Systems and Methods Using Person Recognizability Across a Network of Devices

    公开(公告)号:US20220254190A1

    公开(公告)日:2022-08-11

    申请号:US17622460

    申请日:2019-08-14

    Applicant: Google LLC

    Abstract: The present disclosure is directed to computer-implemented systems and methods for performing recognition over a network of devices. In general, the systems and methods implement a machine-learned recognizability model that can process information such as a person's voice, facial characteristics, or similar information to determine a recognizability score without necessarily generating or storing biometric information that could be used to identify the person. The recognizability score can act as a proxy for the quality of the information as a reference for biometric recognition that can be performed on other devices in the network of devices. Thus a single device can be used to enroll a person in the network (e.g., by capturing a number of photographs of the person). Thereafter, connection to the other devices can utilize a sensor (e.g., a camera) on the other devices to compare features of the reference information to the input received by the sensor.

    Dynamically assigning multi-modality circumstantial data to assistant action requests for correlating with subsequent requests

    公开(公告)号:US11200898B2

    公开(公告)日:2021-12-14

    申请号:US16613686

    申请日:2019-05-31

    Applicant: Google LLC

    Abstract: Implementations set forth herein relate to an automated assistant that uses circumstantial condition data, generated based on circumstantial conditions of an input, to determine whether the input should affect an action been initialized by a particular user. The automated assistant can allow each user to manipulate their respective ongoing action without necessitating interruptions for soliciting explicit user authentication. For example, when an individual in a group of persons interacts with the automated assistant to initialize or affect a particular ongoing action, the automated assistant can generate data that correlates that individual to the particular ongoing action. The data can be generated using a variety of different input modalities, which can be dynamically selected based on changing circumstances of the individual. Therefore, different sets of input modalities can be processed each time a user provides an input for modifying an ongoing action and/or initializing another action.

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