Patents by Inventor John Canny
John Canny 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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Publication number: 20180060696Abstract: A clustering and recommendation machine determines that an item is included in a cluster of items. The machine accesses item data descriptive of the item. The machine accesses a vector that represents the cluster and calculates the likelihood that the item is included in the cluster, based on the item variable and the probability parameter. The machine determines that the item is included in the cluster, based on the likelihood. The machine also recommends an item to a potential buyer. The machine accesses behavior data that represents a first event type pertinent to a first cluster of items. The machine calculates a probability that a second event type pertaining to a second cluster of items will co-occur with the first event type. The machine identifies an item from the second cluster to be recommended and presents a recommendation of the item to the potential buyer.Type: ApplicationFiled: August 18, 2017Publication date: March 1, 2018Inventors: Ye Chen, John Canny
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Patent number: 9852193Abstract: A clustering and recommendation machine determines that an item is included in a cluster of items. The machine accesses item data descriptive of the item. The machine accesses a vector that represents the cluster and calculates the likelihood that the item is included in the cluster, based on the item variable and the probability parameter. The machine determines that the item is included in the cluster, based on the likelihood. The machine also recommends an item to a potential buyer. The machine accesses behavior data that represents a first event type pertinent to a first cluster of items. The machine calculates a probability that a second event type pertaining to a second cluster of items will co-occur with the first event type. The machine identifies an item from the second cluster to be recommended and presents a recommendation of the item to the potential buyer.Type: GrantFiled: January 27, 2010Date of Patent: December 26, 2017Assignee: eBay Inc.Inventors: Ye Chen, John Canny
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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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Patent number: 9760802Abstract: A clustering and recommendation machine determines that an item is included in a cluster of items. The machine accesses item data descriptive of the item. The machine accesses a vector that represents the cluster and calculates the likelihood that the item is included in the cluster, based on the item variable and the probability parameter. The machine determines that the item is included in the cluster, based on the likelihood. The machine also recommends an item to a potential buyer. The machine accesses behavior data that represents a first event type pertinent to a first cluster of items. The machine calculates a probability that a second event type pertaining to a second cluster of items will co-occur with the first event type. The machine identifies an item from the second cluster to be recommended and presents a recommendation of the item to the potential buyer.Type: GrantFiled: January 27, 2010Date of Patent: September 12, 2017Assignee: eBay Inc.Inventors: Ye Chen, John Canny
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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: 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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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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Patent number: 8150723Abstract: A method and a system are provided for large-scale behavioral targeting for advertising over a network, such as the Internet. In one example, the system receives training data that is processed raw data of user behavior. The system generates selected features by performing feature selection on the training data. The system generates feature vectors from the selected features. The system initializes weights of a behavioral targeting model by scanning the feature vectors once. The system then updates the weights of the behavioral targeting model by scanning iteratively the feature vectors using a multiplicative recurrence.Type: GrantFiled: January 9, 2009Date of Patent: April 3, 2012Assignee: Yahoo! Inc.Inventors: Ye Chen, Dmitry Pavlov, Pavel Berkhin, John Canny
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Publication number: 20110184806Abstract: A clustering and recommendation machine determines that an item is included in a cluster of items. The machine accesses item data descriptive of the item. The machine accesses a vector that represents the cluster and calculates the likelihood that the item is included in the cluster, based on the item variable and the probability parameter. The machine determines that the item is included in the cluster, based on the likelihood. The machine also recommends an item to a potential buyer. The machine accesses behavior data that represents a first event type pertinent to a first cluster of items. The machine calculates a probability that a second event type pertaining to a second cluster of items will co-occur with the first event type. The machine identifies an item from the second cluster to be recommended and presents a recommendation of the item to the potential buyer.Type: ApplicationFiled: January 27, 2010Publication date: July 28, 2011Inventors: Ye Chen, John Canny
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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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Publication number: 20110035379Abstract: A clustering and recommendation machine determines that an item is included in a cluster of items. The machine accesses item data descriptive of the item. The machine accesses a vector that represents the cluster and calculates the likelihood that the item is included in the cluster, based on the item variable and the probability parameter. The machine determines that the item is included in the cluster, based on the likelihood. The machine also recommends an item to a potential buyer. The machine accesses behavior data that represents a first event type pertinent to a first cluster of items. The machine calculates a probability that a second event type pertaining to a second cluster of items will co-occur with the first event type. The machine identifies an item from the second cluster to be recommended and presents a recommendation of the item to the potential buyer.Type: ApplicationFiled: January 27, 2010Publication date: February 10, 2011Inventors: Ye Chen, John Canny
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Publication number: 20100306161Abstract: Methods and systems are provided for predicting click through rate in connection with a particular user, keyword-based query, and advertisement using a probabilistic latent variable model. Click through rate may be predicted based on historical sponsored search activity information. Predicted click through rate may be used as a factor in determining advertisement rank.Type: ApplicationFiled: May 29, 2009Publication date: December 2, 2010Applicant: Yahoo! Inc.Inventors: Ye Chen, Dmitry Pavlov, John Canny, Eren Manavoglu
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Publication number: 20100179855Abstract: A method and a system are provided for large-scale behavioral targeting for advertising over a network, such as the Internet. In one example, the system receives training data that is processed raw data of user behavior. The system generates selected features by performing feature selection on the training data. The system generates feature vectors from the selected features. The system initializes weights of a behavioral targeting model by scanning the feature vectors once. The system then updates the weights of the behavioral targeting model by scanning iteratively the feature vectors using a multiplicative recurrence.Type: ApplicationFiled: January 9, 2009Publication date: July 15, 2010Inventors: Ye Chen, Dmitry Pavlov, Pavel Berkhin, John Canny
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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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Patent number: 5856924Abstract: Methods and apparatus are provided for developing a complete set of all admissible Type I and Type II fixture designs for a workpiece. The fixture processor generates the set of all admissible designs based on geometric access constraints and expected applied forces on the workpiece. For instance, the fixture processor may generate a set of admissible fixture designs for first, second and third locators placed in an array of holes on a fixture plate and a translating clamp attached to the fixture plate for contacting the workpiece. In another instance, a fixture vise is used in which first, second, third and fourth locators are used and first and second fixture jaws are tightened to secure the workpiece. The fixture process also ranks the set of admissible fixture designs according to a predetermined quality metric so that the optimal fixture design for the desired purpose may be identified from the set of all admissible fixture designs.Type: GrantFiled: April 28, 1995Date of Patent: January 5, 1999Inventors: Randolph C. Brost, Kenneth Y. Goldberg, John Canny, Aaron S. Wallack
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Patent number: 5546314Abstract: A fixture process and method is provided for developing a complete set of all admissible fixture designs for a workpiece which prevents the workpiece from translating or rotating. The fixture processor generates the set of all admissible designs based on geometric access constraints and expected applied forces on the workpiece. For instance, the fixture processor may generate a set of admissible fixture designs for first, second and third locators placed in an array of holes on a fixture plate and a translating clamp attached to the fixture plate for contacting the workpiece. In another instance, a fixture vice is used in which first, second, third and fourth locators are used and first and second fixture jaws are tightened to secure the workpiece. The fixture process also ranks the set of admissible fixture designs according to a predetermined quality metric so that the optimal fixture design for the desired purpose may be identified from the set of all admissible fixture designs.Type: GrantFiled: June 6, 1995Date of Patent: August 13, 1996Inventors: Randolph C. Brost, Kenneth Y. Goldberg, Aaron S. Wallack, John Canny