WO2007002729A2 - Method and system for predicting consumer behavior - Google Patents
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- WO2007002729A2 WO2007002729A2 PCT/US2006/025104 US2006025104W WO2007002729A2 WO 2007002729 A2 WO2007002729 A2 WO 2007002729A2 US 2006025104 W US2006025104 W US 2006025104W WO 2007002729 A2 WO2007002729 A2 WO 2007002729A2
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- 238000000034 method Methods 0.000 title claims abstract description 58
- 230000011218 segmentation Effects 0.000 claims abstract description 46
- 230000004044 response Effects 0.000 claims abstract description 43
- 230000006399 behavior Effects 0.000 claims abstract description 28
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/16—Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
- G06F15/163—Interprocessor communication
- G06F15/173—Interprocessor communication using an interconnection network, e.g. matrix, shuffle, pyramid, star, snowflake
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- 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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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- 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
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0255—Targeted advertisements based on user history
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- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
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Definitions
- the present invention relates generally to the field of market research, and in particular, it relates to the use of user behavior to define content offered to that user.
- the science of economics is both complicated and inexact, precisely because human behavior is complex. While the question whether consumers will or will not respond to a particular advertisement by taking a desired action, generally purchasing or other wise, remains a matter governed more by intuition than science.
- Market research as a discipline seeks to replace that intuition with objective judgments based on hard data, but to date that effort has not universally succeeded. Opinion pollsters are continually surprised by events, and multi-million dollar marketing campaigns completely fail.
- a weakness of conventional marketing research is a lack of detailed information about actual consumer behavior leading up to a desired action. The fact needs no repetition that neither the general survey nor the focus group truly replicates consumer behavior. Rather, researchers need some method for knowing how real consumers behave in a real marketing setting.
- a behavior module can reside on a user computer, which module can observe and record user behavior in terms of keystrokes, mouse clicks and so on. Also, the behavior module can also observe information about websites visited by the user. In conjunction with software incorporated into the behavior module, data about the web site or web page can be analyzed and the site categorized into one of a set of categories defined by the behavior module. Information identifying the category, as well as information about the user's navigation behavior, such as the when the site was visited, how much time was spent there, and what the user did, can also be gathered by the behavior module. Finally, the behavior module can summarize the information and compact it into a form suitable for transmission, such the form generally known as a "cookie.”
- An aspect of the invention is a method of predicting consumer response to given content.
- the process begins with the step of collecting a dataset of consumer response to the content, each data item including values for a selected set of segmentation variables related to past consumer behavior.
- the dataset contains at least twice the number of entries required to provide statistical validity.
- the process continues by constructing a classification tree structure using the dataset, in which the dataset is subdivided into learning and validation datasets of substantially equal size. Also, the criterion for each successive split is the lowest entropy of segmentation variables not employed to the point of such split.
- Each successive split of the learning dataset is performed only if that split produces child nodes statistically different from one another, and an identical split of the validation data set produces child nodes statistically similar to child nodes produced on the learning dataset.
- the system estimates consumer responses by first receiving a data item related to a new consumer, including values for the segmentation variables and then computing the likely response of the new consumer to the content, employing the classification tree data structure.
- FIG. 1 illustrates the initial stages of an embodiment of the process set out in the claims appended hereto.
- FIG.2 continues the process of Fig. 1 , depicting the detailed computation and analysis portions of the embodiment described.
- FIG. 3 illustrates a binary tree constructed by the process depicted in Fig.
- FIG. 4 sets out a process for employing the process described above in a production environment to provide advertising content to users.
- Answering that question requires, first, that data regarding consumer behavior be gathered. Then, there must be provided a method for analyzing that data to relate it to the inventory of advertising material. Finally, that analysis must be harnessed to select and provide specific content to the user. In general, that process involves several parties: the user (or consumer) who is navigating the internet and is the target of the advertisement; the website operator, who provides the website content but not the advertising content; and the content provider, who selects and provides the actual advertisements.
- the first requirement is the topic of the '066 Application.
- one method for gathering behavioral information about consumers is to monitor behavior directly as the user navigates on the internet, via behavior monitoring software resident on the user's computer. Behavior can be identified in terms of a subject-matter context, and information can also be gathered based on whether the user filled out forms on a page, or clicked on an advertisement. Such behavior records can be kept, summarized, and reported.
- the present invention concerns the second requirement, a process for analyzing data to relate past behavior to specific situations to produce a prediction of future action.
- One approach to that problem was illustrated in the embodiments set out in U.S. Patent Application 11/369,334 entitled “Method for Quantifying the Propensity to Respond to an Advertisement," filed March 7, 2006 by the inventors herein. A different approach is seen in the embodiments set out below.
- Binary trees are a powerful technique for analyzing data, particularly large datasets in which the relationships among variables are not initially well understood. Generally, a binary tree is a data structure consisting of a set of linked nodes, in which ⁇ each node has zero or two "child" nodes.
- Links are referred to as "branches," and the final node on each branch is called the terminal or "leaf' node.
- Each node comprises a subset of the dataset, and the set of terminal nodes constitutes a partition of the dataset as a whole.
- Techniques and procedures involving binary trees in general are known in the art and will not be further addressed here.
- the principles set out in the claims, below, are general in nature, but it is instructive to consider an exemplary embodiment of those principles.
- the embodiment set out here addresses the issues set out in the '066 Application, cited above.
- the challenge can be stated as the requirement to select an advertisement to present to an internet user, representing the advertisement most likely to evoke a positive response from among the multiple advertisements available for display.
- a "positive response" entails the user's clicking on an advertisement, resulting in navigation to another website, display of more detailed information, or similar behavior having commercial significance to the sponsor of the advertisement. That term may have different meanings in other environments in which different embodiments are deployed, as can be imagined by those in the art.
- FIG. 1 An overall process 100 embodying the principles claimed herein is illustrated in Fig. 1. Initially, three data gathering steps must be accomplished. First, the response dataset must be assembled (step 102). Then, the response variables and the segmentation variables must be selected (steps 104, 106). These initial steps are considered in the order presented.
- Response data structures are specific to the application concerned, though they are governed by general principles. As described in the '066 Application, response data are gathered at the user's computer, based on both the user's navigation history (what websites were visited) and also the activity history (what was done at a visited site). In one embodiment, the content provider prepares for processing such data by first determining an extensive list of commercially relevant categories, and then it proceeds to categorize commercially relevant websites. That process is described in U.S. Patent Application 11/377,932, entitled “Method for Providing Content to an Internet User Based on the user's Demonstrated Content Preferences," filed March 16, 2006 and owned by the assignee herein.
- categories should be defined at a relatively fine granularity level to provide useful information. In the embodiment discussed here, over 2000 categories are employed.
- websites can be categorized by an appropriate module at the user's computer, or at a central location, via messages passing back and forth between such a central server and the user's computer.
- the result of such activity is a record at the user's computer that includes recent internet activity, which can be represented by a data structure such as that shown in Table 1, below.
- data can be aggregated by categories (indicated by a Category ID) and can include measures of how recently any activity occurred; a measure of how frequent the activity occurred; and the number of times that a banner was clicked, all further aggregated under the ID of the banner.
- Data such as that shown in Table 1 can be periodically provided to the content provider, either in the form of cookies or messages, as described in the '066 Application. In either event, data concerning activity for a particular user is made available to the content provider.
- activity data (concerning only a given period of time) can be combined with results from two other data sources.
- One source is geographic data, concerning the user computers location as well as any demographic data available about the user. Such data do not vary, and they can be stored at the content provider level and combined with incoming activity data as needed. Additionally, the content provider has information concerning the actually user response to an advertisement — did that user click on a given banner. That data is available separately, with the user's machine ID, and thus that data can be included.
- a dataset can be assembled for each banner ad, having the general structure shown in Table 2, as follows: Category 1 recency
- Choosing the response variables requires an identification of the response desired from the user.
- any click on the presented advertisement qualifies as a target event.
- Other embodiments go further and require that the user not only click on the advertisement, but also take some action after doing so, such as subscribing to the resulting website, or the like.
- either approach is permissible, but the content provider must think through this problem in advance.
- the initial step in designing a system using binary trees is selecting the variables employed in splitting nodes, known as segmentation variables (step 106). Often, the selection of variables flows from the dataset itself.
- the variables include category recency, category usage, and others discussed above.
- An associated issue is the representation of variable values. Many variables exhibit a range of values, a situation which demands choices of how to characterize such values for analysis purposes. It has been found useful to define buckets for such values, which allows the designer to draw lines based on the applied (rather than intrinsic) value of the data.
- Table 3 sets out the segmentation variables employed herein, together with the value characterizations. As seen there, the Category Recency variable is divided into reporting buckets that have greatly different lengths. The most recent time values are emphasized in this structure, as one can readily understand the value to a marketer of knowing that a consumer visited a given website only five minutes previously.
- variable Category Recency is actually some 2000 variables, one for each category, so that an actual category would be, for example, Airline Reservation Recency, measuring the time elapsed since the user has accessed a site in that category.
- Airline Reservation Recency measuring the time elapsed since the user has accessed a site in that category.
- the nature of the problem indicates that selection of a segmentation variable value operates to split the population of a node into two groups.
- one node will consist of those elements having a value less than the segmentation variable value, and the other node all elements with values equal to or greater than that value.
- segmentation variables might not be ordinal in nature. Locations, for example, do not lend themselves to ordered lists such as used for time variables.
- some arbitrary element can be used to signify a split point, such as zipcode, other codes, or simply the position of a value on a list. So long as the listing produces consistent results, the technique for such ordering can be set up as desired.
- Fig. 2 illustrates an embodiment 200 of this process.
- 202 consists of dividing the dataset into two subsets, a learning set and a validation set.
- Tree building proceeds on a node-by-node basis, with testing and validation accomplished on the fly.
- Analysis of each node, in step 204, starts with the learning set, in step 210.
- the segmentation variable is selected and tested empirically, by examining results for each possible segmentation value, step 212.
