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US Patent 9344447 Internal malware data item clustering and analysis

Patent 9344447 was granted and assigned to Palantir Technologies on May, 2016 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
Palantir Technologies
Palantir Technologies
Current Assignee
Palantir Technologies
Palantir Technologies
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
9344447
Patent Inventor Names
Alex Smaliy0
David Cohen0
Ilya Nepomnyashchiy0
Jack Grossman0
James Thompson0
Jason Ma0
Julia Boortz0
Matthew Sprague0
...
Date of Patent
May 17, 2016
Patent Application Number
14486991
Date Filed
September 15, 2014
Patent Citations Received
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US Patent 11997111 Attribute-controlled malware detection
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US Patent 11677764 Automated malware family signature generation
0
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US Patent 11681282 Systems and methods for determining relationships between defects
0
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US Patent 11714869 Automated assistance for generating relevant and valuable search results for an entity of interest
0
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US Patent 11882140 System and method for detecting repetitive cybersecurity attacks constituting an email campaign
0
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US Patent 11895137 Phishing data item clustering and analysis
0
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US Patent 11947569 Framework for exposing network activities
0
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US Patent 11954607 Systems and methods for reducing manufacturing failure rates
0
Patent Primary Examiner
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Lalita M. Hamilton
Patent 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 analysis (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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