Patents by Inventor Geoff Horrell

Geoff Horrell 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).

  • Machine learning-based relationship association and related discovery and
    Patent number: 11222052
    Abstract: Systems and techniques for determining relationships and association significance between entities are disclosed. The systems and techniques automatically identify supply chain relationships between companies based on unstructured text corpora. The system combines Machine Learning models to identify sentences mentioning supply chain between two companies (evidence), and an aggregation layer to take into account the evidence found and assign a confidence score to the relationship between companies.
    Type: Grant
    Filed: May 24, 2019
    Date of Patent: January 11, 2022
    Inventors: Shai Hertz, Mans Olof-Ors, Enav Weinreb, Oren Hazai, Geoff Horrell, Yael Lindman, Yehonatan Mataraso, Phani Nivarthi
  • MACHINE LEARNING-BASED RELATIONSHIP ASSOCIATION AND RELATED DISCOVERY AND SEARCH ENGINES
    Publication number: 20190354544
    Abstract: Systems and techniques for determining relationships and association significance between entities are disclosed. The systems and techniques automatically identify supply chain relationships between companies based on unstructured text corpora. The system combines Machine Learning models to identify sentences mentioning supply chain between two companies (evidence), and an aggregation layer to take into account the evidence found and assign a confidence score to the relationship between companies.
    Type: Application
    Filed: May 24, 2019
    Publication date: November 21, 2019
    Applicant: Refinitiv US Organization LLC
    Inventors: Shai Hertz, Mans Olof-Ors, Enav Weinreb, Oren Hazai, Geoff Horrell, Yael Lindman, Yehonatan Mataraso, Phani Nivarthi
  • Machine learning-based relationship association and related discovery and search engines
    Patent number: 10303999
    Abstract: Systems and techniques for determining relationships and association significance between entities are disclosed. The systems and techniques automatically identify supply chain relationships between companies based on unstructured text corpora. The system combines Machine Learning models to identify sentences mentioning supply chain between two companies (evidence), and an aggregation layer to take into account the evidence found and assign a confidence score to the relationship between companies.
    Type: Grant
    Filed: May 31, 2017
    Date of Patent: May 28, 2019
    Assignee: Refinitiv US Organization LLC
    Inventors: Shai Hertz, Mans Olof-Ors, Enav Weinreb, Oren Hazai, Geoff Horrell, Yael Lindman, Yehonatan Mataraso, Phani Nivarthi
  • MACHINE LEARNING-BASED RELATIONSHIP ASSOCIATION AND RELATED DISCOVERY AND SEARCH ENGINES
    Publication number: 20180082183
    Abstract: Systems and techniques for determining relationships and association significance between entities are disclosed. The systems and techniques automatically identify supply chain relationships between companies based on unstructured text corpora. The system combines Machine Learning models to identify sentences mentioning supply chain between two companies (evidence), and an aggregation layer to take into account the evidence found and assign a confidence score to the relationship between companies.
    Type: Application
    Filed: May 31, 2017
    Publication date: March 22, 2018
    Applicant: Thomson Reuters Global Resources
    Inventors: Shai Hertz, Mans Olof-Ors, Enav Weinreb, Oren Hazai, Geoff Horrell, Yael Lindman, Yoni Mataraso, Phani Nivarthi