- entropy refers to "information entropy”, defined as
- R is the response variable, expressed as a percentage rate. That equation provides calculates the entropy of the complete dataset of a given node.
- the entropy of a given split depends on the sum of the entropies of each child node dataset (conventionally referred to as "Right" and "Left” nodes), as follows:
- Entropy L -[R L log 2 R L + (l - R L )log 2 R L ]
- Entropy R -[R R log 2 R R + (l - R R )log 2 R R ]
- splitting criterion can be expressed as follows:
- n is the number of observations in a given node.
- the results of that test indicate whether any statistical difference exists between the two child nodes, step 220. If no difference exists, then the split does not improve the analytical product of the binary tree, and the parent node in question should be treated as a terminal, or leaf, node.
- the proposed split is collapsed, step 222, and the process loops back to consider other nodes.
- the process proceeds to validate the split, using the validation dataset, in step 224.
- the binary tree constructed using the learning dataset is replicated using the validation dataset, to the point at which the loop starting at step 210 had proceeded, and then the split made at step 216 is replicated with the validation dataset.
- the question is whether the validation dataset tree is the same as or similar to the learning set tree, which again can be addressed with a statistical T-test. Instead of looking for difference, the T-test here looks for similarity, step 228. A positive finding confirms the validity of the tree structure, step 230, and the process loops back, retaining the newly-split node in the tree. If the T-test does not show similarity, the split is collapsed, step 222, before looping back.
- step 206 the loop starting at step 204 and continuing to steps 222 or 230, terminates at step 206, where it is determined whether to perform another loop or end the process.
- the process continues until every node is determined to be a leaf node, or until a predetermined number of node levels has been reached. Both of these criteria are sufficiently known in the art to require no further explanation here. If the process does commence another loop, the segmentation variable used in the previous loop is declared unavailable for further use, precluding the selection of that variable for any other nodes. Thus, if a loop of the process employs "Airline Reservation Recency" as a segmentation variable, that variable cannot be used on any other nodes of the tree.
- a binary tree 250 constructed according to the principles set out in the embodiment described above, is shown in Fig. 3.
- the root node 252 was found to yield minimum entropy using a segmentation variable of recency in the Airline Reservation category, at a value of less than or equal to 7 days.
- child nodes 254 and 260 contain all entries for which activity in the Airline Reservations category was reported within the previous 7 days and beyond that period, respectively.
- the minimum entropy was found using the recency of click in the Airline Reservation category, at a value of less than or equal to 7 days.
- the two child nodes 256 and 258 from that point, however, were found to be terminal, or leaf, nodes, and have no child nodes below them. The fact that a node is found to be a terminal node does not imply that other nodes at the same level are also terminal nodes.
- node 264 is a terminal node, but node 262 is not.
- the set of terminal nodes constitutes a complete portioning of the dataset.
- nodes 256, 258, 266, 268 and 264 are the terminal nodes. It will be noted that because the splitting rules are based on varied crieteria, no implication exists of size of the populations in the nodes. Rather, the nodes report on behavior correlations of commercial interest.
- the response variable rate of the population of a terminal node is calculated, as that data is included in the response dataset (as shown in Fig. 1, step 110).
- the response variable is chosen to be the click rate, and the percentage click rate is shown for each terminal node.
- This latter step allows one to draw useful inference from the tree.
- the sample indicates that a person who had navigated to a website dealing with airline reservations in the previous week, and had clicked on an item in such a site over a week ago would have a 5% probability of clicking on the advertisement under consideration. If that person had clicked on an airline reservations site item within the past week, that person would have only a 1% probability of clicking on the advertisement.
- the "response rate" calculation can be tailored to the business environment of the content provider. For example, if the content provider is compensated by advertiser client based on a set value per click on an advertisement, then that value can be incorporated directly into the tree calculation. If, for example, the compensation was set at $1.00 per click, then showing the advertisement in question to a user who fits into node 258 has an expected return of $.05, which showing the ad to a user from node 256 can be expected to return only $.01. Those in the art can adapt the principles set out above to fit whatever compensation plans that may be devised.
- a process 300 for employing the embodiment discussed above in a production environment is shown in Fig. 4.
- a new user is acquired at step 302, and the task is to determine what content to provide.
- the loop consisting of steps 304, 306 and 312 determines the advertisement having the highest value for the user in question. That result is determined by iterating through every binary tree in the inventory (step 304); at each stage the system uses the user profile to identify the terminal node into which the user fits, and then calculates a value for displaying the associated advertisement to the user.
- This step 306 is carried out exactly as set out above.
- that process allows the system to select the highest value advertisement, at step 308, and to forward that advertisement to the user, step 310.
Abstract
A method of predicting consumer response to given content including collecting a dataset of consumer response to the content, each data item including values for a selected set of segmentation variables related to past consumer behavior. A classification tree sbnucture is then constructed using the dataset, in which the dataset is subdivided into learning and validation datasets. The criterion for successive splits of the dataset is the lowest entropy of segmentation variables not employed to the point of such split. The system estimates consumer responses by first receiving a data item related to a new consumer, including values for the segmentation variables and then computing the likely response of the new consumer to the content, employing the classification tree data structure.
Description
METHOD AND SYSTEM FOR PREDICTING CONSUMER BEHAVIOR
BACKGROUND OF THE INVENTION
[0001] The present invention relates generally to the field of market research, and in particular, it relates to the use of user behavior to define content offered to that user. [0002] The science of economics is both complicated and inexact, precisely because human behavior is complex. While the question whether consumers will or will not respond to a particular advertisement by taking a desired action, generally purchasing or other wise, remains a matter governed more by intuition than science. [0003] Market research as a discipline seeks to replace that intuition with objective judgments based on hard data, but to date that effort has not universally succeeded. Opinion pollsters are continually surprised by events, and multi-million dollar marketing campaigns completely fail.
[0004] A weakness of conventional marketing research is a lack of detailed information about actual consumer behavior leading up to a desired action. The fact needs no repetition that neither the general survey nor the focus group truly replicates consumer behavior. Rather, researchers need some method for knowing how real consumers behave in a real marketing setting.
[0005] The technique of gathering information about consumer behavior on the internet was set out in commonly-owned U.S. Patent Application No. 11/226,066, entitled "Method and Device for Publishing Cross-Network User Behavioral Data" filed on 14 September 2005. (the '"066" Application). That application is incorporated by reference herein for all purposes.
[0006] The technique of the '066 Application teaches how information about user behavior on the internet can be gathered. In sum, that application teaches that a behavior module can reside on a user computer, which module can observe and record user behavior in terms of keystrokes, mouse clicks and so on. Also, the behavior module can also observe information about websites visited by the user. In conjunction with software incorporated into the behavior module, data about the web site or web page can be analyzed and the site categorized into one of a set of categories defined by the behavior module. Information identifying the category, as well as information about the user's navigation behavior, such as the when the site was visited, how much time was spent
there, and what the user did, can also be gathered by the behavior module. Finally, the behavior module can summarize the information and compact it into a form suitable for transmission, such the form generally known as a "cookie."
[0007] What is not taught by the '066 Application, and not seen in the art, is an understanding of how to employ such information to provide content to a user based on what that user wants to see. It remains to the present invention to provide such functionality to the art.
SUMMARY OF THE INVENTION
[0008] An aspect of the invention is a method of predicting consumer response to given content. The process begins with the step of collecting a dataset of consumer response to the content, each data item including values for a selected set of segmentation variables related to past consumer behavior. The dataset contains at least twice the number of entries required to provide statistical validity. The process continues by constructing a classification tree structure using the dataset, in which the dataset is subdivided into learning and validation datasets of substantially equal size. Also, the criterion for each successive split is the lowest entropy of segmentation variables not employed to the point of such split. Each successive split of the learning dataset is performed only if that split produces child nodes statistically different from one another, and an identical split of the validation data set produces child nodes statistically similar to child nodes produced on the learning dataset. The system estimates consumer responses by first receiving a data item related to a new consumer, including values for the segmentation variables and then computing the likely response of the new consumer to the content, employing the classification tree data structure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] FIG. 1 illustrates the initial stages of an embodiment of the process set out in the claims appended hereto.
[0010] FIG.2 continues the process of Fig. 1 , depicting the detailed computation and analysis portions of the embodiment described.
[0011] FIG. 3 illustrates a binary tree constructed by the process depicted in Fig.
3.
[0012] FIG. 4 sets out a process for employing the process described above in a production environment to provide advertising content to users.
DETAILED DESCRIPTION
[0013] The following detailed description is made with reference to the figures.
Preferred embodiments are described to illustrate the present invention, not to limit its scope, which is defined by the claims. Those of ordinary skill in the art will recognize a variety of equivalent variations on the description that follows. [0014] The key problem facing marketers can be stated as follows: What is the probability that a specific customer will respond positively to a particular advertisement? More particularly, the problem can be stated thusly: Given an inventory of existing advertisements, and given information about a consumer's actual behavior, which advertisement has the highest probability of eliciting a positive response from the consumer?
[0015] Answering that question requires, first, that data regarding consumer behavior be gathered. Then, there must be provided a method for analyzing that data to relate it to the inventory of advertising material. Finally, that analysis must be harnessed to select and provide specific content to the user. In general, that process involves several parties: the user (or consumer) who is navigating the internet and is the target of the advertisement; the website operator, who provides the website content but not the advertising content; and the content provider, who selects and provides the actual advertisements.
[0016] The first requirement is the topic of the '066 Application. As explained there, one method for gathering behavioral information about consumers is to monitor behavior directly as the user navigates on the internet, via behavior monitoring software resident on the user's computer. Behavior can be identified in terms of a subject-matter context, and information can also be gathered based on whether the user filled out forms on a page, or clicked on an advertisement. Such behavior records can be kept, summarized, and reported.
