Patents by Inventor George H. John
George H. John has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 10007927Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.Type: GrantFiled: February 29, 2016Date of Patent: June 26, 2018Assignee: EXCALIBUR IP, LLCInventors: Joshua M. Koran, Christina Yip Chung, Abhinav Gupta, George H John, Hongfeng Yin, Long-ji Lin, Richard Frankel
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Patent number: 9965764Abstract: A current behavioral targeting system is first tested using a suite of test data. The output of the test is one or more performance metrics. Next, newly proposed behavioral targeting system created. The newly proposed behavioral targeting system is then evaluated using both the existing source data and a new source data. The evaluation of the newly proposed behavioral targeting system produces one or more performance metrics of the same type earlier calculated. Finally, the two sets of performance metrics are compared. The performance metric difference represents the impact of the new source data.Type: GrantFiled: May 23, 2007Date of Patent: May 8, 2018Assignee: EXCALIBUR IP, LLCInventors: Ankur Jain, Abhinay Gupta, George H. John
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Patent number: 9760907Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.Type: GrantFiled: January 11, 2013Date of Patent: September 12, 2017Assignee: EXCALIBUR IP, LLCInventors: John Canny, Shi Zhonog, Scott Gaffney, Chad Brower, Pavel Berkhin, George H. John
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Publication number: 20170140424Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.Type: ApplicationFiled: January 11, 2013Publication date: May 18, 2017Applicant: YAHOO! INC.Inventors: John Canny, Shi Zhong, Scott Gaffney, Chad Brower, Pavel Berkhin, George H. John
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Publication number: 20160180388Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.Type: ApplicationFiled: February 29, 2016Publication date: June 23, 2016Inventors: Joshua M. Koran, Christina Yip Chung, Abhinav Gupta, George H. John, Hongfeng Yin, Long-ji Lin, Richard Frankel
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Patent number: 9286569Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.Type: GrantFiled: July 29, 2013Date of Patent: March 15, 2016Assignee: YAHOO! INC.Inventors: Joshua M. Koran, Christina Yip Chung, Abhinav Gupta, George H. John, Hongfeng Yin, Long-Ji Lin, Richard Frankel
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Publication number: 20140200999Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.Type: ApplicationFiled: January 11, 2013Publication date: July 17, 2014Applicant: YAHOO! INC.Inventors: John Canny, Shi Zhong, Scott Gaffney, Chad Brower, Pavel Berkhin, George H. John
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Publication number: 20130318024Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.Type: ApplicationFiled: July 29, 2013Publication date: November 28, 2013Applicant: YAHOO! INC.Inventors: Joshua M. Koran, Christina Yip Chung, Abhinav Gupta, George H. John, Hongfeng Yin, Long-Ji Lin, Richard Frankel
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Patent number: 8504575Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.Type: GrantFiled: March 29, 2006Date of Patent: August 6, 2013Assignee: Yahoo! Inc.Inventors: Joshua M. Koran, Christina Yip Chung, Abhinav Gupta, George H. John, Hongfeng Yin, Long-Ji Lin, Richard Frankel
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Publication number: 20130104159Abstract: Users receive a data feed that has information relating to a first media and extracts events from the received data feed. The method generates a profile relating to a first item in the first media, and processes behavior of a first group of users of a second media. The behavior of the first group of users is modeled to generate a scoring function. A system for targeting a user includes a data feed, an event extractor, one or more profiles, a behavior processor, and a model. The data feed has information relating to a first media. The event extractor receives the data feed and extracts particular information based on a second media to generate profile(s). The behavior processor compares the profile to a first group of users of the second media. The model space models user behavior by using the profile.Type: ApplicationFiled: April 13, 2012Publication date: April 25, 2013Inventor: George H. John
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Patent number: 8364627Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.Type: GrantFiled: January 31, 2011Date of Patent: January 29, 2013Assignee: Yahoo! Inc.Inventors: John Canny, Shi Zhong, Scott Gaffney, Chad Brower, Pavel Berkhin, George H. John
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Publication number: 20110131160Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.Type: ApplicationFiled: January 31, 2011Publication date: June 2, 2011Inventors: John Canny, Shi Zhong, Scott Gaffney, Chad Brower, Pavel Berkhin, George H. John
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Patent number: 7921069Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the pre-processed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive mode. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.Type: GrantFiled: June 28, 2007Date of Patent: April 5, 2011Assignee: Yahoo! Inc.Inventors: John Canny, Shi Zhong, Scott Gaffney, Chad Brower, Pavel Berkhin, George H. John
