Patents by Inventor Jennifer Gillenwater

Jennifer Gillenwater 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).

  • System and method for audio snippet generation from a subset of music tracks
    Patent number: 8666749
    Abstract: The disclosure includes a system and method for generating audio snippets from a subset of audio tracks. In some embodiments an audio snippet is an audio summary of a group or collection of songs.
    Type: Grant
    Filed: January 17, 2013
    Date of Patent: March 4, 2014
    Assignee: Google Inc.
    Inventors: Amarnag Subramanya, Jennifer Gillenwater, Garth Griffin, Fernando Pereira, Douglas Eck
  • Training parsers to approximately optimize NDCG
    Patent number: 8473486
    Abstract: A supervised technique uses relevance judgments to train a dependency parser such that it approximately optimizes Normalized Discounted Cumulative Gain (NDCG) in information retrieval. A weighted tree edit distance between the parse tree for a query and the parse tree for a document is added to a ranking function, where the edit distance weights are parameters from the parser. Using parser parameters in the ranking function enables approximate optimization of the parser's parameters for NDCG by adding some constraints to the objective function.
    Type: Grant
    Filed: December 8, 2010
    Date of Patent: June 25, 2013
    Assignee: Microsoft Corporation
    Inventors: Xiaodong He, Jianfeng Gao, Jennifer Gillenwater
  • TRAINING PARSERS TO APPROXIMATELY OPTIMIZE NDCG
    Publication number: 20120150836
    Abstract: A supervised technique uses relevance judgments to train a dependency parser such that it approximately optimizes Normalized Discounted Cumulative Gain (NDCG) in information retrieval. A weighted tree edit distance between the parse tree for a query and the parse tree for a document is added to a ranking function, where the edit distance weights are parameters from the parser. Using parser parameters in the ranking function enables approximate optimization of the parser's parameters for NDCG by adding some constraints to the objective function.
    Type: Application
    Filed: December 8, 2010
    Publication date: June 14, 2012
    Applicant: Microsoft Corporation
    Inventors: Xiaodong He, Jianfeng Gao, Jennifer Gillenwater