Palantir Technologies Inc.

Matthew Gerhardt

Andrew Elder

Eric Knudson

Eli Rosofsky

Christopher Brahms

Colin Hamilton

Jack Grossman

Daniel Erenrich

Arseny Bogomolov

Cooper Bills

Eric Anderson

Ryan Beiermeister

Nathaniel Klein

Eric Raboin

Matthew Levan

Kevin Simons

Tim O'Brien

James Rosswog

Computer implemented systems and methods are disclosed for automatically and canonically identifying related data in various data structures while maintaining secure access to individual data objects and their properties. Data structures may include a plurality of records, wherein each record is associated with a respective entity. Access to individual records, or in some embodiments access to individual attributes of a record, may be restricted to particular users or groups on a per-item basis. In accordance with some embodiments, systems and methods are disclosed for identifying pairs of records, determining a probability that both records relate to a common entity, and securely notifying and presenting users with information regarding the probability while preserving the access restrictions for the individual records and attributes. Record pairs that potentially refer to the same entity may be linked, reconciled, or ignored in response to feedback from users who have access to one or both records.

Secure resolution of canonical entities

Computer implemented systems and methods are disclosed for annotating and linking electronic documents. In accordance with some embodiments, annotations assigned to source electronic documents are received and snippets are generated from the received annotations. The generated snippets are aggregated into clusters, which are used to generate an electronic document. Links between the snippets and their respective source documents may be generated and embedded in the generated electronic document.

Systems and methods for annotating and linking electronic documents

Computer implemented systems and methods are disclosed for identifying and categorizing electronic documents through machine learning. In accordance with some embodiments, a seed set of categorized electronic documents may be used to train a document categorizer based on a machine learning algorithm. The trained document categorizer may categorize electronic documents in a large corpus of electronic documents. Performance metrics associated with performance of the trained document categorizer may be tracked, and additional seed sets of categorized electronic documents may be used to improve the performance of document categorizer by retraining the document categorizer on subsequent seed sets. Additional seed sets may and categorizations may be iterated through until a desired document categorization performance is reached.

Systems and methods for identifying and categorizing electronic documents through machine learning

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Washington, DC, United States of America

Matthew Gerhardt