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    SYSTEMS AND METHODS FOR MULTI-CHANNEL CUSTOMER COMMUNICATIONS CONTENT RECOMMENDER
    3.
    发明申请
    SYSTEMS AND METHODS FOR MULTI-CHANNEL CUSTOMER COMMUNICATIONS CONTENT RECOMMENDER 有权

    公开(公告)号：US20230023552A1

    公开(公告)日：2023-01-26

    申请号：US17959527

    申请日：2022-10-04

    Applicant: OPEN TEXT SA ULC

    Inventor： Byron Steven Pruitt

    IPC: G06Q10/06 ,  G06Q30/02

    Abstract: Interaction events collected across disparate customer communication channels of an enterprise are processed to generate an encoded unique content item identifier for each content item referenced in an interaction event such that the content item is resolvable to a location in a content repository. A training data set is built using the interaction events thus processed and a multi-channel content recommendation model is trained using the training data set. The multi-channel content recommendation model thus trained stores data points representing intersections of customers and content items that the enterprise has been tracking, with each data point having an effectiveness score for an associated content item. The multi-channel content recommendation model thus trained can be queried by content designers of the disparate customer communication channels through a recommender application for content recommendations based on the effectiveness of the content, agnostic to the disparate customer communication channels.

    SYSTEMS AND METHODS FOR MULTI-CHANNEL CUSTOMER COMMUNICATIONS CONTENT RECOMMENDER
    4.
    发明申请
    SYSTEMS AND METHODS FOR MULTI-CHANNEL CUSTOMER COMMUNICATIONS CONTENT RECOMMENDER 有权

    公开(公告)号：US20210279658A1

    公开(公告)日：2021-09-09

    申请号：US17191521

    申请日：2021-03-03

    Applicant: OPEN TEXT SA ULC

    Inventor： Byron Steven Pruitt

    IPC: G06Q10/06 ,  G06Q30/02

    Abstract: Interaction events collected across disparate customer communication channels of an enterprise are processed to generate an encoded unique content item identifier for each content item referenced in an interaction event such that the content item is resolvable to a location in a content repository. A training data set is built using the interaction events thus processed and a multi-channel content recommendation model is trained using the training data set. The multi-channel content recommendation model thus trained stores data points representing intersections of customers and content items that the enterprise has been tracking, with each data point having an effectiveness score for an associated content item. The multi-channel content recommendation model thus trained can be queried by content designers of the disparate customer communication channels through a recommender application for content recommendations based on the effectiveness of the content, agnostic to the disparate customer communication channels.

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