Patents by Inventor John C. Platt
John C. Platt 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: 11745093Abstract: A system enables metadata to be gathered about a data store beginning from the creation and generation of the data store, through subsequent use of the data store. This metadata can include keywords related to the data store and data appearing within the data store. Thus, keywords and other metadata can be generated without owner/creator intervention, with enough semantic meaning to make a discovery process associated with the data store much easier and efficient. Usage of or communication regarding a data store are monitored and keywords are extracted from the usage or communication. The keywords are then written to otherwise associated with metadata of the data store. During searching, keywords in the metadata are made available to be used to attempt to match query terms entered by a searcher.Type: GrantFiled: November 23, 2021Date of Patent: September 5, 2023Assignee: Microsoft Technology Licensing, LLCInventors: John C. Platt, Surajit Chaudhuri, Lev Novik, Henricus Johannes Maria Meijer
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Patent number: 11494414Abstract: The described implementations relate to processing of electronic data. One implementation is manifest as a technique that can include obtaining a relational query that references one or more data items and associating progress intervals with the data items. The technique can also include converting the relational query into a corresponding streaming query, and providing the streaming query and the data items with the progress intervals to a stream engine that produces incremental results of the query. For example, the progress intervals can be based on row numbers of a relational database table. The progress intervals can be used to define event lifetimes of streaming events that are provided as inputs to the stream engine.Type: GrantFiled: October 25, 2018Date of Patent: November 8, 2022Assignee: Microsoft Technology Licensing, LLCInventors: Danyel A. Fisher, Steven M. Drucker, Jonathan D. Goldstein, Badrish Chandramouli, Robert A. DeLine, John C. Platt, Mike Barnett
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Publication number: 20220152474Abstract: A system enables metadata to be gathered about a data store beginning from the creation and generation of the data store, through subsequent use of the data store. This metadata can include keywords related to the data store and data appearing within the data store. Thus, keywords and other metadata can be generated without owner/creator intervention, with enough semantic meaning to make a discovery process associated with the data store much easier and efficient. Usage of or communication regarding a data store are monitored and keywords are extracted from the usage or communication. The keywords are then written to otherwise associated with metadata of the data store. During searching, keywords in the metadata are made available to be used to attempt to match query terms entered by a searcher.Type: ApplicationFiled: November 23, 2021Publication date: May 19, 2022Inventors: John C. PLATT, Surajit CHAUDHURI, Lev NOVIK, Henricus Johannes Maria MEIJER
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Patent number: 11202958Abstract: A system enables metadata to be gathered about a data store beginning from the creation and generation of the data store, through subsequent use of the data store. This metadata can include keywords related to the data store and data appearing within the data store. Thus, keywords and other metadata can be generated without owner/creator intervention, with enough semantic meaning to make a discovery process associated with the data store much easier and efficient. Usage of or communication regarding a data store are monitored and keywords are extracted from the usage or communication. The keywords are then written to otherwise associated with metadata of the data store. During searching, keywords in the metadata are made available to be used to attempt to match query terms entered by a searcher.Type: GrantFiled: April 11, 2012Date of Patent: December 21, 2021Assignee: Microsoft Technology Licensing, LLCInventors: John C. Platt, Surajit Chaudhuri, Lev Novik, Henricus Johannes Maria Meijer
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Patent number: 10313818Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.Type: GrantFiled: January 22, 2018Date of Patent: June 4, 2019Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Piotr Tadeusz Bilinski, Jens Ahrens, Mark R. P. Thomas, Ivan J. Tashev, John C. Platt, David E. Johnston
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Patent number: 10284992Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.Type: GrantFiled: March 30, 2017Date of Patent: May 7, 2019Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Piotr Tadeusz Bilinski, Jens Ahrens, Mark R. P. Thomas, Ivan J. Tashev, John C. Platt, David E. Johnston
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Patent number: 10244341Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.Type: GrantFiled: March 30, 2017Date of Patent: March 26, 2019Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Piotr Tadeusz Bilinski, Jens Ahrens, Mark R. P. Thomas, Ivan J. Tashev, John C. Platt, David E. Johnston
