中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

22 records (took 0.019 seconds)

    Using corrections, of automated assistant functions, for training of on-device machine learning models
    1.
    发明授权
    Using corrections, of automated assistant functions, for training of on-device machine learning models 有权

    公开(公告)号：US12272360B2

    公开(公告)日：2025-04-08

    申请号：US18657405

    申请日：2024-05-07

    Applicant: GOOGLE LLC

    Inventor： Françoise Beaufays ,  Rajiv Mathews ,  Dragan Zivkovic ,  Kurt Partridge ,  Andrew Hard

    IPC: G10L15/22 ,  G10L15/065 ,  G10L15/10 ,  G10L15/30

    Abstract: Processor(s) of a client device can: receive sensor data that captures environmental attributes of an environment of the client device; process the sensor data using a machine learning model to generate a predicted output that dictates whether one or more currently dormant automated assistant functions are activated; making a decision as to whether to trigger the one or more currently dormant automated assistant functions; subsequent to making the decision, determining that the decision was incorrect; and in response to determining that the determination was incorrect, generating a gradient based on comparing the predicted output to ground truth output. In some implementations, the generated gradient is used, by processor(s) of the client device, to update weights of the on-device speech recognition model. In some implementations, the generated gradient is additionally or alternatively transmitted to a remote system for use in remote updating of global weights of a global speech recognition model.

    Keyboard automatic language identification and reconfiguration
    2.
    发明授权
    Keyboard automatic language identification and reconfiguration 有权

    公开(公告)号：US11327652B2

    公开(公告)日：2022-05-10

    申请号：US16989420

    申请日：2020-08-10

    Applicant: Google LLC

    Inventor： Ouais Alsharif ,  Peter Ciccotto ,  Francoise Beaufays ,  Dragan Zivkovic

    IPC: G06F3/04886 ,  G06F3/023 ,  G06F40/263 ,  G06F40/274

    Abstract: A keyboard is described that determines, using a first decoder and based on a selection of keys of a graphical keyboard, text. Responsive to determining that a characteristic of the text satisfies a threshold, a model of the keyboard identifies the target language of the text, and determines whether the target language is different than a language associated with the first decoder. If the target language of the text is not different than the language associated with the first decoder, the keyboard outputs, for display, an indication of first candidate words determined by the first decoder from the text. If the target language of the text is different: the keyboard enables a second decoder, where a language associated with the second decoder matches the target language of the text, and outputs, for display, an indication of second candidate words determined by the second decoder from the text.

    GENERATION AND UTILIZATION OF PSEUDO-CORRECTION(S) TO PREVENT FORGETTING OF PERSONALIZED ON-DEVICE AUTOMATIC SPEECH RECOGNITION (ASR) MODEL(S)
    3.
    发明申请
    GENERATION AND UTILIZATION OF PSEUDO-CORRECTION(S) TO PREVENT FORGETTING OF PERSONALIZED ON-DEVICE AUTOMATIC SPEECH RECOGNITION (ASR) MODEL(S) 有权

    公开(公告)号：US20250157465A1

    公开(公告)日：2025-05-15

    申请号：US19020214

    申请日：2025-01-14

    Applicant: GOOGLE LLC

    Inventor： Rajiv Mathews ,  Dragan Zivkovic ,  Khe Chai Sim

    IPC: G10L15/19 ,  G10L15/06 ,  G10L15/22 ,  G10L15/30

    Abstract: On-device processor(s) of a client device may store, in on-device storage and in association with a time to live (TTL) in the on-device storage, a correction directed to ASR processing of audio data. The correction may include a portion of a given speech hypothesis that was modified to an alternate speech hypothesis. Further, the on-device processor(s) may cause an on-device ASR model to be personalized based on the correction. Moreover, and based on additional ASR processing of additional audio data, the on-device processor(s) may store, in the on-device storage and in association with an additional TTL in the on-device storage, a pseudo-correction directed to the additional ASR processing. Accordingly, the on-device processor(s) may cause the on-device ASR model to be personalized based on the pseudo-correction to prevent forgetting by the on-device ASR model.

