公开(公告)号：US20200272409A1

公开(公告)日：2020-08-27

申请号：US16285941

申请日：2019-02-26

Applicant: Nvidia Corporation

Inventor： Utkarsh Vaidya ,  Sumit Bhattacharya

IPC: G06F3/16 ,  G06N3/04 ,  G06N7/00

Abstract: The disclosure is directed to a process that can predict an audio glitch, and then attempt to preempt the audio glitch. The process can monitor the systems, processes, and execution threads on a larger system or device, such as a mobile device or an in-vehicle device. Using a learning algorithm, such as deep neural network (DNN), the information collected can generate a prediction of whether an audio glitch is likely to occur. An audio glitch can be an audio underrun condition. 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 underrun condition has abated, the system adjustments can be reversed fully or in steps to return the system to its state prior to the system adjustments.

公开(公告)号：US11590929B2

公开(公告)日：2023-02-28

申请号：US16867395

申请日：2020-05-05

Applicant: NVIDIA Corporation

Inventor： Sumit Bhattacharya ,  Jason Conrad Roche ,  Niranjan Avadhanam

IPC: G06F21/32 ,  B60R25/01 ,  B60R25/25 ,  B60R25/30 ,  G05B13/02 ,  G06N3/08 ,  G10L17/00 ,  G10L17/06 ,  G10L17/18 ,  G06V10/25 ,  G06V20/59

Abstract: Systems and methods are disclosed herein for implementation of a vehicle command operation system that may use multi-modal technology to authenticate an occupant of the vehicle to authorize a command and receive natural language commands for vehicular operations. The system may utilize sensors to receive data indicative of a voice command from an occupant of the vehicle. The system may receive second sensor data to aid in the determination of the corresponding vehicular operation in response to the received command. The system may retrieve authentication data for the occupants of the vehicle. The system authenticates the occupant to authorize a vehicular operation command using a neural network based on at least one of the first sensor data, the second sensor data, and the authentication data. Responsive to the authentication, the system may authorize the operation to be performed in the vehicle based on the vehicular operation command.

公开(公告)号：US11567728B2

公开(公告)日：2023-01-31

申请号：US17121373

申请日：2020-12-14

Applicant: Nvidia Corporation

Inventor： Utkarsh Vaidya ,  Sumit Bhattacharya

IPC: G06F3/16 ,  G06N7/00 ,  G06N3/04

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.

公开(公告)号：US20220319503A1

公开(公告)日：2022-10-06

申请号：US17218751

申请日：2021-03-31

Applicant: NVIDIA Corporation

Inventor： Shubhadeep Das ,  Sumit Bhattacharya ,  Ratin Kumar

IPC: G10L15/18 ,  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.

公开(公告)号：US10896021B2

公开(公告)日：2021-01-19

申请号：US16285941

申请日：2019-02-26

Applicant: Nvidia Corporation

Inventor： Utkarsh Vaidya ,  Sumit Bhattacharya

IPC: G06F3/16 ,  G06N7/00 ,  G06N3/04

Abstract: The disclosure is directed to a process that can predict an audio glitch, and then attempt to preempt the audio glitch. The process can monitor the systems, processes, and execution threads on a larger system or device, such as a mobile device or an in-vehicle device. Using a learning algorithm, such as deep neural network (DNN), the information collected can generate a prediction of whether an audio glitch is likely to occur. An audio glitch can be an audio underrun condition. 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 underrun condition has abated, the system adjustments can be reversed fully or in steps to return the system to its state prior to the system adjustments.

公开(公告)号：US20240311080A1

公开(公告)日：2024-09-19

申请号：US18676243

申请日：2024-05-28

Applicant: NVIDIA Corporation

Inventor： Utkarsh Vaidya ,  Sumit Bhattacharya

IPC: G06F3/16 ,  G06N3/045 ,  G06N7/01

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.

公开(公告)号：US12057113B2

公开(公告)日：2024-08-06

申请号：US18329839

申请日：2023-06-06

Applicant: NVIDIA Corporation

Inventor： Shubhadeep Das ,  Sumit Bhattacharya ,  Ratin Kumar

IPC: G10L15/22 ,  G10L13/02 ,  G10L15/18 ,  G10L15/30

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.

公开(公告)号：US20230259540A1

公开(公告)日：2023-08-17

申请号：US17674704

申请日：2022-02-17

Applicant: NVIDIA Corporation

Inventor： Shubhadeep Das ,  Sumit Bhattacharya ,  Ratin Kumar

IPC: G06F16/332 ,  G06F16/33 ,  G06F16/38 ,  G10L13/08 ,  G10L15/08

CPC classification number: G06F16/3329 ,  G06F16/3344 ,  G06F16/38 ,  G10L13/08 ,  G10L15/083

Abstract: In various examples, a conversational artificial intelligence (AI) platform uses structured data and unstructured data to generate responses to queries from users. In an example, if data for a response to a query is not stored in a structured data structured, the conversational AI platform searches for the data in an unstructured data structure.

公开(公告)号：US20230120989A1

公开(公告)日：2023-04-20

申请号：US18067217

申请日：2022-12-16

Applicant: NVIDIA Corporation

Inventor： Shubhadeep Das ,  Sumit Bhattacharya ,  Ratin Kumar

IPC: G10L15/18 ,  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.

公开(公告)号：US11568861B2

公开(公告)日：2023-01-31

申请号：US17218751

申请日：2021-03-31

Applicant: NVIDIA Corporation

Inventor： Shubhadeep Das ,  Sumit Bhattacharya ,  Ratin Kumar

IPC: G10L15/22 ,  G10L15/18 ,  G10L13/02 ,  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.