Systems and methods for automatic clustering and canonical designation of related data in various data structures

US10127289B2 · kind B2 · utility

Assignee

• Palantir Technologies Inc. · US

Inventors

• Lawrence Manning · New York, US
• Rahul Mehta · New York, US
• Daniel Erenrich · Mountain View, US
• Guillem Palou Visa · London, GB
• Roger Hu · New York, US
• Xavier Falco · London, GB
• Rowan Gilmore · London, GB
• Eli Bingham · New York, US
• Jason Prestinario · New York, US
• Yifei Huang · Redwood City, US
• Daniel Fernandez · New York, US
• Jeremy Elser · New York, US
• Clayton Sader · San Francisco, US
• Rahul Agarwal · Livermore, US
• Matthew Elkherj · Menlo Park, US
• Nicholas Latourette · San Francisco, US
• Aleksandr Zamoshchin · San Francisco, US

Key dates

• Filing date: Aug 10, 2016
• Grant date: Nov 13, 2018
• Expiry date: Aug 10, 2036

Classification

• Technology area (CPC G): Physics
• CPC primary: G06F18/23
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.