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:
Jan. 03, 2023

Filed:

Aug. 26, 2020
Applicant:

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

Inventors:

David Cohen, Mountain View, CA (US);

Jason Ma, Mountain View, CA (US);

Bing Jie Fu, Redwood City, CA (US);

Ilya Nepomnyashchiy, Mountain View, CA (US);

Steven Berler, Menlo Park, CA (US);

Alex Smaliy, Palo Alto, CA (US);

Jack Grossman, Albuquerque, NM (US);

James Thompson, London, GB;

Julia Boortz, Menlo Park, CA (US);

Matthew Sprague, Palo Alto, CA (US);

Parvathy Menon, San Jose, CA (US);

Michael Kross, Palo Alto, CA (US);

Michael Harris, Palo Alto, CA (US);

Adam Borochoff, New York, NY (US);

Assignee:

Palantir Technologies Inc., Denver, CO (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
H04L 9/40 (2022.01); G06Q 40/00 (2012.01); G06F 16/28 (2019.01);
U.S. Cl.
CPC ...
H04L 63/1425 (2013.01); G06F 16/285 (2019.01); G06Q 40/12 (2013.12); H04L 63/145 (2013.01); H04L 63/1408 (2013.01);
Abstract

Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as 'summaries' or 'conclusions') of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.


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