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    MIXED CLIENT-SERVER FEDERATED LEARNING OF MACHINE LEARNING MODEL(S)
    1.
    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.

    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：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.

    MIXED CLIENT-SERVER FEDERATED LEARNING OF MACHINE LEARNING MODEL(S)
    3.
    发明公开
    MIXED CLIENT-SERVER FEDERATED LEARNING OF MACHINE LEARNING MODEL(S) 审中-公开

    公开(公告)号：US20230352004A1

    公开(公告)日：2023-11-02

    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

    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.

    USING CORRECTIONS, OF AUTOMATED ASSISTANT FUNCTIONS, FOR TRAINING OF ON-DEVICE MACHINE LEARNING MODELS
    4.
    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.

    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.

    Using corrections, of automated assistant functions, for training of on-device machine learning models
    6.
    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.

    Using corrections, of automated assistant functions, for training of on-device machine learning models
    7.
    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.

    CHAIN OF THOUGHT REASONING FOR ASR
    8.
    发明申请
    CHAIN OF THOUGHT REASONING FOR ASR 有权

    公开(公告)号：US20250118293A1

    公开(公告)日：2025-04-10

    申请号：US18891615

    申请日：2024-09-20

    Applicant: Google LLC

    Inventor： Mingqing Chen ,  Rajiv Mathews ,  Andrew Hard ,  Swaroop Ramaswamy ,  Kilol Gupta

    IPC: G10L15/06 ,  G10L15/26 ,  G10L25/30

    Abstract: A method includes receiving a conversational training dataset including a plurality of conversational training samples, each training sample associated with a corresponding conversation and including: corresponding audio data characterizing a corresponding current utterance spoken by a user during a current turn in the corresponding conversation; a corresponding context for the corresponding current utterance including a transcript of a previous turn in the corresponding conversation that precedes the current turn; a corresponding ground-truth transcription of the corresponding current utterance; and a CoT annotation representing a corresponding logical relationship between the corresponding current utterance and the previous turn. The method also includes, for each corresponding conversational training sample in the conversational training dataset, training a speech model on the corresponding conversational training sample to teach the speech model to learn how to predict the corresponding logical relationship from the corresponding audio data and the corresponding context.

    UTILIZING ELASTIC WEIGHT CONSOLIDATION (EWC) LOSS TERM(S) TO MITIGATE CATASTROPHIC FORGETTING IN TRAINING MACHINE LEARNING MODEL(S)
    9.
    发明申请
    UTILIZING ELASTIC WEIGHT CONSOLIDATION (EWC) LOSS TERM(S) TO MITIGATE CATASTROPHIC FORGETTING IN TRAINING MACHINE LEARNING MODEL(S) 有权

    公开(公告)号：US20250045627A1

    公开(公告)日：2025-02-06

    申请号：US18365487

    申请日：2023-08-04

    Applicant: GOOGLE LLC

    Inventor： Andrew Hard ,  Kurt Partridge ,  Sean Augenstein ,  Rajiv Mathews

    IPC: G06N20/00

    Abstract: Processor(s) of a client device can receive global weights of a global ML model from a remote system, obtain a client device data set, determine a Fisher information matrix for the client data set, and transmit the Fisher information matrix for the client data set to the remote system. Further, processor(s) of the remote system can determine a corresponding elastic weight consolidation (EWC) loss term for each of the global weights based on at least the Fisher information matrix, generate a server update for the global ML model based on (i) processing server data remotely at the remote system and using the global ML model and (ii) based on the corresponding EWC loss term for each of the global weights, and update the global weights of the global ML model based on the server update.

    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：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.

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