Patents by Inventor Mark Eberstein

Mark Eberstein 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).

  • DISPARITY MITIGATION IN MACHINE LEARNING-BASED PREDICTIONS FOR DISTINCT CLASSES OF DATA USING DERIVED INDISCERNIBILITY CONSTRAINTS DURING NEURAL NETWORK TRAINING
    Publication number: 20240135186
    Abstract: A system and method includes generating approximate distributions for distinct classes of data samples; computing a first partial Jensen-Shannon (JS) divergence and a second partial JS divergence based on the approximate distribution of the disparity affected class of data samples with reference to the approximate distribution of the control class of data samples; computing a disparity divergence based on the first partial JS divergence and the second partial JS divergence; generating a distribution-matching term based on the disparity divergence, wherein the distribution-matching term mitigates an inferential disparity between the control class of data samples and the disparity affected class of data samples during a training of an unconstrained artificial neural network; constructing a disparity-constrained loss function based on augmenting a target loss function with the distribution-matching term; and transforming the unconstrained ANN to a disparity-constrained ANN based on a training of the unconstraine
    Type: Application
    Filed: December 27, 2023
    Publication date: April 25, 2024
    Applicant: Fairness-as-a-Service, Inc.
    Inventors: John Wickens-Lamb Merrill, Kareem Saleh, Mark Eberstein
  • Disparity mitigation in machine learning-based predictions for distinct classes of data using derived indiscernibility constraints during neural network training
    Patent number: 11934960
    Abstract: A system and method includes generating approximate distributions for distinct classes of data samples; computing a first partial Jensen-Shannon (JS) divergence and a second partial JS divergence based on the approximate distribution of the disparity affected class of data samples with reference to the approximate distribution of the control class of data samples; computing a disparity divergence based on the first partial JS divergence and the second partial JS divergence; generating a distribution-matching term based on the disparity divergence, wherein the distribution-matching term mitigates an inferential disparity between the control class of data samples and the disparity affected class of data samples during a training of an unconstrained artificial neural network; constructing a disparity-constrained loss function based on augmenting a target loss function with the distribution-matching term; and transforming the unconstrained ANN to a disparity-constrained ANN based on a training of the unconstraine
    Type: Grant
    Filed: May 1, 2023
    Date of Patent: March 19, 2024
    Assignee: Fairness-as-a-Service
    Inventors: John Wickens-Lamb Merrill, Kareem Saleh, Mark Eberstein
  • DISPARITY MITIGATION IN MACHINE LEARNING-BASED PREDICTIONS FOR DISTINCT CLASSES OF DATA USING DERIVED INDISCERNIBILITY CONSTRAINTS DURING NEURAL NETWORK TRAINING
    Publication number: 20230267334
    Abstract: A system and method includes generating approximate distributions for distinct classes of data samples; computing a first partial Jensen-Shannon (JS) divergence and a second partial JS divergence based on the approximate distribution of the disparity affected class of data samples with reference to the approximate distribution of the control class of data samples; computing a disparity divergence based on the first partial JS divergence and the second partial JS divergence; generating a distribution-matching term based on the disparity divergence, wherein the distribution-matching term mitigates an inferential disparity between the control class of data samples and the disparity affected class of data samples during a training of an unconstrained artificial neural network; constructing a disparity-constrained loss function based on augmenting a target loss function with the distribution-matching term; and transforming the unconstrained ANN to a disparity-constrained ANN based on a training of the unconstraine
    Type: Application
    Filed: May 1, 2023
    Publication date: August 24, 2023
    Applicant: Fairness-as-a-Service, Inc.
    Inventors: John Wickens-Lamb Merrill, Kareem Saleh, Mark Eberstein
  • Disparity mitigation in machine learning-based predictions for distinct classes of data using derived indiscernibility constraints during neural network training
    Patent number: 11676037
    Abstract: A system and method includes generating approximate distributions for distinct classes of data samples; computing a first partial Jensen-Shannon (JS) divergence and a second partial JS divergence based on the approximate distribution of the disparity affected class of data samples with reference to the approximate distribution of the control class of data samples; computing a disparity divergence based on the first partial JS divergence and the second partial JS divergence; generating a distribution-matching term based on the disparity divergence, wherein the distribution-matching term mitigates an inferential disparity between the control class of data samples and the disparity affected class of data samples during a training of an unconstrained artificial neural network; constructing a disparity-constrained loss function based on augmenting a target loss function with the distribution-matching term; and transforming the unconstrained ANN to a disparity-constrained ANN based on a training of the unconstraine
    Type: Grant
    Filed: December 5, 2022
    Date of Patent: June 13, 2023
    Assignee: Fairness-as-a-Service, Inc.
