Emotion detection and expression integration in dialog systems

    公开(公告)号:US10372825B2

    公开(公告)日:2019-08-06

    申请号:US15844877

    申请日:2017-12-18

    IPC分类号: G06F17/27 G10L25/63

    摘要: Utilizing a computing device to detect and respond to emotion in dialog systems. The computing device receives a dialog structure comprising a plurality of dialog nodes. The computing device determines a node emotion level for each of the dialog nodes in the dialog structure based on analysis of one or more intents of each of the dialog nodes in the dialog structure. The computing device determines emotional hotspot nodes in the dialog structure, the node emotion level for each of the emotional hotspot nodes exceeding an emotional threshold. The computing device generates one or more responses modifying the node emotion level of each of the emotional hotspot nodes.

    LARGE TAXONOMY CATEGORIZATION
    2.
    发明申请
    LARGE TAXONOMY CATEGORIZATION 有权
    大量的TAXONOMY分类

    公开(公告)号:US20170032018A1

    公开(公告)日:2017-02-02

    申请号:US14583791

    申请日:2014-12-29

    IPC分类号: G06F17/30

    摘要: A method includes associating, in a graph including graph nodes connected via of edges, a respective node weight with each of the graph nodes, and organizing the graph nodes into ancestor nodes, each of the ancestor nodes having one or more descendent nodes so that the ancestor and the descendent nodes include all the graph nodes. For a given descendent node, a respective path to one or more of the ancestor nodes is identified, each of the respective paths including one or more edges, and a given ancestor node having a shortest of the identified paths is determined. A respective edge weight is assigned to each of the one or more edges in the shortest path, and, for the given descendent node, a node loss value is calculated based on the node weight and the respective edge weight of the each of the one or more edges.

    摘要翻译: 一种方法包括在包括通过边缘连接的图形节点的图形的图表中,将相应的节点权重与每个图形节点相关联,并且将图形节点组织成祖先节点,每个祖先节点具有一个或多个后代节点,使得 祖先和后代节点包括所有图形节点。 对于给定的后代节点,识别到祖先节点中的一个或多个的相应路径,确定每个相应路径包括一个或多个边缘,并且确定具有所识别的路径的最短的给定祖先节点。 相应的边缘权重被分配给最短路径中的一个或多个边缘中的每个边缘,并且对于给定的后代节点,基于节点权重和每个边缘权重的相应边缘权重来计算节点丢失值, 更多的边缘

    Large taxonomy categorization
    6.
    发明授权

    公开(公告)号:US09697276B2

    公开(公告)日:2017-07-04

    申请号:US14583791

    申请日:2014-12-29

    IPC分类号: G06F17/30

    摘要: A method includes associating, in a graph including graph nodes connected via of edges, a respective node weight with each of the graph nodes, and organizing the graph nodes into ancestor nodes, each of the ancestor nodes having one or more descendent nodes so that the ancestor and the descendent nodes include all the graph nodes. For a given descendent node, a respective path to one or more of the ancestor nodes is identified, each of the respective paths including one or more edges, and a given ancestor node having a shortest of the identified paths is determined. A respective edge weight is assigned to each of the one or more edges in the shortest path, and, for the given descendent node, a node loss value is calculated based on the node weight and the respective edge weight of the each of the one or more edges.

    OPTIMIZING PERSONALITY TRAITS OF VIRTUAL AGENTS

    公开(公告)号:US20180374000A1

    公开(公告)日:2018-12-27

    申请号:US15634496

    申请日:2017-06-27

    IPC分类号: G06N99/00 G06N3/00 G06N5/04

    摘要: A method, computer system, and a computer program product for optimizing a plurality of personality traits of a virtual agent based on a predicted customer satisfaction value is provided. The present invention may include identifying a customer. The present invention may also include retrieving a plurality of data associated with the customer. The present invention may then include analyzing the received plurality of data using a customer satisfaction prediction model. The present invention may further include generating a plurality of analyzed data from the customer satisfaction prediction model based on the analyzed plurality of data. The present invention may also include generating a plurality of personality traits for a virtual agent from the generated plurality of analyzed data.