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Publication number: 20190057144Abstract: The described implementations relate to processing of electronic data. One implementation is manifest as a technique that can include obtaining a relational query that references one or more data items and associating progress intervals with the data items. The technique can also include converting the relational query into a corresponding streaming query, and providing the streaming query and the data items with the progress intervals to a stream engine that produces incremental results of the query. For example, the progress intervals can be based on row numbers of a relational database table. The progress intervals can be used to define event lifetimes of streaming events that are provided as inputs to the stream engine.Type: ApplicationFiled: October 25, 2018Publication date: February 21, 2019Applicant: Microsoft Technology Licensing, LLCInventors: Danyel A. Fisher, Steven M. Drucker, Jonathan D. Goldstein, Badrish Chandramouli, Robert A. DeLine, John C. Platt, Mike Barnett
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Patent number: 10140366Abstract: In one embodiment, datasets are stored in a catalog. The datasets are enriched by establishing relationships among the domains in different datasets. A user searches for relevant datasets by providing examples of the domains of interest. The system identifies datasets corresponding to the user-provided examples. The system them identifies connected subsets of the datasets that are directly linked or indirectly linked through other domains. The user provides known relationship examples to filter the connected subsets and to identify the connected subsets that are most relevant to the user's query. The selected connected subsets may be further analyzed by business intelligence/analytics to create pivot tables or to process the data.Type: GrantFiled: March 16, 2015Date of Patent: November 27, 2018Assignee: Microsoft Technology Licensing, LLCInventors: John C. Platt, Surajit Chaudhuri, Lev Novik, Henricus Johannes Maria Meijer, Efim Hudis, Kunal Mukerjee, Christopher Alan Hays
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Patent number: 10140358Abstract: The described implementations relate to processing of electronic data. One implementation is manifest as a technique that can include obtaining a relational query that references one or more data items and associating progress intervals with the data items. The technique can also include converting the relational query into a corresponding streaming query, and providing the streaming query and the data items with the progress intervals to a stream engine that produces incremental results of the query. For example, the progress intervals can be based on row numbers of a relational database table. The progress intervals can be used to define event lifetimes of streaming events that are provided as inputs to the stream engine.Type: GrantFiled: February 14, 2017Date of Patent: November 27, 2018Assignee: Microsoft Technology Licensing, LLCInventors: Danyel A. Fisher, Steven M. Drucker, Jonathan D. Goldstein, Badrish Chandramouli, Robert A. DeLine, John C. Platt, Mike Barnett
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Publication number: 20180146318Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.Type: ApplicationFiled: January 22, 2018Publication date: May 24, 2018Applicant: Microsoft Technology Licensing, LLCInventors: Piotr Tadeusz Bilinski, Jens Ahrens, Mark R.P. Thomas, Ivan J. Tashev, John C. Platt, David E. Johnston
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Patent number: 9900722Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.Type: GrantFiled: April 29, 2014Date of Patent: February 20, 2018Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Piotr Tadeusz Bilinski, Jens Ahrens, Mark R. P. Thomas, Ivan J. Tashev, John C. Platt, David E. Johnston
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Patent number: 9877136Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.Type: GrantFiled: April 29, 2014Date of Patent: January 23, 2018Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Piotr Tadeusz Bilinski, Jens Ahrens, Mark R. P. Thomas, Ivan J. Tashev, John C. Platt, David E. Johnston
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Patent number: 9836671Abstract: Disclosed herein are technologies directed to discovering semantic similarities between images and text, which can include performing image search using a textual query, performing text search using an image as a query, and/or generating captions for images using a caption generator. A semantic similarity framework can include a caption generator and can be based on a deep multimodal similar model. The deep multimodal similarity model can receive sentences and determine the relevancy of the sentences based on similarity of text vectors generated for one or more sentences to an image vector generated for an image. The text vectors and the image vector can be mapped in a semantic space, and their relevance can be determined based at least in part on the mapping. The sentence associated with the text vector determined to be the most relevant can be output as a caption for the image.Type: GrantFiled: August 28, 2015Date of Patent: December 5, 2017Assignee: Microsoft Technology Licensing, LLCInventors: Jianfeng Gao, Xiaodong He, Saurabh Gupta, Geoffrey G. Zweig, Forrest Iandola, Li Deng, Hao Fang, Margaret A. Mitchell, John C. Platt, Rupesh Kumar Srivastava
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Publication number: 20170208413Abstract: The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.Type: ApplicationFiled: March 30, 2017Publication date: July 20, 2017Applicant: Microsoft Technology Licensing, LLCInventors: Piotr Tadeusz Bilinski, Jens Ahrens, Mark R. P. Thomas, Ivan J. Tashev, John C. Platt, David E. Johnston
