Patents by Inventor Jason HECKENDORN

Jason HECKENDORN 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).

  • Embedding performance optimization through use of a summary model
    Patent number: 12099539
    Abstract: Aspects of the present disclosure provide techniques for improved text classification. Embodiments include providing, based on a text string, one or more first inputs to a summary model. Embodiments include determining, based on one or more first outputs from the summary model in response to the one or more first inputs, a summarized version of the text string. In some embodiments the summarized version of the text string comprises a number of tokens that is less than or equal to a maximum number of input tokens for a machine learning model. Embodiments include providing, based on the summarized version of the text string, one or more second inputs to the machine learning model. Embodiments include determining one or more attributes of the text string based on one or more second outputs received from the machine learning model in response to the one or more second inputs.
    Type: Grant
    Filed: January 11, 2022
    Date of Patent: September 24, 2024
    Assignee: INTUIT INC.
    Inventors: Krysten Nicole Dell, Jason Heckendorn, Lin Tao, Yingxin Wang
  • EMBEDDING PERFORMANCE OPTIMIZATION THROUGH USE OF A SUMMARY MODEL
    Publication number: 20230222149
    Abstract: Aspects of the present disclosure provide techniques for improved text classification. Embodiments include providing, based on a text string, one or more first inputs to a summary model. Embodiments include determining, based on one or more first outputs from the summary model in response to the one or more first inputs, a summarized version of the text string. In some embodiments the summarized version of the text string comprises a number of tokens that is less than or equal to a maximum number of input tokens for a machine learning model. Embodiments include providing, based on the summarized version of the text string, one or more second inputs to the machine learning model. Embodiments include determining one or more attributes of the text string based on one or more second outputs received from the machine learning model in response to the one or more second inputs.
    Type: Application
    Filed: January 11, 2022
    Publication date: July 13, 2023
    Inventors: Krysten Nicole DELL, Jason HECKENDORN, Lin TAO, Yingxin WANG