COGNITIVE PRINT SPEAKER MODELER
    22.
    发明申请

    公开(公告)号:US20190333520A1

    公开(公告)日:2019-10-31

    申请号:US15966122

    申请日:2018-04-30

    Abstract: Aspects of the present invention provide devices that subtitle streaming video with audio and identify a speaker in a streaming video with audio according to words spoken by the speaker matched to a cognitive print. The cognitive print includes traits classified according a hierarchical long short term model (LSTM). The hierarchical LSTM includes layers of LSTMs and each layer corresponds to the classification of one trait. A processor annotates a subtitle of the words spoken by the speaker, which decorates the subtitle with a label representative of the identified speaker, and streams the decorated subtitle with the streaming video with audio.

    MORPHED CONVERSATIONAL ANSWERING VIA AGENT HIERARCHY OF VARIED GRANULARITY

    公开(公告)号:US20180285753A1

    公开(公告)日:2018-10-04

    申请号:US15471684

    申请日:2017-03-28

    Abstract: A hierarchy of agents is constructed from a set of agents. Each agent in the hierarchy is trained to answer a question according to a corresponding corpus associated with the agent, which contains a portion of knowledge about a subject-matter. The question is submitted to a first subset of agents, the agents in the first subset occupying a first level in the hierarchy. From a first agent in the first subset, a first answer is propagated to a second agent in a second subset of agents, the first agent computing the first answer using a first portion of knowledge about the subject-matter. to form a first morphed answer, a second answer is added to the first answer, the second answer being computed by the second agent using a second portion of knowledge about the subject-matter. The morphed answer is produced in response to the question.

    NONFUNGIBLE TOKEN PATH SYNTHESIS WITH SOCIAL SHARING

    公开(公告)号:US20240062169A1

    公开(公告)日:2024-02-22

    申请号:US17820992

    申请日:2022-08-19

    CPC classification number: G06Q20/065 G06Q40/04 H04L9/50 H04L2209/56

    Abstract: A computer-implemented method includes generating several crypto tokens, each token includes a content portion that is encrypted. For each pair of tokens, a first token and a second token, in response to a correlation score between content portions of the first token and the second token exceeding a predetermined correlation threshold, creating a grid link between the first token and the second token. The tokens are added into a ledger. A group of tokens is created, based on a similarity score. A series of forecasting equations is distributed to the group of tokens. A forecast reveal rate is generated based on the series of forecasting equations, and in response to the forecast reveal rate meeting a forecast threshold, a key is generated for revealing at least one content portions embedded in the group of tokens. The key is released to decrypt the at least one content portion.

    Collaborative interactions and feedback with midair interfaces

    公开(公告)号:US11132060B2

    公开(公告)日:2021-09-28

    申请号:US16209179

    申请日:2018-12-04

    Abstract: In an embodiment, a method includes detecting a motion pattern in proximity to a first midair interface (MAI) device, the motion pattern being of a body part of a user. In an embodiment, the method includes converting the detected motion pattern to a simulated surface of an object projected from a shared MAI device, wherein the first MAI device and the shared MAI device each correspond to a different user. In an embodiment, the method includes causing a behavior change in the simulated surface being projected from the shared MAI device. An embodiment includes a computer usable program product. The computer usable program product includes a computer-readable storage device, and program instructions stored on the storage device.

    SEMI-SUPERVISED REINFORCEMENT LEARNING

    公开(公告)号:US20210089869A1

    公开(公告)日:2021-03-25

    申请号:US16582092

    申请日:2019-09-25

    Abstract: Provided is a method, a system, and a program product for determining a policy using semi-supervised reinforcement learning. The method includes observing a state of an environment by a learning agent. The method also includes taking an action by the learning agent. The method further includes observing a new state of the environment and calculating a reward for the action taken by the learning agent. The method also includes determining whether a policy related to the learning agent should be changed. The determination is conducted by a teaching agent that inputs the state of the environment and the reward as features. The method can also include changing the policy related to the learning agent upon a determination that a label outputted by the teaching agent exceeds a reward threshold.

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