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).

  • Patent number: 10313818
    Abstract: 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: Grant
    Filed: January 22, 2018
    Date of Patent: June 4, 2019
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Piotr Tadeusz Bilinski, Jens Ahrens, Mark R. P. Thomas, Ivan J. Tashev, John C. Platt, David E. Johnston
  • Patent number: 10284992
    Abstract: 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: Grant
    Filed: March 30, 2017
    Date of Patent: May 7, 2019
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Piotr Tadeusz Bilinski, Jens Ahrens, Mark R. P. Thomas, Ivan J. Tashev, John C. Platt, David E. Johnston
  • Patent number: 10244341
    Abstract: 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: Grant
    Filed: March 30, 2017
    Date of Patent: March 26, 2019
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Piotr Tadeusz Bilinski, Jens Ahrens, Mark R. P. Thomas, Ivan J. Tashev, John C. Platt, David E. Johnston
  • Publication number: 20190057144
    Abstract: 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: Application
    Filed: October 25, 2018
    Publication date: February 21, 2019
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Danyel A. Fisher, Steven M. Drucker, Jonathan D. Goldstein, Badrish Chandramouli, Robert A. DeLine, John C. Platt, Mike Barnett
  • Patent number: 10140358
    Abstract: 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: Grant
    Filed: February 14, 2017
    Date of Patent: November 27, 2018
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Danyel A. Fisher, Steven M. Drucker, Jonathan D. Goldstein, Badrish Chandramouli, Robert A. DeLine, John C. Platt, Mike Barnett
  • Patent number: 10140366
    Abstract: 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: Grant
    Filed: March 16, 2015
    Date of Patent: November 27, 2018
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: John C. Platt, Surajit Chaudhuri, Lev Novik, Henricus Johannes Maria Meijer, Efim Hudis, Kunal Mukerjee, Christopher Alan Hays
  • Publication number: 20180146318
    Abstract: 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: Application
    Filed: January 22, 2018
    Publication date: May 24, 2018
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Piotr Tadeusz Bilinski, Jens Ahrens, Mark R.P. Thomas, Ivan J. Tashev, John C. Platt, David E. Johnston
  • Patent number: 9900722
    Abstract: 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: Grant
    Filed: April 29, 2014
    Date of Patent: February 20, 2018
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Piotr Tadeusz Bilinski, Jens Ahrens, Mark R. P. Thomas, Ivan J. Tashev, John C. Platt, David E. Johnston
  • Patent number: 9877136
    Abstract: 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: Grant
    Filed: April 29, 2014
    Date of Patent: January 23, 2018
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Piotr Tadeusz Bilinski, Jens Ahrens, Mark R. P. Thomas, Ivan J. Tashev, John C. Platt, David E. Johnston
  • Patent number: 9836671
    Abstract: 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: Grant
    Filed: August 28, 2015
    Date of Patent: December 5, 2017
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Jianfeng Gao, Xiaodong He, Saurabh Gupta, Geoffrey G. Zweig, Forrest Iandola, Li Deng, Hao Fang, Margaret A. Mitchell, John C. Platt, Rupesh Kumar Srivastava
  • Publication number: 20170208413
    Abstract: 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: Application
    Filed: March 30, 2017
    Publication date: July 20, 2017
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Piotr Tadeusz Bilinski, Jens Ahrens, Mark R. P. Thomas, Ivan J. Tashev, John C. Platt, David E. Johnston
  • Patent number: 9697262
    Abstract: 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: Grant
    Filed: December 17, 2013
    Date of Patent: July 4, 2017
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Badrish Chandramouli, John Wernsing, Jonathan D. Goldstein, Michael Barnett, John C. Platt
  • Publication number: 20170154098
    Abstract: 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: Application
    Filed: February 14, 2017
    Publication date: June 1, 2017
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Danyel A. Fisher, Steven M. Drucker, Jonathan D. Goldstein, Badrish Chandramouli, Robert A. DeLine, John C. Platt, Mike Barnett
  • Patent number: 9607045
    Abstract: 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: Grant
    Filed: December 21, 2012
    Date of Patent: March 28, 2017
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Danyel A. Fisher, Steven M. Drucker, Jonathan D. Goldstein, Badrish Chandramouli, Robert A. DeLine, John C. Platt, Mike Barnett
  • Publication number: 20170061250
    Abstract: 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: Application
    Filed: August 28, 2015
    Publication date: March 2, 2017
    Inventors: Jianfeng Gao, Xiaodong He, Saurabh Gupta, Geoffrey G. Zweig, Forrest Iandola, Li Deng, Hao Fang, Margaret A. Mitchell, John C. Platt, Rupesh Kumar Srivastava
  • Patent number: 9396269
    Abstract: 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: Grant
    Filed: June 28, 2006
    Date of Patent: July 19, 2016
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: 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
  • Publication number: 20150312694
    Abstract: 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: Application
    Filed: April 29, 2014
    Publication date: October 29, 2015
    Applicant: Microsoft Corporation
    Inventors: Piotr Tadeusz Bilinski, Jens Ahrens, Mark R.P. Thomas, Ivan J. Tashev, John C. Platt, David E. Johnston
  • Patent number: 9141704
    Abstract: 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: Grant
    Filed: June 28, 2006
    Date of Patent: September 22, 2015
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: 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
  • Publication number: 20150193533
    Abstract: 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: Application
    Filed: March 16, 2015
    Publication date: July 9, 2015
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: John C. Platt, Surajit Chaudhuri, Lev Novik, Henricus Johannes Maria Meijer, Efim Hudis, Kunal Mukerjee, Christopher Alan Hays
  • Publication number: 20150169683
    Abstract: 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: Application
    Filed: December 17, 2013
    Publication date: June 18, 2015
    Applicant: Microsoft Corporation
    Inventors: Badrish Chandramouli, John Wernsing, Jonathan D. Goldstein, Michael Barnett, John C. Platt