Patents by Inventor Abigail Ward

Abigail Ward 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).

  • Integrating machine learning into control systems for industrial facilities
    Patent number: 11809164
    Abstract: Methods, systems, apparatus and computer program products for implementing machine learning within control systems are disclosed. An industrial facility setting slate can be received from a machine learning system and a determination can be made as to whether to adopt the settings in the industrial facility setting slate. The machine learning model can be a neural network, e.g., a deep neural network, that has been trained, e.g., using reinforcement learning to predict a data setting slate that is predicted to optimize an efficiency of a data center.
    Type: Grant
    Filed: February 25, 2022
    Date of Patent: November 7, 2023
    Assignee: Google LLC
    Inventors: Jim Gao, Christopher Gamble, Amanda Gasparik, Vedavyas Panneershelvam, David Barker, Dustin Reishus, Abigail Ward, Jerry Luo, Brian Kim, Mark Schwabacher, Stephen Webster, Timothy Jason Kieper, Daniel Fuenffinger, Zakerey Bennett
  • POLYPEPTIDE CLEAVAGE METHODS
    Publication number: 20230159598
    Abstract: Methods and constructs for the cleavage of polypeptides at one or more specific positions within the polypeptide are provided.
    Type: Application
    Filed: April 20, 2021
    Publication date: May 25, 2023
    Inventors: Tyler Nusca, Johan Kers, Matthew Weinstock, Michelle Slind, Amy Doerner, Michael Cammarata, Abigail Ward
  • INTEGRATING MACHINE LEARNING INTO CONTROL SYSTEMS FOR INDUSTRIAL FACILITIES
    Publication number: 20220179401
    Abstract: Methods, systems, apparatus and computer program products for implementing machine learning within control systems are disclosed. An industrial facility setting slate can be received from a machine learning system and a determination can be made as to whether to adopt the settings in the industrial facility setting slate. The machine learning model can be a neural network, e.g., a deep neural network, that has been trained, e.g., using reinforcement learning to predict a data setting slate that is predicted to optimize an efficiency of a data center.
    Type: Application
    Filed: February 25, 2022
    Publication date: June 9, 2022
    Inventors: Jim Gao, Christopher Gamble, Amanda Gasparik, Vedavyas Panneershelvam, David Barker, Dustin Reishus, Abigail Ward, Jerry Luo, Brian Kim, Mark Schwabacher, Stephen Webster, Timothy Jason Kieper, Daniel Fuenffinger, Zakerey Bennett
  • INTEGRATING MACHINE LEARNING INTO CONTROL SYSTEMS FOR INDUSTRIAL FACILITIES
    Publication number: 20200050178
    Abstract: Methods, systems, apparatus and computer program products for implementing machine learning within control systems are disclosed. An industrial facility setting slate can be received from a machine learning system and a determination can be made as to whether to adopt the settings in the industrial facility setting slate. The machine learning model can be a neural network, e.g., a deep neural network, that has been trained, e.g., using reinforcement learning to predict a data setting slate that is predicted to optimize an efficiency of a data center.
    Type: Application
    Filed: October 16, 2019
    Publication date: February 13, 2020
    Inventors: Jim Gao, Christopher Gamble, Amanda Gasparik, Vedavyas Panneershelvam, David Barker, Dustin Reishus, Abigail Ward, Jerry Luo, Brian Kim, Mark Schwabacher, Stephen Webster, Timothy Jason Kieper, Daniel Fuenffinger, Zakerey Bennett