Patents by Inventor Eric D. Tryon

Eric D. Tryon 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).

  • Machine learning techniques for natural language processing using predictive entity scoring
    Patent number: 12093651
    Abstract: There is a need for more accurate and more efficient natural language solutions with greater semantic intelligence. This need can be addressed, for example, by natural language processing techniques that utilize predictive entity scoring. In one example, a method includes determining an overall prevalence score for the input entity data object with respect to a scored document corpus and a target section; determining a qualified prevalence score for the input entity data object with respect to a high-scoring subset of the scored document corpus; processing the input entity data object using an entity scoring machine learning model to generate the predicted entity score, wherein the entity scoring machine learning model may characterized by a plurality of multiplicative hyper-parameters and one or more additive hyper-parameters; and performing one or more prediction-based actions based at least in part on the predicted entity score.
    Type: Grant
    Filed: February 9, 2022
    Date of Patent: September 17, 2024
    Assignee: Optum, Inc.
    Inventors: Nathan H. Funk, Eric D. Tryon, Amy L. Jensen, Sudheer Ponnala, M. P. S. Jagannadha Rao, Raghav Bali, Veera Raghavendra Chikka, Subhadip Maji, Anudeep Srivatsav Appe
  • Machine learning techniques for natural language processing using predictive entity scoring
    Patent number: 11853700
    Abstract: There is a need for more accurate and more efficient natural language solutions with greater semantic intelligence. This need can be addressed, for example, by natural language processing techniques that utilize predictive entity scoring. In one example, a method includes determining an overall prevalence score for the input entity data object with respect to a scored document corpus and a target section; determining a qualified prevalence score for the input entity data object with respect to a high-scoring subset of the scored document corpus; processing the input entity data object using an entity scoring machine learning model to generate the predicted entity score, wherein the entity scoring machine learning model may characterized by a plurality of multiplicative hyper-parameters and one or more additive hyper-parameters; and performing one or more prediction-based actions based at least in part on the predicted entity score.
    Type: Grant
    Filed: January 31, 2023
    Date of Patent: December 26, 2023
    Assignee: Optum, Inc.
    Inventors: Nathan H. Funk, Eric D. Tryon, Amy L. Jensen, Sudheer Ponnala, M. P. S. Jagannadha Rao, Raghav Bali, Veera Raghavendra Chikka, Subhadip Maji, Anudeep Srivatsav Appe