The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 06, 2016

Filed:

Apr. 01, 2016
Applicant:

Palantir Technologies Inc., Palo Alto, CA (US);

Inventors:

James Rosswog, Leesburg, VA (US);

Matthew Gerhardt, Washington, DC (US);

Eric Raboin, College Park, MD (US);

Daniel Erenrich, Mountain View, CA (US);

Arseny Bogomolov, Arlington, VA (US);

Cooper Bills, Mountain View, CA (US);

Eric Anderson, Arlington, VA (US);

Jack Grossman, Palo Alto, CA (US);

Kevin Simons, San Francisco, CA (US);

Matthew Levan, Arlington, VA (US);

Nathaniel Klein, Washington, DC (US);

Ryan Beiermeister, Washington, DC (US);

Tim O'Brien, Washington, DC (US);

Assignee:

PALANTIR TECHNOLOGIES INC., Palo Alto, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 15/18 (2006.01); G06N 5/04 (2006.01); G06F 17/30 (2006.01); G06N 7/00 (2006.01); G06N 99/00 (2010.01);
U.S. Cl.
CPC ...
G06N 5/04 (2013.01); G06F 17/30011 (2013.01); G06F 17/30598 (2013.01); G06N 7/005 (2013.01); G06N 99/005 (2013.01);
Abstract

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.


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