Patents by Inventor Aaron Beppu

Aaron Beppu 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 calibrating a machine learning model
    Patent number: 10572832
    Abstract: Systems and methods include: collecting digital threat scores of an incumbent digital threat machine learning model; identifying incumbent and successor digital threat score distributions; identifying quantiles data of the incumbent digital threat score distribution; collecting digital threat scores of a successor digital threat machine learning model; calibrating the digital threat scores of the successor digital threat score distribution based on the quantiles data of the incumbent digital threat score distribution and the incumbent digital threat score distribution; and in response to remapping the digital threat scores of the successor digital threat score distribution, publishing the successor digital scores in lieu of the incumbent digital threat scores based on requests for digital threat scores.
    Type: Grant
    Filed: May 14, 2019
    Date of Patent: February 25, 2020
    Assignee: Sift Science, Inc.
    Inventors: Fred Sadaghiani, Aaron Beppu, Jacob Burnim, Alex Paino
  • SYSTEMS AND METHODS FOR CALIBRATING A MACHINE LEARNING MODEL
    Publication number: 20190272373
    Abstract: Systems and methods include: collecting digital threat scores of an incumbent digital threat machine learning model; identifying incumbent and successor digital threat score distributions; identifying quantiles data of the incumbent digital threat score distribution; collecting digital threat scores of a successor digital threat machine learning model; calibrating the digital threat scores of the successor digital threat score distribution based on the quantiles data of the incumbent digital threat score distribution and the incumbent digital threat score distribution; and in response to remapping the digital threat scores of the successor digital threat score distribution, publishing the successor digital scores in lieu of the incumbent digital threat scores based on requests for digital threat scores.
    Type: Application
    Filed: May 14, 2019
    Publication date: September 5, 2019
    Inventors: Fred Sadaghiani, Aaron Beppu, Jacob Burnim, Alex Paino
  • Systems and methods for calibrating a machine learning model
    Patent number: 10339472
    Abstract: Systems and methods include: collecting digital threat scores of an incumbent digital threat machine learning model; identifying incumbent and successor digital threat score distributions; identifying quantiles data of the incumbent digital threat score distribution; collecting digital threat scores of a successor digital threat machine learning model; calibrating the digital threat scores of the successor digital threat score distribution based on the quantiles data of the incumbent digital threat score distribution and the incumbent digital threat score distribution; and in response to remapping the digital threat scores of the successor digital threat score distribution, publishing the successor digital scores in lieu of the incumbent digital threat scores based on requests for digital threat scores.
    Type: Grant
    Filed: March 30, 2018
    Date of Patent: July 2, 2019
    Assignee: Sift Science, Inc.
    Inventors: Fred Sadaghiani, Aaron Beppu, Jacob Burnim, Alex Paino
  • SYSTEMS AND METHODS FOR CALIBRATING A MACHINE LEARNING MODEL
    Publication number: 20190019109
    Abstract: Systems and methods include: collecting digital threat scores of an incumbent digital threat machine learning model; identifying incumbent and successor digital threat score distributions; identifying quantiles data of the incumbent digital threat score distribution; collecting digital threat scores of a successor digital threat machine learning model; calibrating the digital threat scores of the successor digital threat score distribution based on the quantiles data of the incumbent digital threat score distribution and the incumbent digital threat score distribution; and in response to remapping the digital threat scores of the successor digital threat score distribution, publishing the successor digital scores in lieu of the incumbent digital threat scores based on requests for digital threat scores.
    Type: Application
    Filed: March 30, 2018
    Publication date: January 17, 2019
    Inventors: Fred Sadaghiani, Aaron Beppu, Jacob Burnim, Alex Paino