | Palantir Technologies Inc. | Network graph parser |
US10534595B1 (en) | 2017-06-30 | 2020-01-14 | Palantir Technologies Inc. | Techniques for configuring and validating a data pipeline deployment |
US10552531B2 (en) | 2016-08-11 | 2020-02-04 | Palantir Technologies Inc. | Collaborative spreadsheet data validation and integration |
US10554516B1 (en) | 2016-06-09 | 2020-02-04 | Palantir Technologies Inc. | System to collect and visualize software usage metrics |
US10552524B1 (en) | 2017-12-07 | 2020-02-04 | Palantir Technolgies Inc. | Systems and methods for in-line document tagging and object based data synchronization |
US10558339B1 (en) | 2015-09-11 | 2020-02-11 | Palantir Technologies Inc. | System and method for analyzing electronic communications and a collaborative electronic communications user interface |
US10572576B1 (en) | 2017-04-06 | 2020-02-25 | Palantir Technologies Inc. | Systems and methods for facilitating data object extraction from unstructured documents |
US10579647B1 (en) | 2013-12-16 | 2020-03-03 | Palantir Technologies Inc. | Methods and systems for analyzing entity performance |
US10599762B1 (en) | 2018-01-16 | 2020-03-24 | Palantir Technologies Inc. | Systems and methods for creating a dynamic electronic form |
US10606872B1 (en) | 2017-05-22 | 2020-03-31 | Palantir Technologies Inc. | Graphical user interface for a database system |
US10650086B1 (en) | 2016-09-27 | 2020-05-12 | Palantir Technologies Inc. | Systems, methods, and framework for associating supporting data in word processing |
US10706434B1 (en) | 2015-09-01 | 2020-07-07 | Palantir Technologies Inc. | Methods and systems for determining location information |
US10747952B2 (en) | 2008-09-15 | 2020-08-18 | Palantir Technologies, Inc. | Automatic creation and server push of multiple distinct drafts |
US10754820B2 (en) | 2017-08-14 | 2020-08-25 | Palantir Technologies Inc. | Customizable pipeline for integrating data |
US10795909B1 (en) | 2018-06-14 | 2020-10-06 | Palantir Technologies Inc. | Minimized and collapsed resource dependency path |
US10817513B2 (en) | 2013-03-14 | 2020-10-27 | Palantir Technologies Inc. | Fair scheduling for mixed-query loads |
US10824604B1 (en) | 2017-05-17 | 2020-11-03 | Palantir Technologies Inc. | Systems and methods for data entry |
US10853352B1 (en) | 2017-12-21 | 2020-12-01 | Palantir Technologies Inc. | Structured data collection, presentation, validation and workflow management |
US10885021B1 (en) | 2018-05-02 | 2021-01-05 | Palantir Technologies Inc. | Interactive interpreter and graphical user interface |
US10909130B1 (en) | 2016-07-01 | 2021-02-02 | Palantir Technologies Inc. | Graphical user interface for a database system |
US10924362B2 (en) | 2018-01-15 | 2021-02-16 | Palantir Technologies Inc. | Management of software bugs in a data processing system |
US10977267B1 (en) | 2016-08-17 | 2021-04-13 | Palantir Technologies Inc. | User interface data sample transformer |
US11016936B1 (en) | 2017-09-05 | 2021-05-25 | Palantir Technologies Inc. | Validating data for integration |
US11061542B1 (en) | 2018-06-01 | 2021-07-13 | Palantir Technologies Inc. | Systems and methods for determining and displaying optimal associations of data items |
US11157951B1 (en) | 2016-12-16 | 2021-10-26 | Palantir Technologies Inc. | System and method for determining and displaying an optimal assignment of data items |
US11176116B2 (en) | 2017-12-13 | 2021-11-16 | Palantir Technologies Inc. | Systems and methods for annotating datasets |
US11256762B1 (en) | 2016-08-04 | 2022-02-22 | Palantir Technologies Inc. | System and method for efficiently determining and displaying optimal packages of data items |
US11263263B2 (en) | 2018-05-30 | 2022-03-01 | Palantir Technologies Inc. | Data propagation and mapping system |
US11379525B1 (en) | 2017-11-22 | 2022-07-05 | Palantir Technologies Inc. | Continuous builds of derived datasets in response to other dataset updates |
US11521096B2 (en) | 2014-07-22 | 2022-12-06 | Palantir Technologies Inc. | System and method for determining a propensity of entity to take a specified action |
Families Citing this family (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10938817B2 (en) * | 2018-04-05 | 2021-03-02 | Accenture Global Solutions Limited | Data security and protection system using distributed ledgers to store validated data in a knowledge graph |
US10796380B1 (en) * | 2020-01-30 | 2020-10-06 | Capital One Services, Llc | Employment status detection based on transaction information |
Family Cites Families (335)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH06506548A (en) | 1991-03-12 | 1994-07-21 | ウォング・ラボラトリーズ・インコーポレーテッド | Graphical query front end for database management systems |
US5426747A (en) | 1991-03-22 | 1995-06-20 | Object Design, Inc. | Method and apparatus for virtual memory mapping and transaction management in an object-oriented database system |
US5428737A (en) | 1991-10-16 | 1995-06-27 | International Business Machines Corporation | Comprehensive bilateral translation between SQL and graphically depicted queries |
JPH0689307A (en) | 1992-05-04 | 1994-03-29 | Internatl Business Mach Corp <Ibm> | Device and method for displaying information in database |
JP2710548B2 (en) | 1993-03-17 | 1998-02-10 | インターナショナル・ビジネス・マシーンズ・コーポレイション | Method for retrieving data and converting between Boolean algebraic and graphic representations |
US5918225A (en) | 1993-04-16 | 1999-06-29 | Sybase, Inc. | SQL-based database system with improved indexing methodology |
US5794229A (en) | 1993-04-16 | 1998-08-11 | Sybase, Inc. | Database system with methodology for storing a database table by vertically partitioning all columns of the table |
US5794228A (en) | 1993-04-16 | 1998-08-11 | Sybase, Inc. | Database system with buffer manager providing per page native data compression and decompression |
US5608899A (en) | 1993-06-04 | 1997-03-04 | International Business Machines Corporation | Method and apparatus for searching a database by interactively modifying a database query |
US5911138A (en) | 1993-06-04 | 1999-06-08 | International Business Machines Corporation | Database search facility having improved user interface |
US5613105A (en) | 1993-06-30 | 1997-03-18 | Microsoft Corporation | Efficient storage of objects in a file system |
US6877137B1 (en) | 1998-04-09 | 2005-04-05 | Rose Blush Software Llc | System, method and computer program product for mediating notes and note sub-notes linked or otherwise associated with stored or networked web pages |
US5742806A (en) | 1994-01-31 | 1998-04-21 | Sun Microsystems, Inc. | Apparatus and method for decomposing database queries for database management system including multiprocessor digital data processing system |
US5560005A (en) | 1994-02-25 | 1996-09-24 | Actamed Corp. | Methods and systems for object-based relational distributed databases |
US5542089A (en) | 1994-07-26 | 1996-07-30 | International Business Machines Corporation | Method and apparatus for estimating the number of occurrences of frequent values in a data set |
US6321274B1 (en) | 1996-06-28 | 2001-11-20 | Microsoft Corporation | Multiple procedure calls in a single request |
US5870559A (en) | 1996-10-15 | 1999-02-09 | Mercury Interactive | Software system and associated methods for facilitating the analysis and management of web sites |
US6430305B1 (en) | 1996-12-20 | 2002-08-06 | Synaptics, Incorporated | Identity verification methods |
US5857329A (en) | 1997-03-14 | 1999-01-12 | Deere & Company | One-piece combined muffler exhaust outlet and exhaust gas deflector |
US6026233A (en) | 1997-05-27 | 2000-02-15 | Microsoft Corporation | Method and apparatus for presenting and selecting options to modify a programming language statement |
US6208985B1 (en) | 1997-07-09 | 2001-03-27 | Caseventure Llc | Data refinery: a direct manipulation user interface for data querying with integrated qualitative and quantitative graphical representations of query construction and query result presentation |
US6236994B1 (en) | 1997-10-21 | 2001-05-22 | Xerox Corporation | Method and apparatus for the integration of information and knowledge |
US7168039B2 (en) | 1998-06-02 | 2007-01-23 | International Business Machines Corporation | Method and system for reducing the horizontal space required for displaying a column containing text data |
US6178519B1 (en) | 1998-12-10 | 2001-01-23 | Mci Worldcom, Inc. | Cluster-wide database system |
