Videoconferencing Using Hybrid Edge/Cloud Inference with Machine-Learned Systems

    公开(公告)号:US20220075995A1

    公开(公告)日:2022-03-10

    申请号:US16624450

    申请日:2019-05-20

    Applicant: Google LLC

    Abstract: Systems and methods for providing a multi-step identification process using a hybrid edge/server infrastructure to generate machine learned inferences are provided. One example method includes obtaining sensor data of an environment including one or more user. The method includes selecting at least a portion of the sensor data that is associated with the one or more users. The method includes generating, using one or more machine learning models, an intermediate representation of the selected portion of the sensor data that represents the face of the one or more users. The method includes transmitting the intermediate representation and a request for a user identification based on the intermediate representation, the remote computing system configured to perform additional steps of the multi-step identification process using the intermediate representation and the machine-learned system. The method includes receiving a user identifier for the one or more users.

    Videoconferencing using hybrid edge-cloud inference with machine-learned systems

    公开(公告)号:US12051270B2

    公开(公告)日:2024-07-30

    申请号:US17950727

    申请日:2022-09-22

    Applicant: Google LLC

    Abstract: Systems and methods for providing a multi-step identification process using a hybrid edge/server infrastructure to generate machine learned inferences are provided. One example method includes obtaining sensor data of an environment including one or more user. The method includes selecting at least a portion of the sensor data that is associated with the one or more users. The method includes generating, using one or more machine learning models, an intermediate representation of the selected portion of the sensor data that represents the face of the one or more users. The method includes transmitting the intermediate representation and a request for a user identification based on the intermediate representation, the remote computing system configured to perform additional steps of the multi-step identification process using the intermediate representation and the machine-learned system. The method includes receiving a user identifier for the one or more users.

    Videoconferencing Using Hybrid Edge-Cloud Inference with Machine-Learned Systems

    公开(公告)号:US20230092618A1

    公开(公告)日:2023-03-23

    申请号:US17950727

    申请日:2022-09-22

    Applicant: Google LLC

    Abstract: Systems and methods for providing a multi-step identification process using a hybrid edge/server infrastructure to generate machine learned inferences are provided. One example method includes obtaining sensor data of an environment including one or more user. The method includes selecting at least a portion of the sensor data that is associated with the one or more users. The method includes generating, using one or more machine learning models, an intermediate representation of the selected portion of the sensor data that represents the face of the one or more users. The method includes transmitting the intermediate representation and a request for a user identification based on the intermediate representation, the remote computing system configured to perform additional steps of the multi-step identification process using the intermediate representation and the machine-learned system. The method includes receiving a user identifier for the one or more users.

    Videoconferencing using hybrid edge/cloud inference with machine-learned systems

    公开(公告)号:US11468708B2

    公开(公告)日:2022-10-11

    申请号:US16624450

    申请日:2019-05-20

    Applicant: Google LLC

    Abstract: Systems and methods for providing a multi-step identification process using a hybrid edge/server infrastructure to generate machine learned inferences are provided. One example method includes obtaining sensor data of an environment including one or more user. The method includes selecting at least a portion of the sensor data that is associated with the one or more users. The method includes generating, using one or more machine learning models, an intermediate representation of the selected portion of the sensor data that represents the face of the one or more users. The method includes transmitting the intermediate representation and a request for a user identification based on the intermediate representation, the remote computing system configured to perform additional steps of the multi-step identification process using the intermediate representation and the machine-learned system. The method includes receiving a user identifier for the one or more users.

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