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:
Oct. 03, 2023

Filed:

Jan. 26, 2022
Applicant:

Sedai Inc., Pleasanton, CA (US);

Inventors:

Suresh Mathew, San Ramon, CA (US);

Nikhil Gopinath Kurup, Tampa, FL (US);

Hari Chandrasekhar, Highlands Ranch, CO (US);

Benjamin Thomas, San Jose, CA (US);

Vaibhav Desai, Fremont, CA (US);

Assignee:

Sedai Inc., Pleasanton, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 11/07 (2006.01); G06F 9/50 (2006.01); G06N 3/08 (2023.01); G06F 8/71 (2018.01); G08B 21/18 (2006.01); H04L 43/16 (2022.01); H04L 67/10 (2022.01); H04L 67/00 (2022.01); G06F 11/30 (2006.01); G06F 11/34 (2006.01);
U.S. Cl.
CPC ...
G06F 9/5016 (2013.01); G06F 8/71 (2013.01); G06F 9/5094 (2013.01); G06F 11/079 (2013.01); G06F 11/0721 (2013.01); G06F 11/0769 (2013.01); G06F 11/3006 (2013.01); G06F 11/34 (2013.01); G06F 11/3452 (2013.01); G06N 3/08 (2013.01); G08B 21/182 (2013.01); H04L 43/16 (2013.01); H04L 67/10 (2013.01); H04L 67/34 (2013.01); G06F 2209/501 (2013.01);
Abstract

Implementations described herein relate to methods, systems, and computer-readable media to identify anomalous applications. In some implementations, the methods may include obtaining application metric data that includes application level metrics and instance level metrics for each instance of a plurality of instances associated with a respective application of a plurality of applications operating over a distributed computing system, generating a first anomaly detection score based on the instance level metrics; generating a second anomaly detection score based on one or more input metrics associated with the respective application, generating a third anomaly detection score based on seasonal metric data associated with the respective application and identifying at least one application of the plurality of applications as an anomalous application based on the first anomaly detection score, the second anomaly detection score, and the third anomaly detection score.


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