KR100313198B1 (en) | 1999-03-05 | 2001-11-05 | 윤덕용 | Multi-dimensional Selectivity Estimation Using Compressed Histogram Information |
US7418399B2 (en) | 1999-03-10 | 2008-08-26 | Illinois Institute Of Technology | Methods and kits for managing diagnosis and therapeutics of bacterial infections |
US6560774B1 (en) | 1999-09-01 | 2003-05-06 | Microsoft Corporation | Verifier to check intermediate language |
GB2371901B (en) | 1999-09-21 | 2004-06-23 | Andrew E Borthwick | A probabilistic record linkage model derived from training data |
US7546353B2 (en) | 1999-12-02 | 2009-06-09 | Western Digital Technologies, Inc. | Managed peer-to-peer applications, systems and methods for distributed data access and storage |
US6745382B1 (en) | 2000-04-13 | 2004-06-01 | Worldcom, Inc. | CORBA wrappers for rules automation technology |
US8386945B1 (en) | 2000-05-17 | 2013-02-26 | Eastman Kodak Company | System and method for implementing compound documents in a production printing workflow |
GB2366498A (en) | 2000-08-25 | 2002-03-06 | Copyn Ltd | Method of bookmarking a section of a web-page and storing said bookmarks |
US6795868B1 (en) | 2000-08-31 | 2004-09-21 | Data Junction Corp. | System and method for event-driven data transformation |
US20020065708A1 (en) | 2000-09-22 | 2002-05-30 | Hikmet Senay | Method and system for interactive visual analyses of organizational interactions |
US8707185B2 (en) | 2000-10-10 | 2014-04-22 | Addnclick, Inc. | Dynamic information management system and method for content delivery and sharing in content-, metadata- and viewer-based, live social networking among users concurrently engaged in the same and/or similar content |
US8117281B2 (en) | 2006-11-02 | 2012-02-14 | Addnclick, Inc. | Using internet content as a means to establish live social networks by linking internet users to each other who are simultaneously engaged in the same and/or similar content |
US6976024B1 (en) | 2000-10-12 | 2005-12-13 | International Buisness Machines Corporation | Batch submission API |
US6754640B2 (en) | 2000-10-30 | 2004-06-22 | William O. Bozeman | Universal positive pay match, authentication, authorization, settlement and clearing system |
US6857120B1 (en) | 2000-11-01 | 2005-02-15 | International Business Machines Corporation | Method for characterizing program execution by periodic call stack inspection |
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 |
US7370040B1 (en) | 2000-11-21 | 2008-05-06 | Microsoft Corporation | Searching with adaptively configurable user interface and extensible query language |
US20020103705A1 (en) | 2000-12-06 | 2002-08-01 | Forecourt Communication Group | Method and apparatus for using prior purchases to select activities to present to a customer |
US7529698B2 (en) | 2001-01-16 | 2009-05-05 | Raymond Anthony Joao | Apparatus and method for providing transaction history information, account history information, and/or charge-back information |
US7299202B2 (en) | 2001-02-07 | 2007-11-20 | Exalt Solutions, Inc. | Intelligent multimedia e-catalog |
US20100057622A1 (en) | 2001-02-27 | 2010-03-04 | Faith Patrick L | Distributed Quantum Encrypted Pattern Generation And Scoring |
US7499922B1 (en) | 2001-04-26 | 2009-03-03 | Dakota Software Corp. | Information retrieval system and method |
US6980984B1 (en) | 2001-05-16 | 2005-12-27 | Kanisa, Inc. | Content provider systems and methods using structured data |
US7877421B2 (en) | 2001-05-25 | 2011-01-25 | International Business Machines Corporation | Method and system for mapping enterprise data assets to a semantic information model |
US7155728B1 (en) | 2001-06-28 | 2006-12-26 | Microsoft Corporation | Remoting features |
US7100147B2 (en) | 2001-06-28 | 2006-08-29 | International Business Machines Corporation | Method, system, and program for generating a workflow |
US6643613B2 (en) | 2001-07-03 | 2003-11-04 | Altaworks Corporation | System and method for monitoring performance metrics |
US20030023620A1 (en) | 2001-07-30 | 2003-01-30 | Nicholas Trotta | Creation of media-interaction profiles |
US7028223B1 (en) | 2001-08-13 | 2006-04-11 | Parasoft Corporation | System and method for testing of web services |
US7082365B2 (en) | 2001-08-16 | 2006-07-25 | Networks In Motion, Inc. | Point of interest spatial rating search method and system |
US7165101B2 (en) | 2001-12-03 | 2007-01-16 | Sun Microsystems, Inc. | Transparent optimization of network traffic in distributed systems |
US7519589B2 (en) | 2003-02-04 | 2009-04-14 | Cataphora, Inc. | Method and apparatus for sociological data analysis |
CA3077873A1 (en) | 2002-03-20 | 2003-10-02 | Catalina Marketing Corporation | Targeted incentives based upon predicted behavior |
US20050021397A1 (en) | 2003-07-22 | 2005-01-27 | Cui Yingwei Claire | Content-targeted advertising using collected user behavior data |
US7533026B2 (en) | 2002-04-12 | 2009-05-12 | International Business Machines Corporation | Facilitating management of service elements usable in providing information technology service offerings |
US20040012633A1 (en) | 2002-04-26 | 2004-01-22 | Affymetrix, Inc., A Corporation Organized Under The Laws Of Delaware | System, method, and computer program product for dynamic display, and analysis of biological sequence data |
US20040126840A1 (en) | 2002-12-23 | 2004-07-01 | Affymetrix, Inc. | Method, system and computer software for providing genomic ontological data |
US7127467B2 (en) | 2002-05-10 | 2006-10-24 | Oracle International Corporation | Managing expressions in a database system |
US8244895B2 (en) | 2002-07-15 | 2012-08-14 | Hewlett-Packard Development Company, L.P. | Method and apparatus for applying receiving attributes using constraints |
GB0221257D0 (en) | 2002-09-13 | 2002-10-23 | Ibm | Automated testing |
US7383513B2 (en) | 2002-09-25 | 2008-06-03 | Oracle International Corporation | Graphical condition builder for facilitating database queries |
US20040088177A1 (en) | 2002-11-04 | 2004-05-06 | Electronic Data Systems Corporation | Employee performance management method and system |
US7546607B2 (en) | 2002-11-19 | 2009-06-09 | Microsoft Corporation | Native code exposing virtual machine managed object |
US7243093B2 (en) | 2002-11-27 | 2007-07-10 | International Business Machines Corporation | Federated query management |
US20040111480A1 (en) | 2002-12-09 | 2004-06-10 | Yue Jonathan Zhanjun | Message screening system and method |
US8589273B2 (en) | 2002-12-23 | 2013-11-19 | Ge Corporate Financial Services, Inc. | Methods and systems for managing risk management information |
US7752117B2 (en) | 2003-01-31 | 2010-07-06 | Trading Technologies International, Inc. | System and method for money management in electronic trading environment |
US20040153418A1 (en) | 2003-02-05 | 2004-08-05 | Hanweck Gerald Alfred | System and method for providing access to data from proprietary tools |
US7099888B2 (en) | 2003-03-26 | 2006-08-29 | Oracle International Corporation | Accessing a remotely located nested object |
US7369912B2 (en) | 2003-05-29 | 2008-05-06 | Fisher-Rosemount Systems, Inc. | Batch execution engine with independent batch execution processes |
US7620648B2 (en) | 2003-06-20 | 2009-11-17 | International Business Machines Corporation | Universal annotation configuration and deployment |
US8412566B2 (en) | 2003-07-08 | 2013-04-02 | Yt Acquisition Corporation | High-precision customer-based targeting by individual usage statistics |
US7216133B2 (en) | 2003-07-29 | 2007-05-08 | Microsoft Corporation | Synchronizing logical views independent of physical storage representations |
WO2005036319A2 (en) | 2003-09-22 | 2005-04-21 | Catalina Marketing International, Inc. | Assumed demographics, predicted behaviour, and targeted incentives |
US7584172B2 (en) | 2003-10-16 | 2009-09-01 | Sap Ag | Control for selecting data query and visual configuration |
US7917376B2 (en) | 2003-12-29 | 2011-03-29 | Montefiore Medical Center | System and method for monitoring patient care |
US20050154628A1 (en) | 2004-01-13 | 2005-07-14 | Illumen, Inc. | Automated management of business performance information |
US20050154769A1 (en) | 2004-01-13 | 2005-07-14 | Llumen, Inc. | Systems and methods for benchmarking business performance data against aggregated business performance data |
US7343552B2 (en) | 2004-02-12 | 2008-03-11 | Fuji Xerox Co., Ltd. | Systems and methods for freeform annotations |
