DISTRIBUTED IDENTIFICATION IN NETWORKED SYSTEM

    公开(公告)号:US20210243200A1

    公开(公告)日:2021-08-05

    申请号:US17237573

    申请日:2021-04-22

    Applicant: Google LLC

    Abstract: The present disclosure is generally directed to a data processing system for customizing content in a voice activated computer network environment. With user consent, the data processing system can improve the efficiency and effectiveness of auditory data packet transmission over one or more computer networks by, for example, increasing the accuracy of the voice identification process used in the generation of customized content. The present solution can make accurate identifications while generating fewer audio identification models, which are computationally intensive to generate.

    Multi-User Login Session
    132.
    发明申请

    公开(公告)号:US20190342282A1

    公开(公告)日:2019-11-07

    申请号:US16477062

    申请日:2017-01-20

    Applicant: Google LLC

    Abstract: An example method includes establishing a single-user login session associated with a first user-account such that the single-user login session has read and/or write access to first user data associated with the first user-account. The method further includes accepting, within the single-user login session, a further login associated with a second user-account to convert the single-user login session to a multi-user login session having read and/or write access to second user data associated with the second user-account in addition to having read and/or write access to the first user data. Computer readable media and computing devices related to the example method are disclosed herein as well.

    AUTOMATED ASSISTANT TRAINING AND/OR EXECUTION OF INTER-USER PROCEDURES

    公开(公告)号:US20250147723A1

    公开(公告)日:2025-05-08

    申请号:US19018778

    申请日:2025-01-13

    Applicant: GOOGLE LLC

    Abstract: Implementations relate to an automated assistant that can automate repeatedly performed procedures. The automation can involve communicating with different users, organizations, and/or other automated assistants. The automated assistant, with prior permission from respective user(s), can detect repeated performance of a particular series of manually initiated computational actions. Based on this determination, the automated assistant can determine automated assistant computational action(s) that can be performed by the automated assistant in order to reduce latency in performing a procedure, reduce quantity and/or size of transmissions in performing the procedure, and/or reduce an amount of client device resources required for performing the procedure. Such actions can include communicating with an additional automated assistant that may be associated with another user and/or organization. In these and other manners, manually initiated computational actions that include electronic communications amongst users can be converted to backend operations amongst instances of automated assistants to achieve technical benefits.

    Combining parameters of multiple search queries that share a line of inquiry

    公开(公告)号:US12242472B2

    公开(公告)日:2025-03-04

    申请号:US18228464

    申请日:2023-07-31

    Applicant: GOOGLE LLC

    Abstract: Methods, systems, and computer readable media related to generating a combined search query based on search parameters of a current search query of a user and search parameters of one or more previously submitted search quer(ies) of the user that are determined to be of the same line of inquiry as the current search query. Two or more search queries may be determined to share a line of inquiry when it is determined that they are within a threshold level of semantic similarity to one another. Once a shared line of inquiry has been identified and a combined search query generated, users may interact with the search parameters and/or the search results to update the search parameters of the combined search query.

    Robotic computing device with adaptive user-interaction

    公开(公告)号:US12202125B2

    公开(公告)日:2025-01-21

    申请号:US17533873

    申请日:2021-11-23

    Applicant: GOOGLE LLC

    Abstract: Implementations set forth herein relate to a robotic computing device that can perform certain operations, such as communicating between users in a common space, according to certain preferences of the users. When interacting with a particular user, the robotic computing device can perform an operation at a preferred location relative to the particular user based on an express or implied preference of that particular user. For instance, certain types of operations can be performed at a first location within a room, and other types of operations can be performed at a second location within the room. When an operation involves following or guiding a user, parameters for driving the robotic computing device can be selected based on preferences of the user and/or a context in which the robotic computing device is interacting with the user (e.g., whether or not the context indicates some amount of urgency).

    Speech personalization and federated training using real world noise

    公开(公告)号:US12165630B2

    公开(公告)日:2024-12-10

    申请号:US18356743

    申请日:2023-07-21

    Applicant: Google LLC

    Abstract: A method of training a speech model includes receiving, at a voice-enabled device, a fixed set of training utterances where each training utterance in the fixed set of training utterances includes a transcription paired with a speech representation of the corresponding training utterance. The method also includes sampling noisy audio data from an environment of the voice-enabled device. For each training utterance in the fixed set of training utterances, the method further includes augmenting, using the noisy audio data sampled from the environment of the voice-enabled device, the speech representation of the corresponding training utterance to generate noisy audio samples and pairing each of the noisy audio samples with the corresponding transcription of the corresponding training utterance. The method additionally includes training a speech model on the noisy audio samples generated for each speech representation in the fixed set of training utterances.

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