Patents by Inventor Patrick Shafto

Patrick Shafto 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 for selecting and presenting examples to explain decisions of algorithms
    Patent number: 11468322
    Abstract: A method of generating a set of examples for explaining decisions made by a machine learning program, involving receiving a set of training data for training the program, and for given subsets of the training data, determining each of (a) a probability of a user correctly inferring a future decision of the program after observing the respective decisions of the program for the given subset of the training data, (b) a suitability of a size of the given subset, and (c) an average probability of the user correctly inferring a future decision of the program after observing the respective decisions of the program for an unspecified subset of the training data. The determinations (a), (b) and (c) are used to score each of the given subsets of training data, and a subset of training data is selected as the generated set of examples based on the scores.
    Type: Grant
    Filed: November 27, 2019
    Date of Patent: October 11, 2022
    Assignee: Rutgers, The State University of New Jersey
    Inventors: Patrick Shafto, Scott Cheng-Hsin Yang, Wai Keen Vong, Ravi Sojitra
  • Method For Selecting And Presenting Examples To Explain Decisions Of Algorithms
    Publication number: 20200175367
    Abstract: A method of generating a set of examples for explaining decisions made by a machine learning program, involving receiving a set of training data for training the program, and for given subsets of the training data, determining each of (a) a probability of a user correctly inferring a future decision of the program after observing the respective decisions of the program for the given subset of the training data, (b) a suitability of a size of the given subset, and (c) an average probability of the user correctly inferring a future decision of the program after observing the respective decisions of the program for an unspecified subset of the training data. The determinations (a), (b) and (c) are used to score each of the given subsets of training data, and a subset of training data is selected as the generated set of examples based on the scores.
    Type: Application
    Filed: November 27, 2019
    Publication date: June 4, 2020
    Applicant: Rutgers, The State University of New Jersey; Office of Research Commercialization
    Inventors: Patrick Shafto, Scott Cheng-Hsin Yang, Wai Keen Vong, Ravi Sojitra