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US Patent 11949567 Safeguarding artificial intelligence-based network control

Patent 11949567 was granted and assigned to Ciena (company) on April, 2024 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent
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Patent attributes

Patent Applicant
Ciena (company)
Ciena (company)
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Current Assignee
Ciena (company)
Ciena (company)
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
119495670
Patent Inventor Names
Lyndon Y. Ong0
Raghuraman Ranganathan0
Thomas Triplet0
David Côté0
Date of Patent
April 2, 2024
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Patent Application Number
179717090
Date Filed
October 24, 2022
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Patent Citations
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US Patent 8606105 Virtual core router and switch systems and methods with a hybrid control architecture
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US Patent 9071532 Method for discovery and load balancing of path computation elements based on transport plane link metrics
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US Patent 9774502 Systems and methods for combined software defined networking and distributed network control
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US Patent 9838271 Network service pricing and resource management in a software defined networking environment
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US Patent 10015115 Software defined networking service control systems and methods of remote services
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US Patent 10187318 Dynamic bandwidth control systems and methods in software defined networking
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US Patent 10621019 Using a client to manage remote machine learning jobs
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US Patent 10474955 Network device management
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...
Patent Primary Examiner
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Thu V. Nguyen
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CPC Code
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G06N 20/00
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G06N 20/20
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G06N 7/00
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H04L 41/16
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H04L 12/4641
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G06F 30/27
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Patent 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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