Task resumption in a natural understanding system

    公开(公告)号:US11579841B1

    公开(公告)日:2023-02-14

    申请号:US17547802

    申请日:2021-12-10

    Abstract: A speech-processing system may provide access to one or more skills via spoken commands and/or responses in the form of synthesized speech. The system may be capable of keeping one or more skills active in the background while a user interacts (e.g., provides inputs to and/or receives outputs from) with a skill running in the foreground. A background skill may receive some trigger data, and determine to request the system to return the background skill to the foreground to, for example, request a user input regarding an action previously requested by the user. In some cases, the user may invoke a background skill to continue a previous interaction. The system may return the background skill to the foreground. The resumed skill may continue a previous interaction to, for example, to query the user for instructions, provide an update or alert, or continue a previous output.

    Multi-tasking and skills processing

    公开(公告)号:US11295745B1

    公开(公告)日:2022-04-05

    申请号:US16560103

    申请日:2019-09-04

    Abstract: Described herein is a system for enabling a user to multitask by allowing a user to pause or interrupt an on-going interaction with a skill. The system monitors a state of a skill session, and updates the state to allow the user or system to suspend the session. The user may provide an instruction to pause an active session, causing the system to place the session in a suspended state. The user may then provide an instruction to resume the suspended session, causing the system to place the session in an active state. In other cases, the user input may be a request during an active session that requires invoking another skill. The system may place the current session in a suspended state, and invoke a second skill session to monitor the interaction with a second skill. When the interaction with the second skill is completed, the system may resume the previous session by placing it in an active state.

    Machine learning models for data driven dialog management

    公开(公告)号:US10854191B1

    公开(公告)日:2020-12-01

    申请号:US15710229

    申请日:2017-09-20

    Abstract: Techniques for optimizing a system to improve an overall user satisfaction in a speech controlled system are described. A user speaks an utterance and the system compares an expected sum of user satisfaction values for each action to make a decision as to how best to process the utterance. As a result, the system may make a decision that decreases user satisfaction in the short term but increases user satisfaction in the long term. The system may estimate a user satisfaction value and associate the estimated user satisfaction value with a current dialog state. By tracking user satisfaction values over time, the system may train machine learning models to optimize the expected sum of user satisfaction values. This improves how the system selects an action or application to which to dispatch the dialog state and how a specific application selects an action or intent corresponding to the command.

    DATA DRIVEN DIALOG MANAGEMENT
    6.
    发明公开

    公开(公告)号:US20240153489A1

    公开(公告)日:2024-05-09

    申请号:US18414530

    申请日:2024-01-17

    CPC classification number: G10L15/01 G10L15/16 G10L15/22 G10L2015/225

    Abstract: Techniques for optimizing a system to improve an overall user satisfaction in a speech controlled system are described. A user speaks an utterance and the system compares an expected sum of user satisfaction values for each action to make a decision as to how best to process the utterance. As a result, the system may make a decision that decreases user satisfaction in the short term but increases user satisfaction in the long term. The system may estimate a user satisfaction value and associate the estimated user satisfaction value with a current dialog state. By tracking user satisfaction values over time, the system may train machine learning models to optimize the expected sum of user satisfaction values. This improves how the system selects an action or application to which to dispatch the dialog state and how a specific application selects an action or intent corresponding to the command.

    DATA DRIVEN DIALOG MANAGEMENT
    9.
    发明申请

    公开(公告)号:US20210193116A1

    公开(公告)日:2021-06-24

    申请号:US17106395

    申请日:2020-11-30

    Abstract: Techniques for optimizing a system to improve an overall user satisfaction in a speech controlled system are described. A user speaks an utterance and the system compares an expected sum of user satisfaction values for each action to make a decision as to how best to process the utterance. As a result, the system may make a decision that decreases user satisfaction in the short term but increases user satisfaction in the long term. The system may estimate a user satisfaction value and associate the estimated user satisfaction value with a current dialog state. By tracking user satisfaction values over time, the system may train machine learning models to optimize the expected sum of user satisfaction values. This improves how the system selects an action or application to which to dispatch the dialog state and how a specific application selects an action or intent corresponding to the command.

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