Patents by Inventor James Vincent McFadden
James Vincent McFadden 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: 10713585Abstract: Systems and techniques are provided for template exploration in a large-scale machine learning system. A method may include obtaining multiple base templates, each base template comprising multiple features. A template performance score may be obtained for each base template and a first base template may be selected from among the multiple base templates based on the template performance score of the first base template. Multiple cross-templates may be constructed by generating a cross-template of the selected first base template and each of the multiple base templates. Performance of a machine learning model may be tested based on each cross-template to generate a cross-template performance score for each of the cross-templates. A first cross-template may be selected from among the multiple cross-templates based on the cross-template performance score of the cross-template. Accordingly, the first cross-template may be added to the machine learning model.Type: GrantFiled: December 16, 2013Date of Patent: July 14, 2020Assignee: Google LLCInventors: Tal Shaked, Tushar Deepak Chandra, James Vincent McFadden, Yoram Singer, Tze Way Eugene Ie
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Publication number: 20200151614Abstract: Systems and techniques are provided for template exploration in a large-scale machine learning system. A method may include obtaining multiple base templates, each base template comprising multiple features. A template performance score may be obtained for each base template and a first base template may be selected from among the multiple base templates based on the template performance score of the first base template. Multiple cross-templates may be constructed by generating a cross-template of the selected first base template and each of the multiple base templates. Performance of a machine learning model may be tested based on each cross-template to generate a cross-template performance score for each of the cross-templates. A first cross-template may be selected from among the multiple cross-templates based on the cross-template performance score of the cross-template. Accordingly, the first cross-template may be added to the machine learning model.Type: ApplicationFiled: December 16, 2013Publication date: May 14, 2020Applicant: Google Inc.Inventors: Tal Shaked, Tushar Deepak Chandra, James Vincent McFadden, Yoram Singer, Tze Way Eugene Ie
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Patent number: 10373176Abstract: A method includes presenting a list of one or more videos via a user interface, receiving a selection of a target video to watch from the list, playing the target video in the user interface, and updating the user interface to present one or more suggested videos concurrently with playback of the target video. The one or more suggested videos are predicted to be watched by a user for at least a threshold duration.Type: GrantFiled: October 30, 2017Date of Patent: August 6, 2019Assignee: GOOGLE LLCInventors: Li Wei, James Vincent McFadden
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Patent number: 9805378Abstract: A system receives a user request for a media item and identifies candidate media items for suggesting to the user with the media item. The system predicts a user consumption time for each candidate media item and selects a sub-set of the candidate media items that have higher predicted user consumption times. The system provides the requested media item with the sub-set of the candidate media items.Type: GrantFiled: September 28, 2012Date of Patent: October 31, 2017Assignee: GOOGLE INC.Inventors: Li Wei, James Vincent McFadden
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Publication number: 20170103343Abstract: Mechanisms for recommending content items based on topics are provided. In some implementations, a method for recommending content items is provided that includes: determining a plurality of accessed content items associated with a user, wherein each of the plurality of content items is associated with a plurality of topics; determining the plurality of topics associated with each of the plurality of accessed content items; generating a model of user interests based on the plurality of topics, wherein the model implements a machine learning technique to determine a plurality of weights for assigning to each of the plurality of topics; applying the model to determine, for a plurality of content items, a probability that the user would watch a content item of the plurality of content items; ranking the plurality of content items based on the determined probabilities; and selecting a subset of the plurality of content items to recommend to the user based on the ranked content items.Type: ApplicationFiled: December 20, 2016Publication date: April 13, 2017Inventors: Yangli Hector Yee, James Vincent McFadden, John Kraemer, Dasarathi Sampath
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Patent number: 9552555Abstract: Mechanisms for recommending content items based on topics are provided. In some implementations, a method for recommending content items is provided that includes: determining a plurality of accessed content items associated with a user, wherein each of the plurality of content items is associated with a plurality of topics; determining the plurality of topics associated with each of the plurality of accessed content items; generating a model of user interests based on the plurality of topics, wherein the model implements a machine learning technique to determine a plurality of weights for assigning to each of the plurality of topics; applying the model to determine, for a plurality of content items, a probability that the user would watch a content item of the plurality of content items; ranking the plurality of content items based on the determined probabilities; and selecting a subset of the plurality of content items to recommend to the user based on the ranked content items.Type: GrantFiled: August 3, 2015Date of Patent: January 24, 2017Assignee: Google Inc.Inventors: Yangli Hector Yee, James Vincent McFadden, John Kraemer, Dasarathi Sampath
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Patent number: 9390382Abstract: Systems and techniques are disclosed for training a machine learning model based on one or more regularization penalties associated with one or more features. A template having a lower regularization penalty may be given preference over a template having a higher regularization penalty. A regularization penalty may be determined based on domain knowledge. A restrictive regularization penalty may be assigned to a template based on determining that a template occurrence is below a stability threshold and may be modified if the template occurrence meets or exceeds the stability threshold.Type: GrantFiled: December 30, 2013Date of Patent: July 12, 2016Assignee: Google Inc.Inventors: Yoram Singer, Tal Shaked, Tushar Deepak Chandra, Tze Way Eugene Ie, James Vincent McFadden, Jeremiah Harmsen, Kristen Riedt LeFevre
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Patent number: 9129227Abstract: Mechanisms for recommending content items based on topics are provided. In some implementations, a method for recommending content items is provided that includes: determining a plurality of accessed content items associated with a user, wherein each of the plurality of content items is associated with a plurality of topics; determining the plurality of topics associated with each of the plurality of accessed content items; generating a model of user interests based on the plurality of topics, wherein the model implements a machine learning technique to determine a plurality of weights for assigning to each of the plurality of topics; applying the model to determine, for a plurality of content items, a probability that the user would watch a content item of the plurality of content items; ranking the plurality of content items based on the determined probabilities; and selecting a subset of the plurality of content items to recommend to the user based on the ranked content items.Type: GrantFiled: December 31, 2012Date of Patent: September 8, 2015Assignee: Google Inc.Inventors: Yangli Hector Yee, James Vincent McFadden, John Kraemer, Dasarathi Sampath
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Publication number: 20150186794Abstract: Systems and techniques are disclosed for training a machine learning model based on one or more regularization penalties associated with one or more features. A template having a lower regularization penalty may be given preference over a template having a higher regularization penalty. A regularization penalty may be determined based on domain knowledge. A restrictive regularization penalty may be assigned to a template based on determining that a template occurrence is below a stability threshold and may be modified if the template occurrence meets or exceeds the stability threshold.Type: ApplicationFiled: December 30, 2013Publication date: July 2, 2015Applicant: Google Inc.Inventors: Yoram Singer, Tal Shaked, Tushar Deepak Chandra, Tze Way Eugene Ie, James Vincent McFadden, Jeremiah Harmsen, Kristen Riedt LeFevre