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    PEER RISK BENCHMARKING USING GENERATIVE ADVERSARIAL NETWORKS
    2.
    发明申请
    PEER RISK BENCHMARKING USING GENERATIVE ADVERSARIAL NETWORKS 有权

    公开(公告)号：US20220217056A1

    公开(公告)日：2022-07-07

    申请号：US17704449

    申请日：2022-03-25

    Applicant: Cisco Technology, Inc.

    Inventor： Qihong Shao ,  David John Zacks ,  Xinjun Zhang

    IPC: H04L41/14 ,  H04L41/147 ,  H04L43/06 ,  H04L41/12 ,  H04L43/045 ,  H04L41/5067 ,  H04L43/0817 ,  H04L43/55

    Abstract: A method, computer system, and computer program product are provided for peer risk benchmarking. Customer data for a first network is obtained, wherein the customer data comprises a role of one or more network devices in the first network and a plurality of risk reports corresponding to the one or more network devices, and wherein each risk report is associated with a particular dimension of a plurality of dimensions of risk for the one or more network devices. A network profile image is generated by processing the plurality of risk reports. A generative adversarial network generates a synthetic network profile image from the network profile image, wherein the synthetic network profile image does not include the customer data. A second network is evaluated using the synthetic network profile image to identify differences between the first network and the second network.

    HETEROGENEOUS GRAPH LEARNING-BASED UNIFIED NETWORK REPRESENTATION
    3.
    发明申请
    HETEROGENEOUS GRAPH LEARNING-BASED UNIFIED NETWORK REPRESENTATION 有权

    公开(公告)号：US20240422069A1

    公开(公告)日：2024-12-19

    申请号：US18334735

    申请日：2023-06-14

    Applicant: Cisco Technology, Inc.

    Inventor： Pengfei Sun ,  Qihong Shao ,  David C. White, JR.

    IPC: H04L41/14 ,  H04L41/12 ,  H04L41/16

    Abstract: A heterogeneous graph learning system generates and analyzes network implementations. The heterogeneous graph learning system includes obtaining information describing multiple network implementations including heterogeneous nodes. The heterogeneous graph learning system also includes generating a one-hop graph connecting a particular node of the heterogeneous nodes with a set of related nodes. The one-hop graph connects the particular node with the set of related nodes via corresponding edges. The heterogeneous graph learning system further includes transforming the one-hop graph into a weighted graph based on a Dynamic Meta Path Transformation (DMPT). In the DMPT, each of the corresponding edges connecting the particular node to a corresponding related node among the set of related nodes is associated with a corresponding weight.

    ACCESS POINT COORDINATION USING GRAPHS AND MACHINE LEARNING PROCESSES
    4.
    发明公开
    ACCESS POINT COORDINATION USING GRAPHS AND MACHINE LEARNING PROCESSES 审中-公开

    公开(公告)号：US20240267748A1

    公开(公告)日：2024-08-08

    申请号：US18166264

    申请日：2023-02-08

    Applicant: Cisco Technology, Inc.

    Inventor： Doosan Jung ,  Qixu Gong ,  Qihong Shao

    IPC: H04W16/20 ,  H04L41/16

    CPC classification number: H04W16/20 ,  H04L41/16 ,  H04W84/12

    Abstract: AP coordination, and more specifically intelligent AP coordination using a graph network and reinforcement learning may be provided. AP coordination may include translating a physical space into a logical space, wherein the physical space is being evaluated for AP coordination. A machine learning process may predict signal strengths of signals sent by one or more Access Points (APs) and received by one or more Stations (STAs), wherein the machine learning process uses the logical space, and wherein each STA is in a location of the physical space. One or more AP placements may be evaluated based on the signal strengths, and a recommended AP placement may be determined based on the evaluation.

    PEER RISK BENCHMARKING USING GENERATIVE ADVERSARIAL NETWORKS
    6.
    发明申请
    PEER RISK BENCHMARKING USING GENERATIVE ADVERSARIAL NETWORKS 有权

    公开(公告)号：US20220131761A1

    公开(公告)日：2022-04-28

    申请号：US17077073

    申请日：2020-10-22

    Applicant: Cisco Technology, Inc.