    Inventors: John Wickens-Lamb Merrill, Kareem Saleh, Mark Eberstein
  • DISPARITY MITIGATION IN MACHINE LEARNING-BASED PREDICTIONS FOR DISTINCT CLASSES OF DATA USING DERIVED INDISCERNIBILITY CONSTRAINTS DURING NEURAL NETWORK TRAINING
    Publication number: 20230177346
    Abstract: A system and method includes generating approximate distributions for distinct classes of data samples; computing a first partial Jensen-Shannon (JS) divergence and a second partial JS divergence based on the approximate distribution of the disparity affected class of data samples with reference to the approximate distribution of the control class of data samples; computing a disparity divergence based on the first partial JS divergence and the second partial JS divergence; generating a distribution-matching term based on the disparity divergence, wherein the distribution-matching term mitigates an inferential disparity between the control class of data samples and the disparity affected class of data samples during a training of an unconstrained artificial neural network; constructing a disparity-constrained loss function based on augmenting a target loss function with the distribution-matching term; and transforming the unconstrained ANN to a disparity-constrained ANN based on a training of the unconstraine
    Type: Application
    Filed: December 5, 2022
    Publication date: June 8, 2023
    Inventors: John Wickens-Lamb Merrill, Kareem Saleh, Mark Eberstein
  • SYSTEMS AND METHODS FOR DETECTING A PEAK IN WEB TRAFFIC ON AN ELECTRONIC NETWORK
    Publication number: 20230103048
    Abstract: Systems and methods are disclosed for attributing web traffic to an advertising spot. The method may include receiving traffic data for a web page from a server associated with an advertiser and receiving, from a log provider, a log of a plurality of advertising spots related to the advertiser. A duration of time as a peak may be designated to identify the amount of traffic that is attributable to the one of the plurality of advertising spots.
    Type: Application
    Filed: November 30, 2022
    Publication date: March 30, 2023
    Inventor: Mark EBERSTEIN
  • Systems and methods for detecting a peak in web traffic on an electronic network
    Patent number: 11544738
    Abstract: Systems and methods are disclosed for attributing web traffic to an advertising spot. The method may include receiving traffic data for a web page from a server associated with an advertiser and receiving, from a log provider, a log of a plurality of advertising spots related to the advertiser. A duration of time as a peak may be designated to identify the amount of traffic that is attributable to the one of the plurality of advertising spots.
    Type: Grant
    Filed: October 31, 2019
    Date of Patent: January 3, 2023
    Assignee: Yahoo Ad Tech LLC
    Inventor: Mark Eberstein
  • SYSTEMS AND METHODS FOR DETECTING A PEAK IN WEB TRAFFIC ON AN ELECTRONIC NETWORK
    Publication number: 20200065854
    Abstract: Systems and methods are disclosed for attributing web traffic to an advertising spot. The method may include receiving traffic data for a web page from a server associated with an advertiser and receiving, from a log provider, a log of a plurality of advertising spots related to the advertiser. A duration of time as a peak may be designated to identify the amount of traffic that is attributable to the one of the plurality of advertising spots.
    Type: Application
    Filed: October 31, 2019
    Publication date: February 27, 2020
    Inventor: Mark EBERSTEIN
  • Systems and methods for detecting a peak in web traffic and attributing the traffic to an advertising spot
    Patent number: 10497021
    Abstract: Systems and methods are disclosed for attributing web traffic to an advertising spot. The method may include receiving traffic data for a web page from a server associated with an advertiser and receiving, from a log provider, a log of a plurality of advertising spots related to the advertiser. A duration of time as a peak may be designated to identify the amount of traffic that is attributable to the one of the plurality of advertising spots.
    Type: Grant
    Filed: January 30, 2015
    Date of Patent: December 3, 2019
    Assignee: Oath (Americas) Inc.
    Inventor: Mark Eberstein