Dynamically updatable offline grammar model for resource-constrained offline device

    公开(公告)号:US10521476B2

    公开(公告)日:2019-12-31

    申请号:US15888049

    申请日:2018-02-04

    Applicant: Google LLC

    Abstract: An offline semantic processor of a resource-constrained voice-enabled device such as a mobile device utilizes an offline grammar model with reduced resource requirements to parse voice-based queries received by the device. The offline grammar model may be generated from a larger and more comprehensive grammar model used by an online voice-based query processor, and the generation of the offline grammar model may be based upon query usage data collected from one or more users to enable a subset of more popular voice-based queries from the online grammar model to be incorporated into the offline grammar model. In addition, such a device may collect query usage data and upload such data to an online service to enable an updated offline grammar model to be generated and downloaded back to the device and thereby enable a dynamic update of the offline grammar model to be performed.

    Dynamically updatable offline grammar model for resource-constrained offline device

    公开(公告)号:US10552489B2

    公开(公告)日:2020-02-04

    申请号:US15888049

    申请日:2018-02-04

    Applicant: Google LLC

    Abstract: An offline semantic processor of a resource-constrained voice-enabled device such as a mobile device utilizes an offline grammar model with reduced resource requirements to parse voice-based queries received by the device. The offline grammar model may be generated from a larger and more comprehensive grammar model used by an online voice-based query processor, and the generation of the offline grammar model may be based upon query usage data collected from one or more users to enable a subset of more popular voice-based queries from the online grammar model to be incorporated into the offline grammar model. In addition, such a device may collect query usage data and upload such data to an online service to enable an updated offline grammar model to be generated and downloaded back to the device and thereby enable a dynamic update of the offline grammar model to be performed.

    Context-sensitive dynamic update of voice to text model in a voice-enabled electronic device

    公开(公告)号:US11087762B2

    公开(公告)日:2021-08-10

    申请号:US16665309

    申请日:2019-10-28

    Applicant: Google LLC

    Abstract: A voice to text model used by a voice-enabled electronic device is dynamically and in a context-sensitive manner updated to facilitate recognition of entities that potentially may be spoken by a user in a voice input directed to the voice-enabled electronic device. The dynamic update to the voice to text model may be performed, for example, based upon processing of a first portion of a voice input, e.g., based upon detection of a particular type of voice action, and may be targeted to facilitate the recognition of entities that may occur in a later portion of the same voice input, e.g., entities that are particularly relevant to one or more parameters associated with a detected type of voice action.

    CONTEXT-SENSITIVE DYNAMIC UPDATE OF VOICE TO TEXT MODEL IN A VOICE-ENABLED ELECTRONIC DEVICE

    公开(公告)号:US20200058304A1

    公开(公告)日:2020-02-20

    申请号:US16665309

    申请日:2019-10-28

    Applicant: Google LLC

    Abstract: A voice to text model used by a voice-enabled electronic device is dynamically and in a context-sensitive manner updated to facilitate recognition of entities that potentially may be spoken by a user in a voice input directed to the voice-enabled electronic device. The dynamic update to the voice to text model may be performed, for example, based upon processing of a first portion of a voice input, e.g., based upon detection of a particular type of voice action, and may be targeted to facilitate the recognition of entities that may occur in a later portion of the same voice input, e.g., entities that are particularly relevant to one or more parameters associated with a detected type of voice action.

Patent Agency Ranking