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    Multi-factor authentication utilizing device pairing
    1.
    发明授权
    Multi-factor authentication utilizing device pairing 有权

    公开(公告)号：US11349833B2

    公开(公告)日：2022-05-31

    申请号：US16826231

    申请日：2020-03-21

    Applicant: KYNDRYL, INC.

    Inventor： Sarin Kumar Thayyilsubramanian ,  Debasisha Padhi ,  Anuradha Bhamidipaty ,  Firas Bouz

    IPC: H04L9/40 ,  G06Q20/40 ,  H04W12/06 ,  H04W4/80

    Abstract: Aspects of the present invention disclose a method, computer program product, and system for multi-factor authentication. In response to a request for an action, the method includes one or more processors whether a first authentication credential passes validation. In response to determining that the first authentication credential does pass validation, the method further includes one or more processors determining a second authentication credential, wherein the second authentication credential includes an indication of a wireless connection between a first computing device and a second computing device. The method further includes one or more processors determining whether the second authentication credential passes validation. In response to determining that the second authentication credential passes validation, the method further includes one or more processors allowing execution of the requested response.

    Management of communication notifications based upon geographic location
    2.
    发明授权
    Management of communication notifications based upon geographic location 有权

    公开(公告)号：US11265416B2

    公开(公告)日：2022-03-01

    申请号：US16432258

    申请日：2019-06-05

    Applicant: KYNDRYL, INC.

    Inventor： Garfield W. Vaughn ,  Moncef Benboubakeur ,  Julija Narodicka ,  Anuradha Bhamidipaty

    IPC: H04M1/72457 ,  H04M3/42 ,  H04W4/20 ,  H04M1/72454 ,  H04M1/72484

    Abstract: Embodiments of the present invention provide notification management across a plurality of electronic communication devices based on the present situational context of the intended communication recipient. Some types of relevant situational context information include the geographic location of the intended recipient, what electronic communication devices they may have in their possession, calendar or schedule information, the presence of other people, the identity of other people present in their vicinity, their relationship to the communication sender, and context information concerning the sender, such as how frequently the sender has attempted to communicate with the intended recipient and news information relevant to the known or assumed location of the sender. Notifications are managed by adjusting which electronic communication devices emit notifications and what type of notifications are emitted.

    Semantic consistency of explanations in explainable artificial intelligence applications
    4.
    发明授权
    Semantic consistency of explanations in explainable artificial intelligence applications 有权

    公开(公告)号：US11423334B2

    公开(公告)日：2022-08-23

    申请号：US16870202

    申请日：2020-05-08

    Applicant: KYNDRYL, INC.

    Inventor： Sreekrishnan Venkateswaran ,  Debasisha Padhi ,  Shubhi Asthana ,  Anuradha Bhamidipaty ,  Ashish Kundu

    IPC: G06N20/00 ,  G06F17/16 ,  G06F40/30 ,  G06N3/08

    Abstract: An explainable artificially intelligent (XAI) application contains an ordered sequence of artificially intelligent software modules. When an input dataset is submitted to the application, each module generates an output dataset and an explanation that represents, as a set of Boolean expressions, reasoning by which each output element was chosen. If any pair of explanations are determined to be semantically inconsistent, and if this determination is confirmed by further determining that an apparent inconsistency was not a correct response to an unexpected characteristic of the input dataset, nonzero inconsistency scores are assigned to inconsistent elements of the pair of explanations. If the application's overall inconsistency score exceeds a threshold value, the system forwards information about the explanation, the offending modules, and the input dataset to a downstream machine-learning component that uses this information to train the application to better respond to future input that shares certain characteristics with the current input.

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