Automated assistants that accommodate multiple age groups and/or vocabulary levels

    公开(公告)号:US10573298B2

    公开(公告)日:2020-02-25

    申请号:US15954174

    申请日:2018-04-16

    Applicant: Google LLC

    Abstract: Techniques are described herein for enabling an automated assistant to adjust its behavior depending on a detected age range and/or “vocabulary level” of a user who is engaging with the automated assistant. In various implementations, data indicative of a user's utterance may be used to estimate one or more of the user's age range and/or vocabulary level. The estimated age range/vocabulary level may be used to influence various aspects of a data processing pipeline employed by an automated assistant. In various implementations, aspects of the data processing pipeline that may be influenced by the user's age range/vocabulary level may include one or more of automated assistant invocation, speech-to-text (“STT”) processing, intent matching, intent resolution (or fulfillment), natural language generation, and/or text-to-speech (“TTS”) processing. In some implementations, one or more tolerance thresholds associated with one or more of these aspects, such as grammatical tolerances, vocabularic tolerances, etc., may be adjusted.

    THRESHOLD-BASED ASSEMBLY OF REMOTE AUTOMATED ASSISTANT RESPONSES

    公开(公告)号:US20200050788A1

    公开(公告)日:2020-02-13

    申请号:US16270045

    申请日:2019-02-07

    Applicant: Google LLC

    Abstract: Techniques are described herein for assembling/evaluating automated assistant responses for privacy concerns. In various implementations, a free-form natural language input may be received from a first user and may include a request for information pertaining to a second user. Multiple data sources may be identified that are accessible by an automated assistant to retrieve data associated with the second user. The multiple data sources may collectively include sufficient data to formulate a natural language response to the request. Respective privacy scores associated with the multiple data sources may be used to determine an aggregate privacy score associated with responding to the request. The natural language response may then be output at a client device operated by the first user in response to a determination that the aggregate privacy score associated with the natural language response satisfies a privacy criterion established for the second user with respect to the first user.

    TRANSITIONING BETWEEN PRIVATE AND NON-PRIVATE STATE

    公开(公告)号:US20190394151A1

    公开(公告)日:2019-12-26

    申请号:US16563033

    申请日:2019-09-06

    Applicant: Google LLC

    Abstract: This specification is generally directed to techniques for automatically transitioning applications—especially those that enable exchange of messages between users—into and/or out of a private state based on a variety of signals associated with the messages and/or the participants themselves. In various implementations, an ongoing message exchange thread between two or more participants operating two or more respective message exchange clients may be examined. Based at least in part on the examining, a likelihood may be determined that message(s) directed by one of the participants to another of the participants as part of the ongoing message exchange thread would be deemed private by at least a given participant of the two or more participants. A determination may be made of whether the determined likelihood satisfies one or more thresholds, and in response, one or more of the message exchange clients may be transitioned into a private state.

    Advanced content retrieval
    25.
    发明授权

    公开(公告)号:US10389866B2

    公开(公告)日:2019-08-20

    申请号:US15698734

    申请日:2017-09-08

    Applicant: Google LLC

    Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer-readable storage medium for implementing advanced information retrieval are described. A user may provide fetching parameter values to acquire content. The system may determine whether or not the user-provided fetching parameter values can be satisfied. If the fetching parameters can be satisfied, the system obtains and caches the information.

    Transitioning between private and non-private state

    公开(公告)号:US12120075B2

    公开(公告)日:2024-10-15

    申请号:US18204119

    申请日:2023-05-31

    Applicant: GOOGLE LLC

    Abstract: This specification is generally directed to techniques for automatically transitioning applications—especially those that enable exchange of messages between users—into and/or out of a private state based on a variety of signals associated with the messages and/or the participants themselves. In various implementations, an ongoing message exchange thread between two or more participants operating two or more respective message exchange clients may be examined. Based at least in part on the examining, a likelihood may be determined that message(s) directed by one of the participants to another of the participants as part of the ongoing message exchange thread would be deemed private by at least a given participant of the two or more participants. A determination may be made of whether the determined likelihood satisfies one or more thresholds, and in response, one or more of the message exchange clients may be transitioned into a private state.

