DATA-DRIVEN IDENTIFICATION AND SELECTION OF FEATURES RELATED TO A STATE CHANGE OF A NETWORK COMPONENT

    公开(公告)号:US20210092009A1

    公开(公告)日:2021-03-25

    申请号:US17020384

    申请日:2020-09-14

    Abstract: Techniques and mechanisms for automatically identifying counters/features of a network component that are related to a state change (or event) for the network component or for the network itself. For example, using data obtained from the network component around a time of the state change, delta-averages for the counters/features around the time of the state change may be determined. The delta-averages may be utilized to determine which counters/features are most descriptive for a particular state change. Determining which counters/features are most descriptive may also include determining which counters/features are most relevant, i.e., counters/features that contribute most to preserving the manifold structure of the original data or counters/features with the highest or lowest correlation with the other counters/features in the data set. Thus, the techniques described herein provide for an approach to distill which counters/features contribute the most to a particular state change from a data driven perspective.

    VIRTUAL PROXIMITY RADIUS BASED WEB CONFERENCING

    公开(公告)号:US20220272004A1

    公开(公告)日:2022-08-25

    申请号:US17181243

    申请日:2021-02-22

    Abstract: Techniques for utilizing a communication system that provides access to a representation of a virtual environment to participants. The communication system may establish connections between personal communication bridge(s) associated with participant(s) interacting within a virtual proximity radius of one another's virtual indicator in the virtual environment. The communication system may cause conversation data to be sent each personal communication bridge associated with a participant that is within the virtual proximity radius of the sender, and cause conversation data to be received via the personal communication bridge of a participant that is within the virtual proximity radius of the sender. The communication system may also analyze data associated with the participant profile(s) and transcribed conversation data from the communication bridges(s) to recommend potential conversations of interest to participant(s).

    Virtual proximity radius based web conferencing

    公开(公告)号:US11616701B2

    公开(公告)日:2023-03-28

    申请号:US17181243

    申请日:2021-02-22

    Abstract: Techniques for utilizing a communication system that provides access to a representation of a virtual environment to participants. The communication system may establish connections between personal communication bridge(s) associated with participant(s) interacting within a virtual proximity radius of one another's virtual indicator in the virtual environment. The communication system may cause conversation data to be sent each personal communication bridge associated with a participant that is within the virtual proximity radius of the sender, and cause conversation data to be received via the personal communication bridge of a participant that is within the virtual proximity radius of the sender. The communication system may also analyze data associated with the participant profile(s) and transcribed conversation data from the communication bridges(s) to recommend potential conversations of interest to participant(s).

    Data-driven identification and selection of features related to a state change of a network component

    公开(公告)号:US11283679B2

    公开(公告)日:2022-03-22

    申请号:US17020384

    申请日:2020-09-14

    Abstract: Techniques and mechanisms for automatically identifying counters/features of a network component that are related to a state change (or event) for the network component or for the network itself. For example, using data obtained from the network component around a time of the state change, delta-averages for the counters/features around the time of the state change may be determined. The delta-averages may be utilized to determine which counters/features are most descriptive for a particular state change. Determining which counters/features are most descriptive may also include determining which counters/features are most relevant, i.e., counters/features that contribute most to preserving the manifold structure of the original data or counters/features with the highest or lowest correlation with the other counters/features in the data set. Thus, the techniques described herein provide for an approach to distill which counters/features contribute the most to a particular state change from a data driven perspective.

    Data-driven identification of features related to a state change of a network component

    公开(公告)号:US11115280B2

    公开(公告)日:2021-09-07

    申请号:US16789723

    申请日:2020-02-13

    Abstract: Techniques and mechanisms for automatically identifying counters/features of a network component that are related to a state change (or event) for the network component or for the network itself. For example, using data obtained from the network component around a time of the state change, delta averages for the features around the time of the state change may be determined. The delta averages may be utilized to determine which counters/features are most descriptive for a particular state change. The counter/features that are the most descriptive for a particular state change is as important as the change detection itself. This is especially true since in a case of an event/state change occurring, a large amount of counters/features may react to the state change or event. Thus, the techniques described herein provide for an approach to distill which counters/features contribute the most to a particular state change from a data driven perspective.

    DATA-DRIVEN IDENTIFICATION OF FEATURES RELATED TO A STATE CHANGE OF A NETWORK COMPONENT

    公开(公告)号:US20210092010A1

    公开(公告)日:2021-03-25

    申请号:US16789723

    申请日:2020-02-13

    Abstract: Techniques and mechanisms for automatically identifying counters/features of a network component that are related to a state change (or event) for the network component or for the network itself. For example, using data obtained from the network component around a time of the state change, delta averages for the features around the time of the state change may be determined. The delta averages may be utilized to determine which counters/features are most descriptive for a particular state change. The counter/features that are the most descriptive for a particular state change is as important as the change detection itself. This is especially true since in a case of an event/state change occurring, a large amount of counters/features may react to the state change or event. Thus, the techniques described herein provide for an approach to distill which counters/features contribute the most to a particular state change from a data driven perspective.

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