Patents by Inventor Jill Hagenkord

Jill Hagenkord 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).

  • SYSTEMS AND METHODS FOR ENHANCED USER SPECIFIC PREDICTIONS USING MACHINE LEARNING TECHNIQUES
    Publication number: 20230316092
    Abstract: Data sets can be processed using machine learning or artificial intelligence models to generate outputs predictive of a degree to which performing a protocol can positively modify an expected result associated with a condition. Generating the output may include accessing a user data set, inputting the user data set into a trained machine learning model to generate an output, and selecting an incomplete subset of a set of genes based on the output.
    Type: Application
    Filed: February 17, 2023
    Publication date: October 5, 2023
    Applicant: Color Health, Inc.
    Inventors: Carmen Lai, Jill Hagenkord, Katsuya Noguchi
  • Systems and methods for enhanced user specific predictions using machine learning techniques
    Patent number: 11599800
    Abstract: Data sets can be processed using machine learning or artificial intelligence models to generate outputs predictive of a degree to which performing a protocol can positively modify an expected result associated with a condition. Generating the output may include accessing a user data set, inputting the user data set into a trained machine learning model to generate an output, and selecting an incomplete subset of a set of genes based on the output.
    Type: Grant
    Filed: January 28, 2020
    Date of Patent: March 7, 2023
    Assignee: Color Genomics, Inc.
    Inventors: Carmen Lai, Jill Hagenkord, Katsuya Noguchi
  • SYSTEMS AND METHODS FOR ENHANCED USER SPECIFIC PREDICTIONS USING MACHINE LEARNING TECHNIQUES
    Publication number: 20210232934
    Abstract: Data sets can be processed using machine learning or artificial intelligence models to generate outputs predictive of a degree to which performing a protocol can positively modify an expected result associated with a condition. Generating the output may include accessing a user data set, inputting the user data set into a trained machine learning model to generate an output, and selecting an incomplete subset of a set of genes based on the output.
    Type: Application
    Filed: January 28, 2020
    Publication date: July 29, 2021
    Inventors: Carmen Lai, Jill Hagenkord, Katsuya Noguchi