Patents by Inventor Joshua L. Levenberg

Joshua L. Levenberg 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).

  • Ranking documents based on large data sets
    Patent number: 10055461
    Abstract: A system ranks documents based, at least in part, on a ranking model. The ranking model may be generated to predict the likelihood that a document will be selected. The system may receive a search query and identify documents relating to the search query. The system may then rank the documents based, at least in part, on the ranking model and form search results for the search query from the ranked documents.
    Type: Grant
    Filed: July 31, 2015
    Date of Patent: August 21, 2018
    Assignee: Google LLC
    Inventors: Jeremy Bem, Georges R. Harik, Joshua L. Levenberg, Noam M. Shazeer, Simon Tong
  • Ranking documents based on large data sets
    Patent number: 9116976
    Abstract: A system ranks documents based, at least in part, on a ranking model. The ranking model may be generated to predict the likelihood that a document will be selected. The system may receive a search query and identify documents relating to the search query. The system may then rank the documents based, at least in part, on the ranking model and form search results for the search query from the ranked documents.
    Type: Grant
    Filed: May 11, 2010
    Date of Patent: August 25, 2015
    Assignee: Google Inc.
    Inventors: Jeremy Bem, Georges R. Harik, Joshua L. Levenberg, Noam Shazeer, Simon Tong
  • Large scale machine learning systems and methods
    Patent number: 8688705
    Abstract: A system for generating a model is provided. The system generates, or selects, candidate conditions and generates, or otherwise obtains, statistics regarding the candidate conditions. The system also forms rules based, at least in part, on the statistics and the candidate conditions and selectively adds the rules to the model.
    Type: Grant
    Filed: January 28, 2013
    Date of Patent: April 1, 2014
    Assignee: Google Inc.
    Inventors: Jeremy Bem, Georges R. Harik, Joshua L. Levenberg, Noam Shazeer, Simon Tong
  • Large scale machine learning systems and methods
    Patent number: 8364618
    Abstract: A system for generating a model is provided. The system generates, or selects, candidate conditions and generates, or otherwise obtains, statistics regarding the candidate conditions. The system also forms rules based, at least in part, on the statistics and the candidate conditions and selectively adds the rules to the model.
    Type: Grant
    Filed: June 4, 2012
    Date of Patent: January 29, 2013
    Assignee: Google Inc.
    Inventors: Jeremy Bem, Georges R. Harik, Joshua L. Levenberg, Noam Shazeer, Simon Tong
  • Large scale machine learning systems and methods
    Patent number: 8195674
    Abstract: A system for generating a model is provided. The system generates, or selects, candidate conditions and generates, or otherwise obtains, statistics regarding the candidate conditions. The system also forms rules based, at least in part, on the statistics and the candidate conditions and selectively adds the rules to the model.
    Type: Grant
    Filed: June 24, 2010
    Date of Patent: June 5, 2012
    Assignee: Google Inc.
    Inventors: Jeremy Bem, Georges R. Harik, Joshua L. Levenberg, Noam Shazeer, Simon Tong
  • Large scale machine learning systems and methods
    Patent number: 7769763
    Abstract: A system for generating a model is provided. The system generates, or selects, candidate conditions and generates, or otherwise obtains, statistics regarding the candidate conditions. The system also forms rules based, at least in part, on the statistics and the candidate conditions and selectively adds the rules to the model.
    Type: Grant
    Filed: April 17, 2007
    Date of Patent: August 3, 2010
    Assignee: Google Inc.
    Inventors: Jeremy Bem, Georges R. Harik, Joshua L. Levenberg, Noam Shazeer, Simon Tong
  • Model generation for ranking documents based on large data sets
    Patent number: 7743050
    Abstract: A system ranks documents based, at least in part, on a ranking model. The ranking model may be generated to predict the likelihood that a document will be selected. The system may receive a search query and identify documents relating to the search query. The system may then rank the documents based, at least in part, on the ranking model and form search results for the search query from the ranked documents.
    Type: Grant
    Filed: April 18, 2007
    Date of Patent: June 22, 2010
    Assignee: Google Inc.
    Inventors: Jeremy Bem, Georges R. Harik, Joshua L. Levenberg, Noam Shazeer, Simon Tong
  • Ranking documents based on large data sets
    Patent number: 7231399
    Abstract: A system ranks documents based, at least in part, on a ranking model. The ranking model may be generated to predict the likelihood that a document will be selected. The system may receive a search query and identify documents relating to the search query. The system may then rank the documents based, at least in part, on the ranking model and form search results for the search query from the ranked documents.
    Type: Grant
    Filed: November 14, 2003
    Date of Patent: June 12, 2007
    Assignee: Google Inc.
    Inventors: Jeremy Bem, Georges R. Harik, Joshua L. Levenberg, Noam Shazeer, Simon Tong
  • Large scale machine learning systems and methods
    Patent number: 7222127
    Abstract: A system for generating a model is provided. The system generates, or selects, candidate conditions and generates, or otherwise obtains, statistics regarding the candidate conditions. The system also forms rules based, at least in part, on the statistics and the candidate conditions and selectively adds the rules to the model.
    Type: Grant
    Filed: December 15, 2003
    Date of Patent: May 22, 2007
    Assignee: Google Inc.
    Inventors: Jeremy Bem, Georges R. Harik, Joshua L. Levenberg, Noam Shazeer, Simon Tong