Google Inc.

Sangsoo Sung

Yuli Gao

Andre Rohe

Andrew Stephen Tomkins

Prathab Murugesan

Alexander Sobol

Todd Christopher Jackson

Dandapani Sivakumar

Andrew Ames Bunner

Luca De Alfaro

Lucian Florin Cionca

Justin Lewis Kosslyn

Ya Luo

Krishna Asur Bharat

Robin Dua

Fergus Gerard Hurley

Maxim Gubin

Kenneth Wayne Dauber

Sasha Sobol

Bryan Christopher Horling

Okan Kolak

Pedro Jose Moreno Mengibar

Yonatan Zunger

Daniel Belov

Jinan Lou

Michael Schwarz

Harish Rajamani

Mohit Oberoi

Prathab Murugesa

Implementations disclosed herein relate to generating and/or utilizing, by a client device, a text-response map that is stored locally on the client device. The text-response map can include a plurality of mappings, where each of the mappings define a corresponding direct relationship between corresponding text and a corresponding response. Each of the mappings is defined in the text-response map based on the corresponding text being previously generated from previous audio data captured by the client device and based on the corresponding response being previously received from a remote system in response to transmitting, to the remote system, at least one of the previous audio data and the corresponding text.

Rendering responses to a spoken utterance of a user utilizing a local text-response map

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for list accumulation and reminder triggering. In one aspect, a method includes receiving, at a data processing apparatus, a first input comprising a plurality of terms; determining, by the data processing apparatus, that one or more of the terms in the first input specify an item that belongs to a reminder category, the reminder category being associated with a collection of items that belong to the category and for which reminders may be generated; and in response to the determination, storing data that associates a reminder type with the item specified by the one or more terms.

List accumulation and reminder triggering

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

Data associated with a selectively offline capable voice action is locally persisted in a voice-enabled electronic device whenever such an action cannot be competed locally due to the device being offline to enable the action to later be completed after online connectivity has been restored. Synchronization with an online service and/or another electronic device, and/or retrieval of context sensitive data from an online service may be performed after online connectivity has been restored to enable the voice action to thereafter be completed.

Local persisting of data for selectively offline capable voice action in a voice-enabled electronic device

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

Methods, systems, and apparatus, for detour based content selection. In one aspect, a method includes receiving, at a data processing apparatus, location data for a plurality of users, the location data describing, for each user, locations of a user device of the user; determining, from the location data, route data for the plurality of users, the route data describing, for each user, a route traveled by the user; determining, by the data processing apparatus, for at least one or more of the users, a detour from the route traveled by the user to a retail location of the offeror; determining, by the data processing apparatus, for each of the at least one or more of the users, whether the user is eligible for an offer of the offeror based on the detour; and for each user eligible for an offer, enabling the offeror to provide an offer to the user.

Detour based content selections

A system and method for generating a stream of content includes a content stream module that generates a model based on user input and/or prior activities using heterogeneous data sources. The heterogeneous data sources include search, entertainment, social activity and activity on third-party sites. The content stream module retrieves candidate content items that have interests that are similar to the user. The candidate content items are compared to the model and scored based upon interestingness of the content item to the user. The content stream module generates the stream of content from the candidate content items.

Scoring stream items with models based on user interests

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Palo Alto, CA, United States of America

Sangsoo Sung