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Patent number: 9697262Abstract: Some examples include high-performance query processing of real-time and offline temporal-relational data. Further, some implementations include processing streaming data events by annotating individual events with a first timestamp (e.g., a “sync-time”) and second timestamp that may identify additional event information. The stream of incoming data events may be organized into a sequence of data batches that each include multiple data events. The individual data batches in the sequence may be processed in a non-decreasing “sync-time” order.Type: GrantFiled: December 17, 2013Date of Patent: July 4, 2017Assignee: Microsoft Technology Licensing, LLCInventors: Badrish Chandramouli, John Wernsing, Jonathan D. Goldstein, Michael Barnett, John C. Platt
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Publication number: 20170154098Abstract: The described implementations relate to processing of electronic data. One implementation is manifest as a technique that can include obtaining a relational query that references one or more data items and associating progress intervals with the data items. The technique can also include converting the relational query into a corresponding streaming query, and providing the streaming query and the data items with the progress intervals to a stream engine that produces incremental results of the query. For example, the progress intervals can be based on row numbers of a relational database table. The progress intervals can be used to define event lifetimes of streaming events that are provided as inputs to the stream engine.Type: ApplicationFiled: February 14, 2017Publication date: June 1, 2017Applicant: Microsoft Technology Licensing, LLCInventors: Danyel A. Fisher, Steven M. Drucker, Jonathan D. Goldstein, Badrish Chandramouli, Robert A. DeLine, John C. Platt, Mike Barnett
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Patent number: 9607045Abstract: The described implementations relate to processing of electronic data. One implementation is manifest as a technique that can include obtaining a relational query that references one or more data items and associating progress intervals with the data items. The technique can also include converting the relational query into a corresponding streaming query, and providing the streaming query and the data items with the progress intervals to a stream engine that produces incremental results of the query. For example, the progress intervals can be based on row numbers of a relational database table. The progress intervals can be used to define event lifetimes of streaming events that are provided as inputs to the stream engine.Type: GrantFiled: December 21, 2012Date of Patent: March 28, 2017Assignee: Microsoft Technology Licensing, LLCInventors: Danyel A. Fisher, Steven M. Drucker, Jonathan D. Goldstein, Badrish Chandramouli, Robert A. DeLine, John C. Platt, Mike Barnett
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Publication number: 20170061250Abstract: Disclosed herein are technologies directed to discovering semantic similarities between images and text, which can include performing image search using a textual query, performing text search using an image as a query, and/or generating captions for images using a caption generator. A semantic similarity framework can include a caption generator and can be based on a deep multimodal similar model. The deep multimodal similarity model can receive sentences and determine the relevancy of the sentences based on similarity of text vectors generated for one or more sentences to an image vector generated for an image. The text vectors and the image vector can be mapped in a semantic space, and their relevance can be determined based at least in part on the mapping. The sentence associated with the text vector determined to be the most relevant can be output as a caption for the image.Type: ApplicationFiled: August 28, 2015Publication date: March 2, 2017Inventors: Jianfeng Gao, Xiaodong He, Saurabh Gupta, Geoffrey G. Zweig, Forrest Iandola, Li Deng, Hao Fang, Margaret A. Mitchell, John C. Platt, Rupesh Kumar Srivastava
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Patent number: 9396269Abstract: Architecture that monitors interaction data (e.g., search queries, query results and click-through rates), and provides users with links to other users that fall into similar categories with respect to the foregoing monitored activities (e.g., providing links to individuals and groups that share common interests and/or profiles). A search engine can be interactively coupled with one or more social networks, and that maps individuals and/or groups within respective social networks to subsets of categories associated with searches. A database stores mapped information which can be continuously updated and reorganized as links within the system mapping become stronger or weaker. The architecture can comprise a social network system that includes a database for mapping search-related information to an entity of a social network, and a search component for processing a search query for search results and returning a link to an entity of a social network based on the search query.Type: GrantFiled: June 28, 2006Date of Patent: July 19, 2016Assignee: Microsoft Technology Licensing, LLCInventors: Christopher A. Meek, Eric J. Horvitz, Joshua T. Goodman, Gary W. Flake, Oliver Hurst-Hiller, Anoop Gupta, Ramez Naam, Kenneth A. Moss, William H. Gates, III, John C. Platt, Trenholme J. Griffin, Bradly A. Brunell