Dynamically preventing audio artifacts

    公开(公告)号:US11995378B2

    公开(公告)日:2024-05-28

    申请号:US18161326

    申请日:2023-01-30

    CPC classification number: G06F3/165 G06F3/162 G06N3/045 G06N7/01

    Abstract: The disclosure is directed to a process that can predict and prevent an audio artifact from occurring. The process can monitor the systems, processes, and execution threads on a larger system/device, such as a mobile or in-vehicle device. Using a learning algorithm, such as deep neural network (DNN), the information collected can generate a prediction of whether an audio artifact is likely to occur. The process can use a second learning algorithm, which also can be a DNN, to generate recommended system adjustments that can attempt to prevent the audio glitch from occurring. The recommendations can be for various systems and components on the device, such as changing the processing system frequency, the memory frequency, and the audio buffer size. After the audio artifact has been prevented, the system adjustments can be reversed fully or in steps to return the system to its state prior to the system adjustments.

    USING A NATURAL LANGUAGE MODEL TO INTERFACE WITH A CLOSED DOMAIN SYSTEM

    公开(公告)号:US20230317067A1

    公开(公告)日:2023-10-05

    申请号:US18329839

    申请日:2023-06-06

    CPC classification number: G10L15/1815 G10L13/02 G10L15/30 G10L15/22

    Abstract: In various examples, systems and methods of the present disclosure combine open and closed dialog systems into an intelligent dialog management system. A text query may be processed by a natural language understanding model trained to associate the text query with a domain tag, intent classification, and/or input slots. Using the domain tag, the natural language understanding model may identify information in the text query corresponding to input slots needed for answering the text query. The text query and related information may then be passed to a dialog manager to direct the text query to the proper domain dialog system. Responses retrieved from the domain dialog system may be provided to the user via text output and/or via a text to speech component of the dialog management system.

    USING A NATURAL LANGUAGE MODEL TO INTERFACE WITH A CLOSED DOMAIN SYSTEM

    公开(公告)号:US20240363104A1

    公开(公告)日:2024-10-31

    申请号:US18766466

    申请日:2024-07-08

    CPC classification number: G10L15/1815 G10L13/02 G10L15/22 G10L15/30

    Abstract: In various examples, systems and methods of the present disclosure combine open and closed dialog systems into an intelligent dialog management system. A text query may be processed by a natural language understanding model trained to associate the text query with a domain tag, intent classification, and/or input slots. Using the domain tag, the natural language understanding model may identify information in the text query corresponding to input slots needed for answering the text query. The text query and related information may then be passed to a dialog manager to direct the text query to the proper domain dialog system. Responses retrieved from the domain dialog system may be provided to the user via text output and/or via a text to speech component of the dialog management system.

    Conversational AI platforms with closed domain and open domain dialog integration

    公开(公告)号:US11769495B2

    公开(公告)日:2023-09-26

    申请号:US18067217

    申请日:2022-12-16

    CPC classification number: G10L15/1815 G10L13/02 G10L15/22 G10L15/30

    Abstract: In various examples, systems and methods of the present disclosure combine open and closed dialog systems into an intelligent dialog management system. A text query may be processed by a natural language understanding model trained to associate the text query with a domain tag, intent classification, and/or input slots. Using the domain tag, the natural language understanding model may identify information in the text query corresponding to input slots needed for answering the text query. The text query and related information may then be passed to a dialog manager to direct the text query to the proper domain dialog system. Responses retrieved from the domain dialog system may be provided to the user via text output and/or via a text to speech component of the dialog management system.

    SYSTEMS AND METHODS FOR PEDESTRIAN CROSSING RISK ASSESSMENT AND DIRECTIONAL WARNING

    公开(公告)号:US20220012988A1

    公开(公告)日:2022-01-13

    申请号:US16922601

    申请日:2020-07-07

    Abstract: Systems and methods are disclosed herein for a pedestrian crossing warning system that may use multi-modal technology to determine attributes of a person and provide a warning to the person in response to a calculated risk level to effect a reduction of the risk level. The system may utilize sensors to receive data indicative of a trajectory of a person external to the vehicle. Specific attributes of the person such as age or walking aids may be determined. Based on the trajectory data and the specific attributes, a risk level may be determined by the system using a machine learning model. The system may cause emission of a warning to the person in response to the risk level.

    DYNAMICALLY PREVENTING AUDIO ARTIFACTS
    9.
    发明公开

    公开(公告)号:US20230168857A1

    公开(公告)日:2023-06-01

    申请号:US18161326

    申请日:2023-01-30

    CPC classification number: G06F3/165 G06F3/162 G06N3/045 G06N7/01

    Abstract: The disclosure is directed to a process that can predict and prevent an audio artifact from occurring. The process can monitor the systems, processes, and execution threads on a larger system/ device, such as a mobile or in-vehicle device. Using a learning algorithm, such as deep neural network (DNN), the information collected can generate a prediction of whether an audio artifact is likely to occur. The process can use a second learning algorithm, which also can be a DNN, to generate recommended system adjustments that can attempt to prevent the audio glitch from occurring. The recommendations can be for various systems and components on the device, such as changing the processing system frequency, the memory frequency, and the audio buffer size. After the audio artifact has been prevented, the system adjustments can be reversed fully or in steps to return the system to its state prior to the system adjustments.

    DYNAMICALLY PREVENTING AUDIO ARTIFACTS

    公开(公告)号:US20210103425A1

    公开(公告)日:2021-04-08

    申请号:US17121373

    申请日:2020-12-14

    Abstract: The disclosure is directed to a process that can predict and prevent an audio artifact from occurring. The process can monitor the systems, processes, and execution threads on a larger system/device, such as a mobile or in-vehicle device. Using a learning algorithm, such as deep neural network (DNN), the information collected can generate a prediction of whether an audio artifact is likely to occur. The process can use a second learning algorithm, which also can be a DNN, to generate recommended system adjustments that can attempt to prevent the audio glitch from occurring. The recommendations can be for various systems and components on the device, such as changing the processing system frequency, the memory frequency, and the audio buffer size. After the audio artifact has been prevented, the system adjustments can be reversed fully or in steps to return the system to its state prior to the system adjustments.

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