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. 17, 2023

Filed:

Mar. 14, 2022
Applicant:

Ciena Corporation, Hanover, MD (US);

Inventors:

David Côté, Gatineau, CA;

Merlin Davies, Montréal, CA;

Olivier Simard, Montréal, CA;

Emil Janulewicz, Ottawa, CA;

Thomas Triplet, Manotick, CA;

Assignee:

Ciena Corporation, Hanover, MD (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
H04L 29/06 (2006.01); H04L 9/40 (2022.01); H04L 43/045 (2022.01); H04L 41/14 (2022.01); G06F 17/18 (2006.01); G06N 20/00 (2019.01); H04L 41/0677 (2022.01); G06F 15/76 (2006.01); G06N 3/08 (2023.01); G06N 20/20 (2019.01); G06N 20/10 (2019.01); G06F 18/2411 (2023.01); G06F 18/2413 (2023.01); G06N 5/01 (2023.01); G06N 3/02 (2006.01); G06N 5/04 (2023.01);
U.S. Cl.
CPC ...
H04L 63/1425 (2013.01); G06F 15/76 (2013.01); G06F 17/18 (2013.01); G06F 18/2411 (2023.01); G06F 18/2413 (2023.01); G06N 3/08 (2013.01); G06N 5/01 (2023.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01); H04L 41/0677 (2013.01); H04L 41/145 (2013.01); H04L 43/045 (2013.01); H04L 63/1441 (2013.01); G06N 3/02 (2013.01); G06N 5/04 (2013.01);
Abstract

Systems and methods include receiving a machine learning model that is configured to detect anomalies in network devices operating in a multi-layer network, wherein the machine learning model is trained via unsupervised learning that includes training the machine learning model with unlabeled data that describes an operational status of the network devices over time; receiving live data related to a current operational status of the network devices; analyzing the live data with the machine learning model; and detecting an anomaly related to any of the network device based on the analyzing.


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