FEDERATED LEARNING USING SECURE CENTERS OF CLIENT DEVICE EMBEDDINGS

    公开(公告)号:US20220383197A1

    公开(公告)日:2022-12-01

    申请号:US17828613

    申请日:2022-05-31

    Abstract: Certain aspects of the present disclosure provide techniques for training a machine learning model. The method generally includes receiving, at a local device from a server, information defining a global version of a machine learning model. A local version of the machine learning model and a local center associated with the local version of the machine learning model are generated based on embeddings generated from local data at a client device and the global version of the machine learning model. A secure center different from the local center is generated based, at least in part, on information about secure centers shared by a plurality of other devices participating in a federated learning scheme. Information about the local version of the machine learning model and information about the secure center is transmitted by the local device to the server.

    HYBRID LANGUAGE TRANSLATION ON MOBILE DEVICES

    公开(公告)号:US20240104311A1

    公开(公告)日:2024-03-28

    申请号:US17952025

    申请日:2022-09-23

    CPC classification number: G06F40/58 G06F40/51 G10L15/16

    Abstract: A processor-implemented method for recognizing a natural language on a mobile device includes receiving an audio input. The method further includes using a neural network to generate local text corresponding to the audio input. The method still further includes generating a local confidence value for accuracy of the local text. The method includes transmitting, to a remote device, data corresponding to the audio input. The method further includes receiving remote text corresponding to the data, along with a remote confidence score for accuracy of the remote text. The method still further includes outputting the local text in response to the local confidence value being higher than the remote confidence score, and outputting the remote text in response to the remote confidence score being higher than the local confidence value.

    Suggesting a New and Easier System Function by Detecting User's Action Sequences

    公开(公告)号:US20240045782A1

    公开(公告)日:2024-02-08

    申请号:US17818064

    申请日:2022-08-08

    CPC classification number: G06F11/3438 G06F2201/81

    Abstract: Embodiments include methods performed by a processor of a computing device for suggesting more efficient action sequences to a user. The methods may include recognizing a user action sequence including one or more user actions performed by the user to achieve a result, determining a first difficulty rating of the user action sequence, determining whether a cluster of multiple system action sequences exists within a cluster database in which each system action sequence of the one or more system action sequences produces the result. Methods may further include comparing the first difficulty rating to one or more difficulty ratings of the one or more system action sequences in response to determining that the cluster of multiple system action sequences exists within the cluster database, and displaying, via a display interface of the computing device, one or more system action sequences with a lower difficulty rating than the first difficulty rating.

    Presenting A Facial Expression In A Virtual Meeting

    公开(公告)号:US20220368856A1

    公开(公告)日:2022-11-17

    申请号:US17320627

    申请日:2021-05-14

    Abstract: Embodiment systems and methods for presenting a facial expression in a virtual meeting may include detecting a user facial expression of a user based on information received from a sensor of the computing device, determining whether the detected user facial expression is approved for presentation on an avatar in a virtual meeting, generating an avatar exhibiting a facial expression consistent with the detected user facial expression in response to determining that the detected user facial expression is approved for presentation on an avatar in the virtual meeting, generating an avatar exhibiting a facial expression that is approved for presentation in response to determining that the detected user facial expression is not approved for presentation on an avatar in the virtual meeting, and presenting the generated avatar in the virtual meeting.

    METHOD AND APPARATUS FOR ACTIVATING SPEECH RECOGNITION

    公开(公告)号:US20210056974A1

    公开(公告)日:2021-02-25

    申请号:US16547263

    申请日:2019-08-21

    Abstract: A device to process an audio signal representing input sound includes a user voice verifier configured to generate a first indication based on whether the audio signal represents a user's voice. The device includes a speaking target detector configured to generate a second indication based on whether the audio signal represents at least one of a command or a question. The device includes an activation signal unit configured to selectively generate an activation signal based on the first indication and the second indication. The device also includes an automatic speech recognition engine configured to be activated, responsive to the activation signal, to process the audio signal.

    VIRTUAL ASSISTANT DEVICE
    6.
    发明申请

    公开(公告)号:US20200372906A1

    公开(公告)日:2020-11-26

    申请号:US16418783

    申请日:2019-05-21

    Abstract: A device includes a screen and one or more processors configured to provide, at the screen, a graphical user interface (GUI) configured to display data associated with multiple devices on the screen. The GUI is also configured to illustrate a label and at least one control input for each device of the multiple devices. The GUI is also configured to provide feedback to a user. The feedback indicates that a verbal command is not recognized with an action to be performed. The GUI is also configured to provide instructions for the user on how to teach the one or more processors which action is to be performed in response to receiving the verbal command.

    COMMON ACTION LOCALIZATION
    7.
    发明公开

    公开(公告)号:US20240303987A1

    公开(公告)日:2024-09-12

    申请号:US18360741

    申请日:2023-07-27

    Abstract: Aspects of the disclosure are directed to an apparatus configured to perform common-action localization. In certain aspects, the apparatus may receive a query video comprising a plurality of frames, wherein a first query proposal is determined based on a subset of frames of the plurality of frames, the first query proposal indicative of an action depicted on the subset of frames. In certain aspects, the apparatus may determine a first attendance for a first support video of a plurality of support videos. In certain aspects, the apparatus may determine a second attendance for a second support video of the plurality of support videos after computing the first attendance.

    ACOUSTIC-BASED POSITIONING WITH DYNAMIC FREQUENCY PILOT TONE

    公开(公告)号:US20240248202A1

    公开(公告)日:2024-07-25

    申请号:US18157017

    申请日:2023-01-19

    CPC classification number: G01S15/104 G01S7/539 G01S15/62

    Abstract: Aspects presented herein may enable a wireless device to dynamically change the frequencies of its pilot tones based on the distance of one or more objects detected, thereby enabling the wireless device to utilize the advantages of both high frequency pilot tone and low frequency pilot tone. In one aspect, a wireless device transmits a first pilot tone at a first frequency. The wireless device detects whether there is an object within a specified distance of the wireless device based on a reflected signal of the first pilot tone. The wireless device transmits a second pilot tone at a second frequency based on at least one object being detected within the specified distance, where the second frequency is higher than the first frequency. The wireless device calculates a first distance of the at least one object with respect to the wireless device based the second pilot tone.

    CLIENT-AGNOSTIC LEARNING AND ZERO-SHOT ADAPTATION FOR FEDERATED DOMAIN GENERALIZATION

    公开(公告)号:US20240112039A1

    公开(公告)日:2024-04-04

    申请号:US18238998

    申请日:2023-08-28

    CPC classification number: G06N3/098 H04L67/10

    Abstract: Example implementations include methods, apparatuses, and computer-readable mediums of federated learning by a federated client device, comprising identifying client invariant information of a neural network for performing a machine learning (ML) task in a first domain known to a federated server. The implementations further comprising transmitting the client invariant information to the federated server, the federated server configured to generate a ML model for performing the ML task in a domain unknown to the federated server based on the client invariant information and other client invariant information of another neural network for performing the ML task in a second domain known to the federated server.

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