    Inventor： Qihong Shao ,  David John Zacks ,  Xinjun Zhang

    IPC: H04L12/24 ,  H04L12/26

    Abstract: A method, computer system, and computer program product are provided for peer risk benchmarking. Customer data for a first network is obtained, wherein the customer data comprises a role of one or more network devices in the first network and a plurality of risk reports corresponding to the one or more network devices, and wherein each risk report is associated with a particular dimension of a plurality of dimensions of risk for the one or more network devices. A network profile image is generated by processing the plurality of risk reports. A generative adversarial network generates a synthetic network profile image from the network profile image, wherein the synthetic network profile image does not include the customer data. A second network is evaluated using the synthetic network profile image to identify differences between the first network and the second network.

    Peer risk benchmarking using generative adversarial networks
    7.
    发明授权
    Peer risk benchmarking using generative adversarial networks 有权

    公开(公告)号：US11316750B1

    公开(公告)日：2022-04-26

    申请号：US17077073

    申请日：2020-10-22

    Applicant: Cisco Technology, Inc.

    Inventor： Qihong Shao ,  David John Zacks ,  Xinjun Zhang

    IPC: G06F15/173 ,  H04L41/14 ,  H04L41/12 ,  H04L41/147 ,  H04L43/0817 ,  H04L41/5067 ,  H04L43/045 ,  H04L43/06 ,  H04L43/55

    Abstract: A method, computer system, and computer program product are provided for peer risk benchmarking. Customer data for a first network is obtained, wherein the customer data comprises a role of one or more network devices in the first network and a plurality of risk reports corresponding to the one or more network devices, and wherein each risk report is associated with a particular dimension of a plurality of dimensions of risk for the one or more network devices. A network profile image is generated by processing the plurality of risk reports. A generative adversarial network generates a synthetic network profile image from the network profile image, wherein the synthetic network profile image does not include the customer data. A second network is evaluated using the synthetic network profile image to identify differences between the first network and the second network.

    PERSONA-BASED MULTI-SCALE NETWORK RELATED DIGEST GENERATION
    8.
    发明申请
    PERSONA-BASED MULTI-SCALE NETWORK RELATED DIGEST GENERATION 有权

    公开(公告)号：US20250080410A1

    公开(公告)日：2025-03-06

    申请号：US18457804

    申请日：2023-08-29

    Applicant: Cisco Technology, Inc.

    Inventor： Daniel Shan-Shea Chen ,  Pengfei Sun ,  Qihong Shao ,  Gurvinder P. Singh

    IPC: H04L41/069 ,  G06F40/166 ,  G06F40/30 ,  H04L41/0654 ,  H04L41/16

    Abstract: Methods are provided for generating digests of network-related notifications specifically tailored to user's personas and adaptable across multiple timescale frequencies. Specifically, the methods involve obtaining user data of a user associated with an enterprise network and a plurality of network-related notifications. Each of the plurality of network-related notifications relates to network operations or network configurations. The methods further involve determining a network persona of the user in a context of the enterprise network based on the user data and generating a digest of the plurality of network-related notifications based on the network persona. The digest includes a semantic summary for each of the plurality of network-related notifications that is specific to the network persona. The methods further involve providing the digest for performing one or more actions associated with the enterprise network.

    HIERARCHICAL AUTO SUMMARY GENERATION WITH MULTI-TASK LEARNING IN NETWORKING RECOMMENDATIONS
    9.
    发明公开
    HIERARCHICAL AUTO SUMMARY GENERATION WITH MULTI-TASK LEARNING IN NETWORKING RECOMMENDATIONS 审中-公开

    公开(公告)号：US20240314020A1

    公开(公告)日：2024-09-19

    申请号：US18184972

    申请日：2023-03-16

    Applicant: Cisco Technology, Inc.

    Inventor： Qixu Gong ,  Benjamin L. Chang ,  Qihong Shao ,  Derek William Engi ,  Jaime Madruga Rita

    IPC: H04L41/0631 ,  H04L41/069 ,  H04L41/16

    CPC classification number: H04L41/065 ,  H04L41/069 ,  H04L41/16

    Abstract: Methods are provided for generating hierarchical summaries with actionable recommendations having various granularities. Specifically, the methods involve obtaining notifications related to network issues and generating meta-semantic data that includes a summary of each of the notifications. The methods further involve obtaining inventory data of network devices in a plurality of domains of a network. The inventory data includes configuration information of the network devices. The methods further involve generating a multi-level hierarchical summary specific to the network based on the inventory data and the meta-semantic data. The multi-level hierarchical summary includes a first level specific to one or more affected network devices and a second level specific to a group of network devices. The methods further involve providing the multi-level hierarchical summary for performing one or more actions associated with the network.

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