Patents by Inventor Yael Lindman

Yael Lindman 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
  • Self-service classification system
    Patent number: 10990897
    Abstract: Systems, technologies and techniques for generating a customized classification model are disclosed. The system and technologies, such as THOMSON REUTERS SELF-SERVICE CLASSIFICATION™, employ part machine learning and part an user interactive approach to generate a customized classification model. The system combines a novel approach for text classification using a smaller initial set of data to initiate training, with a unique workflow and user interaction for customization.
    Type: Grant
    Filed: April 4, 2017
    Date of Patent: April 27, 2021
    Inventors: Hila Zarosim, Oren Hazai, Ofri Rom, Ehud Azikri, Lior Weintraub, Yael Lindman, Enav Weinreb, Savva Khalaman, Yossi Ben-Shlomo, Dmitry Levinson, Evyatar Sharabi, Alexandra Rabinovich Goldshlager, Shai Hertz
  • 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
  • Self-Service Classification System
    Publication number: 20170286869
    Abstract: Systems, technologies and techniques for generating a customized classification model are disclosed. The system and technologies, such as THOMSON REUTERS SELF-SERVICE CLASSIFICATION™, employ part machine learning and part an user interactive approach to generate a customized classification model. The system combines a novel approach for text classification using a smaller initial set of data to initiate training, with a unique workflow and user interaction for customization.
    Type: Application
    Filed: April 4, 2017
    Publication date: October 5, 2017
    Inventors: Hila Zarosim, Oren Hazai, Ofri Rom, Ehud Azikri, Lior Weintraub, Yael Lindman, Enav Weinreb, Savva Khalaman, Yossi Ben-Shlomo, Dmitry Levinson, Evyatar Sharabi, Alexandra Rabinovich Goldshlager, Shai Hertz