Patents by Inventor Max Chickering
Max Chickering 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: 11023677Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: GrantFiled: July 13, 2016Date of Patent: June 1, 2021Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Patrice Y. Simard, David Max Chickering, David G. Grangier, Aparna Lakshmiratan, Saleema A. Amershi
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Patent number: 9779081Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: GrantFiled: April 21, 2016Date of Patent: October 3, 2017Assignee: Microsoft Technology Licensing, LLCInventors: Patrice Y. Simard, David Max Chickering, David G. Grangier, Denis X. Charles, Leon Bottou, Carlos Garcia Jurado Suarez
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Patent number: 9582490Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: GrantFiled: November 8, 2013Date of Patent: February 28, 2017Assignee: Microsoft Technolog Licensing, LLCInventors: Patrice Y. Simard, David Max Chickering, Aparna Lakshmiratan, Denis X. Charles, Leon Bottou
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Patent number: 9569541Abstract: Systems, methods, and computer storage media having computer-executable instructions embodied thereon that facilitate evaluation of digital content preferences are provided. A user is presented with items of digital content and permitted to manipulate the arrangement of the digital content items in the context of a layout area. Based on the user's manipulation of the digital content items, a user preference regarding an arrangement of digital content, such as a location preference, a position preference, and/or a usage preference, is identified. In embodiments, such a user preference can be utilized to later display digital content to a user in accordance therewith.Type: GrantFiled: December 31, 2009Date of Patent: February 14, 2017Assignee: Microsoft Technology Licensing, LLCInventors: Reid Andersen, David Max Chickering, Ewa Dominowska, Matt Jacobsen, Anton Mityagin
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Publication number: 20170039486Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: ApplicationFiled: July 13, 2016Publication date: February 9, 2017Inventors: Patrice Y. SIMARD, David Max CHICKERING, David G. GRANGIER, Aparna LAKSHMIRATAN, Saleema A. AMERSHI
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Patent number: 9489373Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: GrantFiled: November 8, 2013Date of Patent: November 8, 2016Assignee: Microsoft Technology Licensing, LLCInventors: Patrice Y. Simard, David Max Chickering, David G. Grangier, Denis X. Charles, Leon Bottou, Saleema A. Amershi, Aparna Lakshmiratan, Carlos Garcia Jurado Suarez
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Patent number: 9430460Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: GrantFiled: November 8, 2013Date of Patent: August 30, 2016Assignee: Microsoft Technology Licensing, LLCInventors: Patrice Y. Simard, David Max Chickering, David G. Grangier, Aparna Lakshmiratan, Saleema A. Amershi
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Publication number: 20160239761Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: ApplicationFiled: April 21, 2016Publication date: August 18, 2016Inventors: PATRICE Y. SIMARD, DAVID MAX CHICKERING, DAVID G. GRANGIER, DENIS X. CHARLES, LEON BOTTOU, CARLOS GARCIA JURADO SUAREZ
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Patent number: 9355088Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: GrantFiled: November 8, 2013Date of Patent: May 31, 2016Assignee: Microsoft Technology Licensing, LLCInventors: Patrice Y. Simard, David Max Chickering, David G. Grangier, Denis X. Charles, Leon Bottou, Carlos Garcia Jurado Suarez
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Patent number: 9104960Abstract: Methods, systems, and computer-storage media having computer-usable instructions embodied thereon for calculating event probabilities are provided. The event may be a click probability. Event probabilities are calculated using a system optimized for runtime model accuracy with an operable learning algorithm. Bin counting techniques are used to calculate event probabilities based on a count of event occurrences and non-event occurrences. Linear parameters, such and counts of clicks and non-clicks, may also be used in the system to allow for runtime adjustments.Type: GrantFiled: June 20, 2011Date of Patent: August 11, 2015Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Leon Bottou, Kumar Chellapilla, Patrice Y. Simard, David Max Chickering
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Publication number: 20150019461Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: ApplicationFiled: November 8, 2013Publication date: January 15, 2015Applicant: Microsoft CorporationInventors: PATRICE Y. SIMARD, DAVID MAX CHICKERING, DAVID G. GRANGIER, DENIS X. CHARLES, LEON BOTTOU, SALEEMA A. AMERSHI, APARNA LAKSHMIRATAN, CARLOS GARCIA JURADO SUAREZ
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Publication number: 20150019204Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: ApplicationFiled: November 8, 2013Publication date: January 15, 2015Applicant: Microsoft CorporationInventors: PATRICE Y. SIMARD, DAVID MAX CHICKERING, DAVID G. GRANGIER, DENIS X. CHARLES, LEON BOTTOU, CARLOS GARCIA JURADO SUAREZ
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Publication number: 20150019460Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: ApplicationFiled: November 8, 2013Publication date: January 15, 2015Applicant: Microsoft CorporationInventors: PATRICE Y. SIMARD, DAVID MAX CHICKERING, APARNA LAKSHMIRATAN, DENIS X. CHARLES, LEON BOTTOU
