Patents by Inventor Michael Thomas Wegan

Michael Thomas Wegan 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).

  • Privacy and proprietary-information preserving collaborative multi-party machine learning
    Patent number: 11893520
    Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.
    Type: Grant
    Filed: June 7, 2022
    Date of Patent: February 6, 2024
    Assignee: Cerebri AI Inc.
    Inventors: Gabriel Mauricio Silberman, Alain Charles Briancon, Lee David Harper, Luke Philip Reding, David Alexander Curry, Jean Joseph Belanger, Michael Thomas Wegan, Thejas Narayana Prasad
  • COLLABORATIVE MULTI-PARTIES/MULTI-SOURCES MACHINE LEARNING FOR AFFINITY ASSESSMENT, PERFORMANCE SCORING, AND RECOMMENDATION MAKING
    Publication number: 20230206124
    Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.
    Type: Application
    Filed: December 21, 2022
    Publication date: June 29, 2023
    Inventors: Gabriel Mauricio Silberman, Alain Charles Briancon, Lee David Harper, Luke Philip Reding, David Alexander Curry, Jean Joseph Belanger, Michael Thomas Wegan, Thejas Narayana Prasad
  • PRIVACY & PROPRIETARY-INFORMATION PRESERVING COLLABORATIVE MULTI-PARTY MACHINE LEARNING
    Publication number: 20230080773
    Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.
    Type: Application
    Filed: June 7, 2022
    Publication date: March 16, 2023
    Inventors: Gabriel Mauricio Silberman, Alain Charles Briancon, Lee David Harper, Luke Philip Reding, David Alexander Curry, Jean Joseph Belanger, Michael Thomas Wegan, Thejas Narayana Prasad
  • Collaborative multi-parties/multi-sources machine learning for affinity assessment, performance scoring, and recommendation making
    Patent number: 11556846
    Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.
    Type: Grant
    Filed: October 3, 2019
    Date of Patent: January 17, 2023
    Assignee: Cerebri AI Inc.
    Inventors: Gabriel Mauricio Silberman, Alain Charles Briancon, Lee David Harper, Luke Philip Reding, David Alexander Curry, Jean Joseph Belanger, Michael Thomas Wegan, Thejas Narayana Prasad
  • Privacy and proprietary-information preserving collaborative multi-party machine learning
    Patent number: 11386295
    Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.
    Type: Grant
    Filed: October 3, 2018
    Date of Patent: July 12, 2022
    Assignee: Cerebri AI Inc.
    Inventors: Gabriel Mauricio Silberman, Alain Charles Briancon, Lee David Harper, Luke Philip Reding, David Alexander Curry, Jean Joseph Belanger, Michael Thomas Wegan, Thejas Narayana Prasad
  • COLLABORATIVE MULTI-PARTIES/MULTI-SOURCES MACHINE LEARNING FOR AFFINITY ASSESSMENT, PERFORMANCE SCORING, AND RECOMMENDATION MAKING
    Publication number: 20200111022
    Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.
    Type: Application
    Filed: October 3, 2019
    Publication date: April 9, 2020
    Inventors: Gabriel Mauricio Silberman, Alain Charles Briancon, Lee David Harper, Luke Philip Reding, David Alexander Curry, Jean Joseph Belanger, Michael Thomas Wegan, Thejas Narayana Prasad
  • PRIVACY & PROPRIETARY-INFORMATION PRESERVING COLLABORATIVE MULTI-PARTY MACHINE LEARNING
    Publication number: 20200042828
    Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.
    Type: Application
    Filed: October 3, 2018
    Publication date: February 6, 2020
    Inventors: Gabriel Mauricio Silberman, Alain Charles Briancon, Lee David Harper, Luke Philip Reding, David Alexander Curry, Jean Joseph Belanger, Michael Thomas Wegan, Thejas Narayana Prasad