[0017] The present invention concerns the second requirement, a process for analyzing data to relate past behavior to specific situations to produce a prediction of future action. One approach to that problem was illustrated in the embodiments set out in U.S. Patent Application 11/369,334 entitled "Method for Quantifying the Propensity to Respond to an Advertisement," filed March 7, 2006 by the inventors herein. A different approach is seen in the embodiments set out below.
[0018] Binary trees are a powerful technique for analyzing data, particularly large datasets in which the relationships among variables are not initially well understood. Generally, a binary tree is a data structure consisting of a set of linked nodes, in which ■ each node has zero or two "child" nodes. Links are referred to as "branches," and the final node on each branch is called the terminal or "leaf' node. Each node comprises a subset of the dataset, and the set of terminal nodes constitutes a partition of the dataset as a whole. Techniques and procedures involving binary trees in general are known in the art and will not be further addressed here.
[0019] The principles set out in the claims, below, are general in nature, but it is instructive to consider an exemplary embodiment of those principles. The embodiment set out here addresses the issues set out in the '066 Application, cited above. In general, the challenge can be stated as the requirement to select an advertisement to present to an internet user, representing the advertisement most likely to evoke a positive response from among the multiple advertisements available for display. Here, a "positive response" entails the user's clicking on an advertisement, resulting in navigation to another website, display of more detailed information, or similar behavior having commercial significance to the sponsor of the advertisement. That term may have different meanings in other environments in which different embodiments are deployed, as can be imagined by those in the art.
[0020] An overall process 100 embodying the principles claimed herein is illustrated in Fig. 1. Initially, three data gathering steps must be accomplished. First, the response dataset must be assembled (step 102). Then, the response variables and the segmentation variables must be selected (steps 104, 106). These initial steps are considered in the order presented.
[0021] Response data structures are specific to the application concerned, though they are governed by general principles. As described in the '066 Application, response data are gathered at the user's computer, based on both the user's navigation history (what websites were visited) and also the activity history (what was done at a visited site). In one embodiment, the content provider prepares for processing such data by first determining an extensive list of commercially relevant categories, and then it proceeds to categorize commercially relevant websites. That process is described in U.S. Patent Application 11/377,932, entitled "Method for Providing Content to an Internet User Based on the user's Demonstrated Content Preferences," filed March 16, 2006 and owned by the assignee herein. As noted there, categories should be defined at a relatively
fine granularity level to provide useful information. In the embodiment discussed here, over 2000 categories are employed. As a user navigates the web, websites can be categorized by an appropriate module at the user's computer, or at a central location, via messages passing back and forth between such a central server and the user's computer. [0022] The result of such activity is a record at the user's computer that includes recent internet activity, which can be represented by a data structure such as that shown in Table 1, below. As shown there, data can be aggregated by categories (indicated by a Category ID) and can include measures of how recently any activity occurred; a measure of how frequent the activity occurred; and the number of times that a banner was clicked, all further aggregated under the ID of the banner.
Table 1. Data from User
[0023] Data such as that shown in Table 1 can be periodically provided to the content provider, either in the form of cookies or messages, as described in the '066 Application. In either event, data concerning activity for a particular user is made available to the content provider.
[0024] At the content provider level, activity data (concerning only a given period of time) can be combined with results from two other data sources. One source is geographic data, concerning the user computers location as well as any demographic data available about the user. Such data do not vary, and they can be stored at the content provider level and combined with incoming activity data as needed. Additionally, the content provider has information concerning the actually user response to an advertisement — did that user click on a given banner. That data is available separately, with the user's machine ID, and thus that data can be included. [0025] From all the data received from users, combined with that from banner clicks, a dataset can be assembled for each banner ad, having the general structure shown in Table 2, as follows:
Category 1 recency
Category 1 frequency
Category 2 recency
Category 2 frequency
Category n recency
Category n frequency
Banner ID
Number of impressions
Number of clicks
Counter
Geographic data
Table 2. Analysis data input
[0026] It should be understood that the description above addresses a single user computer, but in practice a large number of user computers all send information to a central processing repository. It should also be understood that separate datasets are assembled for each banner advertisement, differing only in the identification of the advertisement concerned. As used below, the term "dataset" applies to data related to one advertisement.
[0027] Choosing the response variables (step 104) requires an identification of the response desired from the user. In one embodiment, any click on the presented advertisement qualifies as a target event. Other embodiments go further and require that the user not only click on the advertisement, but also take some action after doing so, such as subscribing to the resulting website, or the like. For analytical purposes, either approach is permissible, but the content provider must think through this problem in advance.
[0028] The initial step in designing a system using binary trees is selecting the variables employed in splitting nodes, known as segmentation variables (step 106). Often, the selection of variables flows from the dataset itself. In the embodiment set out herein, the variables include category recency, category usage, and others discussed
above. An associated issue is the representation of variable values. Many variables exhibit a range of values, a situation which demands choices of how to characterize such values for analysis purposes. It has been found useful to define buckets for such values, which allows the designer to draw lines based on the applied (rather than intrinsic) value of the data. Table 3, below, sets out the segmentation variables employed herein, together with the value characterizations. As seen there, the Category Recency variable is divided into reporting buckets that have greatly different lengths. The most recent time values are emphasized in this structure, as one can readily understand the value to a marketer of knowing that a consumer visited a given website only five minutes previously.
Table 3. Segmentation Variables
[0029] Two points should be made about the segmentation variables employed for this embodiment. First, several of the variables are actually clusters of variables. Thus, for example, the variable Category Recency is actually some 2000 variables, one for each category, so that an actual category would be, for example, Airline Reservation Recency, measuring the time elapsed since the user has accessed a site in that category. Second, the nature of the problem indicates that selection of a segmentation variable value operates to split the population of a node into two groups. Thus, when analyzing the populations of child nodes resulting from a given split, or proposed split, one node will consist of those elements having a value less than the segmentation variable value, and the other node all elements with values equal to or greater than that value. For example, if one were considering a split employing the segmentation variable "Airline Reservation Category Usage", at a value of 3 days, then one node would consist of the
cumulation of the buckets labeled "1 day" and "2 days", and the other the contents of buckets labeled "3 days," "4 or 5 days," "6 to 10 days," "11 to 30 days," and "31 to 60 days."
[0030] Also, it should be noted that some segmentation variables might not be ordinal in nature. Locations, for example, do not lend themselves to ordered lists such as used for time variables. Here, some arbitrary element can be used to signify a split point, such as zipcode, other codes, or simply the position of a value on a list. So long as the listing produces consistent results, the technique for such ordering can be set up as desired.
[0031] These data form inputs to the process of building and validating a binary tree, step 108. Fig. 2 illustrates an embodiment 200 of this process. The first action, step
202, consists of dividing the dataset into two subsets, a learning set and a validation set.
These sets should be indistinguishable to the extent possible, and the selection criterion should be chosen with a view to avoiding the introduction of any biasing factors.
[0032] The general process of building a binary tree is known in the art and will not be set out in any detail here. Rather, the discussion that follows will build on conventional techniques by concentrating on those additions and improvements that characterize the claimed process.
[0033] Tree building proceeds on a node-by-node basis, with testing and validation accomplished on the fly. Analysis of each node, in step 204, starts with the learning set, in step 210. The segmentation variable is selected and tested empirically, by examining results for each possible segmentation value, step 212. For each possible value of each possible segmentation value (step 208) (see below), the system proceeds to calculate an entropy value, in step 212.
[0034] As used here, "entropy" refers to "information entropy", defined as
Entropy = -[R log2R + (1 - R) log2R]
where R is the response variable, expressed as a percentage rate. That equation provides calculates the entropy of the complete dataset of a given node. The entropy of a given split depends on the sum of the entropies of each child node dataset (conventionally referred to as "Right" and "Left" nodes), as follows:
EntropyL = -[RL log2 RL + (l - RL )log2 RL ]
Entropy R = -[RR log2 RR + (l - RR )log2 RR ]
It has been found that superior results are obtained by performing a split at the segmentation variable value that provides the minimum entropy level after the split. Thus, the splitting criterion can be expressed as follows:
[0035] Those principles can be put into practice as follows. At a given node, an iterative process is performed to calculate the net entropy for every value of every available segmentation variable (see below) (step 214). The segmentation variable yielding the lowest entropy level is selected, and the split is performed, at step 216. [0036] The split is then subjected to a two-part test to ensure validity and robustness. The first question to be addressed is whether the split should be made at all, which is addressed by determining the statistical difference between the populations of the two child nodes. That difference is measured by performing a statistical T-test to compare the two child nodes, step 218. That test is known in the art and will not be set out in detail here. The results of that test indicate whether any statistical difference exists between the two child nodes, step 220. If no difference exists, then the split does not improve the analytical product of the binary tree, and the parent node in question should be treated as a terminal, or leaf, node. The proposed split is collapsed, step 222, and the process loops back to consider other nodes.
[0037] It should be noted at this point that the directions, or rules, for performing each node split are saved to provide a set of directions for replicating the binary tree. A number of possible structures for this process are known in the art, and details of the same can be left to the discretion of skilled practitioners.
[0038] If the split does produce useful results, then the process proceeds to validate the split, using the validation dataset, in step 224. There, the binary tree constructed using the learning dataset is replicated using the validation dataset, to the point at which the loop starting at step 210 had proceeded, and then the split made at step 216 is replicated with the validation dataset. At this point the question is whether the validation dataset tree is the same as or similar to the learning set tree, which again can
be addressed with a statistical T-test. Instead of looking for difference, the T-test here looks for similarity, step 228. A positive finding confirms the validity of the tree structure, step 230, and the process loops back, retaining the newly-split node in the tree. If the T-test does not show similarity, the split is collapsed, step 222, before looping back.
