中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

19 records (took 0.017 seconds)

    Predicting the efficacy of issues detected with machine executed digitized intellectual capital
    11.
    发明授权
    Predicting the efficacy of issues detected with machine executed digitized intellectual capital 有权

    公开(公告)号：US11444853B2

    公开(公告)日：2022-09-13

    申请号：US17153252

    申请日：2021-01-20

    Applicant: Cisco Technology, Inc.

    Inventor： Donald Mark Allen ,  Dmitri Goloubev

    IPC: H04L41/50 ,  H04L41/082 ,  G06F11/34 ,  H04L41/16

    Abstract: A digitized Intellectual Capital (IC) system obtains code modules configured to detect one or more issues in a computing system. The IC system selects from the code modules to generate a first set of code modules based on a corresponding value metric. The corresponding value metric for each code module in the first set of code modules is higher than a predetermined threshold. The IC system also samples from the remainder of the code modules unselected for the first set of code modules to generate a second set of code modules. The IC system runs the first set of code modules and the second set of code modules to detect the one or more issues and updates the corresponding value metric for at least one code module.

    Sensor fusion for trustworthy device identification and monitoring
    12.
    发明授权
    Sensor fusion for trustworthy device identification and monitoring 有权

    公开(公告)号：US11368848B2

    公开(公告)日：2022-06-21

    申请号：US16278430

    申请日：2019-02-18

    Applicant: Cisco Technology, Inc.

    Inventor： Charles Calvin Byers ,  M. David Hanes ,  Gonzalo Salgueiro ,  Dmitri Goloubev ,  Joseph Michael Clarke

    IPC: H04W12/65 ,  G06N20/00 ,  H04W12/00 ,  H04W12/63 ,  H04W12/122

    Abstract: Presented herein are methodologies to on-board and monitor Internet of Things (IoT) devices on a network. The methodology includes receiving at a server, from a plurality of IoT devices communicating over a network, data representative of external environmental factors being experienced by individual ones of the plurality of IoT devices at a predetermined location; generating, using machine learning, an aggregated model of the external environmental factors at the predetermined location; receiving, at the server, a communication indicative that a new IoT device seeks to join the network at the predetermined location; receiving, from the new IoT device, data representative of external environmental factors being experienced by the new IoT device; determining whether there is a discrepancy between the external environmental factors of the new IoT device and the aggregated model; and when there is such a discrepancy, prohibiting the new IoT device from joining the network.

    Knowledge aggregation for GAN-based anomaly detectors
    13.
    发明授权
    Knowledge aggregation for GAN-based anomaly detectors 有权

    公开(公告)号：US10742516B1

    公开(公告)日：2020-08-11

    申请号：US16268853

    申请日：2019-02-06

    Applicant: Cisco Technology, Inc.

    Inventor： Volodymyr Iashyn ,  Borys Viacheslavovych Berlog ,  Dmitri Goloubev

    IPC: G06K9/62 ,  H04L12/24 ,  G06N20/00 ,  H04L29/08

    Abstract: Systems, methods, and computer-readable media for distributing machine learning. In some examples, a first GAN model is deployed to a first network edge device and a second GAN model is deployed to a second network edge device. A generator of the first GAN model can be trained using real telemetry data of a first computing node and a generator of the second GAN model can be trained using real telemetry data of a second IoT device. The generator of the first GAN model and the generator of the second GAN model can be received. Additionally, a unified generator of a unified GAN model can be trained using the generator of the first GAN model and the generator of the second GAN model. Subsequently, the unified GAN model can be deployed to a third computing node for monitoring operation of the third IoT device.

    ANOMALY CLASSIFICATION WITH ATTENDANT WORD ENRICHMENT
    14.
    发明申请
    ANOMALY CLASSIFICATION WITH ATTENDANT WORD ENRICHMENT 有权

    公开(公告)号：US20240370656A1

    公开(公告)日：2024-11-07

    申请号：US18743282

    申请日：2024-06-14

    Applicant: Cisco Technology, Inc.

    Inventor： Dmitri Goloubev ,  Nassim Benoussaid ,  Volodymyr Iashyn ,  Borys Viacheslavovych Berlog ,  Carlos M. Pignataro

    IPC: G06F40/30 ,  G06F16/28 ,  G06F16/35 ,  G06F40/279 ,  G06N20/00

    Abstract: A method includes associating anomalous first text, from a first unstructured data set, with a first classification; processing the first unstructured data set using at least one of ML or AI to identify a second text that is in close context to the first text, and adding the second text to a text list associated with the first classification; enriching the text list by processing the second text to generate a third text, and adding the third text to the text list to produce an enriched text list and such that the third text is also associated with the first classification; matching the text in the enriched text list to text in a second unstructured data set; and classifying the text in the second unstructured data set as having the first classification when the text in the second unstructured data set matches text in the enriched text list.

    KNOWLEDGE AGGREGATION FOR GAN-BASED ANOMALY DETECTORS
    17.
    发明申请
    KNOWLEDGE AGGREGATION FOR GAN-BASED ANOMALY DETECTORS 审中-公开

    公开(公告)号：US20200252296A1

    公开(公告)日：2020-08-06

    申请号：US16268853

    申请日：2019-02-06

    Applicant: Cisco Technology, Inc.

    Inventor： Volodymyr Iashyn ,  Borys Viacheslavovych Berlog ,  Dmitri Goloubev

    IPC: H04L12/24 ,  G06N20/00

    Abstract: Systems, methods, and computer-readable media for distributing machine learning. In some examples, a first GAN model is deployed to a first network edge device and a second GAN model is deployed to a second network edge device. A generator of the first GAN model can be trained using real telemetry data of a first computing node and a generator of the second GAN model can be trained using real telemetry data of a second IoT device. The generator of the first GAN model and the generator of the second GAN model can be received. Additionally, a unified generator of a unified GAN model can be trained using the generator of the first GAN model and the generator of the second GAN model. Subsequently, the unified GAN model can be deployed to a third computing node for monitoring operation of the third IoT device.

Patent Agency Ranking