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Publication number: 20150019463Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.Type: ApplicationFiled: November 8, 2013Publication date: January 15, 2015Applicant: Microsoft CorporationInventors: PATRICE Y. SIMARD, DAVID MAX CHICKERING, DAVID G. GRANGIER, APARNA LAKSHMIRATAN, SALEEMA A. AMERSHI
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Patent number: 8862468Abstract: A system and method of refining context-free grammars (CFGs). The method includes deriving back-off grammar (BOG) rules from an initially developed CFG and utilizing the initial CFG and the derived BOG rules to recognize user utterances. Based on a response of the initial CFG and the derived BOG rules to the user utterances, at least a portion of the derived BOG rules are utilized to modify the initial CFG and thereby produce a refined CFG. The above method can carried out iterativey, with each new iteration utilizing a refined CFG from preceding iterations.Type: GrantFiled: December 22, 2011Date of Patent: October 14, 2014Assignee: Microsoft CorporationInventors: Timothy Paek, Max Chickering, Eric Badger
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Patent number: 8589233Abstract: For a multi-party online advertising exchange including advertising and publishing entities and one or more third party participants, the disclosed systems and methods enable third party participation in arbitrage opportunities in online advertising transactions. A plurality of underlying transaction details are abstracted and provided to the third party participants without loss of generalization and while preserving relationships in the transaction data, to enable a third party share risk in advertising transactions. Various system refinements are provided and disclosed according to a host of optional embodiments.Type: GrantFiled: June 15, 2007Date of Patent: November 19, 2013Assignee: Microsoft CorporationInventors: Gary W. Flake, Brett D. Brewer, Christopher A. Meek, David Max Chickering, Jody D. Biggs, Ewa Dominowska, Brian Burdick
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Patent number: 8533049Abstract: For a multi-party advertising exchange, including publishing entities and advertising entities from disparate advertising networks, which facilitates transactions for publishing inventory, a value add broker is provided to aggregate information from third parties having valuable information for input to the exchange or to perform services that are valuable to transactions in the exchange. The valuable information or services further facilitate the transactions for the publishing inventory automatically generating a benefit for the third parties providing the valuable information or services commensurate with the value added to the transactions.Type: GrantFiled: June 13, 2007Date of Patent: September 10, 2013Assignee: Microsoft CorporationInventors: Gary W. Flake, Brett D. Brewer, Christopher A. Meek, David Max Chickering, Jody D. Biggs, Ewa Dominowska, Brian Burdick
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Publication number: 20120323677Abstract: Methods, systems, and computer-storage media having computer-usable instructions embodied thereon for calculating event probabilities are provided. The event may be a click probability. Event probabilities are calculated using a system optimized for runtime model accuracy with an operable learning algorithm. Bin counting techniques are used to calculate event probabilities based on a count of event occurrences and non-event occurrences. Linear parameters, such and counts of clicks and non-clicks, may also be used in the system to allow for runtime adjustments.Type: ApplicationFiled: June 20, 2011Publication date: December 20, 2012Applicant: MICROSOFT CORPORATIONInventors: LEON BOTTOU, KUMAR CHELLAPILLA, PATRICE Y. SIMARD, DAVID MAX CHICKERING
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Publication number: 20120150772Abstract: A social newsfeed being delivered to a user is triaged. A personalized model is established which predicts the importance to the user of data elements within a current social newsfeed being delivered to the user. The personalized model is established based on implicit actions the user takes in response to receiving previous social newsfeeds. The personalized model is then used to triage the data elements within the current social newsfeed.Type: ApplicationFiled: December 10, 2010Publication date: June 14, 2012Applicant: MICROSOFT CORPORATIONInventors: Tim Paek, Scott Joseph Counts, Michael Gamon, Aaron Hoff, Max Chickering
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Publication number: 20120095752Abstract: A system and method of refining context-free grammars (CFGs). The method includes deriving back-off grammar (BOG) rules from an initially developed CFG and utilizing the initial CFG and the derived BOG rules to recognize user utterances. Based on a response of the initial CFG and the derived BOG rules to the user utterances, at least a portion of the derived BOG rules are utilized to modify the initial CFG and thereby produce a refined CFG. The above method can carried out iterativey, with each new iteration utilizing a refined CFG from preceding iterations.Type: ApplicationFiled: December 22, 2011Publication date: April 19, 2012Applicant: Microsoft CorporationInventors: Timothy Paek, Max Chickering, Eric Badger