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Patent number: 7882111Abstract: A method of determining content relevance for a user sets a user preference, which is related to a first area of content. The method calculates a set of scores, by using a combination, of the user preference, affinity data, and a parametric weight. The method organizes the content by using the set of scores, such that the organization of the content has a desirable relationship to the user, and recommends the selected content. Preferably, the method precomputes the affinity data and/or the parametric weight to generate and store the precompiled data for later retrieval. The affinity data describes a relationship between a first item of content and a second item of content, and the parametric weight describes an attribute of the second item. Additional embodiments include a system implementation and computer readable medium.Type: GrantFiled: June 1, 2007Date of Patent: February 1, 2011Assignee: Yahoo! Inc.Inventors: Shu-Yao Chien, Amitabh Seth, Nikolai Rochnik, George H. John
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Publication number: 20090006363Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the pre-processed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive mode. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.Type: ApplicationFiled: June 28, 2007Publication date: January 1, 2009Inventors: John Canny, Shi Zhong, Scott Gaffney, Chad Brower, Pavel Berkhin, George H. John
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Publication number: 20080306931Abstract: A system and method to facilitate automatic weighting of events in a network and targeting of advertising information to users within the network based on assigned event weights are described. Multiple events associated with a user are retrieved from a data storage module. Each event is further analyzed to extract one or more event features. A weight parameter value is further calculated for each retrieved event. Each event is further assigned to a predetermined category based on the calculated weight parameter value. Finally, each event and the associated weight parameter value are stored within the data storage module in connection with the predetermined category.Type: ApplicationFiled: June 6, 2007Publication date: December 11, 2008Inventors: Chad Brower, George H. John
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Publication number: 20080301118Abstract: A method of determining content relevance for a user sets a user preference, which is related to a first area of content. The method calculates a set of scores, by using a combination, of the user preference, affinity data, and a parametric weight. The method organizes the content by using the set of scores, such that the organization of the content has a desirable relationship to the user, and recommends the selected content. Preferably, the method precomputes the affinity data and/or the parametric weight to generate and store the precompiled data for later retrieval. The affinity data describes a relationship between a first item of content and a second item of content, and the parametric weight describes an attribute of the second item. Additional embodiments include a system implementation and computer readable medium.Type: ApplicationFiled: June 1, 2007Publication date: December 4, 2008Inventors: Shu-Yao Chien, Amitabh Seth, Nikolai Rochnik, George H. John
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Publication number: 20080300894Abstract: A method of targeting users receives a data feed that has information relating to a first media and extracts events from the received data feed. The method generates a profile relating to a first item in the first media, and processes behavior of a first group of users of a second media. The method models the behavior of the first group of users, and generates a scoring function by using the modeling. A system for targeting a user includes a data feed, an event extractor, one or more profiles, a behavior processor, and a model. The data feed has information relating to a first media. The event extractor is for receiving the data feed and extracting particular information based on a second media. The profile(s) are based on the extracted information. The behavior processor is for receiving the profile and comparing the profile to a first group of users of the second media. The model space is for receiving an output of the behavior processor and modeling user behavior by using the profile.Type: ApplicationFiled: June 1, 2007Publication date: December 4, 2008Inventor: George H. John
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Publication number: 20080294495Abstract: A current behavioral targeting system is first tested using a suite of test data. The output of the test is one or more performance metrics. Next, newly proposed behavioral targeting system created. The newly proposed behavioral targeting system is then evaluated using both the existing source data and a new source data. The evaluation of the newly proposed behavioral targeting system produces one or more performance metrics of the same type earlier calculated. Finally, the two sets of performance metrics are compared. The performance metric difference represents the impact of the new source data.Type: ApplicationFiled: May 23, 2007Publication date: November 27, 2008Inventors: Ankur Jain, Abhinay Gupta, George H. John