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    SCALING DIGITAL PERSONAL ASSISTANT AGENTS ACROSS DEVICES
    2.
    发明申请
    SCALING DIGITAL PERSONAL ASSISTANT AGENTS ACROSS DEVICES 有权
    缩放数位个人助理代理

    公开(公告)号：US20160179464A1

    公开(公告)日：2016-06-23

    申请号：US14580093

    申请日：2014-12-22

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Mouni Reddy ,  Vishwac Sena Kannan ,  Aleksandar Uzelac ,  Daniel J. Hwang ,  Molly Rose Suver ,  Lisa Joy Stifelman

    IPC: G06F3/16 ,  G10L21/10 ,  G10L15/26

    CPC classification number: G06F3/167 ,  G06F9/451 ,  G10L13/00 ,  G10L15/1822 ,  G10L15/22

    Abstract: A method for providing digital personal assistant responses may include receiving, by a digital personal assistant associated with a plurality of reactive agents, a user input initiating a dialog with the digital personal assistant within the computing device. In response to receiving the input, an operation mode of the computing device may be detected from a plurality of available operation modes. One of the plurality of reactive agents can be selected based on the received input. A plurality of response strings associated with the selected reactive agent can be accessed. At least one of the plurality of response strings is selected based at least on the operation mode and at least one hardware characteristic of the computing device. The selected at least one of the plurality of response strings is providing during the dialog, as a response to the user input.

    Abstract translation: 用于提供数字个人助理响应的方法可以包括由与多个无效代理相关联的数字个人助理接收在计算设备内与数字个人助理发起对话的用户输入。 响应于接收到输入，可以从多个可用操作模式检测计算设备的操作模式。 可以基于所接收的输入来选择多个反应剂中的一个。 可以访问与所选择的反应剂相关联的多个响应串。 至少基于操作模式和计算设备的至少一个硬件特性来选择多个响应串中的至少一个。 所选择的多个响应串中的至少一个在对话期间提供作为对用户输入的响应。

    EXTENDING DIGITAL PERSONAL ASSISTANT ACTION PROVIDERS
    7.
    发明申请
    EXTENDING DIGITAL PERSONAL ASSISTANT ACTION PROVIDERS 审中-公开
    扩展数字个人助理行动提供者

    公开(公告)号：US20160155442A1

    公开(公告)日：2016-06-02

    申请号：US14676603

    申请日：2015-04-01

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Vishwac Sena Kannan ,  Aleksandar Uzelac ,  Daniel J. Hwang ,  Robert L. Chambers ,  David Pinch ,  Zachary Thomas John Siddall

    IPC: G10L15/22 ,  G06F9/48 ,  G06F3/16

    CPC classification number: G10L15/22 ,  G06F3/167 ,  G06F9/4881 ,  G06F9/5027 ,  G10L2015/223

    Abstract: An electronic device can receive user input via voice or text that includes tasks to be performed. A digital personal assistant infrastructure service can control to which registered action provider the task is assigned. Per-task action provider preferences can be stored. If a preferred action provider is not able to complete the task, the task can still be performed by a registered action provider that has appropriate capabilities. Machine learning can determine a user's preferences. Resource conservation and effective user interaction can result.

    Abstract translation: 电子设备可以通过包括要执行的任务的语音或文本接收用户输入。 数字个人助理基础设施服务可以控制任务被分配到哪个注册的动作提供者。 可以存储每个任务操作提供程序的首选项。 如果首选操作提供程序无法完成任务，则该任务仍然可以由具有适当功能的注册动作提供者执行。 机器学习可以确定用户的偏好。 可以导致资源节约和有效的用户交互。

    Contextual feedback, with expiration indicator, to a natural understanding system in a chat bot
    8.
    发明授权
    Contextual feedback, with expiration indicator, to a natural understanding system in a chat bot 有权

    公开(公告)号：US11575624B2

    公开(公告)日：2023-02-07

    申请号：US17095161

    申请日：2020-11-11

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： John A. Taylor ,  Vishwac Sena Kannan

    IPC: H04L51/02 ,  G06F40/40

    Abstract: A chat bot computing system includes a bot controller and a natural language processor. The natural language processor receives a first textual input and identifies concepts represented by the first textual input. An indication of the concepts is output to the bot controller which generates a response to the first textual input. The concepts output by the natural language processor are also fed back into the input to the natural language processor, as context information, along with an expiration indicator when a second textual input is received. The natural language processor then identifies concepts represented in the second textual input, based on the second natural language, textual input and unexpired context information.

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