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    Neural flow attestation
    2.
    发明授权
    Neural flow attestation 有权

    公开(公告)号：US12141704B2

    公开(公告)日：2024-11-12

    申请号：US18236590

    申请日：2023-08-22

    Applicant: International Business Machines Corporation

    Inventor： Zhongshu Gu ,  Xiaokui Shu ,  Hani Jamjoom ,  Tengfei Ma

    IPC: G06N3/10 ,  G06N3/04 ,  G06N3/08

    Abstract: Mechanisms are provided to implement a neural flow attestation engine and perform computer model execution integrity verification based on neural flows. Input data is input to a trained computer model that includes a plurality of layers of neurons. The neural flow attestation engine records, for a set of input data instances in the input data, an output class generated by the trained computer model and a neural flow through the plurality of layers of neurons to thereby generate recorded neural flows. The trained computer model is deployed to a computing platform, and the neural flow attestation engine verifies the execution integrity of the deployed trained computer model based on a runtime neural flow of the deployed trained computer model and the recorded neural flows.

    Neural Flow Attestation
    3.
    发明申请
    Neural Flow Attestation 有权

    公开(公告)号：US20210232933A1

    公开(公告)日：2021-07-29

    申请号：US16750328

    申请日：2020-01-23

    Applicant: International Business Machines Corporation

    Inventor： Zhongshu Gu ,  Xiaokui Shu ,  Hani Jamjoom ,  Tengfei Ma

    IPC: G06N3/10 ,  G06N3/04 ,  G06N3/08

    Abstract: Mechanisms are provided to implement a neural flow attestation engine and perform computer model execution integrity verification based on neural flows. Input data is input to a trained computer model that includes a plurality of layers of neurons. The neural flow attestation engine records, for a set of input data instances in the input data, an output class generated by the trained computer model and a neural flow through the plurality of layers of neurons to thereby generate recorded neural flows. The trained computer model is deployed to a computing platform, and the neural flow attestation engine verifies the execution integrity of the deployed trained computer model based on a runtime neural flow of the deployed trained computer model and the recorded neural flows.

    Neural flow attestation
    6.
    发明授权
    Neural flow attestation 有权

    公开(公告)号：US11783201B2

    公开(公告)日：2023-10-10

    申请号：US16750328

    申请日：2020-01-23

    Applicant: International Business Machines Corporation

    Inventor： Zhongshu Gu ,  Xiaokui Shu ,  Hani Jamjoom ,  Tengfei Ma

    IPC: G06N3/10 ,  G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/10 ,  G06N3/04 ,  G06N3/08

    Abstract: Mechanisms are provided to implement a neural flow attestation engine and perform computer model execution integrity verification based on neural flows. Input data is input to a trained computer model that includes a plurality of layers of neurons. The neural flow attestation engine records, for a set of input data instances in the input data, an output class generated by the trained computer model and a neural flow through the plurality of layers of neurons to thereby generate recorded neural flows. The trained computer model is deployed to a computing platform, and the neural flow attestation engine verifies the execution integrity of the deployed trained computer model based on a runtime neural flow of the deployed trained computer model and the recorded neural flows.

    Neural Flow Attestation
    9.
    发明公开
    Neural Flow Attestation 审中-公开

    公开(公告)号：US20230394324A1

    公开(公告)日：2023-12-07

    申请号：US18236590

    申请日：2023-08-22

    Applicant: International Business Machines Corporation

    Inventor： Zhongshu Gu ,  XIAOKUI SHU ,  Hani Jamjoom ,  Tengfei Ma

    IPC: G06N3/10 ,  G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/10 ,  G06N3/08 ,  G06N3/04

    Abstract: Mechanisms are provided to implement a neural flow attestation engine and perform computer model execution integrity verification based on neural flows. Input data is input to a trained computer model that includes a plurality of layers of neurons. The neural flow attestation engine records, for a set of input data instances in the input data, an output class generated by the trained computer model and a neural flow through the plurality of layers of neurons to thereby generate recorded neural flows. The trained computer model is deployed to a computing platform, and the neural flow attestation engine verifies the execution integrity of the deployed trained computer model based on a runtime neural flow of the deployed trained computer model and the recorded neural flows.

    Predicting user attentiveness to electronic notifications
    10.
    发明授权
    Predicting user attentiveness to electronic notifications 有权

    公开(公告)号：US10832160B2

    公开(公告)日：2020-11-10

    申请号：US15139716

    申请日：2016-04-27

    Applicant: International Business Machines Corporation

    Inventor： Hani Jamjoom ,  David M. Lubensky ,  Justin G. Manweiler ,  Katherine Vogt ,  Justin D. Weisz

    IPC: G06N20/00 ,  G06Q10/10 ,  H04L12/58

    Abstract: A database comprises historical information of a user's response to previous notifications. The database is accessed to determine a time at which to provide a (new) notification to the user, utilizing at least: a) current user activity status (e.g., determined from measurement information collected from one or more personal devices and/or user calendar events; b) time/day; and c) context information about the notification (e.g., geo-location, indoors/outdoors) including notification type (e.g., calendar entry, email, IM). The user gets the notification via a portable device at the determined time. A machine learning model can select the determined time by discriminating features of the previous notifications for which the user immediately attended versus those that were deferred and/or ignored. Content of the notification can also be altered in view of such discriminating features so as to increase a likelihood the user will immediately attend to the provided notification.

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