US7085890B2 (en) | 2004-02-19 | 2006-08-01 | International Business Machines Corporation | Memory mapping to reduce cache conflicts in multiprocessor systems |
US20060026120A1 (en) | 2004-03-24 | 2006-02-02 | Update Publications Lp | Method and system for collecting, processing, and distributing residential property data |
US20050226473A1 (en) | 2004-04-07 | 2005-10-13 | Subramanyan Ramesh | Electronic Documents Signing and Compliance Monitoring Invention |
CN101288060B (en) | 2004-05-25 | 2012-11-07 | 波斯蒂尼公司 | Electronic message source reputation information system |
GB2414576A (en) | 2004-05-25 | 2005-11-30 | Arion Human Capital Ltd | Business communication monitoring system detecting anomalous communication patterns |
US8055672B2 (en) | 2004-06-10 | 2011-11-08 | International Business Machines Corporation | Dynamic graphical database query and data mining interface |
US7617232B2 (en) | 2004-09-02 | 2009-11-10 | Microsoft Corporation | Centralized terminology and glossary development |
US7406592B1 (en) | 2004-09-23 | 2008-07-29 | American Megatrends, Inc. | Method, system, and apparatus for efficient evaluation of boolean expressions |
US7512738B2 (en) | 2004-09-30 | 2009-03-31 | Intel Corporation | Allocating call stack frame entries at different memory levels to functions in a program |
US7366723B2 (en) | 2004-10-05 | 2008-04-29 | Sap Ag | Visual query modeling for configurable patterns |
GB0422750D0 (en) | 2004-10-13 | 2004-11-17 | Ciphergrid Ltd | Remote database technique |
US20060080616A1 (en) | 2004-10-13 | 2006-04-13 | Xerox Corporation | Systems, methods and user interfaces for document workflow construction |
CA2484694A1 (en) | 2004-10-14 | 2006-04-14 | Alcatel | Database ram cache |
US20060129992A1 (en) | 2004-11-10 | 2006-06-15 | Oberholtzer Brian K | Software test and performance monitoring system |
US7797197B2 (en) | 2004-11-12 | 2010-09-14 | Amazon Technologies, Inc. | Method and system for analyzing the performance of affiliate sites |
US7899796B1 (en) | 2004-11-23 | 2011-03-01 | Andrew Borthwick | Batch automated blocking and record matching |
US20060143079A1 (en) | 2004-12-29 | 2006-06-29 | Jayanta Basak | Cross-channel customer matching |
US8700414B2 (en) | 2004-12-29 | 2014-04-15 | Sap Ag | System supported optimization of event resolution |
US7783679B2 (en) | 2005-01-12 | 2010-08-24 | Computer Associates Think, Inc. | Efficient processing of time series data |
US8091784B1 (en) | 2005-03-09 | 2012-01-10 | Diebold, Incorporated | Banking system controlled responsive to data bearing records |
US7483028B2 (en) | 2005-03-15 | 2009-01-27 | Microsoft Corporation | Providing 1D and 2D connectors in a connected diagram |
WO2006102270A2 (en) | 2005-03-22 | 2006-09-28 | Cooper Kim A | Performance motivation systems and methods for contact centers |
US7596528B1 (en) | 2005-03-31 | 2009-09-29 | Trading Technologies International, Inc. | System and method for dynamically regulating order entry in an electronic trading environment |
US7672968B2 (en) | 2005-05-12 | 2010-03-02 | Apple Inc. | Displaying a tooltip associated with a concurrently displayed database object |
US8020110B2 (en) | 2005-05-26 | 2011-09-13 | Weisermazars Llp | Methods for defining queries, generating query results and displaying same |
US8161122B2 (en) | 2005-06-03 | 2012-04-17 | Messagemind, Inc. | System and method of dynamically prioritized electronic mail graphical user interface, and measuring email productivity and collaboration trends |
EP1732034A1 (en) | 2005-06-06 | 2006-12-13 | First Data Corporation | System and method for authorizing electronic payment transactions |
US8341259B2 (en) | 2005-06-06 | 2012-12-25 | Adobe Systems Incorporated | ASP for web analytics including a real-time segmentation workbench |
US7571192B2 (en) | 2005-06-15 | 2009-08-04 | Oracle International Corporation | Methods and apparatus for maintaining consistency during analysis of large data sets |
US20070005582A1 (en) | 2005-06-17 | 2007-01-04 | Honeywell International Inc. | Building of database queries from graphical operations |
WO2006137530A1 (en) | 2005-06-24 | 2006-12-28 | Justsystems Corporation | Document processing apparatus |
CA2615659A1 (en) | 2005-07-22 | 2007-05-10 | Yogesh Chunilal Rathod | Universal knowledge management and desktop search system |
US20070178501A1 (en) | 2005-12-06 | 2007-08-02 | Matthew Rabinowitz | System and method for integrating and validating genotypic, phenotypic and medical information into a database according to a standardized ontology |
US7421429B2 (en) | 2005-08-04 | 2008-09-02 | Microsoft Corporation | Generate blog context ranking using track-back weight, context weight and, cumulative comment weight |
CN1913441A (en) | 2005-08-09 | 2007-02-14 | 张永敏 | Continuous changed data set transmission and updating method |
US8095866B2 (en) | 2005-09-09 | 2012-01-10 | Microsoft Corporation | Filtering user interface for a data summary table |
US20070094248A1 (en) | 2005-09-26 | 2007-04-26 | Bea Systems, Inc. | System and method for managing content by workflows |
US20090168163A1 (en) | 2005-11-01 | 2009-07-02 | Global Bionic Optics Pty Ltd. | Optical lens systems |
US8726144B2 (en) | 2005-12-23 | 2014-05-13 | Xerox Corporation | Interactive learning-based document annotation |
US7870512B2 (en) | 2005-12-28 | 2011-01-11 | Sap Ag | User interface (UI) prototype using UI taxonomy |
US7801912B2 (en) | 2005-12-29 | 2010-09-21 | Amazon Technologies, Inc. | Method and apparatus for a searchable data service |
US7831917B1 (en) | 2005-12-30 | 2010-11-09 | Google Inc. | Method, system, and graphical user interface for identifying and communicating with meeting spots |
US8712828B2 (en) | 2005-12-30 | 2014-04-29 | Accenture Global Services Limited | Churn prediction and management system |
US20070192281A1 (en) | 2006-02-02 | 2007-08-16 | International Business Machines Corporation | Methods and apparatus for displaying real-time search trends in graphical search specification and result interfaces |
US20070185867A1 (en) | 2006-02-03 | 2007-08-09 | Matteo Maga | Statistical modeling methods for determining customer distribution by churn probability within a customer population |
US7743056B2 (en) | 2006-03-31 | 2010-06-22 | Aol Inc. | Identifying a result responsive to a current location of a client device |
US20080040275A1 (en) | 2006-04-25 | 2008-02-14 | Uc Group Limited | Systems and methods for identifying potentially fraudulent financial transactions and compulsive spending behavior |
US7853573B2 (en) | 2006-05-03 | 2010-12-14 | Oracle International Corporation | Efficient replication of XML data in a relational database management system |
US20070260582A1 (en) | 2006-05-05 | 2007-11-08 | Inetsoft Technology | Method and System for Visual Query Construction and Representation |
US7756843B1 (en) | 2006-05-25 | 2010-07-13 | Juniper Networks, Inc. | Identifying and processing confidential information on network endpoints |
US9195985B2 (en) | 2006-06-08 | 2015-11-24 | Iii Holdings 1, Llc | Method, system, and computer program product for customer-level data verification |
US8230332B2 (en) | 2006-08-30 | 2012-07-24 | Compsci Resources, Llc | Interactive user interface for converting unstructured documents |
US8054756B2 (en) | 2006-09-18 | 2011-11-08 | Yahoo! Inc. | Path discovery and analytics for network data |
US7792353B2 (en) | 2006-10-31 | 2010-09-07 | Hewlett-Packard Development Company, L.P. | Retraining a machine-learning classifier using re-labeled training samples |
US8229902B2 (en) | 2006-11-01 | 2012-07-24 | Ab Initio Technology Llc | Managing storage of individually accessible data units |
US7853614B2 (en) | 2006-11-27 | 2010-12-14 | Rapleaf, Inc. | Hierarchical, traceable, and association reputation assessment of email domains |
US7680939B2 (en) | 2006-12-20 | 2010-03-16 | Yahoo! Inc. | Graphical user interface to manipulate syndication data feeds |
US8290838B1 (en) | 2006-12-29 | 2012-10-16 | Amazon Technologies, Inc. | Indicating irregularities in online financial transactions |
US8799871B2 (en) | 2007-01-08 | 2014-08-05 | The Mathworks, Inc. | Computation of elementwise expression in parallel |
US8171418B2 (en) | 2007-01-31 | 2012-05-01 | Salesforce.Com, Inc. | Method and system for presenting a visual representation of the portion of the sets of data that a query is expected to return |
CN101246486B (en) | 2007-02-13 | 2012-02-01 | 国际商业机器公司 | Method and apparatus for improved process of expressions |
