Techniques for providing predictive interface elements

    公开(公告)号:US11294784B1

    公开(公告)日:2022-04-05

    申请号:US16584524

    申请日:2019-09-26

    Abstract: Systems and methods are described herein for providing predictive user interface elements. A computing system may train a machine-learning model to identify issues that are likely being experienced by users contacting a customer service system based at least in part on historical user account data of a plurality of user accounts. When a request for assistance is received, user account data corresponding to the request may be obtained and provided to the model to identify issues likely experienced by a user. A number of graphical user interface (GUI) elements (e.g., “match cards”), each corresponding to one of the identified issues, may be generated, ranked, and presented in accordance with the ranking. Each GUI element may be selectable. Upon selection additional data likely to be pertinent to the selected issue may be presented alleviating a need to search for this data as would be the case in conventional systems.

    Transparent contact transfer system

    公开(公告)号:US10142486B1

    公开(公告)日:2018-11-27

    申请号:US15699835

    申请日:2017-09-08

    Abstract: A centralized system is provided for managing customer contacts in a customer service center and transferring those contacts between agents in a transparent manner. When an agent determines that a different representative is more likely or capable of resolving an issue associated with a customer contact, the system facilitates the transfer of the contact to another agent. The transfer can be transparent to both the sending agent and receiving agent so that both agents are informed in real time, via a dynamic user interface, regarding the status of the transfer of the current contact. In some embodiments, the transfer may not be limited to only transferring actual communication with the customer to the receiving agent, but may be an integrated process that includes the transfer of records, assignments, and other data-driven associations from the sending agent to the receiving agent.

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