Invention Application
- Patent Title: SYSTEMS AND METHODS FOR AUTONOMOUS NETWORK MANAGEMENT USING DEEP REINFORCEMENT LEARNING
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Application No.: US17101749Application Date: 2020-11-23
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Publication No.: US20220167188A1Publication Date: 2022-05-26
- Inventor: Said Soulhi , Bryan Christopher Larish
- Applicant: Verizon Patent and Licensing Inc.
- Applicant Address: US NJ Basking Ridge
- Assignee: Verizon Patent and Licensing Inc.
- Current Assignee: Verizon Patent and Licensing Inc.
- Current Assignee Address: US NJ Basking Ridge
- Main IPC: H04W24/08
- IPC: H04W24/08 ; H04L12/26 ; H04L12/24 ; H04W24/02 ; H04W28/02

Abstract:
A system described herein may provide a technique for analyzing metrics, parameters, attributes, and/or other information associated with networks or other devices or systems associated with high-dimensional data in order to determine potential configuration changes that may be made to such networks or other devices or systems in order to optimize and/or otherwise enhance the operation of such networks or other devices or systems. Multiple autoencoders associated with multiple dimensions may be used to calculate reconstruction errors or other features of data (e.g., metrics, parameters, etc.) that may be used to define operating or performance states of the network. Operating or performance states of network components may be mapped to quantum state objects (“QSOs”) for analysis using artificial intelligence and/or machine learning techniques or other suitable techniques.
Public/Granted literature
- US11601830B2 Systems and methods for autonomous network management using deep reinforcement learning Public/Granted day:2023-03-07
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