US7689624B2 (en) | 2007-03-01 | 2010-03-30 | Microsoft Corporation | Graph-based search leveraging sentiment analysis of user comments |
US8180717B2 (en) | 2007-03-20 | 2012-05-15 | President And Fellows Of Harvard College | System for estimating a distribution of message content categories in source data |
US8036971B2 (en) | 2007-03-30 | 2011-10-11 | Palantir Technologies, Inc. | Generating dynamic date sets that represent market conditions |
US20080255973A1 (en) | 2007-04-10 | 2008-10-16 | Robert El Wade | Sales transaction analysis tool and associated method of use |
US7930547B2 (en) | 2007-06-15 | 2011-04-19 | Alcatel-Lucent Usa Inc. | High accuracy bloom filter using partitioned hashing |
US8386996B2 (en) | 2007-06-29 | 2013-02-26 | Sap Ag | Process extension wizard for coherent multi-dimensional business process models |
US20090006150A1 (en) | 2007-06-29 | 2009-01-01 | Sap Ag | Coherent multi-dimensional business process model |
WO2009009623A1 (en) | 2007-07-09 | 2009-01-15 | Tailwalker Technologies, Inc. | Integrating a methodology management system with project tasks in a project management system |
US7761525B2 (en) | 2007-08-23 | 2010-07-20 | International Business Machines Corporation | System and method for providing improved time references in documents |
US8631015B2 (en) | 2007-09-06 | 2014-01-14 | Linkedin Corporation | Detecting associates |
US20090083275A1 (en) | 2007-09-24 | 2009-03-26 | Nokia Corporation | Method, Apparatus and Computer Program Product for Performing a Visual Search Using Grid-Based Feature Organization |
US8849728B2 (en) | 2007-10-01 | 2014-09-30 | Purdue Research Foundation | Visual analytics law enforcement tools |
US8484115B2 (en) | 2007-10-03 | 2013-07-09 | Palantir Technologies, Inc. | Object-oriented time series generator |
US8214308B2 (en) | 2007-10-23 | 2012-07-03 | Sas Institute Inc. | Computer-implemented systems and methods for updating predictive models |
US7650310B2 (en) | 2007-10-30 | 2010-01-19 | Intuit Inc. | Technique for reducing phishing |
US20090126020A1 (en) | 2007-11-09 | 2009-05-14 | Norton Richard Elliott | Engine for rule based content filtering |
US9898767B2 (en) | 2007-11-14 | 2018-02-20 | Panjiva, Inc. | Transaction facilitating marketplace platform |
US8417715B1 (en) | 2007-12-19 | 2013-04-09 | Tilmann Bruckhaus | Platform independent plug-in methods and systems for data mining and analytics |
US20090161147A1 (en) | 2007-12-20 | 2009-06-25 | Sharp Laboratories Of America, Inc. | Personal document container |
US20090172674A1 (en) | 2007-12-28 | 2009-07-02 | International Business Machines Corporation | Managing the computer collection of information in an information technology environment |
US8055633B2 (en) | 2008-01-21 | 2011-11-08 | International Business Machines Corporation | Method, system and computer program product for duplicate detection |
US7877367B2 (en) | 2008-01-22 | 2011-01-25 | International Business Machines Corporation | Computer method and apparatus for graphical inquiry specification with progressive summary |
KR100915295B1 (en) | 2008-01-22 | 2009-09-03 | 성균관대학교산학협력단 | System and method for search service having a function of automatic classification of search results |
US20090193012A1 (en) | 2008-01-29 | 2009-07-30 | James Charles Williams | Inheritance in a Search Index |
US20090199047A1 (en) | 2008-01-31 | 2009-08-06 | Yahoo! Inc. | Executing software performance test jobs in a clustered system |
US9274923B2 (en) | 2008-03-25 | 2016-03-01 | Wind River Systems, Inc. | System and method for stack crawl testing and caching |
US8121962B2 (en) | 2008-04-25 | 2012-02-21 | Fair Isaac Corporation | Automated entity identification for efficient profiling in an event probability prediction system |
US20120053990A1 (en) * | 2008-05-07 | 2012-03-01 | Nice Systems Ltd. | System and method for predicting customer churn |
US20090282068A1 (en) | 2008-05-12 | 2009-11-12 | Shockro John J | Semantic packager |
US20090307049A1 (en) | 2008-06-05 | 2009-12-10 | Fair Isaac Corporation | Soft Co-Clustering of Data |
US8860754B2 (en) | 2008-06-22 | 2014-10-14 | Tableau Software, Inc. | Methods and systems of automatically generating marks in a graphical view |
US8499287B2 (en) | 2008-06-23 | 2013-07-30 | Microsoft Corporation | Analysis of thread synchronization events |
US7908521B2 (en) | 2008-06-25 | 2011-03-15 | Microsoft Corporation | Process reflection |
AU2009201514A1 (en) | 2008-07-11 | 2010-01-28 | Icyte Pty Ltd | Annotation system and method |
CN102150129A (en) | 2008-08-04 | 2011-08-10 | 奎德公司 | Entity performance analysis engines |
US10747952B2 (en) | 2008-09-15 | 2020-08-18 | Palantir Technologies, Inc. | Automatic creation and server push of multiple distinct drafts |
KR101495132B1 (en) | 2008-09-24 | 2015-02-25 | 삼성전자주식회사 | Mobile terminal and method for displaying data thereof |
CN101685449B (en) | 2008-09-26 | 2012-07-11 | 国际商业机器公司 | Method and system for connecting tables in a plurality of heterogeneous distributed databases |
US20100114887A1 (en) | 2008-10-17 | 2010-05-06 | Google Inc. | Textual Disambiguation Using Social Connections |
US8391584B2 (en) | 2008-10-20 | 2013-03-05 | Jpmorgan Chase Bank, N.A. | Method and system for duplicate check detection |
US9032254B2 (en) | 2008-10-29 | 2015-05-12 | Aternity Information Systems Ltd. | Real time monitoring of computer for determining speed and energy consumption of various processes |
US8103962B2 (en) | 2008-11-04 | 2012-01-24 | Brigham Young University | Form-based ontology creation and information harvesting |
US20100131502A1 (en) | 2008-11-25 | 2010-05-27 | Fordham Bradley S | Cohort group generation and automatic updating |
US8805861B2 (en) | 2008-12-09 | 2014-08-12 | Google Inc. | Methods and systems to train models to extract and integrate information from data sources |
US8312038B2 (en) | 2008-12-18 | 2012-11-13 | Oracle International Corporation | Criteria builder for query builder |
US8719350B2 (en) | 2008-12-23 | 2014-05-06 | International Business Machines Corporation | Email addressee verification |
US20100169376A1 (en) | 2008-12-29 | 2010-07-01 | Yahoo! Inc. | Visual search engine for personal dating |
US20100262688A1 (en) | 2009-01-21 | 2010-10-14 | Daniar Hussain | Systems, methods, and devices for detecting security vulnerabilities in ip networks |
US20100191563A1 (en) | 2009-01-23 | 2010-07-29 | Doctors' Administrative Solutions, Llc | Physician Practice Optimization Tracking |
WO2010085773A1 (en) | 2009-01-24 | 2010-07-29 | Kontera Technologies, Inc. | Hybrid contextual advertising and related content analysis and display techniques |
US8073857B2 (en) | 2009-02-17 | 2011-12-06 | International Business Machines Corporation | Semantics-based data transformation over a wire in mashups |
US8473454B2 (en) | 2009-03-10 | 2013-06-25 | Xerox Corporation | System and method of on-demand document processing |
US20100235915A1 (en) | 2009-03-12 | 2010-09-16 | Nasir Memon | Using host symptoms, host roles, and/or host reputation for detection of host infection |
US9268761B2 (en) | 2009-06-05 | 2016-02-23 | Microsoft Technology Licensing, Llc | In-line dynamic text with variable formatting |
US8495151B2 (en) | 2009-06-05 | 2013-07-23 | Chandra Bodapati | Methods and systems for determining email addresses |
US20110004498A1 (en) | 2009-07-01 | 2011-01-06 | International Business Machines Corporation | Method and System for Identification By A Cardholder of Credit Card Fraud |
US9104695B1 (en) | 2009-07-27 | 2015-08-11 | Palantir Technologies, Inc. | Geotagging structured data |
US8606804B2 (en) | 2009-08-05 | 2013-12-10 | Microsoft Corporation | Runtime-defined dynamic queries |
US9280777B2 (en) | 2009-09-08 | 2016-03-08 | Target Brands, Inc. | Operations dashboard |
US20110066497A1 (en) | 2009-09-14 | 2011-03-17 | Choicestream, Inc. | Personalized advertising and recommendation |
US8214490B1 (en) | 2009-09-15 | 2012-07-03 | Symantec Corporation | Compact input compensating reputation data tracking mechanism |
US20110074811A1 (en) | 2009-09-25 | 2011-03-31 | Apple Inc. | Map Layout for Print Production |
US20110078173A1 (en) | 2009-09-30 | 2011-03-31 | Avaya Inc. | Social Network User Interface |
US8595058B2 (en) | 2009-10-15 | 2013-11-26 | Visa U.S.A. | Systems and methods to match identifiers |
US9158816B2 (en) | 2009-10-21 | 2015-10-13 | Microsoft Technology Licensing, Llc | Event processing with XML query based on reusable XML query template |
