Patents by Inventor Samuel ANTHONY

Samuel ANTHONY 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).

  • Systems and methods for machine learning enhanced by human measurements
    Patent number: 11042785
    Abstract: In various embodiments, training objects are classified by human annotators, psychometric data characterizing the annotation of the training objects is acquired, a human-weighted loss function based at least in part on the classification data and the psychometric data is computationally derived, and one or more features of a query object are computationally classifies based at least in part on the human-weighted loss function.
    Type: Grant
    Filed: April 7, 2020
    Date of Patent: June 22, 2021
    Assignee: PRESIDENT AND FELLOWS OF HARVARD COLLEGE
    Inventors: David Cox, Walter Scheirer, Samuel Anthony, Ken Nakayama
  • SYSTEMS AND METHODS FOR MACHINE LEARNING ENHANCED BY HUMAN MEASUREMENTS
    Publication number: 20200242416
    Abstract: In various embodiments, training objects are classified by human annotators, psychometric data characterizing the annotation of the training objects is acquired, a human-weighted loss function based at least in part on the classification data and the psychometric data is computationally derived, and one or more features of a query object are computationally classifies based at least in part on the human-weighted loss function.
    Type: Application
    Filed: April 7, 2020
    Publication date: July 30, 2020
    Inventors: David COX, Walter SCHEIRER, Samuel ANTHONY, Ken NAKAYAMA
  • Systems and methods for machine learning enhanced by human measurements
    Patent number: 10650280
    Abstract: In various embodiments, training objects are classified by human annotators, psychometric data characterizing the annotation of the training objects is acquired, a human-weighted loss function based at least in part on the classification data and the psychometric data is computationally derived, and one or more features of a query object are computationally classified based at least its part on the human-weighted loss function.
    Type: Grant
    Filed: April 11, 2019
    Date of Patent: May 12, 2020
    Assignee: PRESIDENT AND FELLOWS OF HARVARD COLLEGE
    Inventors: David Cox, Walter Scheirer, Samuel Anthony, Ken Nakayama
  • SYSTEMS AND METHODS FOR MACHINE LEARNING ENHANCED BY HUMAN MEASUREMENTS
    Publication number: 20190236413
    Abstract: In various embodiments, training objects are classilied by human annotators, psychometric data characterizing the annotation of the training objects is acquired, a human-weighted loss function based at least in part on the colassification data and the psychometric data is computationally derived, and one or more features of a query object are computationally classified based at least its part on the human-weighted loss function.
    Type: Application
    Filed: April 11, 2019
    Publication date: August 1, 2019
    Inventors: David COX, Walter SCHEIRER, Samuel ANTHONY, Ken NAKAYAMA
  • Systems and methods for machine learning enhanced by human measurements
    Patent number: 10303982
    Abstract: In various embodiments, training objects are classified by human annotators, psychometric data characterizing the annotation of the training objects is acquired, a human-weighted loss function based at least in part on the classification data and the psychometric data is computationally derived, and one or more features of a query object are computationally classified based at least in part on the human-weighted loss function.
    Type: Grant
    Filed: July 25, 2018
    Date of Patent: May 28, 2019
    Assignee: PRESIDENT AND FELLOWS OF HARVARD COLLEGE
    Inventors: David Cox, Walter Scheirer, Samuel Anthony, Ken Nakayama
  • SYSTEMS AND METHODS FOR MACHINE LEARNING ENHANCED BY HUMAN MEASUREMENTS
    Publication number: 20180357515
    Abstract: In various embodiments, training objects are classified by human annotators, psychometric data characterizing the annotation of the training objects is acquired, a human-weighted loss function based at least in part on the classification data and the psychometric data is computationally derived, and one or more features of a query object are computationally classified based at least in part on the human-weighted loss function.
    Type: Application
    Filed: July 25, 2018
    Publication date: December 13, 2018
    Inventors: David COX, Walter SCHEIRER, Samuel ANTHONY, Ken NAKAYAMA
  • Systems and methods for machine learning enhanced by human measurements
    Patent number: 10062011
    Abstract: In various embodiments, training objects are classified by human annotators, psychometric data characterizing the annotation of the training objects is acquired, a human-weighted loss function based at least in part on the classification data and the psychometric data is computationally derived, and one or more features of a query object are computationally classified based at least in part on the human-weighted loss function.
    Type: Grant
    Filed: September 13, 2017
    Date of Patent: August 28, 2018
    Assignee: PRESIDENT AND FELLOWS OF HARVARD COLLEGE
    Inventors: David Cox, Walter Scheirer, Samuel Anthony, Ken Nakayama
  • SYSTEMS AND METHODS FOR MACHINE LEARNING ENHANCED BY HUMAN MEASUREMENTS
    Publication number: 20180012106
    Abstract: In various embodiments, training objects are classified by human annotators, psychometric data characterizing the annotation of the training objects is acquired, a human-weighted loss function based at least in part on the classification data and the psychometric data is computationally derived, and one or more features of a query object are computationally classified based at least in part on the human-weighted loss function.
    Type: Application
    Filed: September 13, 2017
    Publication date: January 11, 2018
    Inventors: David COX, Walter SCHEIRER, Samuel ANTHONY, Ken NAKAYAMA
  • Systems and methods for machine learning enhanced by human measurements
    Patent number: 9792532
    Abstract: In various embodiments, training objects are classified by human annotators, psychometric data characterizing the annotation of the training objects is acquired, a human-weighted loss function based at least in part on the classification data and the psychometric data is computationally derived, and one or more features of a query object are computationally classified based at least in part on the human-weighted loss function.
    Type: Grant
    Filed: June 26, 2014
    Date of Patent: October 17, 2017
    Assignee: PRESIDENT AND FELLOWS OF HARVARD COLLEGE
    Inventors: David Cox, Walter Scheirer, Samuel Anthony, Ken Nakayama
  • SYSTEMS AND METHODS FOR MACHINE LEARNING ENHANCED BY HUMAN MEASUREMENTS
    Publication number: 20160148077
    Abstract: In various embodiments, training objects are classified by human annotators, psychometric data characterizing the annotation of the training objects is acquired, a human-weighted loss function based at least in part on the classification data and the psychometric data is computationally derived, and one or more features of a query object are computationally classified based at least in part on the human-weighted loss function.
    Type: Application
    Filed: June 26, 2014
    Publication date: May 26, 2016
    Inventors: David COX, Walter SCHEIRER, Samuel ANTHONY, Ken NAKAYAMA