Education reward system and method
    12.
    发明授权

    公开(公告)号:US10902735B2

    公开(公告)日:2021-01-26

    申请号:US15808256

    申请日:2017-11-09

    Abstract: A reward learning system includes a user interface configured to receive modes of user information related to a state of the user. A cognitive computing system includes a reward system. The reward system includes a dynamically upgraded profile model of the user which is updated in accordance with the user information related to the state. The reward system is updated by machine learning employing feedback from user responses measured by the user interface and searched information by the cognitive computing system. The reward system includes an increasing reward protocol based on learned user preferences and responses and rewarded in accordance with user achievements.

    Configurable sensor array for a multi-target environment

    公开(公告)号:US10969863B2

    公开(公告)日:2021-04-06

    申请号:US16406735

    申请日:2019-05-08

    Abstract: A method of operating a configurable gaze tracking system includes initializing a plurality of sensors by determining positional information of the sensors, wherein the sensors establish a virtual framework, initializing a plurality of target objects by determining positional information of the target objects within the virtual framework, determining a current user using data output by the sensors, determining a gaze of the current user, matching the gaze to one of the target objects in the virtual framework, wherein a target object matched to the gaze is a current target object, and activating the current target object to receive input.

    Node relevance determination in an evolving network

    公开(公告)号:US10756977B2

    公开(公告)日:2020-08-25

    申请号:US15987048

    申请日:2018-05-23

    Abstract: Methods and systems for determining a time dependent relevancy score of an agent node among an evolving heterogeneous network are described. A processor may expand the heterogeneous network by generating temporal heterogeneous networks representing states of the heterogeneous network at different times. The processor may extract a set of agent nodes from each temporal heterogeneous network and may generate a relationship network based on the extracted agent nodes for each temporal heterogeneous network. The processor may remove the agent node from the temporal heterogeneous network to generate a conditional relationship network excluding the removed agent node. The processor may determine a relevancy score for the agent node based on the corresponding relationship network and the conditional relationship network. Each relevancy score for the agent node may correspond to a temporal heterogeneous network and may indicate an impact of removing the agent node from the corresponding temporal heterogeneous network.

    NODE RELEVANCE DETERMINATION IN AN EVOLVING NETWORK

    公开(公告)号:US20190363937A1

    公开(公告)日:2019-11-28

    申请号:US15987048

    申请日:2018-05-23

    Abstract: Methods and systems for determining a time dependent relevancy score of an agent node among an evolving heterogeneous network are described. A processor may expand the heterogeneous network by generating temporal heterogeneous networks representing states of the heterogeneous network at different times. The processor may extract a set of agent nodes from each temporal heterogeneous network and may generate a relationship network based on the extracted agent nodes for each temporal heterogeneous network. The processor may remove the agent node from the temporal heterogeneous network to generate a conditional relationship network excluding the removed agent node. The processor may determine a relevancy score for the agent node based on the corresponding relationship network and the conditional relationship network. Each relevancy score for the agent node may correspond to a temporal heterogeneous network and may indicate an impact of removing the agent node from the corresponding temporal heterogeneous network.

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