    Training Speech Recognizers Based On Biased Transcriptions
    4.
    发明公开
    Training Speech Recognizers Based On Biased Transcriptions 审中-公开

    公开(公告)号：US20240257799A1

    公开(公告)日：2024-08-01

    申请号：US18161608

    申请日：2023-01-30

    Applicant: Google LLC

    Inventor： Dragan Zivkovic ,  Agoston Weisz

    IPC: G10L15/06 ,  G10L15/08 ,  G10L15/22

    CPC classification number: G10L15/063 ,  G10L15/08 ,  G10L15/22 ,  G10L2015/0636 ,  G10L2015/088 ,  G10L2015/223

    Abstract: A method includes receiving a biased transcription for a voice command spoken by a user and captured by a user device, the biased transcription biased to include a biasing phrase from a set of biasing phrases specific to the user. The method also includes instructing an application executing on the user device to perform an action specified by the biased transcription for the voice command, and receiving one or more user behavior signals responsive to the application performing the action specified by the biased transcription. The method further includes generating, as output from a confidence model, a confidence score of the biased transcription based on the one or more user behavior signals input to the confidence model and, based on the confidence score output from the confidence model, training a speech recognizer on the biased transcription.

    USING CORRECTIONS, OF AUTOMATED ASSISTANT FUNCTIONS, FOR TRAINING OF ON-DEVICE MACHINE LEARNING MODELS
    5.
    发明公开
    USING CORRECTIONS, OF AUTOMATED ASSISTANT FUNCTIONS, FOR TRAINING OF ON-DEVICE MACHINE LEARNING MODELS 审中-公开

    公开(公告)号：US20230352019A1

    公开(公告)日：2023-11-02

    申请号：US18218818

    申请日：2023-07-06

    Applicant: GOOGLE LLC

    Inventor： Françoise Beaufays ,  Rajiv Mathews ,  Dragan Zivkovic ,  Kurt Partridge ,  Andrew Hard

    IPC: G10L15/22 ,  G10L15/065 ,  G10L15/10 ,  G10L15/30

    CPC classification number: G10L15/22 ,  G10L15/065 ,  G10L15/10 ,  G10L15/30

    Abstract: Processor(s) of a client device can: receive sensor data that captures environmental attributes of an environment of the client device; process the sensor data using a machine learning model to generate a predicted output that dictates whether one or more currently dormant automated assistant functions are activated; making a decision as to whether to trigger the one or more currently dormant automated assistant functions; subsequent to making the decision, determining that the decision was incorrect; and in response to determining that the determination was incorrect, generating a gradient based on comparing the predicted output to ground truth output. In some implementations, the generated gradient is used, by processor(s) of the client device, to update weights of the on-device speech recognition model. In some implementations, the generated gradient is additionally or alternatively transmitted to a remote system for use in remote updating of global weights of a global speech recognition model.

    Modality Learning On Mobile Devices
    6.
    发明申请
    Modality Learning On Mobile Devices 有权

    公开(公告)号：US20220413696A1

    公开(公告)日：2022-12-29

    申请号：US17823545

    申请日：2022-08-31

    Applicant: Google LLC

    Inventor： Yu Ouyang ,  Diego Melendo Casado ,  Mohammadinamul Hasan Sheik ,  Francoise Beaufays ,  Dragan Zivkovic ,  Meltem Oktem

    IPC: G06F3/04886 ,  G06F3/16 ,  G06F1/16 ,  G06F3/023 ,  G06F3/04883 ,  G06F40/166 ,  G06F40/289

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for cross input modality learning in a mobile device are disclosed. In one aspect, a method includes activating a first modality user input mode in which user inputs by way of a first modality are recognized using a first modality recognizer; and receiving a user input by way of the first modality. The method includes, obtaining, as a result of the first modality recognizer recognizing the user input, a transcription that includes a particular term; and generating an input context data structure that references at least the particular term. The method further includes, transmitting, by the first modality recognizer, the input context data structure to a second modality recognizer for use in updating a second modality recognition model associated with the second modality recognizer.

