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    Systems and methods to prioritize chat rooms using machine learning
    12.
    发明授权
    Systems and methods to prioritize chat rooms using machine learning 有权

    公开(公告)号：US11206236B2

    公开(公告)日：2021-12-21

    申请号：US16448257

    申请日：2019-06-21

    Applicant: Cisco Technology, Inc.

    Inventor： Eric Chen ,  Keith Griffin

    IPC: G06N20/00 ,  H04L29/08 ,  H04L12/58 ,  G06F3/0482

    Abstract: Chat room content classification, in an online communication environment, where higher applicable chat rooms are prioritized for a user, is provided. First, an chat room service receives chat room content for at least a first chat room and a second chat room. A chat room analyzer can then analyze a characteristic(s) associated with the first chat room and/or the second chat room. Based on the characteristic, the chat room determines that the first chat room is more applicable to the user. Then, a user interface may be presented to the user where the first chat room is prioritized (or ranked) over the second chat room.

    Noise mitigation using machine learning
    13.
    发明授权
    Noise mitigation using machine learning 有权

    公开(公告)号：US10446170B1

    公开(公告)日：2019-10-15

    申请号：US16012565

    申请日：2018-06-19

    Applicant: Cisco Technology, Inc.

    Inventor： Eric Chen ,  Asbjørn Therkelsen ,  Espen Moberg ,  Wei-Lien Hsu

    IPC: G10L21/0216 ,  G10L25/30 ,  G06F17/18 ,  G06N7/00 ,  G06N20/00

    Abstract: This disclosure relates to solutions for eliminating undesired audio artifacts, such as background noises, on an audio channel. A process for implementing the technology can include receiving a set of audio segments, analyzing the segments using a first ML model to identify a first probability of unwanted background noises in the segments, and if the first probability exceeds a threshold, analyzing the segments using a second ML model to determine a second probability that the one or more background features exist in the segments. In some aspects, the process can include attenuating audio artifacts in the segments, if the second probability exceeds a second threshold. In some implementations, dynamic time stretching and shrinking can be applied to the noise attenuation. Systems and machine-readable media are also provided.

    CONTINUAL LEARNING FOR MULTI MODAL SYSTEMS USING CROWD SOURCING
    15.
    发明申请
    CONTINUAL LEARNING FOR MULTI MODAL SYSTEMS USING CROWD SOURCING 审中-公开

    公开(公告)号：US20190318198A1

    公开(公告)日：2019-10-17

    申请号：US15992013

    申请日：2018-05-29

    Applicant: Cisco Technology, Inc.

    Inventor： Keith Griffin ,  Eric Chen

    IPC: G06K9/62 ,  G06F15/18

    Abstract: Systems, methods, and devices are disclosed for training a model. Media data is separated into one or more clusters, each cluster based on a feature from a first model. The media data of each cluster is sampled and, based on an analysis of the sampled media data, an accuracy of the media data of each cluster is determined. The accuracy is associated with the feature from the first model. Based on a subset dataset of the media data being outside a threshold accuracy, the subset dataset is automatically forwarded to a crowd source service. Verification of the subset dataset is received from the crowd source service, and the verified subset dataset is added to the first model.

    Data sovereignty compliant machine learning
    16.
    发明授权
    Data sovereignty compliant machine learning 有权

    公开(公告)号：US10963813B2

    公开(公告)日：2021-03-30

    申请号：US15582089

    申请日：2017-04-28

    Applicant: Cisco Technology, Inc.

    Inventor： Eric Chen

    IPC: G06N20/00 ,  G06F9/50

    Abstract: The subject disclosure relates to systems for managing the deployment and updating of incremental machine learning models across multiple geographic sovereignties. In some aspects, systems of the subject technology are configured to perform operations including: receiving a first machine learning model via a first coordination agent, the first machine learning model based on a first training data set corresponding with a first sovereign region, sending the first machine learning model to a second coordination agent in a second sovereign region, wherein the second sovereign region is different from the first sovereign region, and receiving a second machine learning model from the second coordination agent, wherein the second machine learning model is based on updates to the first machine learning model using a second training data set corresponding with the second sovereign region. Methods and machine-readable media are also provided.

    DATA SOVEREIGNTY COMPLIANT MACHINE LEARNING
    19.
    发明申请
    DATA SOVEREIGNTY COMPLIANT MACHINE LEARNING 审中-公开

    公开(公告)号：US20180314981A1

    公开(公告)日：2018-11-01

    申请号：US15582089

    申请日：2017-04-28

    Applicant: Cisco Technology, Inc.

    Inventor： Eric Chen

    IPC: G06N99/00

    CPC classification number: G06N99/005 ,  G06F9/5072

    Abstract: The subject disclosure relates to systems for managing the deployment and updating of incremental machine learning models across multiple geographic sovereignties. In some aspects, systems of the subject technology are configured to perform operations including: receiving a first machine learning model via a first coordination agent, the first machine learning model based on a first training data set corresponding with a first sovereign region, sending the first machine learning model to a second coordination agent in a second sovereign region, wherein the second sovereign region is different from the first sovereign region, and receiving a second machine learning model from the second coordination agent, wherein the second machine learning model is based on updates to the first machine learning model using a second training data set corresponding with the second sovereign region. Methods and machine-readable media are also provided.

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