US9165304B2 (en) | 2009-10-23 | 2015-10-20 | Service Management Group, Inc. | Analyzing consumer behavior using electronically-captured consumer location data |
US20110099133A1 (en) | 2009-10-28 | 2011-04-28 | Industrial Technology Research Institute | Systems and methods for capturing and managing collective social intelligence information |
CN102054015B (en) | 2009-10-28 | 2014-05-07 | 财团法人工业技术研究院 | System and method of organizing community intelligent information by using organic matter data model |
US20110131547A1 (en) | 2009-12-01 | 2011-06-02 | International Business Machines Corporation | Method and system defining and interchanging diagrams of graphical modeling languages |
US11122009B2 (en) | 2009-12-01 | 2021-09-14 | Apple Inc. | Systems and methods for identifying geographic locations of social media content collected over social networks |
US8645478B2 (en) | 2009-12-10 | 2014-02-04 | Mcafee, Inc. | System and method for monitoring social engineering in a computer network environment |
GB2476121A (en) | 2009-12-14 | 2011-06-15 | Colin Westlake | Linking interactions using a reference for an internet user's web session |
US20110153384A1 (en) | 2009-12-17 | 2011-06-23 | Matthew Donald Horne | Visual comps builder |
EP2524299A4 (en) | 2010-01-11 | 2013-11-13 | Panjiva Inc | Evaluating public records of supply transactions for financial investment decisions |
US9026552B2 (en) | 2010-01-18 | 2015-05-05 | Salesforce.Com, Inc. | System and method for linking contact records to company locations |
US20110208822A1 (en) | 2010-02-22 | 2011-08-25 | Yogesh Chunilal Rathod | Method and system for customized, contextual, dynamic and unified communication, zero click advertisement and prospective customers search engine |
US20110208565A1 (en) | 2010-02-23 | 2011-08-25 | Michael Ross | complex process management |
US8478709B2 (en) | 2010-03-08 | 2013-07-02 | Hewlett-Packard Development Company, L.P. | Evaluation of client status for likelihood of churn |
US20110231296A1 (en) | 2010-03-16 | 2011-09-22 | UberMedia, Inc. | Systems and methods for interacting with messages, authors, and followers |
US8739118B2 (en) | 2010-04-08 | 2014-05-27 | Microsoft Corporation | Pragmatic mapping specification, compilation and validation |
US8306846B2 (en) | 2010-04-12 | 2012-11-06 | First Data Corporation | Transaction location analytics systems and methods |
US20110258216A1 (en) | 2010-04-20 | 2011-10-20 | International Business Machines Corporation | Usability enhancements for bookmarks of browsers |
US8874432B2 (en) | 2010-04-28 | 2014-10-28 | Nec Laboratories America, Inc. | Systems and methods for semi-supervised relationship extraction |
US8255399B2 (en) | 2010-04-28 | 2012-08-28 | Microsoft Corporation | Data classifier |
US8626770B2 (en) | 2010-05-03 | 2014-01-07 | International Business Machines Corporation | Iceberg query evaluation implementing a compressed bitmap index |
US20110289397A1 (en) | 2010-05-19 | 2011-11-24 | Mauricio Eastmond | Displaying Table Data in a Limited Display Area |
US20110295649A1 (en) | 2010-05-31 | 2011-12-01 | International Business Machines Corporation | Automatic churn prediction |
US8799867B1 (en) | 2010-06-08 | 2014-08-05 | Cadence Design Systems, Inc. | Methods, systems, and articles of manufacture for synchronizing software verification flows |
US8756224B2 (en) | 2010-06-16 | 2014-06-17 | Rallyverse, Inc. | Methods, systems, and media for content ranking using real-time data |
US8380719B2 (en) | 2010-06-18 | 2013-02-19 | Microsoft Corporation | Semantic content searching |
US8352908B2 (en) | 2010-06-28 | 2013-01-08 | International Business Machines Corporation | Multi-modal conversion tool for form-type applications |
US8407341B2 (en) | 2010-07-09 | 2013-03-26 | Bank Of America Corporation | Monitoring communications |
CA2707916C (en) | 2010-07-14 | 2015-12-01 | Ibm Canada Limited - Ibm Canada Limitee | Intelligent timesheet assistance |
US8554653B2 (en) | 2010-07-22 | 2013-10-08 | Visa International Service Association | Systems and methods to identify payment accounts having business spending activities |
US8775530B2 (en) | 2010-08-25 | 2014-07-08 | International Business Machines Corporation | Communication management method and system |
US20120066166A1 (en) | 2010-09-10 | 2012-03-15 | International Business Machines Corporation | Predictive Analytics for Semi-Structured Case Oriented Processes |
US20120078595A1 (en) | 2010-09-24 | 2012-03-29 | Nokia Corporation | Method and apparatus for ontology matching |
US8549004B2 (en) | 2010-09-30 | 2013-10-01 | Hewlett-Packard Development Company, L.P. | Estimation of unique database values |
WO2012054860A1 (en) | 2010-10-22 | 2012-04-26 | Daniel Paul Miranker | Accessing relational databases as resource description framework databases |
JP5706137B2 (en) | 2010-11-22 | 2015-04-22 | インターナショナル・ビジネス・マシーンズ・コーポレーションInternational Business Machines Corporation | Method and computer program for displaying a plurality of posts (groups of data) on a computer screen in real time along a plurality of axes |
US8543694B2 (en) | 2010-11-24 | 2013-09-24 | Logrhythm, Inc. | Scalable analytical processing of structured data |
CN102546446A (en) | 2010-12-13 | 2012-07-04 | 太仓市浏河镇亿网行网络技术服务部 | Email device |
US20120159449A1 (en) | 2010-12-15 | 2012-06-21 | International Business Machines Corporation | Call Stack Inspection For A Thread Of Execution |
US9141405B2 (en) | 2010-12-15 | 2015-09-22 | International Business Machines Corporation | User interface construction |
US20120173381A1 (en) | 2011-01-03 | 2012-07-05 | Stanley Benjamin Smith | Process and system for pricing and processing weighted data in a federated or subscription based data source |
IL211163A0 (en) | 2011-02-10 | 2011-04-28 | Univ Ben Gurion | A method for generating a randomized data structure for representing sets, based on bloom filters |
KR101950529B1 (en) | 2011-02-24 | 2019-02-20 | 렉시스넥시스, 어 디비젼 오브 리드 엘서비어 인크. | Methods for electronic document searching and graphically representing electronic document searches |
US8966486B2 (en) | 2011-05-03 | 2015-02-24 | Microsoft Corporation | Distributed multi-phase batch job processing |
US9104765B2 (en) | 2011-06-17 | 2015-08-11 | Robert Osann, Jr. | Automatic webpage characterization and search results annotation |
US9781540B2 (en) * | 2011-07-07 | 2017-10-03 | Qualcomm Incorporated | Application relevance determination based on social context |
US8726379B1 (en) | 2011-07-15 | 2014-05-13 | Norse Corporation | Systems and methods for dynamic protection from electronic attacks |
US8982130B2 (en) | 2011-07-15 | 2015-03-17 | Green Charge Networks | Cluster mapping to highlight areas of electrical congestion |
US20130024268A1 (en) | 2011-07-22 | 2013-01-24 | Ebay Inc. | Incentivizing the linking of internet content to products for sale |
US9996807B2 (en) | 2011-08-17 | 2018-06-12 | Roundhouse One Llc | Multidimensional digital platform for building integration and analysis |
US20130054551A1 (en) | 2011-08-24 | 2013-02-28 | Sap Ag | Global product database |
US8630892B2 (en) | 2011-08-31 | 2014-01-14 | Accenture Global Services Limited | Churn analysis system |
GB201115083D0 (en) | 2011-08-31 | 2011-10-19 | Data Connection Ltd | Identifying data items |
US8949164B1 (en) | 2011-09-08 | 2015-02-03 | George O. Mohler | Event forecasting system |
WO2013044141A2 (en) | 2011-09-22 | 2013-03-28 | Capgemini U.S. Llc | Process transformation and transitioning apparatuses, methods and systems |
US8433702B1 (en) | 2011-09-28 | 2013-04-30 | Palantir Technologies, Inc. | Horizon histogram optimizations |
US8560494B1 (en) | 2011-09-30 | 2013-10-15 | Palantir Technologies, Inc. | Visual data importer |
US20130086482A1 (en) | 2011-09-30 | 2013-04-04 | Cbs Interactive, Inc. | Displaying plurality of content items in window |
BR112014008351A2 (en) | 2011-10-05 | 2017-04-18 | Mastercard International Inc | naming mechanism |
US8626545B2 (en) | 2011-10-17 | 2014-01-07 | CrowdFlower, Inc. | Predicting future performance of multiple workers on crowdsourcing tasks and selecting repeated crowdsourcing workers |
US8843421B2 (en) | 2011-11-01 | 2014-09-23 | Accenture Global Services Limited | Identification of entities likely to engage in a behavior |
US9159024B2 (en) | 2011-12-07 | 2015-10-13 | Wal-Mart Stores, Inc. | Real-time predictive intelligence platform |
