Machine learning-based relationship association and related discovery and search engines

US10303999B2 · kind B2 · utility

Assignee

• REFINITIV US ORGANIZATION LLC · US

Inventors

• Shai Hertz · Nes Ziona, IL
• Mans Olof-Ors · Zug, CH
• Enav Weinreb · Petah Tikva, IL
• Oren Hazai · Nes Ziona, IL
• Geoff Horrell · London, GB
• Yael Lindman · Shoham, IL
• Yehonatan Mataraso · New Hyde Park, US
• Phani Nivarthi · West New York, US

Key dates

• Filing date: May 31, 2017
• Grant date: May 28, 2019
• Expiry date: Aug 17, 2037

Classification

• Technology area (CPC G): Physics
• CPC primary: G06F16/951
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

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.