[0039] The loop starting at step 204 and continuing to steps 222 or 230, terminates at step 206, where it is determined whether to perform another loop or end the process. The process continues until every node is determined to be a leaf node, or until a predetermined number of node levels has been reached. Both of these criteria are sufficiently known in the art to require no further explanation here. If the process does commence another loop, the segmentation variable used in the previous loop is declared unavailable for further use, precluding the selection of that variable for any other nodes. Thus, if a loop of the process employs "Airline Reservation Recency" as a segmentation variable, that variable cannot be used on any other nodes of the tree. [0040] A binary tree 250, constructed according to the principles set out in the embodiment described above, is shown in Fig. 3. The root node 252 was found to yield minimum entropy using a segmentation variable of recency in the Airline Reservation category, at a value of less than or equal to 7 days. Thus, child nodes 254 and 260 contain all entries for which activity in the Airline Reservations category was reported within the previous 7 days and beyond that period, respectively. At node 254, the minimum entropy was found using the recency of click in the Airline Reservation category, at a value of less than or equal to 7 days. The two child nodes 256 and 258 from that point, however, were found to be terminal, or leaf, nodes, and have no child nodes below them. The fact that a node is found to be a terminal node does not imply that other nodes at the same level are also terminal nodes. As can be seen, node 264 is a terminal node, but node 262 is not.
[0041] The set of terminal nodes constitutes a complete portioning of the dataset.
Here, nodes 256, 258, 266, 268 and 264 are the terminal nodes. It will be noted that because the splitting rules are based on varied crieteria, no implication exists of size of the populations in the nodes. Rather, the nodes report on behavior correlations of commercial interest.
[0042] It is also possible to calculate the response variable rate of the population of a terminal node, as that data is included in the response dataset (as shown in Fig. 1, step 110). Here, the response variable is chosen to be the click rate, and the percentage
click rate is shown for each terminal node. This latter step allows one to draw useful inference from the tree. Thus, one can see that the sample indicates that a person who had navigated to a website dealing with airline reservations in the previous week, and had clicked on an item in such a site over a week ago would have a 5% probability of clicking on the advertisement under consideration. If that person had clicked on an airline reservations site item within the past week, that person would have only a 1% probability of clicking on the advertisement.
[0043] The "response rate" calculation can be tailored to the business environment of the content provider. For example, if the content provider is compensated by advertiser client based on a set value per click on an advertisement, then that value can be incorporated directly into the tree calculation. If, for example, the compensation was set at $1.00 per click, then showing the advertisement in question to a user who fits into node 258 has an expected return of $.05, which showing the ad to a user from node 256 can be expected to return only $.01. Those in the art can adapt the principles set out above to fit whatever compensation plans that may be devised. For example, if compensation is tied to some more detailed response than a simple click, such as subscription to a site, or an actual purchase, that criterion is straightforwardly added to the data collected, and the results are reflected in each terminal node. [0044] Using the process set out above, a tree is constructed for every advertisement in the operator's inventory. Those in the art will be able to determine appropriate intervals for refreshing these data and the resulting trees, in order to ensure the data remain valid and to identify any emerging trends. Also, as new advertisements are developed, they can be offered initially on a test basis, to gather sufficient data to enable the construction of a binary tree, and afterward they can enter a normal production cycle. These and other details of managing the use of such trees are within the skill of those in the art.
[0045] A process 300 for employing the embodiment discussed above in a production environment is shown in Fig. 4. There, a new user is acquired at step 302, and the task is to determine what content to provide. The loop consisting of steps 304, 306 and 312 determines the advertisement having the highest value for the user in question. That result is determined by iterating through every binary tree in the inventory (step 304); at each stage the system uses the user profile to identify the terminal node into which the user fits, and then calculates a value for displaying the associated advertisement to the user. This step 306 is carried out exactly as set out above. When
completed, at step 312, that process allows the system to select the highest value advertisement, at step 308, and to forward that advertisement to the user, step 310. [0046] While the present invention is disclosed by reference to the preferred embodiments and examples detailed above, it is understood that these examples are intended in an illustrative rather than in a limiting sense. Computer-assisted processing is implicated in the described embodiments. It is contemplated that modifications and combinations will readily occur to those skilled in the art, which modifications and combinations will be within the spirit of the invention and the scope of the following claims. [0047] We claim as follows:
Claims
1. Method of predicting consumer response to given content, including the steps of collecting a dataset of consumer response to the content, each data item including values for a selected set of segmentation variables related to past consumer behavior and the dataset containing at least twice the number of entries to provide statistical validity; constructing a classification tree structure using the dataset, wherein the dataset is subdivided into learning and validation datasets of substantially equal size; the criterion for each successive split is the lowest entropy of segmentation variables not employed to the point of such split; and each successive split of the learning dataset is performed only if such split produces child nodes statistically different from one another; and an identical split of the validation data set produces child nodes statistically similar to child nodes produced on the learning dataset; receiving a data item related to a new consumer, including values for the segmentation variables; computing the likely response of the new consumer to the content, employing the classification tree data structure.
2. The method of Claim 1 , wherein the segmentation variables include data relating to internet navigation history of the consumer.
3. The method of Claim 1, wherein the segmentation variables include information related to categories of websites visited by the consumer.
4. The method of Claim 1, wherein the subdivision of the dataset is made on the basis of a /ariable independent of the segmentation variables or the consumer response.
5. The method of Claim 1, further including the step of calculating the value of the consumer response to the provider of the content.
6. The method of Claim 1, wherein the process is repeated for a plurality of content items, producing a library of classification data structures.
7. Method of predicting consumer response to given content presented in connection with viewing a website on the internet, including the steps of collecting a dataset of consumer response to the content, each data item including values for a selected set of segmentation variables related to past consumer internet behavior, the dataset containing at least twice the number of entries to provide statistical validity; constructing a classification tree structure using the dataset, wherein the dataset is subdivided into learning and validation datasets of substantially equal size; the criterion for each successive split is the lowest entropy of segmentation variables not employed to the point of such split; and each successive split of the learning dataset is performed only if such split produces child nodes statistically different from one another; and an identical split of the validation data set produces child nodes statistically similar to child nodes produced on the learning dataset; receiving a data item related to a new internet consumer, including values for the segmentation variables; computing the likely response of the new consumer to the content, employing the classification tree data structure.
8. The method of Claim 7, wherein the segmentation variables include data relating to internet navigation history of the consumer.
9. The method of Claim 7, wherein the segmentation variables include information related to categories of websites visited by the consumer.
10. The method of Claim 7, wherein the subdivision of the dataset is made on the basis of a variable independent of the segmentation variables or the consumer response.
11. The method of Claim 7, further including the step of calculating the value of the consumer response to the provider of the content.
12. The method of Claim 7, wherein the process is repeated for a plurality of content items, producing a library of classification data structures.
13. A classification tree data structure useful for predicting consumer response to given content, wherein the tree structure is constructed by a process including the steps of subdividing the dataset into learning and validation datasets of substantially equal size; determining each successive split based on the lowest entropy of segmentation variables not employed to the point of such split; and performing successive split of the learning dataset only if such split produces child nodes statistically different from one another; and an identical split of the validation data set produces child nodes statistically similar to child nodes produced on the learning dataset.
14. The classification tree structure of Claim 13 , wherein the segmentation variables include data relating to internet navigation history of the consumer.
15. The classification tree structure of Claim 13, wherein the segmentation variables include information related to categories of websites visited by the consumer.
16. The classification tree structure of Claim 13, wherein the subdivision of the dataset is made on the basis of a variable independent of the segmentation variables or the consumer response.
17. The classification tree structure of Claim 13, further including the step of calculating the value of the consumer response to the provider of the content.
18. Method of predicting consumer response to given content, including the steps of assembling a library of binary tree tools, including the steps of building a consumer response dataset, including the steps of exposing consumers to selected content; collecting each consumer response, measured as a value of a response variable; collecting consumer segmentation characteristics, measured as values of each of a set of consumer segmentation variables; continuing the collection until the dataset consists of at least twice the number of data items required for a statistically valid sample; dividing the dataset into a learning set and a validation set, based on a variable independent of either the response variable or any segmentation variable, the datasets being substantially equal in size and each being sufficiently large to provide statistical reliability; constructing a binary tree by successively splitting nodes, each splitting step including the steps of employing the learning dataset to obtain a proposed split, including splitting the node hypothetically, based on each value of each segmentation variable; calculating the entropy of each hypothetical split; choosing the split having the minimum entropy as the proposed split; performing a statistical test on the resulting nodes to determine whether they differ statistically; collapsing the proposed split in the event no difference is found; validating the proposed split, including replicating the proposed split on the validation dataset; performing a statistical test on the resulting nodes to determine whether they are statistically similar to like nodes of the proposed split; collapsing the proposed split in the event that no similarity is found; continuing the tree construction process, with each successive split employing only those segmentation variables not employed in an adopted split; receiving data concerning an individual consumer, including values for the set of segmentation variables; determining the most appropriate content to present to the consumer, including the steps of obtaining a value for the consumer dataset for each binary tree tool in the library; and selecting the content associated with the binary tree tool producing the highest response value.
