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:
Apr. 04, 2023

Filed:

Dec. 28, 2017
Applicant:

Elasticsearch B.v., Mountain View, CA (US);

Inventors:

Stephen Dodson, London, GB;

Thomas Veasey, York, GB;

Assignee:

ELASTICSEARCH B.V., Amsterdam, NL;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
H04L 9/40 (2022.01); G06N 5/025 (2023.01); G06F 7/483 (2006.01); G06F 16/28 (2019.01); H04L 43/16 (2022.01); H04L 41/14 (2022.01); H04L 41/147 (2022.01); H04L 41/22 (2022.01); H04L 41/5019 (2022.01); H04L 43/0876 (2022.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
H04L 63/1425 (2013.01); G06F 7/483 (2013.01); G06F 16/285 (2019.01); G06N 5/025 (2013.01); H04L 41/145 (2013.01); H04L 41/147 (2013.01); H04L 43/16 (2013.01); H04L 63/1416 (2013.01); G06F 2207/4818 (2013.01); G06N 20/00 (2019.01); H04L 41/22 (2013.01); H04L 41/5019 (2013.01); H04L 43/0876 (2013.01);
Abstract

Clustering and outlier detection in anomaly and causation detection for computing environments is disclosed. An example method includes receiving an input stream having data instances, each of the data instances having multi-dimensional attribute sets, identifying any of outliers and singularities in the data instances, extracting the outliers and singularities, grouping two or more of the data instances into one or more groups based on correspondence between the multi-dimensional attribute sets and a clustering type, and displaying the grouped data instances that are not extracted in a plurality of clustering maps on an interactive graphical user interface, wherein each of the plurality of clustering maps is based on a unique clustering type.


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