Computerized dialog system improvements based on conversation data

    公开(公告)号:US11605386B2

    公开(公告)日:2023-03-14

    申请号:US17000397

    申请日:2020-08-24

    Abstract: The computer receives a group of conversation data associated with the escalation node, identifies agent responses in the conversation data, and clusters them into agent response types. The computer identifies dialog state feature value sets for the conversations. The computer identifies feature value set associations with response types, and generates, Boolean expressions representing the feature value sets associated with each of the response types. The computer makes a recommendation to add to at least one child node for the escalation node, with the child node corresponding to one of the response types. The child node has, as an entry condition, the Boolean expression for the response type to which the child node corresponds. The child node has as an action, which according to some aspects, provides a response representative of the cluster of agent responses for the response type to which the child node corresponds.

    Calculating expertise confidence based on content and social proximity

    公开(公告)号:US11151663B2

    公开(公告)日:2021-10-19

    申请号:US16185013

    申请日:2018-11-09

    Abstract: A document-oriented search can be executed to generate a set of document results, at least one of the documents associated with at least one potential expert. The document results can be analyzed to produce a list of potential experts. An expertise score for at least one of the potential experts can be calculated based on a content score and a metadata score for the at least one of the potential experts. A confidence score for the potential expert can be calculated based on a diversity-constrained content score and a diversity-constrained metadata score for the at least one of the potential experts, the diversity-constrained content score and the diversity-constrained metadata score calculated using an evidence diversity score for the at least one of the potential experts. A list of experts with associated confidence scores that are above a confidence score threshold can be sent to a client device.

    Context-based personalized summarization of missed messages

    公开(公告)号:US10574613B2

    公开(公告)日:2020-02-25

    申请号:US15478540

    申请日:2017-04-04

    Abstract: A method, computer system, and a computer program product for generating a chat summary personalized to a user is provided. The present invention may include receiving a plurality of input interactions associated with the user. The present invention may include determining a user profile based on the received plurality of input interactions, whereby the determined user profile includes a plurality of topics of interest. The present invention may include receiving a plurality of missed messages. The present invention may include determining a plurality of message clusters from the plurality of missed messages, whereby a topic is associated with each message cluster. The present invention may include ranking the determined plurality of message clusters based on comparing the topic associated with each message cluster to the plurality of topics of interest. The present invention may include presenting the ranked plurality of message clusters to the user.

    CONTEXT-BASED PERSONALIZED GROUP CHAT SUMMARIZATION OF MISSED MESSAGES

    公开(公告)号:US20180287981A1

    公开(公告)日:2018-10-04

    申请号:US15478540

    申请日:2017-04-04

    Abstract: A method, computer system, and a computer program product for generating a chat summary personalized to a user is provided. The present invention may include receiving a plurality of input interactions associated with the user. The present invention may include determining a user profile based on the received plurality of input interactions, whereby the determined user profile includes a plurality of topics of interest. The present invention may include receiving a plurality of missed messages. The present invention may include determining a plurality of message clusters from the plurality of missed messages, whereby a topic is associated with each message cluster. The present invention may include ranking the determined plurality of message clusters based on comparing the topic associated with each message cluster to the plurality of topics of interest. The present invention may include presenting the ranked plurality of message clusters to the user.

    Calculating expertise confidence based on content and social proximity

    公开(公告)号:US11151664B2

    公开(公告)日:2021-10-19

    申请号:US16435567

    申请日:2019-06-10

    Abstract: A document-oriented search can be executed to generate a set of document results, at least one of the documents associated with at least one potential expert. The document results can be analyzed to produce a list of potential experts. An expertise score for at least one of the potential experts can be calculated based on a content score and a metadata score for the at least one of the potential experts. A confidence score for the potential expert can be calculated based on a diversity-constrained content score and a diversity-constrained metadata score for the at least one of the potential experts, the diversity-constrained content score and the diversity-constrained metadata score calculated using an evidence diversity score for the at least one of the potential experts. A list of experts with associated confidence scores that are above a confidence score threshold can be sent to a client device.

    Calculating expertise confidence based on content and social proximity

    公开(公告)号:US10319048B2

    公开(公告)日:2019-06-11

    申请号:US14837770

    申请日:2015-08-27

    Abstract: A method includes executing, via a processor, a document-oriented search based on a query in an index of documents to generate a set of document results, each document associated with at least one potential expert. The method includes analyzing the document results to produce a list of potential experts. The method includes calculating an expertise score for each potential expert based on a calculated content score and metadata score for each potential expert. The method includes calculating an evidence diversity score for each potential expert. The method includes calculating a confidence score for each potential expert based on a diversity-constrained content score and a diversity-constrained metadata score for each potential expert. The method includes displaying a list of potential experts with associated confidence scores.

    CALCULATING EXPERTISE CONFIDENCE BASED ON CONTENT AND SOCIAL PROXIMITY

    公开(公告)号:US20190079936A1

    公开(公告)日:2019-03-14

    申请号:US16185013

    申请日:2018-11-09

    Abstract: A document-oriented search can be executed to generate a set of document results, at least one of the documents associated with at least one potential expert. The document results can be analyzed to produce a list of potential experts. An expertise score for at least one of the potential experts can be calculated based on a content score and a metadata score for the at least one of the potential experts. A confidence score for the potential expert can be calculated based on a diversity-constrained content score and a diversity-constrained metadata score for the at least one of the potential experts, the diversity-constrained content score and the diversity-constrained metadata score calculated using an evidence diversity score for the at least one of the potential experts. A list of experts with associated confidence scores that are above a confidence score threshold can be sent to a client device.

    Calculating expertise confidence based on content and social proximity

    公开(公告)号:US10146839B2

    公开(公告)日:2018-12-04

    申请号:US14573561

    申请日:2014-12-17

    Abstract: A method includes executing, via a processor, a document-oriented search based on a query in an index of documents to generate a set of document results, each document associated with at least one potential expert. The method includes analyzing the document results to produce a list of potential experts. The method includes calculating an expertise score for each potential expert based on a calculated content score and metadata score for each potential expert. The method includes calculating an evidence diversity score for each potential expert. The method includes calculating a confidence score for each potential expert based on a diversity-constrained content score and a diversity-constrained metadata score for each potential expert. The method includes displaying a list of potential experts with associated confidence scores.

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