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. 02, 2024

Filed:

Oct. 24, 2022
Applicant:

Ciena Corporation, Hanover, MD (US);

Inventors:

Lyndon Y. Ong, Sunnyvale, CA (US);

David Côté, Gatineau, CA;

Raghuraman Ranganathan, Bellaire, TX (US);

Thomas Triplet, Manotick, CA;

Assignee:

Ciena Corporation, Hanover, MD (US);

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
H04L 41/16 (2022.01); G06F 30/27 (2020.01); G06N 7/00 (2023.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); H04L 12/46 (2006.01);
U.S. Cl.
CPC ...
H04L 41/16 (2013.01); G06F 30/27 (2020.01); G06N 7/00 (2013.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); H04L 12/4641 (2013.01);
Abstract

Artificial Intelligence (AI)-based network control includes obtaining data from a network having a plurality of network elements; analyzing the data with one or more Machine Learning (ML) algorithms to determine one or more actions for network control; analyzing the determined one or more actions to determine any risks associated therewith; and one of allowing, modifying, and blocking the determined one or more actions based on the determined risks to safeguard the network. The risks can be based on one or more of (1) non-deterministic behavior AI inference which is statistical in nature, (2) unbounded uncertainty of the AI inference that can result in arbitrarily large inaccuracy on rare occasions, (3) unpredictable behavior of the AI inference in presence of input data that is different than data in training and testing datasets, and (4) malicious input data.


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