Patents by Inventor Iman MALIK

Iman MALIK 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).

  • Method of and server for training a machine learning algorithm for estimating uncertainty of a sequence of models
    Patent number: 11562203
    Abstract: There is provided a method and server for estimating an uncertainty parameter of a sequence of computer-implemented models comprising at least one machine learning algorithm (MLA). A set of labelled digital documents is received, which is to be processed by the sequence of models. For a given model of the sequence of models, at least one of a respective set of input features, a respective set of model-specific features and a respective set of output features are received. The set of predictions output by the sequence of models is received. A second MLA is trained to estimate uncertainty of the sequence of models based on the set of labelled digital documents, and the at least one of the respective set of input features, the respective set of model-specific features, the respective set of output features, and the set of predictions.
    Type: Grant
    Filed: December 30, 2019
    Date of Patent: January 24, 2023
    Assignee: ServiceNow Canada Inc.
    Inventors: Gabrielle Gauthier Melançon, Waseem Gharbieh, Iman Malik, William Xavier Snelgrove
  • METHOD OF AND SERVER FOR TRAINING A MACHINE LEARNING ALGORITHM FOR ESTIMATING UNCERTAINTY OF A SEQUENCE OF MODELS
    Publication number: 20210201112
    Abstract: There is provided a method and server for estimating an uncertainty parameter of a sequence of computer-implemented models comprising at least one machine learning algorithm (MLA). A set of labelled digital documents is received, which is to be processed by the sequence of models. For a given model of the sequence of models, at least one of a respective set of input features, a respective set of model-specific features and a respective set of output features are received. The set of predictions output by the sequence of models is received. A second MLA is trained to estimate uncertainty of the sequence of models based on the set of labelled digital documents, and the at least one of the respective set of input features, the respective set of model-specific features, the respective set of output features, and the set of predictions.
    Type: Application
    Filed: December 30, 2019
    Publication date: July 1, 2021
    Applicant: ELEMENT AI Inc.
    Inventors: Gabrielle GAUTHIER MELANÇON, Waseem GHARBIEH, Iman MALIK, William Xavier SNELGROVE