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PCT/US2006/025103 WO2007002728A2 (en) | 2005-06-28 | 2006-06-28 | Method and system for controlling and adapting a media stream |
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Families Citing this family (64)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9542690B2 (en) | 2006-07-18 | 2017-01-10 | American Express Travel Related Services Company, Inc. | System and method for providing international coupon-less discounts |
US9430773B2 (en) | 2006-07-18 | 2016-08-30 | American Express Travel Related Services Company, Inc. | Loyalty incentive program using transaction cards |
US9558505B2 (en) | 2006-07-18 | 2017-01-31 | American Express Travel Related Services Company, Inc. | System and method for prepaid rewards |
US8402163B2 (en) * | 2007-02-21 | 2013-03-19 | John Almeida | Target advertising to a specific user offered through an intermediary internet service provider, server or wireless network |
US8041778B2 (en) * | 2007-04-26 | 2011-10-18 | Microsoft Corporation | Extended browser data storage |
US8255267B2 (en) * | 2007-07-13 | 2012-08-28 | Wahrheit, Llc | System and method for determining relative preferences |
US7774488B2 (en) | 2008-03-12 | 2010-08-10 | International Business Machines Corporation | Method and system for switching media streams in a client system based on environmental changes |
US7779140B2 (en) * | 2008-03-14 | 2010-08-17 | International Business Machines Corporation | Method and system for switching media streams in a client system as directed by a control system |
US20090319359A1 (en) * | 2008-06-18 | 2009-12-24 | Vyrl Mkt, Inc. | Social behavioral targeting based on influence in a social network |
EP2304676A1 (en) * | 2008-06-23 | 2011-04-06 | Double Verify Inc. | Automated monitoring and verification of internet based advertising |
US8346749B2 (en) * | 2008-06-27 | 2013-01-01 | Microsoft Corporation | Balancing the costs of sharing private data with the utility of enhanced personalization of online services |
US20120296701A1 (en) * | 2008-07-14 | 2012-11-22 | Wahrheit, Llc | System and method for generating recommendations |
US20100088152A1 (en) * | 2008-10-02 | 2010-04-08 | Dominic Bennett | Predicting user response to advertisements |
US20100088177A1 (en) * | 2008-10-02 | 2010-04-08 | Turn Inc. | Segment optimization for targeted advertising |
KR101010285B1 (en) * | 2008-11-21 | 2011-01-24 | 삼성전자주식회사 | History Operation Method For Web Page And Apparatus using the same |
US20100198685A1 (en) * | 2009-01-30 | 2010-08-05 | Microsoft Corporation | Predicting web advertisement click success by using head-to-head ratings |
US8539359B2 (en) * | 2009-02-11 | 2013-09-17 | Jeffrey A. Rapaport | Social network driven indexing system for instantly clustering people with concurrent focus on same topic into on-topic chat rooms and/or for generating on-topic search results tailored to user preferences regarding topic |
US20110093375A1 (en) * | 2009-10-15 | 2011-04-21 | Sony Corporation | System and method for supporting a bidding procedure in an electronic network |
CN102238152B (en) * | 2010-05-06 | 2015-09-23 | 华为技术有限公司 | Control the methods, devices and systems of content report behavior |
US20120042263A1 (en) | 2010-08-10 | 2012-02-16 | Seymour Rapaport | Social-topical adaptive networking (stan) system allowing for cooperative inter-coupling with external social networking systems and other content sources |
KR101890448B1 (en) * | 2010-12-22 | 2018-08-21 | 톰슨 라이센싱 | Usage data feedback loop |
WO2012094352A1 (en) | 2011-01-04 | 2012-07-12 | Thomson Licensing | Media asset usage data reporting that indicates corresponding content creator |
US8620770B1 (en) | 2011-03-30 | 2013-12-31 | Amazon Technologies, Inc. | Inferring user intent based on hybrid navigation paths |
US8775275B1 (en) * | 2011-03-30 | 2014-07-08 | Amazon Technologies, Inc. | Inferring user intent based on network navigation paths |
US8732569B2 (en) * | 2011-05-04 | 2014-05-20 | Google Inc. | Predicting user navigation events |
US8676937B2 (en) | 2011-05-12 | 2014-03-18 | Jeffrey Alan Rapaport | Social-topical adaptive networking (STAN) system allowing for group based contextual transaction offers and acceptances and hot topic watchdogging |
US8788711B2 (en) * | 2011-06-14 | 2014-07-22 | Google Inc. | Redacting content and inserting hypertext transfer protocol (HTTP) error codes in place thereof |
US9769285B2 (en) | 2011-06-14 | 2017-09-19 | Google Inc. | Access to network content |
US8650139B2 (en) | 2011-07-01 | 2014-02-11 | Google Inc. | Predicting user navigation events |
US8745212B2 (en) | 2011-07-01 | 2014-06-03 | Google Inc. | Access to network content |
US8744988B1 (en) | 2011-07-15 | 2014-06-03 | Google Inc. | Predicting user navigation events in an internet browser |
US8655819B1 (en) | 2011-09-15 | 2014-02-18 | Google Inc. | Predicting user navigation events based on chronological history data |
US8600921B2 (en) | 2011-09-15 | 2013-12-03 | Google Inc. | Predicting user navigation events in a browser using directed graphs |
US8849699B2 (en) * | 2011-09-26 | 2014-09-30 | American Express Travel Related Services Company, Inc. | Systems and methods for targeting ad impressions |
US9104664B1 (en) | 2011-10-07 | 2015-08-11 | Google Inc. | Access to search results |
US9584579B2 (en) | 2011-12-01 | 2017-02-28 | Google Inc. | Method and system for providing page visibility information |
US8793235B2 (en) | 2012-01-19 | 2014-07-29 | Google Inc. | System and method for improving access to search results |
US9697529B2 (en) | 2012-03-13 | 2017-07-04 | American Express Travel Related Services Company, Inc. | Systems and methods for tailoring marketing |
US20130246176A1 (en) | 2012-03-13 | 2013-09-19 | American Express Travel Related Services Company, Inc. | Systems and Methods Determining a Merchant Persona |
US9049546B2 (en) * | 2012-04-10 | 2015-06-02 | Yellowpages.Com Llc | User description based on a context of travel |
US8849312B2 (en) | 2012-04-10 | 2014-09-30 | Yellowpages.Com Llc | User description based on contexts of location and time |
US9946792B2 (en) | 2012-05-15 | 2018-04-17 | Google Llc | Access to network content |
US8984091B1 (en) | 2012-08-03 | 2015-03-17 | Google Inc. | Providing content based on timestamp of last request for content |
US8887239B1 (en) | 2012-08-08 | 2014-11-11 | Google Inc. | Access to network content |
US9710822B2 (en) | 2012-09-16 | 2017-07-18 | American Express Travel Related Services Company, Inc. | System and method for creating spend verified reviews |
US10664883B2 (en) | 2012-09-16 | 2020-05-26 | American Express Travel Related Services Company, Inc. | System and method for monitoring activities in a digital channel |
SG11201404254PA (en) * | 2012-09-18 | 2014-08-28 | Singapore First Aid Training Ct Pte Ltd | A mannequin for practicing cardiopulmonary resuscitation |
US9141722B2 (en) | 2012-10-02 | 2015-09-22 | Google Inc. | Access to network content |
US10504132B2 (en) | 2012-11-27 | 2019-12-10 | American Express Travel Related Services Company, Inc. | Dynamic rewards program |
US20140278973A1 (en) * | 2013-03-15 | 2014-09-18 | MaxPoint Interactive, Inc. | System and method for audience targeting |
CN103824214A (en) * | 2014-03-17 | 2014-05-28 | 联想(北京)有限公司 | Information processing method and device and electronic equipment |
US10395237B2 (en) | 2014-05-22 | 2019-08-27 | American Express Travel Related Services Company, Inc. | Systems and methods for dynamic proximity based E-commerce transactions |
US9123054B1 (en) | 2014-07-17 | 2015-09-01 | Mastercard International Incorporated | Method and system for maintaining privacy in scoring of consumer spending behavior |
US20160094600A1 (en) * | 2014-09-30 | 2016-03-31 | The Nielsen Company (Us), Llc | Methods and apparatus to measure exposure to streaming media |
US10026097B2 (en) * | 2015-02-18 | 2018-07-17 | Oath (Americas) Inc. | Systems and methods for inferring matches and logging-in of online users across devices |
US11087356B2 (en) | 2015-08-24 | 2021-08-10 | Google Llc | Dynamically varying remarketing based on evolving user interests |
US10565627B2 (en) * | 2015-12-30 | 2020-02-18 | Google Llc | Systems and methods for automatically generating remarketing lists |
US10346871B2 (en) * | 2016-04-22 | 2019-07-09 | Facebook, Inc. | Automatic targeting of content by clustering based on user feedback data |
US11023925B1 (en) | 2016-11-18 | 2021-06-01 | Wells Fargo Bank, N.A. | Enhanced advertisement click-through customer data |
US20180285469A1 (en) * | 2017-03-31 | 2018-10-04 | Facebook, Inc. | Optimizing determination of content item values |
US10657558B1 (en) | 2017-05-16 | 2020-05-19 | Mather Economics, LLC | System and method for using a plurality of different data sources to control displayed content |
US10860642B2 (en) | 2018-06-21 | 2020-12-08 | Google Llc | Predicting topics of potential relevance based on retrieved/created digital media files |
CN110569431A (en) * | 2019-08-14 | 2019-12-13 | 深圳市赛为智能股份有限公司 | public opinion information monitoring method and device, computer equipment and storage medium |
US11551251B2 (en) * | 2020-11-12 | 2023-01-10 | Rodney Yates | System and method for transactional data acquisition, aggregation, processing, and dissemination in coordination with a preference matching algorithm |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5918014A (en) * | 1995-12-27 | 1999-06-29 | Athenium, L.L.C. | Automated collaborative filtering in world wide web advertising |
US20020035568A1 (en) * | 2000-04-28 | 2002-03-21 | Benthin Mark Louis | Method and apparatus supporting dynamically adaptive user interactions in a multimodal communication system |
US20020087499A1 (en) * | 2001-01-03 | 2002-07-04 | Stockfisch Thomas P. | Methods and systems of classifying multiple properties simultaneously using a decision tree |
