Patents by Inventor Navdeep Gill

Navdeep Gill 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).

  • Model interpretation
    Patent number: 11922283
    Abstract: An indication of a selection of an entry associated with a machine learning model is received. One or more interpretation views associated with one or more machine learning models are dynamically updated based on the selected entry.
    Type: Grant
    Filed: April 20, 2018
    Date of Patent: March 5, 2024
    Assignee: H2O.ai Inc.
    Inventors: Mark Chan, Navdeep Gill, Patrick Hall
  • Model interpretation
    Patent number: 11893467
    Abstract: Input data associated with a machine learning model is classified into a plurality of clusters. A plurality of linear surrogate models are generated. One of the plurality of linear surrogate models corresponds to one of the plurality of clusters. A linear surrogate model is configured to output a corresponding prediction based on input data associated with a corresponding cluster. Prediction data associated with the machine learning model and prediction data associated with the plurality of linear surrogate models are outputted.
    Type: Grant
    Filed: May 20, 2022
    Date of Patent: February 6, 2024
    Assignee: H2O.ai Inc.
    Inventors: Mark Chan, Navdeep Gill, Patrick Hall
  • MODEL INTERPRETATION
    Publication number: 20240005218
    Abstract: An indication of a selection of an entry associated with a machine learning model is received. One or more interpretation views associated with one or more machine learning models are dynamically updated based on the selected entry.
    Type: Application
    Filed: April 21, 2023
    Publication date: January 4, 2024
    Inventors: Mark Chan, Navdeep Gill, Patrick Hall
  • MODEL INTERPRETATION
    Publication number: 20220374746
    Abstract: Input data associated with a machine learning model is classified into a plurality of clusters. A plurality of linear surrogate models are generated. One of the plurality of linear surrogate models corresponds to one of the plurality of clusters. A linear surrogate model is configured to output a corresponding prediction based on input data associated with a corresponding cluster. Prediction data associated with the machine learning model and prediction data associated with the plurality of linear surrogate models are outputted.
    Type: Application
    Filed: May 20, 2022
    Publication date: November 24, 2022
    Inventors: Mark Chan, Navdeep Gill, Patrick Hall
  • Model interpretation
    Patent number: 11386342
    Abstract: Input data associated with a machine learning model is classified into a plurality of clusters. A plurality of linear surrogate models are generated. One of the plurality of linear surrogate models corresponds to one of the plurality of clusters. A linear surrogate model is configured to output a corresponding prediction based on input data associated with a corresponding cluster. Prediction data associated with the machine learning model and prediction data associated with the plurality of linear surrogate models are outputted.
    Type: Grant
    Filed: April 20, 2018
    Date of Patent: July 12, 2022
    Assignee: H2O.ai Inc.
    Inventors: Mark Chan, Navdeep Gill, Patrick Hall
  • MODEL INTERPRETATION
    Publication number: 20190325335
    Abstract: An indication of a selection of an entry associated with a machine learning model is received. One or more interpretation views associated with one or more machine learning models are dynamically updated based on the selected entry.
    Type: Application
    Filed: April 20, 2018
    Publication date: October 24, 2019
    Inventors: Mark Chan, Navdeep Gill, Patrick Hall
  • MODEL INTERPRETATION
    Publication number: 20190325333
    Abstract: Input data associated with a machine learning model is classified into a plurality of clusters. A plurality of linear surrogate models are generated. One of the plurality of linear surrogate models corresponds to one of the plurality of clusters. A linear surrogate model is configured to output a corresponding prediction based on input data associated with a corresponding cluster. Prediction data associated with the machine learning model and prediction data associated with the plurality of linear surrogate models are outputted.
    Type: Application
    Filed: April 20, 2018
    Publication date: October 24, 2019
    Inventors: Mark Chan, Navdeep Gill, Patrick Hall