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:
Jun. 09, 2020

Filed:

Aug. 30, 2018
Applicant:

Intelligent Fusion Technology, Inc, Germantown, MD (US);

Inventors:

Bin Jia, Germantown, MD (US);

Cailing Dong, Germantown, MD (US);

Zhijiang Chen, Germantown, MD (US);

Kuo-Chu Chang, Fairfax, VA (US);

Nichole Sullivan, Germantown, MD (US);

Genshe Chen, Germantown, MD (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/00 (2020.01); G06F 40/295 (2020.01); G06N 5/04 (2006.01); G06F 16/28 (2019.01); G06F 40/30 (2020.01); G06F 40/253 (2020.01); G06F 40/268 (2020.01);
U.S. Cl.
CPC ...
G06F 40/295 (2020.01); G06F 16/288 (2019.01); G06F 40/253 (2020.01); G06F 40/30 (2020.01); G06N 5/047 (2013.01); G06F 40/268 (2020.01);
Abstract

A method for pattern discovery and real-time anomaly detection based on knowledge graph, comprising: based on a dataset including messages collected within a certain period, constructing a local knowledge graph (KG); applying a statistical relational learning (SRL) model to predict hidden relations between entities to obtain an updated local KG; from all SPO triples of the updated local KG, discovering a normalcy pattern that includes frequent entities, frequent relations, and frequent SPO triples; and in response to receiving streaming data from a message bus, extracting a plurality of entities, a plurality of relations, and a plurality of SPO triples, from the streaming data for comparison with the normalcy pattern using semantic distance, thereby determining whether there is an abnormal entity, relation, or SPO triple in the streaming data.


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