US20030176931A1 (en) * | 2002-03-11 | 2003-09-18 | International Business Machines Corporation | Method for constructing segmentation-based predictive models |
Family Cites Families (185)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5734863A (en) * | 1986-04-14 | 1998-03-31 | National Instruments Corporation | Method and apparatus for providing improved type compatibility and data structure organization in a graphical data flow diagram |
US5481741A (en) * | 1986-04-14 | 1996-01-02 | National Instruments Corporation | Method and apparatus for providing attribute nodes in a graphical data flow environment |
EP0477448B1 (en) * | 1990-09-28 | 1995-07-12 | Hewlett-Packard Company | Network monitoring device and system |
US5898434A (en) * | 1991-05-15 | 1999-04-27 | Apple Computer, Inc. | User interface system having programmable user interface elements |
US5469553A (en) * | 1992-04-16 | 1995-11-21 | Quantum Corporation | Event driven power reducing software state machine |
US5951300A (en) * | 1997-03-10 | 1999-09-14 | Health Hero Network | Online system and method for providing composite entertainment and health information |
US5887133A (en) * | 1997-01-15 | 1999-03-23 | Health Hero Network | System and method for modifying documents sent over a communications network |
AU1170295A (en) * | 1993-10-29 | 1995-05-22 | Kevin L. Keithley | Interactive multimedia communications system which accesses industry-specific information |
US5499340A (en) * | 1994-01-12 | 1996-03-12 | Isogon Corporation | Method and apparatus for computer program usage monitoring |
US5608850A (en) * | 1994-04-14 | 1997-03-04 | Xerox Corporation | Transporting a display object coupled to a viewpoint within or between navigable workspaces |
US5724567A (en) * | 1994-04-25 | 1998-03-03 | Apple Computer, Inc. | System for directing relevance-ranked data objects to computer users |
US5627886A (en) * | 1994-09-22 | 1997-05-06 | Electronic Data Systems Corporation | System and method for detecting fraudulent network usage patterns using real-time network monitoring |
US5615325A (en) * | 1994-09-29 | 1997-03-25 | Intel Corporation | Graphical viewer for heirarchical datasets |
US5717923A (en) * | 1994-11-03 | 1998-02-10 | Intel Corporation | Method and apparatus for dynamically customizing electronic information to individual end users |
US5758257A (en) * | 1994-11-29 | 1998-05-26 | Herz; Frederick | System and method for scheduling broadcast of and access to video programs and other data using customer profiles |
US5617526A (en) * | 1994-12-13 | 1997-04-01 | Microsoft Corporation | Operating system provided notification area for displaying visual notifications from application programs |
US5530852A (en) * | 1994-12-20 | 1996-06-25 | Sun Microsystems, Inc. | Method for extracting profiles and topics from a first file written in a first markup language and generating files in different markup languages containing the profiles and topics for use in accessing data described by the profiles and topics |
US5708780A (en) * | 1995-06-07 | 1998-01-13 | Open Market, Inc. | Internet server access control and monitoring systems |
US5883955A (en) * | 1995-06-07 | 1999-03-16 | Digital River, Inc. | On-line try before you buy software distribution system |
US5721908A (en) * | 1995-06-07 | 1998-02-24 | International Business Machines Corporation | Computer network for WWW server data access over internet |
US5761499A (en) * | 1995-12-21 | 1998-06-02 | Novell, Inc. | Method for managing globally distributed software components |
US6026368A (en) * | 1995-07-17 | 2000-02-15 | 24/7 Media, Inc. | On-line interactive system and method for providing content and advertising information to a targeted set of viewers |
US5649186A (en) * | 1995-08-07 | 1997-07-15 | Silicon Graphics Incorporated | System and method for a computer-based dynamic information clipping service |
US5712979A (en) * | 1995-09-20 | 1998-01-27 | Infonautics Corporation | Method and apparatus for attaching navigational history information to universal resource locator links on a world wide web page |
US5708709A (en) * | 1995-12-08 | 1998-01-13 | Sun Microsystems, Inc. | System and method for managing try-and-buy usage of application programs |
US5745681A (en) * | 1996-01-11 | 1998-04-28 | Sun Microsystems, Inc. | Stateless shopping cart for the web |
US5823879A (en) * | 1996-01-19 | 1998-10-20 | Sheldon F. Goldberg | Network gaming system |
US5872850A (en) * | 1996-02-02 | 1999-02-16 | Microsoft Corporation | System for enabling information marketplace |
US5704017A (en) * | 1996-02-16 | 1997-12-30 | Microsoft Corporation | Collaborative filtering utilizing a belief network |
US6047327A (en) * | 1996-02-16 | 2000-04-04 | Intel Corporation | System for distributing electronic information to a targeted group of users |
AU2230597A (en) * | 1996-02-28 | 1997-09-16 | Aim Corporation | Communication system for distributing such message as advertisement to user of terminal equipment |
US6604726B2 (en) * | 1996-04-15 | 2003-08-12 | Teknocraft, Inc. | Proportional solenoid-controlled fluid valve assembly without non-magnetic alignment support element |
US5848396A (en) * | 1996-04-26 | 1998-12-08 | Freedom Of Information, Inc. | Method and apparatus for determining behavioral profile of a computer user |
US5793972A (en) * | 1996-05-03 | 1998-08-11 | Westminster International Computers Inc. | System and method providing an interactive response to direct mail by creating personalized web page based on URL provided on mail piece |
US5715453A (en) * | 1996-05-31 | 1998-02-03 | International Business Machines Corporation | Web server mechanism for processing function calls for dynamic data queries in a web page |
US5886683A (en) * | 1996-06-25 | 1999-03-23 | Sun Microsystems, Inc. | Method and apparatus for eyetrack-driven information retrieval |
US5920697A (en) * | 1996-07-11 | 1999-07-06 | Microsoft Corporation | Method of automatic updating and use of routing information by programmable and manual routing information configuration based on least lost routing |
US5933811A (en) * | 1996-08-20 | 1999-08-03 | Paul D. Angles | System and method for delivering customized advertisements within interactive communication systems |
US5890152A (en) * | 1996-09-09 | 1999-03-30 | Seymour Alvin Rapaport | Personal feedback browser for obtaining media files |
US6029182A (en) * | 1996-10-04 | 2000-02-22 | Canon Information Systems, Inc. | System for generating a custom formatted hypertext document by using a personal profile to retrieve hierarchical documents |
US6006252A (en) * | 1996-10-08 | 1999-12-21 | Wolfe; Mark A. | System and method for communicating information relating to a network resource |
US5999526A (en) * | 1996-11-26 | 1999-12-07 | Lucent Technologies Inc. | Method and apparatus for delivering data from an information provider using the public switched network |
US5907838A (en) * | 1996-12-10 | 1999-05-25 | Seiko Epson Corporation | Information search and collection method and system |
US6347398B1 (en) * | 1996-12-12 | 2002-02-12 | Microsoft Corporation | Automatic software downloading from a computer network |
US5978833A (en) * | 1996-12-31 | 1999-11-02 | Intel Corporation | Method and apparatus for accessing and downloading information from the internet |
US6029145A (en) * | 1997-01-06 | 2000-02-22 | Isogon Corporation | Software license verification process and apparatus |
BR9807467B1 (en) * | 1997-01-06 | 2010-11-16 | method and system for monitoring the use of television media distribution network. | |
US7363291B1 (en) * | 2002-03-29 | 2008-04-22 | Google Inc. | Methods and apparatus for increasing efficiency of electronic document delivery to users |
US6076166A (en) * | 1997-01-17 | 2000-06-13 | Philips Electronics North America Corporation | Personalizing hospital intranet web sites |
WO1998035468A2 (en) * | 1997-01-27 | 1998-08-13 | Benjamin Slotznick | System for delivering and displaying primary and secondary information |
US5875296A (en) * | 1997-01-28 | 1999-02-23 | International Business Machines Corporation | Distributed file system web server user authentication with cookies |
US6892226B1 (en) * | 1997-03-27 | 2005-05-10 | Intel Corporation | System for delivery of dynamic content to a client device |
US6714975B1 (en) * | 1997-03-31 | 2004-03-30 | International Business Machines Corporation | Method for targeted advertising on the web based on accumulated self-learning data, clustering users and semantic node graph techniques |
US6233564B1 (en) * | 1997-04-04 | 2001-05-15 | In-Store Media Systems, Inc. | Merchandising using consumer information from surveys |
US6892354B1 (en) * | 1997-04-16 | 2005-05-10 | Sony Corporation | Method of advertising on line during a communication link idle time |
US5983190A (en) * | 1997-05-19 | 1999-11-09 | Microsoft Corporation | Client server animation system for managing interactive user interface characters |
US6026933A (en) * | 1997-05-29 | 2000-02-22 | Cosco, Inc. | Step stool |
US6029141A (en) * | 1997-06-27 | 2000-02-22 | Amazon.Com, Inc. | Internet-based customer referral system |
US6014711A (en) * | 1997-08-29 | 2000-01-11 | Nortel Networks Corporation | Apparatus and method for providing electronic mail relay translation services |
US5978807A (en) * | 1997-09-30 | 1999-11-02 | Sony Corporation | Apparatus for and method of automatically downloading and storing internet web pages |
US6157924A (en) * | 1997-11-07 | 2000-12-05 | Bell & Howell Mail Processing Systems Company | Systems, methods, and computer program products for delivering information in a preferred medium |
US6182066B1 (en) * | 1997-11-26 | 2001-01-30 | International Business Machines Corp. | Category processing of query topics and electronic document content topics |
US6335963B1 (en) * | 1997-12-01 | 2002-01-01 | Nortel Networks Limited | System and method for providing notification of a received electronic mail message |
