Patents by Inventor Yaxiao Song

Yaxiao Song 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).

  • Sentiment-based recommendations as a function of grounding factors associated with a user
    Patent number: 10650804
    Abstract: A “Facet Recommender” creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks.
    Type: Grant
    Filed: May 14, 2018
    Date of Patent: May 12, 2020
    Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
    Inventors: Bill Dolan, Margaret Mitchell, Jay Banerjee, Pallavi Choudhury, Susan Hendrich, Rebecca Mason, Ron Owens, Mouni Reddy, Yaxiao Song, Kristina Toutanova, Liang Xu, Xuetao Yin
  • SENTIMENT-BASED RECOMMENDATIONS AS A FUNCTION OF GROUNDING FACTORS ASSOCIATED WITH A USER
    Publication number: 20180261211
    Abstract: A “Facet Recommender” creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks.
    Type: Application
    Filed: May 14, 2018
    Publication date: September 13, 2018
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Bill Dolan, Margaret Mitchell, Jay Banerjee, Pallavi Choudhury, Susan Hendrich, Rebecca Mason, Ron Owens, Mouni Reddy, Yaxiao Song, Kristina Toutanova, Liang Xu, Xuetao Yin
  • Facet recommendations from sentiment-bearing content
    Patent number: 9978362
    Abstract: A “Facet Recommender” creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks.
    Type: Grant
    Filed: September 2, 2014
    Date of Patent: May 22, 2018
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Bill Dolan, Margaret Mitchell, Jay Banerjee, Pallavi Choudhury, Susan Hendrich, Rebecca Mason, Ron Owens, Mouni Reddy, Yaxiao Song, Kristina Toutanova, Liang Xu, Xuetao Yin
  • FACET RECOMMENDATIONS FROM SENTIMENT-BEARING CONTENT
    Publication number: 20160063993
    Abstract: A “Facet Recommender” creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks.
    Type: Application
    Filed: September 2, 2014
    Publication date: March 3, 2016
    Inventors: Bill Dolan, Margaret Mitchell, Jay Banerjee, Pallavi Choudhury, Susan Hendrich, Rebecca Mason, Ron Owens, Mouni Reddy, Yaxiao Song, Kristina Toutanova, Liang Xu, Xuetao Yin