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    Interactive Voice Navigation
    1.
    发明公开
    Interactive Voice Navigation 审中-公开

    公开(公告)号：US20230160710A1

    公开(公告)日：2023-05-25

    申请号：US17251244

    申请日：2020-08-12

    申请人： Google LLC

    发明人： Victor Carbune ,  Matthew Sharifi ,  Blaise Aguera-Arcas

    IPC分类号： G01C21/36 ,  G10L15/22 ,  G06F3/16 ,  G01C21/34

    CPC分类号： G01C21/3608 ,  G10L15/22 ,  G06F3/165 ,  G01C21/3415 ,  G10L2015/228 ,  G10L2015/223 ,  G10L2015/225 ,  G10L15/30

    摘要： The present disclosure is directed to interactive voice navigation. In particular, a computing system can provide audio information including one or more navigation instructions to a user via a computing system associated with the user. The computing system can activate an audio sensor associated with the computing system. The computing system can collect, using the audio sensor, audio data associated with the user. The computing system can determine, based on the audio data, whether the audio data is associated with one or more navigation instructions. The computing system can, in accordance with a determination that the audio data is associated with one or more navigation instructions, determine a context-appropriate audio response. The computing system can provide the context-appropriate audio response to the user.

    EPHEMERAL LEARNING AND/OR FEDERATED LEARNING OF AUDIO-BASED MACHINE LEARNING MODEL(S) FROM STREAM(S) OF AUDIO DATA GENERATED VIA RADIO STATION(S)
    5.
    发明公开
    EPHEMERAL LEARNING AND/OR FEDERATED LEARNING OF AUDIO-BASED MACHINE LEARNING MODEL(S) FROM STREAM(S) OF AUDIO DATA GENERATED VIA RADIO STATION(S) 审中-公开

    公开(公告)号：US20240071406A1

    公开(公告)日：2024-02-29

    申请号：US18074739

    申请日：2022-12-05

    申请人： GOOGLE LLC

    发明人： Johan Schalkwyk ,  Blaise Aguera-Arcas ,  Diego Melendo Casado ,  Oren Litvin

    IPC分类号： G10L25/51 ,  G10L15/00 ,  G10L15/18

    CPC分类号： G10L25/51 ,  G10L15/005 ,  G10L15/18

    摘要： Implementations disclosed herein are directed to utilizing ephemeral learning techniques and/or federated learning techniques to update audio-based machine learning (ML) model(s) based on processing streams of audio data generated via radio station(s) across the world. This enables the audio-based ML model(s) to learn representations and/or understand languages across the world, including tail languages for which there is no/minimal audio data. In various implementations, one or more deduping techniques may be utilized to ensure the same stream of audio data is not overutilized in updating the audio-based ML model(s). In various implementations, a given client device may determine whether to employ an ephemeral learning technique or a federated learning technique based on, for instance, a connection status with a remote system. Generally, the streams of audio data are received at client devices, but the ephemeral learning techniques may be implemented at the client device and/or at the remote system.

    Machine learning for interpretation of subvocalizations
    6.
    发明授权
    Machine learning for interpretation of subvocalizations 有权

    公开(公告)号：US11580978B2

    公开(公告)日：2023-02-14

    申请号：US17103345

    申请日：2020-11-24

    申请人： Google LLC

    发明人： Yaroslav Volovich ,  Ant Oztaskent ,  Blaise Aguera-Arcas

    IPC分类号： G10L15/22 ,  G10L15/18 ,  H04R1/08 ,  H04R1/10

    摘要： Provided is an in-ear device and associated computational support system that leverages machine learning to interpret sensor data descriptive of one or more in-ear phenomena during subvocalization by the user. An electronic device can receive sensor data generated by at least one sensor at least partially positioned within an ear of a user, wherein the sensor data was generated by the at least one sensor concurrently with the user subvocalizing a subvocalized utterance. The electronic device can then process the sensor data with a machine-learned subvocalization interpretation model to generate an interpretation of the subvocalized utterance as an output of the machine-learned subvocalization interpretation model.

    Privacy-First On-Device Federated Health Modeling and Intervention
    8.
    发明申请
    Privacy-First On-Device Federated Health Modeling and Intervention 有权

    公开(公告)号：US20210090750A1

    公开(公告)日：2021-03-25

    申请号：US17051188

    申请日：2018-09-27

    申请人： Google LLC

    发明人： Adam Sadilek ,  Blaise Aguera-Arcas ,  Keith Allen Bonawitz

    IPC分类号： G16H50/80 ,  G16H10/60 ,  G16H50/20 ,  G16H50/30 ,  G16H40/67 ,  G06F21/62 ,  G06N20/00

    摘要： The present disclosure provides systems and methods that leverage machine-learned models in conjunction with user-associated data and disease prevalence mapping to predict disease infections with improved user privacy. In one example, a computer-implemented method can include obtaining, by a user computing device associated with a user, a machine-learned prediction model configured to predict a probability that the user may be infected with a disease based at least in part on user-associated data associated with the user. The method can further include receiving, by the user computing device, the user-associated data associated with the user. The method can further include providing, by the user computing device, the user-associated data as input to the machine-learned prediction model, the machine-learned prediction model being implemented on the user computing device. The method can further include receiving, by the user computing device, a current disease prediction for the user as an output of the machine-learned prediction model. The method can further include providing, by the user computing device, data indicative of the current disease prediction for the user to a central computing system for use in updating a prevalence map that models prevalence of the disease over a plurality of geographic locations.