US6505385B2 (en) * | 1997-12-22 | 2003-01-14 | Sama S.P.A. | Magnetic closure with mutual interlock for bags, knapsacks, items of clothing and the like |
US6052709A (en) * | 1997-12-23 | 2000-04-18 | Bright Light Technologies, Inc. | Apparatus and method for controlling delivery of unsolicited electronic mail |
CA2316256C (en) * | 1997-12-24 | 2009-02-24 | America Online, Inc. | Localization of clients and servers |
US6222520B1 (en) * | 1997-12-31 | 2001-04-24 | At&T Corp. | Information display for a visual communication device |
US6185558B1 (en) * | 1998-03-03 | 2001-02-06 | Amazon.Com, Inc. | Identifying the items most relevant to a current query based on items selected in connection with similar queries |
US6643624B2 (en) * | 1998-03-09 | 2003-11-04 | Yan Philippe | Method and system for integrating transaction mechanisms over multiple internet sites |
US6199079B1 (en) * | 1998-03-09 | 2001-03-06 | Junglee Corporation | Method and system for automatically filling forms in an integrated network based transaction environment |
US6192380B1 (en) * | 1998-03-31 | 2001-02-20 | Intel Corporation | Automatic web based form fill-in |
US6133912A (en) * | 1998-05-04 | 2000-10-17 | Montero; Frank J. | Method of delivering information over a communication network |
AU749314B2 (en) * | 1998-05-15 | 2002-06-20 | Unicast Communications Corporation | A technique for implementing browser-initiated network-distributed advertising and for interstitially displaying an advertisement |
US6185614B1 (en) * | 1998-05-26 | 2001-02-06 | International Business Machines Corp. | Method and system for collecting user profile information over the world-wide web in the presence of dynamic content using document comparators |
US6154771A (en) * | 1998-06-01 | 2000-11-28 | Mediastra, Inc. | Real-time receipt, decompression and play of compressed streaming video/hypervideo; with thumbnail display of past scenes and with replay, hyperlinking and/or recording permissively intiated retrospectively |
US6208339B1 (en) * | 1998-06-19 | 2001-03-27 | International Business Machines Corporation | User-interactive data entry display system with entry fields having distinctive and changeable autocomplete |
JP3511029B2 (en) * | 1998-06-30 | 2004-03-29 | 株式会社博報堂 | Notification information display device, notification information display system, and recording medium |
US6308202B1 (en) * | 1998-09-08 | 2001-10-23 | Webtv Networks, Inc. | System for targeting information to specific users on a computer network |
AU5465099A (en) * | 1998-08-04 | 2000-02-28 | Rulespace, Inc. | Method and system for deriving computer users' personal interests |
US6317722B1 (en) * | 1998-09-18 | 2001-11-13 | Amazon.Com, Inc. | Use of electronic shopping carts to generate personal recommendations |
US6381735B1 (en) * | 1998-10-02 | 2002-04-30 | Microsoft Corporation | Dynamic classification of sections of software |
CA2328480A1 (en) * | 1998-12-12 | 2000-06-22 | The Brodia Group | Trusted agent for electronic commerce |
US6338059B1 (en) * | 1998-12-17 | 2002-01-08 | International Business Machines Corporation | Hyperlinked search interface for distributed database |
US6084628A (en) * | 1998-12-18 | 2000-07-04 | Telefonaktiebolaget Lm Ericsson (Publ) | System and method of providing targeted advertising during video telephone calls |
US6760916B2 (en) * | 2000-01-14 | 2004-07-06 | Parkervision, Inc. | Method, system and computer program product for producing and distributing enhanced media downstreams |
GB2345158A (en) * | 1998-12-23 | 2000-06-28 | Ibm | Publish and subscribe data processing with ability to specify a local publication/subscription |
US6055573A (en) * | 1998-12-30 | 2000-04-25 | Supermarkets Online, Inc. | Communicating with a computer based on an updated purchase behavior classification of a particular consumer |
US6332127B1 (en) * | 1999-01-28 | 2001-12-18 | International Business Machines Corporation | Systems, methods and computer program products for providing time and location specific advertising via the internet |
US6366298B1 (en) * | 1999-06-03 | 2002-04-02 | Netzero, Inc. | Monitoring of individual internet usage |
AU4481600A (en) * | 1999-04-22 | 2000-11-10 | Qode.Com, Inc. | System and method for providing electronic information upon receipt of a scannedbar code |
US6847969B1 (en) * | 1999-05-03 | 2005-01-25 | Streetspace, Inc. | Method and system for providing personalized online services and advertisements in public spaces |
US20050038819A1 (en) * | 2000-04-21 | 2005-02-17 | Hicken Wendell T. | Music Recommendation system and method |
US7010497B1 (en) * | 1999-07-08 | 2006-03-07 | Dynamiclogic, Inc. | System and method for evaluating and/or monitoring effectiveness of on-line advertising |
US6356908B1 (en) * | 1999-07-30 | 2002-03-12 | International Business Machines Corporation | Automatic web page thumbnail generation |
US6938027B1 (en) * | 1999-09-02 | 2005-08-30 | Isogon Corporation | Hardware/software management, purchasing and optimization system |
US6360221B1 (en) * | 1999-09-21 | 2002-03-19 | Neostar, Inc. | Method and apparatus for the production, delivery, and receipt of enhanced e-mail |
WO2001029727A2 (en) * | 1999-10-21 | 2001-04-26 | Adfluence, Inc. | Network methods for interactive advertising and direct marketing |
US6857024B1 (en) * | 1999-10-22 | 2005-02-15 | Cisco Technology, Inc. | System and method for providing on-line advertising and information |
US7630986B1 (en) * | 1999-10-27 | 2009-12-08 | Pinpoint, Incorporated | Secure data interchange |
US6697825B1 (en) * | 1999-11-05 | 2004-02-24 | Decentrix Inc. | Method and apparatus for generating and modifying multiple instances of element of a web site |
US6526411B1 (en) * | 1999-11-15 | 2003-02-25 | Sean Ward | System and method for creating dynamic playlists |
US6848004B1 (en) * | 1999-11-23 | 2005-01-25 | International Business Machines Corporation | System and method for adaptive delivery of rich media content to a user in a network based on real time bandwidth measurement & prediction according to available user bandwidth |
JP2003527627A (en) * | 1999-12-02 | 2003-09-16 | ゼド インコーポレイテッド | Data processing system for targeted content |
US20020010757A1 (en) * | 1999-12-03 | 2002-01-24 | Joel Granik | Method and apparatus for replacement of on-line advertisements |
US6513052B1 (en) * | 1999-12-15 | 2003-01-28 | Imation Corp. | Targeted advertising over global computer networks |
AU2592701A (en) * | 1999-12-23 | 2001-07-03 | My-E-Surveys.Com, Llc | System and methods for internet commerce and communication based on customer interaction and preferences |
US6801906B1 (en) * | 2000-01-11 | 2004-10-05 | International Business Machines Corporation | Method and apparatus for finding information on the internet |
US20040193488A1 (en) * | 2000-01-19 | 2004-09-30 | Denis Khoo | Method and system for advertising over a data network |
US6721741B1 (en) * | 2000-01-24 | 2004-04-13 | Friskit, Inc. | Streaming media search system |
US7328189B2 (en) * | 2000-01-26 | 2008-02-05 | Paybyclick Corporation | Method and apparatus for conducting electronic commerce transactions using electronic tokens |
US6850967B1 (en) * | 2000-02-19 | 2005-02-01 | Hewlett-Packard Development Company, L.P. | System and method for ensuring transparent sychronization of multiple applications across remote systems |
US6877027B1 (en) * | 2000-02-19 | 2005-04-05 | Hewlett-Packard Development Company, L.P. | System and method for providing synchronization verification of multiple applications across remote systems |
US6701362B1 (en) * | 2000-02-23 | 2004-03-02 | Purpleyogi.Com Inc. | Method for creating user profiles |
IL134943A0 (en) * | 2000-03-08 | 2001-05-20 | Better T V Technologies Ltd | Method for personalizing information and services from various media sources |
US7076561B1 (en) * | 2000-03-08 | 2006-07-11 | Music Choice | Personalized audio system and method |
AU2001243637A1 (en) * | 2000-03-14 | 2001-09-24 | Blue Dolphin Group, Inc. | Method of selecting content for a user |
US6311194B1 (en) * | 2000-03-15 | 2001-10-30 | Taalee, Inc. | System and method for creating a semantic web and its applications in browsing, searching, profiling, personalization and advertising |
US6757661B1 (en) * | 2000-04-07 | 2004-06-29 | Netzero | High volume targeting of advertisements to user of online service |
US20020032592A1 (en) * | 2000-04-17 | 2002-03-14 | Steve Krasnick | Online meeting planning program |
US6976090B2 (en) * | 2000-04-20 | 2005-12-13 | Actona Technologies Ltd. | Differentiated content and application delivery via internet |
WO2001082160A1 (en) * | 2000-04-26 | 2001-11-01 | Voltage Inc. | Advertisement distribution determining/optimizing method |
US20020016736A1 (en) * | 2000-05-03 | 2002-02-07 | Cannon George Dewey | System and method for determining suitable breaks for inserting content |
US7003734B1 (en) * | 2000-05-05 | 2006-02-21 | Point Roll, Inc. | Method and system for creating and displaying images including pop-up images on a visual display |
US20020010626A1 (en) * | 2000-05-22 | 2002-01-24 | Eyal Agmoni | Internert advertising and information delivery system |
WO2001093096A2 (en) * | 2000-05-30 | 2001-12-06 | Koki Uchiyama | Distributed monitoring system providing knowledge services |
US7421645B2 (en) * | 2000-06-06 | 2008-09-02 | Microsoft Corporation | Method and system for providing electronic commerce actions based on semantically labeled strings |
US7739335B2 (en) * | 2000-06-22 | 2010-06-15 | Sony Corporation | Method and apparatus for providing a customized selection of audio content over the internet |
WO2002003256A1 (en) * | 2000-07-05 | 2002-01-10 | Camo, Inc. | Method and system for the dynamic analysis of data |
US6748395B1 (en) * | 2000-07-14 | 2004-06-08 | Microsoft Corporation | System and method for dynamic playlist of media |
US20040073485A1 (en) * | 2000-07-25 | 2004-04-15 | Informlink, Inc. | Method for an on-line promotion server |
