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    Using corrections, of automated assistant functions, for training of on-device machine learning models
    21.
    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：US12014739B2

    公开(公告)日：2024-06-18

    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.

    Unsupervised federated learning of machine learning model layers
    22.
    发明授权
    Unsupervised federated learning of machine learning model layers 有权

    公开(公告)号：US12014724B2

    公开(公告)日：2024-06-18

    申请号：US16973605

    申请日：2020-07-20

    Applicant: Google LLC

    Inventor： Françoise Beaufays ,  Khe Chai Sim ,  Johan Schalkwyk

    IPC: G10L15/06 ,  G10L15/187 ,  G10L15/22 ,  G10L15/30

    CPC classification number: G10L15/063 ,  G10L15/187 ,  G10L15/22 ,  G10L15/30 ,  G10L2015/0635

    Abstract: Implementations disclosed herein are directed to unsupervised federated training of global machine learning (“ML”) model layers that, after the federated training, can be combined with additional layer(s), thereby resulting in a combined ML model. Processor(s) can: detect audio data that captures a spoken utterance of a user of a client device; process, using a local ML model, the audio data to generate predicted output(s); generate, using unsupervised learning locally at the client device, a gradient based on the predicted output(s); transmit the gradient to a remote system; update weight(s) of the global ML model layers based on the gradient; subsequent to updating the weight(s), train, using supervised learning remotely at the remote system, a combined ML model that includes the updated global ML model layers and additional layer(s); transmit the combined ML model to the client device; and use the combined ML model to make prediction(s) at the client device.

    Using corrections, of automated assistant functions, for training of on-device machine learning models
    24.
    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：US11741953B2

    公开(公告)日：2023-08-29

    申请号：US16973572

    申请日：2019-11-08

    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.

    ON-DEVICE SPEECH SYNTHESIS OF TEXTUAL SEGMENTS FOR TRAINING OF ON-DEVICE SPEECH RECOGNITION MODEL
    25.
    发明申请
    ON-DEVICE SPEECH SYNTHESIS OF TEXTUAL SEGMENTS FOR TRAINING OF ON-DEVICE SPEECH RECOGNITION MODEL 有权

    公开(公告)号：US20220005458A1

    公开(公告)日：2022-01-06

    申请号：US17479285

    申请日：2021-09-20

    Applicant: Google LLC

    Inventor： Françoise Beaufays ,  Johan Schalkwyk ,  Khe Chai Sim

    IPC: G10L13/047 ,  G10L15/06

    Abstract: Processor(s) of a client device can: identify a textual segment stored locally at the client device; process the textual segment, using a speech synthesis model stored locally at the client device, to generate synthesized speech audio data that includes synthesized speech of the identified textual segment; process the synthesized speech, using an on-device speech recognition model that is stored locally at the client device, to generate predicted output; and generate a gradient based on comparing the predicted output to ground truth output that corresponds to the textual segment. 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.

    USING CORRECTIONS, OF PREDICTED TEXTUAL SEGMENTS OF SPOKEN UTTERANCES, FOR TRAINING OF ON-DEVICE SPEECH RECOGNITION MODEL
    26.
    发明申请
    USING CORRECTIONS, OF PREDICTED TEXTUAL SEGMENTS OF SPOKEN UTTERANCES, FOR TRAINING OF ON-DEVICE SPEECH RECOGNITION MODEL 有权

    公开(公告)号：US20210327410A1

    公开(公告)日：2021-10-21

    申请号：US17250165

    申请日：2019-10-11

    Applicant: Google LLC

    Inventor： Françoise Beaufays ,  Johan Schalkwyk ,  Giovanni Motta

    IPC: G10L15/00 ,  G10L25/51 ,  G06F3/0484 ,  G06F3/0488

    Abstract: Processor(s) of a client device can: receive audio data that captures a spoken utterance of a user of the client device; process, using an on-device speech recognition model, the audio data to generate a predicted textual segment that is a prediction of the If spoken utterance; cause at least part of the predicted textual segment to be rendered (e.g., visually and/or audibly); receive further user interface input that is a correction of the predicted textual segment to an alternate textual segment; and generate a gradient based on comparing at least part of the predicted output to ground truth output that corresponds to the alternate textual segment. The gradient is used, by processor(s) of the client device, to update weights of the on-device speech recognition model and/or is transmitted to a remote system for use in remote updating of global weights of a global speech recognition model.

    ON-DEVICE SPEECH SYNTHESIS OF TEXTUAL SEGMENTS FOR TRAINING OF ON-DEVICE SPEECH RECOGNITION MODEL
    27.
    发明申请
    ON-DEVICE SPEECH SYNTHESIS OF TEXTUAL SEGMENTS FOR TRAINING OF ON-DEVICE SPEECH RECOGNITION MODEL 有权

    公开(公告)号：US20210104223A1

    公开(公告)日：2021-04-08

    申请号：US16959546

    申请日：2019-10-02

    Applicant: Google LLC

    Inventor： Françoise Beaufays ,  Johan Schalkwyk ,  Khe Chai Sim

    IPC: G10L13/047 ,  G10L15/06

    Abstract: Processor(s) of a client device can: identify a textual segment stored locally at the client device; process the textual segment, using a speech synthesis model stored locally at the client device, to generate synthesized speech audio data that includes synthesized speech of the identified textual segment; process the synthesized speech, using an on-device speech recognition model that is stored locally at the client device, to generate predicted output; and generate a gradient based on comparing the predicted output to ground truth output that corresponds to the textual segment. 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.

    MIXED CLIENT-SERVER FEDERATED LEARNING OF MACHINE LEARNING MODEL(S)
    28.
    发明申请
    MIXED CLIENT-SERVER FEDERATED LEARNING OF MACHINE LEARNING MODEL(S) 有权

    公开(公告)号：US20250037707A1

    公开(公告)日：2025-01-30

    申请号：US18917696

    申请日：2024-10-16

    Applicant: GOOGLE LLC

    Inventor： Françoise Beaufays ,  Andrew Hard ,  Swaroop Indra Ramaswamy ,  Om Dipakbhai Thakkar ,  Rajiv Mathews

    IPC: G10L15/065 ,  G10L13/04 ,  G10L15/26 ,  G10L15/30

    Abstract: Implementations disclosed herein are directed to federated learning of machine learning (“ML”) model(s) based on gradient(s) generated at corresponding client devices and a remote system. Processor(s) of the corresponding client devices can process client data generated locally at the corresponding client devices using corresponding on-device ML model(s) to generate corresponding predicted outputs, generate corresponding client gradients based on the corresponding predicted outputs, and transmit the corresponding client gradients to the remote system. Processor(s) of the remote system can process remote data obtained from remote database(s) using global ML model(s) to generate additional corresponding predicted outputs, generate corresponding remote gradients based on the additional corresponding predicted outputs. Further, the remote system can utilize the corresponding client gradients and the corresponding remote gradients to update the global ML model(s) or weights thereof. The updated global ML model(s) and/or the updated weights thereof can be transmitted back to the corresponding client devices.

    EPHEMERAL LEARNING OF MACHINE LEARNING MODEL(S)
    29.
    发明申请
    EPHEMERAL LEARNING OF MACHINE LEARNING MODEL(S) 有权

    公开(公告)号：US20250016387A1

    公开(公告)日：2025-01-09

    申请号：US18890050

    申请日：2024-09-19

    Applicant: GOOGLE LLC

    Inventor： Françoise Beaufays ,  Khe Chai Sim ,  Trevor Strohman ,  Oren Litvin

    IPC: H04N21/233 ,  G06F18/214 ,  G06N20/00 ,  H04N21/232

    Abstract: Implementations disclosed herein are directed to ephemeral learning of machine learning (“ML”) model(s) based on gradient(s) generated at a remote system (e.g., remote server(s)). Processor(s) of the remote system can receive stream(s) of audio data capturing spoken utterance(s) from a client device of a user. A fulfillment pipeline can process the stream(s) of audio data to cause certain fulfillment(s) of the spoken utterance(s) to be performed. Meanwhile, a training pipeline can process the stream(s) of audio data to generate gradient(s) using unsupervised learning techniques. Subsequent to the processing by the fulfillment pipeline and/or the training pipeline, the stream(s) of audio data are discarded by the remote system. Accordingly, the ML model(s) can be trained at the remote system without storing or logging of the stream(s) of audio data by non-transient memory thereof, thereby providing more efficient training mechanisms for training the ML model(s) and also increasing security of user data.

    UNSUPERVISED FEDERATED LEARNING OF MACHINE LEARNING MODEL LAYERS
    30.
    发明公开
    UNSUPERVISED FEDERATED LEARNING OF MACHINE LEARNING MODEL LAYERS 审中-公开

    公开(公告)号：US20240296834A1

    公开(公告)日：2024-09-05

    申请号：US18659940

    申请日：2024-05-09

    Applicant: GOOGLE LLC

    Inventor： Françoise Beaufays ,  Khe Chai Sim ,  Johan Schalkwyk

    IPC: G10L15/06 ,  G10L15/187 ,  G10L15/22 ,  G10L15/30

    CPC classification number: G10L15/063 ,  G10L15/187 ,  G10L15/22 ,  G10L15/30 ,  G10L2015/0635

    Abstract: Implementations disclosed herein are directed to unsupervised federated training of global machine learning (“ML”) model layers that, after the federated training, can be combined with additional layer(s), thereby resulting in a combined ML model. Processor(s) can: detect audio data that captures a spoken utterance of a user of a client device; process, using a local ML model, the audio data to generate predicted output(s); generate, using unsupervised learning locally at the client device, a gradient based on the predicted output(s); transmit the gradient to a remote system; update weight(s) of the global ML model layers based on the gradient; subsequent to updating the weight(s), train, using supervised learning remotely at the remote system, a combined ML model that includes the updated global ML model layers and additional layer(s); transmit the combined ML model to the client device; and use the combined ML model to make prediction(s) at the client device.

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