Patents by Inventor Efraim David Feinstein

Efraim David Feinstein 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).

  • INTEGRATED MACHINE LEARNING AND RULES PLATFORM FOR IMPROVED ACCURACY AND ROOT CAUSE ANALYSIS
    Publication number: 20240037425
    Abstract: Aspects of the present disclosure provide techniques for machine learning and rules integration. Embodiments include receiving input values corresponding to a subset of a set of input variables associated with an automated determination. Embodiments include generating a directed acyclic graph (DAG) representing a set of constraints corresponding to the set of input variables. The set of constraints relate to one or more machine learning models and one or more rules. Embodiments include receiving one or more outputs from the one or more machine learning models based on one or more of the input values. Embodiments include determining outcomes for the one or more rules based on at least one of the input values. Embodiments include populating the DAG based on the input values, the one or more outputs, and the outcomes. Embodiments include making the automated determination based on logic represented by the DAG.
    Type: Application
    Filed: May 8, 2023
    Publication date: February 1, 2024
    Inventors: Sricharan Kallur Palli KUMAR, Conrad DE PEUTER, Efraim David FEINSTEIN, Nagaraj JANARDHANA, Yi Xu NG, Ian Andrew SEBANJA
  • Integrated machine learning and rules platform for improved accuracy and root cause analysis
    Patent number: 11687799
    Abstract: Aspects of the present disclosure provide techniques for machine learning and rules integration. Embodiments include receiving input values corresponding to a subset of a set of input variables associated with an automated determination. Embodiments include generating a directed acyclic graph (DAG) representing a set of constraints corresponding to the set of input variables. The set of constraints relate to one or more machine learning models and one or more rules. Embodiments include receiving one or more outputs from the one or more machine learning models based on one or more of the input values. Embodiments include determining outcomes for the one or more rules based on at least one of the input values. Embodiments include populating the DAG based on the input values, the one or more outputs, and the outcomes. Embodiments include making the automated determination based on logic represented by the DAG.
    Type: Grant
    Filed: July 28, 2022
    Date of Patent: June 27, 2023
    Assignee: INTUIT, INC.
    Inventors: Sricharan Kallur Palli Kumar, Conrad De Peuter, Efraim David Feinstein, Nagaraj Janardhana, Yi Xu Ng, Ian Andrew Sebanja
  • MULTIPLE INPUT MACHINE LEARNING FRAMEWORK FOR ANOMALY DETECTION
    Publication number: 20230132720
    Abstract: A method that includes extracting image features of a document image, executing an optical character recognition (OCR) engine on the document image to obtain OCR output, and extracting OCR features from the OCR output. The method further includes executing an anomaly detection model using features including the OCR features and the image features to generate anomaly score, and presenting anomaly score.
    Type: Application
    Filed: October 29, 2021
    Publication date: May 4, 2023
    Applicant: Intuit Inc.
    Inventors: Fadoua Khmaissia, Efraim David Feinstein, Preeti Duraipandian