Patents by Inventor Patrick Kenichi Herring

Patrick Kenichi Herring 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).

  • METHODS FOR PREDICTING LIKELIHOOD OF SUCCESSFUL EXPERIMENTAL SYNTHESIS OF COMPUTER-GENERATED MATERIALS BY COMBINING NETWORK ANALYSIS AND MACHINE LEARNING
    Publication number: 20230135041
    Abstract: One aspect of the disclosure relates to systems and methods for determining probabilities of successful synthesis of materials in the real world at one or more points in time. The probabilities of successful synthesis of materials in the real world at one or more points in time can be determined by representing the materials and their pre-defined relationships respectively as nodes and edges in a network form, and computation of the parameters of the nodes in the network as input to a classification model for successful synthesis. The classification model being configured to determine probabilities of successful synthesis of materials in the real world at one or more points in time.
    Type: Application
    Filed: December 28, 2022
    Publication date: May 4, 2023
    Inventors: MURATAHAN AYKOL, SANTOSH KARTHIK SURAM, LINDA HUNG, PATRICK KENICHI HERRING
  • Methods for predicting likelihood of successful experimental synthesis of computer-generated materials by combining network analysis and machine learning
    Patent number: 11580431
    Abstract: One aspect of the disclosure relates to systems and methods for determining probabilities of successful synthesis of materials in the real world at one or more points in time. The probabilities of successful synthesis of materials in the real world at one or more points in time can be determined by representing the materials and their pre-defined relationships respectively as nodes and edges in a network form, and computation of the parameters of the nodes in the network as input to a classification model for successful synthesis. The classification model being configured to determine probabilities of successful synthesis of materials in the real world at one or more points in time.
    Type: Grant
    Filed: June 8, 2018
    Date of Patent: February 14, 2023
    Assignee: Toyota Research Institute, Inc.
    Inventors: Muratahan Aykol, Santosh Karthik Suram, Linda Hung, Patrick Kenichi Herring
  • METHODS FOR PREDICTING LIKELIHOOD OF SUCCESSFUL EXPERIMENTAL SYNTHESIS OF COMPUTER-GENERATED MATERIALS BY COMBINING NETWORK ANALYSIS AND MACHINE LEARNING
    Publication number: 20190378029
    Abstract: One aspect of the disclosure relates to systems and methods for determining probabilities of successful synthesis of materials in the real world at one or more points in time. The probabilities of successful synthesis of materials in the real world at one or more points in time can be determined by representing the materials and their pre-defined relationships respectively as nodes and edges in a network form, and computation of the parameters of the nodes in the network as input to a classification model for successful synthesis. The classification model being configured to determine probabilities of successful synthesis of materials in the real world at one or more points in time.
    Type: Application
    Filed: June 8, 2018
    Publication date: December 12, 2019
    Inventors: Muratahan Aykol, Santosh Karthik Suram, Linda Hung, Patrick Kenichi Herring