CN103167093A (en) | 2011-12-08 | 2013-06-19 | 青岛海信移动通信技术股份有限公司 | Filling method of mobile phone email address |
US9026480B2 (en) | 2011-12-21 | 2015-05-05 | Telenav, Inc. | Navigation system with point of interest classification mechanism and method of operation thereof |
US8880420B2 (en) | 2011-12-27 | 2014-11-04 | Grubhub, Inc. | Utility for creating heatmaps for the study of competitive advantage in the restaurant marketplace |
US8843431B2 (en) | 2012-01-16 | 2014-09-23 | International Business Machines Corporation | Social network analysis for churn prediction |
US8909648B2 (en) | 2012-01-18 | 2014-12-09 | Technion Research & Development Foundation Limited | Methods and systems of supervised learning of semantic relatedness |
US9279898B2 (en) | 2012-02-09 | 2016-03-08 | Pgs Geophysical As | Methods and systems for correction of streamer-depth bias in marine seismic surveys |
US8965422B2 (en) | 2012-02-23 | 2015-02-24 | Blackberry Limited | Tagging instant message content for retrieval using mobile communication devices |
US20130226944A1 (en) | 2012-02-24 | 2013-08-29 | Microsoft Corporation | Format independent data transformation |
US9378526B2 (en) | 2012-03-02 | 2016-06-28 | Palantir Technologies, Inc. | System and method for accessing data objects via remote references |
JP2013191187A (en) | 2012-03-15 | 2013-09-26 | Fujitsu Ltd | Processing device, program and processing system |
US20130263019A1 (en) | 2012-03-30 | 2013-10-03 | Maria G. Castellanos | Analyzing social media |
US9298856B2 (en) | 2012-04-23 | 2016-03-29 | Sap Se | Interactive data exploration and visualization tool |
US9043710B2 (en) | 2012-04-26 | 2015-05-26 | Sap Se | Switch control in report generation |
US10304036B2 (en) | 2012-05-07 | 2019-05-28 | Nasdaq, Inc. | Social media profiling for one or more authors using one or more social media platforms |
EP2662782A1 (en) | 2012-05-10 | 2013-11-13 | Siemens Aktiengesellschaft | Method and system for storing data in a database |
US10163158B2 (en) | 2012-08-27 | 2018-12-25 | Yuh-Shen Song | Transactional monitoring system |
US20140068487A1 (en) | 2012-09-05 | 2014-03-06 | Roche Diagnostics Operations, Inc. | Computer Implemented Methods For Visualizing Correlations Between Blood Glucose Data And Events And Apparatuses Thereof |
US9798768B2 (en) | 2012-09-10 | 2017-10-24 | Palantir Technologies, Inc. | Search around visual queries |
US20140095509A1 (en) | 2012-10-02 | 2014-04-03 | Banjo, Inc. | Method of tagging content lacking geotags with a location |
WO2014058889A1 (en) | 2012-10-08 | 2014-04-17 | Fisher-Rosemount Systems, Inc. | Configurable user displays in a process control system |
US9104786B2 (en) | 2012-10-12 | 2015-08-11 | International Business Machines Corporation | Iterative refinement of cohorts using visual exploration and data analytics |
US8688573B1 (en) | 2012-10-16 | 2014-04-01 | Intuit Inc. | Method and system for identifying a merchant payee associated with a cash transaction |
US9471370B2 (en) | 2012-10-22 | 2016-10-18 | Palantir Technologies, Inc. | System and method for stack-based batch evaluation of program instructions |
US9348677B2 (en) | 2012-10-22 | 2016-05-24 | Palantir Technologies Inc. | System and method for batch evaluation programs |
US8914886B2 (en) | 2012-10-29 | 2014-12-16 | Mcafee, Inc. | Dynamic quarantining for malware detection |
US9378030B2 (en) | 2013-10-01 | 2016-06-28 | Aetherpal, Inc. | Method and apparatus for interactive mobile device guidance |
US10504127B2 (en) | 2012-11-15 | 2019-12-10 | Home Depot Product Authority, Llc | System and method for classifying relevant competitors |
US20140143009A1 (en) | 2012-11-16 | 2014-05-22 | International Business Machines Corporation | Risk reward estimation for company-country pairs |
US20140156527A1 (en) | 2012-11-30 | 2014-06-05 | Bank Of America Corporation | Pre-payment authorization categorization |
US20140157172A1 (en) | 2012-11-30 | 2014-06-05 | Drillmap | Geographic layout of petroleum drilling data and methods for processing data |
US10672008B2 (en) | 2012-12-06 | 2020-06-02 | Jpmorgan Chase Bank, N.A. | System and method for data analytics |
US9497289B2 (en) | 2012-12-07 | 2016-11-15 | Genesys Telecommunications Laboratories, Inc. | System and method for social message classification based on influence |
US10108668B2 (en) | 2012-12-14 | 2018-10-23 | Sap Se | Column smart mechanism for column based database |
US9294576B2 (en) | 2013-01-02 | 2016-03-22 | Microsoft Technology Licensing, Llc | Social media impact assessment |
US20140195515A1 (en) | 2013-01-10 | 2014-07-10 | I3 Analytics | Methods and systems for querying and displaying data using interactive three-dimensional representations |
US8639552B1 (en) | 2013-01-24 | 2014-01-28 | Broadvision, Inc. | Systems and methods for creating and sharing tasks |
US9805407B2 (en) | 2013-01-25 | 2017-10-31 | Illumina, Inc. | Methods and systems for using a cloud computing environment to configure and sell a biological sample preparation cartridge and share related data |
US20140222793A1 (en) | 2013-02-07 | 2014-08-07 | Parlance Corporation | System and Method for Automatically Importing, Refreshing, Maintaining, and Merging Contact Sets |
US20140222521A1 (en) | 2013-02-07 | 2014-08-07 | Ibms, Llc | Intelligent management and compliance verification in distributed work flow environments |
US9264393B2 (en) | 2013-02-13 | 2016-02-16 | International Business Machines Corporation | Mail server-based dynamic workflow management |
US8744890B1 (en) | 2013-02-14 | 2014-06-03 | Aktana, Inc. | System and method for managing system-level workflow strategy and individual workflow activity |
US20140244388A1 (en) | 2013-02-28 | 2014-08-28 | MetroStar Systems, Inc. | Social Content Synchronization |
US9286618B2 (en) | 2013-03-08 | 2016-03-15 | Mastercard International Incorporated | Recognizing and combining redundant merchant designations in a transaction database |
US10140664B2 (en) | 2013-03-14 | 2018-11-27 | Palantir Technologies Inc. | Resolving similar entities from a transaction database |
GB2513720A (en) | 2013-03-15 | 2014-11-05 | Palantir Technologies Inc | Computer-implemented systems and methods for comparing and associating objects |
US8924388B2 (en) | 2013-03-15 | 2014-12-30 | Palantir Technologies Inc. | Computer-implemented systems and methods for comparing and associating objects |
US9501202B2 (en) | 2013-03-15 | 2016-11-22 | Palantir Technologies, Inc. | Computer graphical user interface with genomic workflow |
US9898167B2 (en) | 2013-03-15 | 2018-02-20 | Palantir Technologies Inc. | Systems and methods for providing a tagging interface for external content |
GB2513721A (en) | 2013-03-15 | 2014-11-05 | Palantir Technologies Inc | Computer-implemented systems and methods for comparing and associating objects |
US9372929B2 (en) | 2013-03-20 | 2016-06-21 | Securboration, Inc. | Methods and systems for node and link identification |
US20140351006A1 (en) | 2013-05-22 | 2014-11-27 | Cube, Co. | System and method for generating and utilizing global information from transaction records |
US9576248B2 (en) | 2013-06-01 | 2017-02-21 | Adam M. Hurwitz | Record linkage sharing using labeled comparison vectors and a machine learning domain classification trainer |
US8938686B1 (en) | 2013-10-03 | 2015-01-20 | Palantir Technologies Inc. | Systems and methods for analyzing performance of an entity |
US8812960B1 (en) | 2013-10-07 | 2014-08-19 | Palantir Technologies Inc. | Cohort-based presentation of user interaction data |
US9792194B2 (en) | 2013-10-18 | 2017-10-17 | International Business Machines Corporation | Performance regression manager for large scale systems |
US8832594B1 (en) | 2013-11-04 | 2014-09-09 | Palantir Technologies Inc. | Space-optimized display of multi-column tables with selective text truncation based on a combined text width |
US9356937B2 (en) | 2013-11-13 | 2016-05-31 | International Business Machines Corporation | Disambiguating conflicting content filter rules |
US9105000B1 (en) | 2013-12-10 | 2015-08-11 | Palantir Technologies Inc. | Aggregating data from a plurality of data sources |
US10356032B2 (en) | 2013-12-26 | 2019-07-16 | Palantir Technologies Inc. | System and method for detecting confidential information emails |
US8832832B1 (en) | 2014-01-03 | 2014-09-09 | Palantir Technologies Inc. | IP reputation |
US8935201B1 (en) | 2014-03-18 | 2015-01-13 | Palantir Technologies Inc. | Determining and extracting changed data from a data source |
