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:
Nov. 19, 2019

Filed:

Mar. 15, 2017
Applicant:

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

Inventors:

Ezra Spiro, New York City, NY (US);

Joseph Staehle, New York City, NY (US);

Andrew Levine, New York City, NY (US);

Juan Ricafort, New York City, NY (US);

Alvaro Morales, New York City, NY (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04L 29/06 (2006.01); G06F 16/2457 (2019.01); G06F 3/0482 (2013.01); G06F 21/55 (2013.01); H04L 12/58 (2006.01); G06F 16/951 (2019.01);
U.S. Cl.
CPC ...
H04L 63/1425 (2013.01); G06F 3/0482 (2013.01); G06F 16/24578 (2019.01); G06F 16/951 (2019.01); G06F 21/552 (2013.01); H04L 51/12 (2013.01); H04L 63/1416 (2013.01); H04L 63/1433 (2013.01); H04L 63/1483 (2013.01);
Abstract

A data analysis system receives potentially undesirable electronic communications and automatically groups them in computationally-efficient data clusters, automatically analyze those data clusters, automatically tags and groups those data clusters, and provides results of the automated analysis and grouping in an optimized way to an analyst. The automated analysis of the data clusters may include an automated application of various criteria or rules so as to generate an ordered display of the groups of related data clusters such that the analyst may quickly and efficiently evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters.


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