    EVALUATING ON-DEVICE MACHINE LEARNING MODEL(S) BASED ON PERFORMANCE MEASURES OF CLIENT DEVICE(S) AND/OR THE ON-DEVICE MACHINE LEARNING MODEL(S)
    7.
    发明申请
    EVALUATING ON-DEVICE MACHINE LEARNING MODEL(S) BASED ON PERFORMANCE MEASURES OF CLIENT DEVICE(S) AND/OR THE ON-DEVICE MACHINE LEARNING MODEL(S) 有权

    公开(公告)号：US20220309389A1

    公开(公告)日：2022-09-29

    申请号：US17215588

    申请日：2021-03-29

    Applicant: Google LLC

    Inventor： Dragan Zivkovic ,  Akash Agrawal ,  Françoise Beaufays ,  Tamar Lucassen

    IPC: G06N20/00 ,  G06N5/04

    Abstract: Implementations disclosed herein are directed to systems and methods for evaluating on-device machine learning (ML) model(s) based on performance measure(s) of client device(s) and/or the on-device ML model(s). The client device(s) can include on-device memory that stores the on-device ML model(s) and a plurality of testing instances for the on-device ML model(s). When certain condition(s) are satisfied, the client device(s) can process, using the on-device ML model(s), the plurality of testing instances to generate the performance measure(s). The performance measure(s) can include, for example, latency measure(s), memory consumption measure(s), CPU usage measure(s), ML model measure(s) (e.g., precision and/or recall), and/or other measures. In some implementations, the on-device ML model(s) can be activated (or kept active) for use locally at the client device(s) based on the performance measure(s). In other implementations, the on-device ML model(s) can be sparsified based on the performance measure(s).

    Keyboard Automatic Language Identification and Reconfiguration
    8.
    发明申请
    Keyboard Automatic Language Identification and Reconfiguration 有权

    公开(公告)号：US20220229548A1

    公开(公告)日：2022-07-21

    申请号：US17658233

    申请日：2022-04-06

    Applicant: Google LLC

    Inventor： Ouais Alsharif ,  Peter Ciccotto ,  Francoise Beaufays ,  Dragan Zivkovic

    IPC: G06F3/04886 ,  G06F3/023 ,  G06F40/263 ,  G06F40/274

    Abstract: A keyboard is described that determines, using a first decoder and based on a selection of keys of a graphical keyboard, text. Responsive to determining that a characteristic of the text satisfies a threshold, a model of the keyboard identifies the target language of the text, and determines whether the target language is different than a language associated with the first decoder. If the target language of the text is not different than the language associated with the first decoder, the keyboard outputs, for display, an indication of first candidate words determined by the first decoder from the text. If the target language of the text is different: the keyboard enables a second decoder, where a language associated with the second decoder matches the target language of the text, and outputs, for display, an indication of second candidate words determined by the second decoder from the text.

    USING CORRECTIONS, OF AUTOMATED ASSISTANT FUNCTIONS, FOR TRAINING OF ON-DEVICE MACHINE LEARNING MODELS
    9.
    发明公开
    USING CORRECTIONS, OF AUTOMATED ASSISTANT FUNCTIONS, FOR TRAINING OF ON-DEVICE MACHINE LEARNING MODELS 审中-公开

    公开(公告)号：US20240296843A1

    公开(公告)日：2024-09-05

    申请号：US18657405

    申请日：2024-05-07

    Applicant: GOOGLE LLC

    Inventor： Françoise Beaufays ,  Rajiv Mathews ,  Dragan Zivkovic ,  Kurt Partridge ,  Andrew Hard

    IPC: G10L15/22 ,  G10L15/065 ,  G10L15/10 ,  G10L15/30

    CPC classification number: G10L15/22 ,  G10L15/065 ,  G10L15/10 ,  G10L15/30

    Abstract: Processor(s) of a client device can: receive sensor data that captures environmental attributes of an environment of the client device; process the sensor data using a machine learning model to generate a predicted output that dictates whether one or more currently dormant automated assistant functions are activated; making a decision as to whether to trigger the one or more currently dormant automated assistant functions; subsequent to making the decision, determining that the decision was incorrect; and in response to determining that the determination was incorrect, generating a gradient based on comparing the predicted output to ground truth output. In some implementations, the generated gradient is used, by processor(s) of the client device, to update weights of the on-device speech recognition model. In some implementations, the generated gradient is additionally or alternatively transmitted to a remote system for use in remote updating of global weights of a global speech recognition model.

Patent Agency Ranking