US9129219B1 (en) | 2014-06-30 | 2015-09-08 | Palantir Technologies, Inc. | Crime risk forecasting |
US9256664B2 (en) | 2014-07-03 | 2016-02-09 | Palantir Technologies Inc. | System and method for news events detection and visualization |
US20160026923A1 (en) | 2014-07-22 | 2016-01-28 | Palantir Technologies Inc. | System and method for determining a propensity of entity to take a specified action |
US20160055501A1 (en) | 2014-08-19 | 2016-02-25 | Palantir Technologies Inc. | System and method for determining a cohort |
-
2014
- 2014-12-05 US US14/562,524 patent/US20160026923A1/en not_active Abandoned
-
2017
- 2017-08-29 US US15/689,757 patent/US11521096B2/en active Active
-
2022
- 2022-10-07 US US17/961,822 patent/US11861515B2/en active Active
Cited By (98)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10747952B2 (en) | 2008-09-15 | 2020-08-18 | Palantir Technologies, Inc. | Automatic creation and server push of multiple distinct drafts |
US9880987B2 (en) | 2011-08-25 | 2018-01-30 | Palantir Technologies, Inc. | System and method for parameterizing documents for automatic workflow generation |
US10706220B2 (en) | 2011-08-25 | 2020-07-07 | Palantir Technologies, Inc. | System and method for parameterizing documents for automatic workflow generation |
US11138180B2 (en) | 2011-09-02 | 2021-10-05 | Palantir Technologies Inc. | Transaction protocol for reading database values |
US10331797B2 (en) | 2011-09-02 | 2019-06-25 | Palantir Technologies Inc. | Transaction protocol for reading database values |
US9621676B2 (en) | 2012-03-02 | 2017-04-11 | Palantir Technologies, Inc. | System and method for accessing data objects via remote references |
US9471370B2 (en) | 2012-10-22 | 2016-10-18 | Palantir Technologies, Inc. | System and method for stack-based batch evaluation of program instructions |
US11182204B2 (en) | 2012-10-22 | 2021-11-23 | Palantir Technologies Inc. | System and method for batch evaluation programs |
US9898335B1 (en) | 2012-10-22 | 2018-02-20 | Palantir Technologies Inc. | System and method for batch evaluation programs |
US9652291B2 (en) | 2013-03-14 | 2017-05-16 | Palantir Technologies, Inc. | System and method utilizing a shared cache to provide zero copy memory mapped database |
US10817513B2 (en) | 2013-03-14 | 2020-10-27 | Palantir Technologies Inc. | Fair scheduling for mixed-query loads |
US9852205B2 (en) | 2013-03-15 | 2017-12-26 | Palantir Technologies Inc. | Time-sensitive cube |
US9740369B2 (en) | 2013-03-15 | 2017-08-22 | Palantir Technologies Inc. | Systems and methods for providing a tagging interface for external content |
US9898167B2 (en) | 2013-03-15 | 2018-02-20 | Palantir Technologies Inc. | Systems and methods for providing a tagging interface for external content |
US10809888B2 (en) | 2013-03-15 | 2020-10-20 | Palantir Technologies, Inc. | Systems and methods for providing a tagging interface for external content |
US10977279B2 (en) | 2013-03-15 | 2021-04-13 | Palantir Technologies Inc. | Time-sensitive cube |
US10452678B2 (en) | 2013-03-15 | 2019-10-22 | Palantir Technologies Inc. | Filter chains for exploring large data sets |
US10360705B2 (en) | 2013-05-07 | 2019-07-23 | Palantir Technologies Inc. | Interactive data object map |
US9953445B2 (en) | 2013-05-07 | 2018-04-24 | Palantir Technologies Inc. | Interactive data object map |
US9996229B2 (en) | 2013-10-03 | 2018-06-12 | Palantir Technologies Inc. | Systems and methods for analyzing performance of an entity |
US11138279B1 (en) | 2013-12-10 | 2021-10-05 | Palantir Technologies Inc. | System and method for aggregating data from a plurality of data sources |
US10198515B1 (en) | 2013-12-10 | 2019-02-05 | Palantir Technologies Inc. | System and method for aggregating data from a plurality of data sources |
US10579647B1 (en) | 2013-12-16 | 2020-03-03 | Palantir Technologies Inc. | Methods and systems for analyzing entity performance |
US10025834B2 (en) | 2013-12-16 | 2018-07-17 | Palantir Technologies Inc. | Methods and systems for analyzing entity performance |
US9734217B2 (en) | 2013-12-16 | 2017-08-15 | Palantir Technologies Inc. | Methods and systems for analyzing entity performance |
US9727622B2 (en) | 2013-12-16 | 2017-08-08 | Palantir Technologies, Inc. | Methods and systems for analyzing entity performance |
US10180977B2 (en) | 2014-03-18 | 2019-01-15 | Palantir Technologies Inc. | Determining and extracting changed data from a data source |
US9449074B1 (en) | 2014-03-18 | 2016-09-20 | Palantir Technologies Inc. | Determining and extracting changed data from a data source |
US11521096B2 (en) | 2014-07-22 | 2022-12-06 | Palantir Technologies Inc. | System and method for determining a propensity of entity to take a specified action |
US11861515B2 (en) | 2014-07-22 | 2024-01-02 | Palantir Technologies Inc. | System and method for determining a propensity of entity to take a specified action |
US10242072B2 (en) | 2014-12-15 | 2019-03-26 | Palantir Technologies Inc. | System and method for associating related records to common entities across multiple lists |
US9891808B2 (en) | 2015-03-16 | 2018-02-13 | Palantir Technologies Inc. | Interactive user interfaces for location-based data analysis |
US10459619B2 (en) | 2015-03-16 | 2019-10-29 | Palantir Technologies Inc. | Interactive user interfaces for location-based data analysis |
US10103953B1 (en) | 2015-05-12 | 2018-10-16 | Palantir Technologies Inc. | Methods and systems for analyzing entity performance |
US10163061B2 (en) * | 2015-06-18 | 2018-12-25 | International Business Machines Corporation | Quality-directed adaptive analytic retraining |
US10444940B2 (en) | 2015-08-17 | 2019-10-15 | Palantir Technologies Inc. | Interactive geospatial map |
US10444941B2 (en) | 2015-08-17 | 2019-10-15 | Palantir Technologies Inc. | Interactive geospatial map |
US10706434B1 (en) | 2015-09-01 | 2020-07-07 | Palantir Technologies Inc. | Methods and systems for determining location information |
US20180210935A1 (en) * | 2015-09-04 | 2018-07-26 | Palantir Technologies Inc. | Systems and methods for importing data from electronic data files |
US9514205B1 (en) | 2015-09-04 | 2016-12-06 | Palantir Technologies Inc. | Systems and methods for importing data from electronic data files |
US10380138B1 (en) | 2015-09-04 | 2019-08-13 | Palantir Technologies Inc. | Systems and methods for importing data from electronic data files |
US10545985B2 (en) * | 2015-09-04 | 2020-01-28 | Palantir Technologies Inc. | Systems and methods for importing data from electronic data files |
US9946776B1 (en) | 2015-09-04 | 2018-04-17 | Palantir Technologies Inc. | Systems and methods for importing data from electronic data files |
US10558339B1 (en) | 2015-09-11 | 2020-02-11 | Palantir Technologies Inc. | System and method for analyzing electronic communications and a collaborative electronic communications user interface |
US11907513B2 (en) | 2015-09-11 | 2024-02-20 | Palantir Technologies Inc. | System and method for analyzing electronic communications and a collaborative electronic communications user interface |
US10114884B1 (en) | 2015-12-16 | 2018-10-30 | Palantir Technologies Inc. | Systems and methods for attribute analysis of one or more databases |
US11106701B2 (en) | 2015-12-16 | 2021-08-31 | Palantir Technologies Inc. | Systems and methods for attribute analysis of one or more databases |
US11829928B2 (en) | 2015-12-18 | 2023-11-28 | Palantir Technologies Inc. | Misalignment detection system for efficiently processing database-stored data and automatically generating misalignment information for display in interactive user interfaces |
US10373099B1 (en) | 2015-12-18 | 2019-08-06 | Palantir Technologies Inc. | Misalignment detection system for efficiently processing database-stored data and automatically generating misalignment information for display in interactive user interfaces |
US10452673B1 (en) | 2015-12-29 | 2019-10-22 | Palantir Technologies Inc. | Systems and user interfaces for data analysis including artificial intelligence algorithms for generating optimized packages of data items |
US9652510B1 (en) | 2015-12-29 | 2017-05-16 | Palantir Technologies Inc. | Systems and user interfaces for data analysis including artificial intelligence algorithms for generating optimized packages of data items |
US11444854B2 (en) | 2016-06-09 | 2022-09-13 | Palantir Technologies Inc. | System to collect and visualize software usage metrics |