    On-device machine learning platform to enable sharing of machine learned models between applications

    公开(公告)号:US12020127B2

    公开(公告)日:2024-06-25

    申请号:US18075943

    申请日:2022-12-06

    Applicant: Google LLC

    CPC classification number: G06N20/00

    Abstract: The present disclosure provides an on-device machine learning platform that enables sharing of machine-learned models between applications on a computing device. For example, a first application which has a machine-learned model for a specific task can expose the model to other applications through a system level application programming interface (API) for the other applications to use. Communications using the API can be handled by the on-device machine learning platform. In some implementations, some exchange of resources (e.g., computing resources) can be provided so that the first application is compensated for sharing the machine-learned model (e.g., on a per model invocation basis).

    GENERATING AND/OR PRIORITIZING PRE-CALL CONTENT FOR RENDERING WHEN AWAITING ACCEPTANCE OF AN INCOMING CALL

    公开(公告)号:US20240040037A1

    公开(公告)日:2024-02-01

    申请号:US18378080

    申请日:2023-10-09

    Applicant: GOOGLE LLC

    Abstract: Implementations set forth herein relate to generating a pre-call analysis for one or more users that are receiving and/or initializing a call with one or more other users, and/or prioritizing pre-call content according to whether security-related value was gleaned from provisioning certain pre-call content. One or more machine learning models can be employed for determining the pre-call content to be cached and/or presented prior to a user accepting a call from another user. Feedback provided before, during, and/or after the call can be used as a basis from which to prioritize certain content and/or sources of content when generating pre-call content for a subsequent call. Other information, such as contextual data (e.g., calendar entries, available peripheral devices, location, etc.) corresponding to the previous call and/or the subsequent call, can also be used as a basis from which to provide a pre-call analysis.

    Developing event-specific provisional knowledge graphs

    公开(公告)号:US11853902B2

    公开(公告)日:2023-12-26

    申请号:US17572903

    申请日:2022-01-11

    Applicant: GOOGLE LLC

    CPC classification number: G06N5/02 G06F40/205 G06F40/295 G06F40/35 H04L51/046

    Abstract: Techniques and a framework are described herein for constructing and/or updating, e.g., on top of a general-purpose knowledge graph, an “event-specific provisional knowledge graph.” In various implementations, live data stream(s) may be analyzed to identify entity(s) associated with a developing event. The entity(s) may form part of a general-purpose knowledge graph that includes entity nodes and edges between the entity nodes. Based on the identified one or more entities, an event-specific provisional knowledge graph may be constructed or updated in association with the developing event. In some implementations, the event-specific provisional knowledge graph may be queried for new information about the developing event. Computing devices may be caused to render, as output, the new information.

    DETERMINING WHETHER AND/OR WHEN TO PROVIDE NOTIFICATIONS, BASED ON APPLICATION CONTENT, TO MITIGATE COMPUTATIONALLY WASTEFUL APPLICATION-LAUNCHING BEHAVIOR

    公开(公告)号:US20230229530A1

    公开(公告)日:2023-07-20

    申请号:US18123870

    申请日:2023-03-20

    Applicant: GOOGLE LLC

    CPC classification number: G06F9/542 H04L51/224

    Abstract: Implementations set forth herein relate to intervening notifications provided by an application for mitigating computationally wasteful application launching behavior that is exhibited by some users. A state of a module of a target application can be identified by emulating user inputs previously provided by the user to the target application. In this way, the state of the module can be determined without visibly launching the target application. When the state of the module is determined to satisfy criteria for providing a notification to the user, the application can render a notification for the user. The application can provide intervening notifications for a variety of different target applications in order to reduce a frequency at which the user launches and closes applications to check for variations in target application content.

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