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    SYSTEMS AND METHODS FOR AUTONOMOUS NETWORK MANAGEMENT USING DEEP REINFORCEMENT LEARNING
    4.
    发明申请
    SYSTEMS AND METHODS FOR AUTONOMOUS NETWORK MANAGEMENT USING DEEP REINFORCEMENT LEARNING 有权

    公开(公告)号：US20220167188A1

    公开(公告)日：2022-05-26

    申请号：US17101749

    申请日：2020-11-23

    Applicant: Verizon Patent and Licensing Inc.

    Inventor： Said Soulhi ,  Bryan Christopher Larish

    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.

    Systems and methods for managing network performance based on defining rewards for a reinforcement learning model
    9.
    发明授权
    Systems and methods for managing network performance based on defining rewards for a reinforcement learning model 有权

    公开(公告)号：US11063841B2

    公开(公告)日：2021-07-13

    申请号：US16683966

    申请日：2019-11-14

    Applicant: Verizon Patent and Licensing Inc.

    Inventor： Said Soulhi ,  Bryan Christopher Larish

    IPC: H04L12/24 ,  H04L12/26

    Abstract: A device may receive network policies of a network, and network performance data identifying KPIs of the network, and may generate an embedded space of reconstructed data that is embedded in an original space that includes the KPIs. The device may calculate reconstruction errors based on differences between the reconstructed data and the network performance data, and may calculate a convex hull of the original space. The device may calculate a convex hull of the embedded space, and may determine reward metrics based on the reconstruction errors, the convex hull of the original space, and the convex hull of the embedded space. The device may define performance baselines associated with portions, and may generate a new reward for a portion based on a particular reconstruction error, a particular convex hull of the embedded space, and a particular performance baseline. The device may perform actions based on the new reward.

    METHOD AND SYSTEM FOR ANOMALY DETECTION AND NETWORK DEPLOYMENT BASED ON QUANTITATIVE ASSESSMENT
    10.
    发明申请
    METHOD AND SYSTEM FOR ANOMALY DETECTION AND NETWORK DEPLOYMENT BASED ON QUANTITATIVE ASSESSMENT 审中-公开

    公开(公告)号：US20200267174A1

    公开(公告)日：2020-08-20

    申请号：US16797192

    申请日：2020-02-21

    Applicant: Verizon Patent and Licensing Inc.

    Inventor： Said Soulhi ,  Bryan Christopher Larish

    IPC: H04L29/06 ,  H04L12/26 ,  H04L12/24

    Abstract: A method, a device, and a non-transitory storage medium provide a validation and anomaly detection service. The service includes quantitatively assessing latent space data representative of network performance data, which may be generated by a generative model, based on quantitative values pertaining to quantitative criteria. The quantitative criteria may include Hausdorff distances, divergence, joint entropy, and/or total correlation. The service further includes generating geogrid data for services areas of deployed network devices and service areas for prospective and new deployments based on selected latent space data and corresponding network performance data.

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