US10554516B1 (en) | 2016-06-09 | 2020-02-04 | Palantir Technologies Inc. | System to collect and visualize software usage metrics |
US10909130B1 (en) | 2016-07-01 | 2021-02-02 | Palantir Technologies Inc. | Graphical user interface for a database system |
US11256762B1 (en) | 2016-08-04 | 2022-02-22 | Palantir Technologies Inc. | System and method for efficiently determining and displaying optimal packages of data items |
US11366959B2 (en) | 2016-08-11 | 2022-06-21 | Palantir Technologies Inc. | Collaborative spreadsheet data validation and integration |
US10552531B2 (en) | 2016-08-11 | 2020-02-04 | Palantir Technologies Inc. | Collaborative spreadsheet data validation and integration |
US11488058B2 (en) | 2016-08-15 | 2022-11-01 | Palantir Technologies Inc. | Vector generation for distributed data sets |
US10373078B1 (en) | 2016-08-15 | 2019-08-06 | Palantir Technologies Inc. | Vector generation for distributed data sets |
US10977267B1 (en) | 2016-08-17 | 2021-04-13 | Palantir Technologies Inc. | User interface data sample transformer |
US11475033B2 (en) | 2016-08-17 | 2022-10-18 | Palantir Technologies Inc. | User interface data sample transformer |
US10650086B1 (en) | 2016-09-27 | 2020-05-12 | Palantir Technologies Inc. | Systems, methods, and framework for associating supporting data in word processing |
US10754627B2 (en) | 2016-11-07 | 2020-08-25 | Palantir Technologies Inc. | Framework for developing and deploying applications |
US11397566B2 (en) | 2016-11-07 | 2022-07-26 | Palantir Technologies Inc. | Framework for developing and deploying applications |
US10152306B2 (en) | 2016-11-07 | 2018-12-11 | Palantir Technologies Inc. | Framework for developing and deploying applications |
US10261763B2 (en) | 2016-12-13 | 2019-04-16 | Palantir Technologies Inc. | Extensible data transformation authoring and validation system |
US10860299B2 (en) | 2016-12-13 | 2020-12-08 | Palantir Technologies Inc. | Extensible data transformation authoring and validation system |
US11157951B1 (en) | 2016-12-16 | 2021-10-26 | Palantir Technologies Inc. | System and method for determining and displaying an optimal assignment of data items |
US10509844B1 (en) | 2017-01-19 | 2019-12-17 | Palantir Technologies Inc. | Network graph parser |
US10762291B2 (en) | 2017-03-02 | 2020-09-01 | Palantir Technologies Inc. | Automatic translation of spreadsheets into scripts |
US10180934B2 (en) | 2017-03-02 | 2019-01-15 | Palantir Technologies Inc. | Automatic translation of spreadsheets into scripts |
US11200373B2 (en) | 2017-03-02 | 2021-12-14 | Palantir Technologies Inc. | Automatic translation of spreadsheets into scripts |
US10572576B1 (en) | 2017-04-06 | 2020-02-25 | Palantir Technologies Inc. | Systems and methods for facilitating data object extraction from unstructured documents |
US11244102B2 (en) | 2017-04-06 | 2022-02-08 | Palantir Technologies Inc. | Systems and methods for facilitating data object extraction from unstructured documents |
US11500827B2 (en) | 2017-05-17 | 2022-11-15 | Palantir Technologies Inc. | Systems and methods for data entry |
US10824604B1 (en) | 2017-05-17 | 2020-11-03 | Palantir Technologies Inc. | Systems and methods for data entry |
US11860831B2 (en) | 2017-05-17 | 2024-01-02 | Palantir Technologies Inc. | Systems and methods for data entry |
US10606872B1 (en) | 2017-05-22 | 2020-03-31 | Palantir Technologies Inc. | Graphical user interface for a database system |
US10534595B1 (en) | 2017-06-30 | 2020-01-14 | Palantir Technologies Inc. | Techniques for configuring and validating a data pipeline deployment |
US10204119B1 (en) | 2017-07-20 | 2019-02-12 | Palantir Technologies, Inc. | Inferring a dataset schema from input files |
US10540333B2 (en) | 2017-07-20 | 2020-01-21 | Palantir Technologies Inc. | Inferring a dataset schema from input files |
US11379407B2 (en) | 2017-08-14 | 2022-07-05 | Palantir Technologies Inc. | Customizable pipeline for integrating data |
US11886382B2 (en) | 2017-08-14 | 2024-01-30 | Palantir Technologies Inc. | Customizable pipeline for integrating data |
US10754820B2 (en) | 2017-08-14 | 2020-08-25 | Palantir Technologies Inc. | Customizable pipeline for integrating data |
US11016936B1 (en) | 2017-09-05 | 2021-05-25 | Palantir Technologies Inc. | Validating data for integration |
US11379525B1 (en) | 2017-11-22 | 2022-07-05 | Palantir Technologies Inc. | Continuous builds of derived datasets in response to other dataset updates |
US10552524B1 (en) | 2017-12-07 | 2020-02-04 | Palantir Technolgies Inc. | Systems and methods for in-line document tagging and object based data synchronization |
US10360252B1 (en) | 2017-12-08 | 2019-07-23 | Palantir Technologies Inc. | Detection and enrichment of missing data or metadata for large data sets |
US11645250B2 (en) | 2017-12-08 | 2023-05-09 | Palantir Technologies Inc. | Detection and enrichment of missing data or metadata for large data sets |
US11176116B2 (en) | 2017-12-13 | 2021-11-16 | Palantir Technologies Inc. | Systems and methods for annotating datasets |
US10853352B1 (en) | 2017-12-21 | 2020-12-01 | Palantir Technologies Inc. | Structured data collection, presentation, validation and workflow management |
US10924362B2 (en) | 2018-01-15 | 2021-02-16 | Palantir Technologies Inc. | Management of software bugs in a data processing system |
US10599762B1 (en) | 2018-01-16 | 2020-03-24 | Palantir Technologies Inc. | Systems and methods for creating a dynamic electronic form |
US11392759B1 (en) | 2018-01-16 | 2022-07-19 | Palantir Technologies Inc. | Systems and methods for creating a dynamic electronic form |
US10885021B1 (en) | 2018-05-02 | 2021-01-05 | Palantir Technologies Inc. | Interactive interpreter and graphical user interface |
US11263263B2 (en) | 2018-05-30 | 2022-03-01 | Palantir Technologies Inc. | Data propagation and mapping system |
US11061542B1 (en) | 2018-06-01 | 2021-07-13 | Palantir Technologies Inc. | Systems and methods for determining and displaying optimal associations of data items |
US10795909B1 (en) | 2018-06-14 | 2020-10-06 | Palantir Technologies Inc. | Minimized and collapsed resource dependency path |
Also Published As
Publication number | Publication date |
---|---|
US11861515B2 (en) | 2024-01-02 |
US20230034067A1 (en) | 2023-02-02 |
US20180082305A1 (en) | 2018-03-22 |
US11521096B2 (en) | 2022-12-06 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US11861515B2 (en) | System and method for determining a propensity of entity to take a specified action | |
US11580680B2 (en) | Systems and interactive user interfaces for dynamic retrieval, analysis, and triage of data items | |
US20210209696A1 (en) | Real-time analysis using a database to generate data for transmission to computing devices | |
US11048706B2 (en) | Malicious activity detection system capable of efficiently processing data accessed from databases and generating alerts for display in interactive user interfaces | |
US10186000B2 (en) | Simplified tax interview | |
US10872383B2 (en) | Using a model to estimate a payment delinquency for an invoice | |
US20130282571A1 (en) | System and method for dynamic contact management | |
US9804915B2 (en) | Integrated production support | |
US20200210391A1 (en) | Automated audit balance and control processes for data stores | |
US20220365948A1 (en) | Systems and methods for facilitating data transformation | |
Blevi et al. | Process mining on the loan application process of a Dutch Financial Institute | |
US9501378B2 (en) | Client events monitoring | |
US11282038B2 (en) | Information system with embedded insights | |
US11263382B1 (en) | Data normalization and irregularity detection system | |
US11379929B2 (en) | Advice engine | |
US20150220860A1 (en) | Method and a system for optimal debt collection | |
US20220138658A1 (en) | Automated selection of vehicle repairs for reinspection | |
US11620715B2 (en) | Systems and methods for generating insurance policies with predesignated policy levels and reimbursement controls | |
US10984427B1 (en) | Approaches for analyzing entity relationships | |
US20140316851A1 (en) | Predicting customer receptivity for commercial engagement |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: PALANTIR TECHNOLOGIES INC., CALIFORNIA Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:ERENRICH, DANIEL;MUKHERJEE, ANIRVAN;SIGNING DATES FROM 20150907 TO 20151012;REEL/FRAME:036782/0051 |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |