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    Ephemeral learning of machine learning model(s)
    1.
    发明授权
    Ephemeral learning of machine learning model(s) 有权

    公开(公告)号：US12126845B2

    公开(公告)日：2024-10-22

    申请号：US17533779

    申请日：2021-11-23

    Applicant: GOOGLE LLC

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

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

    CPC classification number: 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.

    USING CORRECTIONS, OF AUTOMATED ASSISTANT FUNCTIONS, FOR TRAINING OF ON-DEVICE MACHINE LEARNING MODELS
    2.
    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.

    ON-DEVICE PERSONALIZATION OF SPEECH SYNTHESIS FOR TRAINING OF SPEECH RECOGNITION MODEL(S)
    3.
    发明申请
    ON-DEVICE PERSONALIZATION OF SPEECH SYNTHESIS FOR TRAINING OF SPEECH RECOGNITION MODEL(S) 有权

    公开(公告)号：US20230068897A1

    公开(公告)日：2023-03-02

    申请号：US17983671

    申请日：2022-11-09

    Applicant: GOOGLE LLC

    Inventor： Françoise Beaufays ,  Johan Schalkwyk ,  Khe Chai Sim

    IPC: G10L13/047 ,  G10L13/033 ,  G10L13/10

    Abstract: Processor(s) of a client device can: identify a textual segment stored locally at the client device; process the textual segment, using an on-device TTS generator model, to generate synthesized speech audio data that includes synthesized speech of the textual segment; process the synthesized speech, using an on-device ASR model to generate predicted ASR output; and generate a gradient based on comparing the predicted ASR output to ground truth output corresponding to the textual segment. Processor(s) of the client device can also: process the synthesized speech audio data using an on-device TTS generator model to make a prediction; and generate a gradient based on the prediction. In these implementations, the generated gradient(s) can be used to update weight(s) of the respective on-device model(s) and/or transmitted to a remote system for use in remote updating of respective global model(s). The updated weight(s) and/or the updated model(s) can be transmitted to client device(s).

    On-device personalization of speech synthesis for training of speech model(s)
    4.
    发明授权
    On-device personalization of speech synthesis for training of speech model(s) 有权

    公开(公告)号：US11545133B2

    公开(公告)日：2023-01-03

    申请号：US17082518

    申请日：2020-10-28

    Applicant: Google LLC

    Inventor： Françoise Beaufays ,  Johan Schalkwyk ,  Khe Chai Sim

    IPC: G10L15/18 ,  G10L15/07 ,  G10L13/047 ,  G10L13/033 ,  G10L13/10

    Abstract: Processor(s) of a client device can: identify a textual segment stored locally at the client device; process the textual segment, using an on-device TTS generator model, to generate synthesized speech audio data that includes synthesized speech of the textual segment; process the synthesized speech, using an on-device ASR model to generate predicted ASR output; and generate a gradient based on comparing the predicted ASR output to ground truth output corresponding to the textual segment. Processor(s) of the client device can also: process the synthesized speech audio data using an on-device TTS generator model to make a prediction; and generate a gradient based on the prediction. In these implementations, the generated gradient(s) can be used to update weight(s) of the respective on-device model(s) and/or transmitted to a remote system for use in remote updating of respective global model(s). The updated weight(s) and/or the updated model(s) can be transmitted to client device(s).

    USING CORRECTIONS, OF AUTOMATED ASSISTANT FUNCTIONS, FOR TRAINING OF ON-DEVICE MACHINE LEARNING MODELS
    5.
    发明申请
    USING CORRECTIONS, OF AUTOMATED ASSISTANT FUNCTIONS, FOR TRAINING OF ON-DEVICE MACHINE LEARNING MODELS 有权

    公开(公告)号：US20210327421A1

    公开(公告)日：2021-10-21

    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

    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.

    DECENTRALIZED LEARNING OF LARGE MACHINE LEARNING (ML) MODEL(S)
    6.
    发明申请
    DECENTRALIZED LEARNING OF LARGE MACHINE LEARNING (ML) MODEL(S) 有权

    公开(公告)号：US20250078812A1

    公开(公告)日：2025-03-06

    申请号：US18794773

    申请日：2024-08-05

    Applicant: GOOGLE LLC

    Inventor： Yonghui Xiao ,  Françoise Beaufays ,  Yuxin Ding

    IPC: G10L15/06 ,  G06N3/098 ,  G10L15/183 ,  G10L15/30

    Abstract: Implementations described herein are directed to a framework for decentralized learning of large global machine learning (ML) model(s). In various implementations, remote processor(s) of a remote system can identify a global ML model, select client devices to participate in a given round of decentralized learning of the global ML model, and transmit, to each of the client devices, a processed version of the global ML model that is of a reduced transferrable size. Further, client device processor(s) of a client device can receive the processed version of the global ML model, obtain corresponding client data, perform partial model training, based on processing the corresponding client data, for the processed version of the global ML model to generate a corresponding update, and transmit the corresponding update back to the remote system. Moreover, the remote processor(s) can update, based on at least the corresponding update, the global ML model.

    USING CORRECTIONS, OF PREDICTED TEXTUAL SEGMENTS OF SPOKEN UTTERANCES, FOR TRAINING OF ON-DEVICE SPEECH RECOGNITION MODEL
    7.
    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：US20250069588A1

    公开(公告)日：2025-02-27

    申请号：US18947557

    申请日：2024-11-14

    Applicant: GOOGLE LLC

    Inventor： Françoise Beaufays ,  Johan Schalkwyk ,  Giovanni Motta

    IPC: G10L15/00 ,  G06F3/04842 ,  G06F3/04883 ,  G10L25/51

    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 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.

    Mixed client-server federated learning of machine learning model(s)
    8.
    发明授权
    Mixed client-server federated learning of machine learning model(s) 有权

    公开(公告)号：US12205575B2

    公开(公告)日：2025-01-21

    申请号：US18218319

    申请日：2023-07-05

    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.

    Mixed client-server federated learning of machine learning model(s)
    10.
    发明授权
    Mixed client-server federated learning of machine learning model(s) 有权

    公开(公告)号：US11749261B2

    公开(公告)日：2023-09-05

    申请号：US17197954

    申请日：2021-03-10

    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

    CPC classification number: 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.

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