STEPWISE RELATIONSHIP CADENCE MANAGEMENT
    12.
    发明申请

    公开(公告)号:US20200374258A1

    公开(公告)日:2020-11-26

    申请号:US16418923

    申请日:2019-05-21

    Abstract: Stepwise relationship cadence management can include generating a discourse cadence and confidence (DCC) measure based on a response message. The response message is made in replying to an originating message during a multi-party discourse over an electronic communication channel. The DCC measure indicates a likelihood of improving cadence and confidence with respect to an originator of the originating message and is based on a stepwise relational confidence model (SRCM) generated from an analysis of a plurality of prior multi-party discourses. Stepwise relationship cadence management can also include prompting a user to provide a follow-on message in response to determining that the response message made in replying to the originating message is not likely to improve cadence and confidence.

    COLLABORATION GROUP RECOMMENDATIONS DERIVED FROM REQUEST-ACTION CORRELATIONS

    公开(公告)号:US20180174204A1

    公开(公告)日:2018-06-21

    申请号:US15895909

    申请日:2018-02-13

    CPC classification number: G06Q30/0282 G06Q10/101 H04L67/22

    Abstract: In response to a user-initiated interaction request sent by a user using an electronic communication, subsequent actions performed by other users that received the user-initiated interaction request are analyzed. A determination is made as to whether the subsequent actions performed by the other users that received the user-initiated interaction request correlate to an intended interaction result of the user-initiated interaction request. A visual representation of a collaboration model that correlates probabilities of successful collaborations between the user and the other users is generated in accordance with determined correlations between the subsequent actions performed by the other users and the intended interaction result. A collaboration recommendation based upon a degree of correlation between the subsequent actions performed by the other users and the intended interaction result represented within the collaboration model is provided in association with the visual representation of the collaboration model.

    Presentation content effectiveness using attraction modeling

    公开(公告)号:US12224877B2

    公开(公告)日:2025-02-11

    申请号:US17660237

    申请日:2022-04-22

    Abstract: An embodiment for determining presentation content effectiveness using attraction modeling is provided. The embodiment may include receiving presentation content from a meeting host during on online collaborative meeting. The embodiment may also include capturing one or more actions of one or more users during a display of the presentation content to the one or more users. The embodiment may further include creating an audience attention model. The embodiment may also include in response to determining at least one user is distracted from the presentation content, modifying the display of the presentation content for each distracted user in accordance with one or more characteristics associated with each distracted user. The embodiment may further include categorizing the one or more users into one or more groups. The embodiment may also include providing visual feedback to the meeting host.

    COGNITIVE ASSISTANT VOICE AMELIORATION MODEL

    公开(公告)号:US20240428774A1

    公开(公告)日:2024-12-26

    申请号:US18338432

    申请日:2023-06-21

    Abstract: According to one embodiment, a method, computer system, and computer program product for augmenting a digital audio representation of a voice is provided. The embodiment may include identifying a current voice waveform of a user. The current voice waveform corresponds to captured speech output of the user. The embodiment may include comparing one or more frequency components of the current voice waveform to one or more corresponding frequency components of a baseline voice waveform of the user. In response to determining that at least one of the one or more frequency components of the current waveform fail a threshold degree of match to at least one corresponding frequency component of the baseline voice waveform, the embodiment may include augmenting the captured speech output via a generative artificial intelligence (AI) voice model trained to produce speech which mimics a voice and a speaking style of the user.

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