US6681223B1 (en) * | 2000-07-27 | 2004-01-20 | International Business Machines Corporation | System and method of performing profile matching with a structured document |
US6990633B1 (en) * | 2000-07-28 | 2006-01-24 | Seiko Epson Corporation | Providing a network-based personalized newspaper with personalized content and layout |
US6523021B1 (en) * | 2000-07-31 | 2003-02-18 | Microsoft Corporation | Business directory search engine |
US6874018B2 (en) * | 2000-08-07 | 2005-03-29 | Networks Associates Technology, Inc. | Method and system for playing associated audible advertisement simultaneously with the display of requested content on handheld devices and sending a visual warning when the audio channel is off |
US20020042750A1 (en) * | 2000-08-11 | 2002-04-11 | Morrison Douglas C. | System method and article of manufacture for a visual self calculating order system over the world wide web |
US7072847B2 (en) * | 2000-08-25 | 2006-07-04 | Jonas Ulenas | Method and apparatus for obtaining consumer product preferences through product selection and evaluation |
US7861174B2 (en) * | 2000-09-08 | 2010-12-28 | Oracle International Corporation | Method and system for assembling concurrently-generated content |
ES2191605T3 (en) * | 2000-09-11 | 2003-09-16 | Mediabricks Ab | METHOD FOR PROVIDING A CONTENT OF MEDIA ON A DIGITAL NETWORK. |
US7287071B2 (en) * | 2000-09-28 | 2007-10-23 | Vignette Corporation | Transaction management system |
US20020040374A1 (en) * | 2000-10-04 | 2002-04-04 | Kent Donald A. | Method for personalizing and customizing publications and customized publications produced thereby |
JP4529058B2 (en) * | 2000-10-12 | 2010-08-25 | ソニー株式会社 | Distribution system |
US20060015390A1 (en) * | 2000-10-26 | 2006-01-19 | Vikas Rijsinghani | System and method for identifying and approaching browsers most likely to transact business based upon real-time data mining |
US7051084B1 (en) * | 2000-11-02 | 2006-05-23 | Citrix Systems, Inc. | Methods and apparatus for regenerating and transmitting a partial page |
US6957390B2 (en) * | 2000-11-30 | 2005-10-18 | Mediacom.Net, Llc | Method and apparatus for providing dynamic information to a user via a visual display |
US20020094868A1 (en) * | 2001-01-16 | 2002-07-18 | Alma Tuck | Methods for interactive internet advertising, apparatuses and systems including same |
US7174305B2 (en) * | 2001-01-23 | 2007-02-06 | Opentv, Inc. | Method and system for scheduling online targeted content delivery |
KR100861625B1 (en) * | 2001-01-23 | 2008-10-07 | 소니 가부시끼 가이샤 | Communication apparatus, communication method, electronic device, control method of the electronic device, and recording medium |
US20020103798A1 (en) * | 2001-02-01 | 2002-08-01 | Abrol Mani S. | Adaptive document ranking method based on user behavior |
US8494950B2 (en) * | 2001-03-09 | 2013-07-23 | Miodrag Kostic | System for conducting an exchange of click-through traffic on internet web sites |
WO2002076077A1 (en) * | 2001-03-16 | 2002-09-26 | Leap Wireless International, Inc. | Method and system for distributing content over a wireless communications system |
US20030041050A1 (en) * | 2001-04-16 | 2003-02-27 | Greg Smith | System and method for web-based marketing and campaign management |
US6993532B1 (en) * | 2001-05-30 | 2006-01-31 | Microsoft Corporation | Auto playlist generator |
US7181488B2 (en) * | 2001-06-29 | 2007-02-20 | Claria Corporation | System, method and computer program product for presenting information to a user utilizing historical information about the user |
US20030014304A1 (en) * | 2001-07-10 | 2003-01-16 | Avenue A, Inc. | Method of analyzing internet advertising effects |
US7620911B2 (en) * | 2001-07-12 | 2009-11-17 | Autodesk, Inc. | Collapsible dialog window |
US20030023698A1 (en) * | 2001-07-25 | 2003-01-30 | International Business Machines Corporation | Method and apparatus for remotely configuring and displaying information |
US20030028870A1 (en) * | 2001-08-01 | 2003-02-06 | Weisman Mitchell T. | Distribution of downloadable software over a network |
US7043471B2 (en) * | 2001-08-03 | 2006-05-09 | Overture Services, Inc. | Search engine account monitoring |
US20030074448A1 (en) * | 2001-08-10 | 2003-04-17 | Tadashi Kinebuchi | Multimedia information system and computer program |
US7007074B2 (en) * | 2001-09-10 | 2006-02-28 | Yahoo! Inc. | Targeted advertisements using time-dependent key search terms |
US20030052913A1 (en) * | 2001-09-19 | 2003-03-20 | Barile Steven E. | Method and apparatus to supply relevant media content |
US20040010629A1 (en) * | 2001-11-01 | 2004-01-15 | Telecommunications Research Associates | System for accelerating delivery of electronic presentations |
US7162739B2 (en) * | 2001-11-27 | 2007-01-09 | Claria Corporation | Method and apparatus for blocking unwanted windows |
US20030106058A1 (en) * | 2001-11-30 | 2003-06-05 | Koninklijke Philips Electronics N.V. | Media recommender which presents the user with rationale for the recommendation |
US20030115157A1 (en) * | 2001-12-14 | 2003-06-19 | Edgar Circenis | Multi-system capacity on demand computer pricing |
US9485532B2 (en) * | 2002-04-11 | 2016-11-01 | Arris Enterprises, Inc. | System and method for speculative tuning |
JP4018450B2 (en) * | 2002-05-27 | 2007-12-05 | キヤノン株式会社 | Document management system, document management apparatus, authentication method, computer readable program, and storage medium |
KR20050054874A (en) * | 2002-06-17 | 2005-06-10 | 포르토 라넬리, 에스. 에이 | Enabling communication between users surfing the same web page |
US20040000446A1 (en) * | 2002-07-01 | 2004-01-01 | Barber Harold P. | Seismic signaling apparatus and method for enhancing signal repeatability |
US8090798B2 (en) * | 2002-08-12 | 2012-01-03 | Morganstein | System and methods for direct targeted media advertising over peer-to-peer networks |
US7349827B1 (en) * | 2002-09-18 | 2008-03-25 | Doubleclick Inc. | System and method for reporting website activity based on inferred attribution methodology |
US6829599B2 (en) * | 2002-10-02 | 2004-12-07 | Xerox Corporation | System and method for improving answer relevance in meta-search engines |
US20040111314A1 (en) * | 2002-10-16 | 2004-06-10 | Ford Motor Company | Satisfaction prediction model for consumers |
US20040181604A1 (en) * | 2003-03-13 | 2004-09-16 | Immonen Pekka S. | System and method for enhancing the relevance of push-based content |
KR20060006919A (en) * | 2003-04-14 | 2006-01-20 | 코닌클리케 필립스 일렉트로닉스 엔.브이. | Generation of implicit tv recommender via shows image content |
JP2004355376A (en) * | 2003-05-29 | 2004-12-16 | Nec Corp | Method and system for utilizing customer information |
US20060136728A1 (en) * | 2003-08-15 | 2006-06-22 | Gentry Craig B | Method and apparatus for authentication of data streams with adaptively controlled losses |
US20050086109A1 (en) * | 2003-10-17 | 2005-04-21 | Mcfadden Jeffrey A. | Methods and apparatus for posting messages on documents delivered over a computer network |
KR100650404B1 (en) * | 2003-11-24 | 2006-11-28 | 엔에이치엔(주) | On-line Advertising System And Method |
US7363282B2 (en) * | 2003-12-03 | 2008-04-22 | Microsoft Corporation | Search system using user behavior data |
US7487435B2 (en) * | 2003-12-12 | 2009-02-03 | Dynamic Logic, Inc. | Method and system for conducting an on-line survey |
US7765592B2 (en) * | 2004-01-10 | 2010-07-27 | Microsoft Corporation | Changed file identification, software conflict resolution and unwanted file removal |
US20050273463A1 (en) * | 2004-06-07 | 2005-12-08 | Meir Zohar | System for calculating client sessions information |
US20060212349A1 (en) * | 2005-02-24 | 2006-09-21 | Shane Brady | Method and system for delivering targeted banner electronic communications |
US8806327B2 (en) * | 2005-08-15 | 2014-08-12 | Iii Holdings 1, Llc | System and method for displaying unrequested information within a web browser |
-
2006
- 2006-06-28 JP JP2008519504A patent/JP2008547136A/en active Pending
- 2006-06-28 US US11/427,282 patent/US20060293957A1/en not_active Abandoned
- 2006-06-28 WO PCT/US2006/025104 patent/WO2007002729A2/en active Application Filing
- 2006-06-28 WO PCT/US2006/025102 patent/WO2007002727A2/en active Application Filing
- 2006-06-28 WO PCT/US2006/025103 patent/WO2007002728A2/en active Application Filing
- 2006-06-28 US US11/427,243 patent/US20070005791A1/en not_active Abandoned
- 2006-06-28 US US11/427,226 patent/US20070005425A1/en not_active Abandoned
-
2007
- 2007-12-20 GB GB0724938A patent/GB2441708A/en not_active Withdrawn
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5918014A (en) * | 1995-12-27 | 1999-06-29 | Athenium, L.L.C. | Automated collaborative filtering in world wide web advertising |
US20020035568A1 (en) * | 2000-04-28 | 2002-03-21 | Benthin Mark Louis | Method and apparatus supporting dynamically adaptive user interactions in a multimodal communication system |
US20020087499A1 (en) * | 2001-01-03 | 2002-07-04 | Stockfisch Thomas P. | Methods and systems of classifying multiple properties simultaneously using a decision tree |
US20030176931A1 (en) * | 2002-03-11 | 2003-09-18 | International Business Machines Corporation | Method for constructing segmentation-based predictive models |
Non-Patent Citations (1)
Title |
---|
MITCHELL T.: 'Decision Tree Learning based on Machine Learning', [Online] 05 April 2003, XP003009834 Retrieved from the Internet: <URL:http://www.web.archive.org/web/2003040 5202241> * |
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US20070005425A1 (en) | 2007-01-04 |
JP2008547136A (en) | 2008-12-25 |
GB2441708A (en) | 2008-03-12 |
WO2007002728A2 (en) | 2007-01-04 |
WO2007002729A3 (en) | 2007-03-22 |
GB0724938D0 (en) | 2008-01-30 |
US20070005791A1 (en) | 2007-01-04 |
WO2007002727A2 (en) | 2007-01-04 |
US20060293957A1 (en) | 2006-12-28 |
WO2007002727A3 (en) | 2007-09-27 |
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