Google Inc.

Bryan Christopher Horling

David Kogan

Maryam Garrett

Okan Kolak

Wan Fen Nicole Quah

Wangqing Yuan

Ruijie He

Wei Chen

Michael Itz

Behshad Behzadi

Zaheed Sabur

Oren Eli Zamir

Lucas Mirelmann

Daniel Cotting

Denis Burakov

Sergey Nazarov

Michael Golikov

Steve Cheng

Mario Bertschler

Bohdan Vlasyuk

Luv Kothari

Paul Haahr

Philip Andrew McDonnell

Owen Daniel Otto

Kavi J Goel

Jason Luther Smart

Andre Rohe

Sangsoo Sung

Beverly Yang

Antoine Jean Bruguier

Lucian Florin Cionca

Alexander Sobol

Steven D Baker

Ronald Ho

Maureen Heymans

Matthew E Kulick

Pavan Kumar Desikan

Marcel M M Yung

Lan Huo

Michael Y Brukman

Daniel Kunkle

Ashutosh Shukla

Nathan Wiegand

Benedict Liang

Rosa Wu

Richard Sproat

Brian E Roark

Vincent Lacey

Guanglei Wang

Michael Anthony Schuler

Anarghya Mitra

Angel L Rodriguez

Vivek Paul Gundotra

Pavani Naishadh Diwanji

Sagar Kamdar

Jun Gong

Shaun Michael Post

David Mark Byttow

Sara Lee Su

Molly Murphy

Jennifer Ellen Fernquist

Sergey Igorevich Nazarov

Divya Sharma

Onur Ozdemir

Shaun Post

Harish Rajamani

Ron Eden

Sean Jordan

Hongtao Zhong

Afsaneh Hajiamin Shirazi

Bilgenhan Uygar Oztekin

Yossi Matias

Avinatan Hassidim

Alon Ziv

Implementations relate to determining a well-formed phrase to suggest to a user to submit in lieu of a not well-formed phrase. The suggestion is rendered via an interface that is provided to a client device of the user. Those implementations relate to determining that a phrase is not well-formed, identifying alternate phrases that are related to the not well-formed phrase, and scoring the alternate phrases to select one or more of the alternate phrases to render via the interface. Some of those implementations are related to identifying that the phrase is not well-formed based on occurrences of the phrase in documents that are generated by a source with the language of the phrase as the primary language of the creator.

Providing a well-formed alternate phrase as a suggestion in lieu of a not well-formed phrase

The various implementations described herein include methods, devices, and systems for monitoring activity in a home environment. In one aspect, a method performed at a voice-assistant device includes: detecting a sound; obtaining a determination as to whether the sound meets one or more monitoring criteria; and in accordance with a determination that the sound meets the one or more monitoring criteria, generating a notification.

Systems, methods, and devices for activity monitoring via a home assistant

Methods, apparatus, and computer readable media related to interaction between a user and an automated assistant during a dialog between the user and the automated assistant. Some implementations are directed to adapting a graphical and/or audible presentation of search results provided by the automated assistant for presentation to the user. The adaptation may be in response to attribute(s), of one or more of the search results, referenced in spoken and/or typed textual input provided by the user during the dialog. Some of those implementations may enable a user to provide textual input to navigate the search results within the dialog and within resource and/or interface constraints associated with the dialog. Some of those implementations may additionally and/or alternatively enable adapting, based on textual input provided by a user to the automated assistant, when and/or whether search results having certain attributes are provided to the user by the automated assistant.

Using user input to adapt search results provided for presentation to the user

Implementations can reduce the time required to obtain responses from an automated assistant through proactive caching, locally at a client device, of proactive assistant cache entries—and through on-device utilization of the proactive assistant cache entries. Different proactive cache entries can be provided to different client devices, and various implementations relate to technique(s) utilized in determining which proactive cache entries to provide to which client devices. In some of those implementations, in determining which proactive cache entries to provide (proactively or in response to a request) to a given client device, a remote system selects, from a superset of candidate proactive cache entries, a subset of the cache entries for providing to the given client device.

Proactive caching of assistant action content to enable resolution of spoken or typed utterances

Some implementations are directed to generating a personal database entry for a user based on free-form natural language input formulated by the user via one or more user interface input devices of a computing device of the user. The generated personal database entry may include one or more terms of the natural language input and descriptive metadata determined based on one or more terms of the natural language input and/or based on contextual features associated with receiving the natural language input. Some implementations are directed to generating, based on one or more personal database entries of a user, output that is responsive to further free-form natural language input of the user. For example, one or more entries that are responsive to further natural language input of the user can be identified based on matching content of those entries to one or more search parameters determined based on the further input.

Generating a personal database entry for a user based on natural language user interface input of the user and generating output based on the entry in response to further natural language user interface input of the user

One or more servers receive a natural language query from a client device associated with a user. The one or more servers classify the natural language query as a query that seeks information previously accessed by the user. The one or more servers then obtain a response to the natural language query from one or more collections of documents, wherein each document in the one or more collections of documents was previously accessed by the user. The one or more servers generate search results based on the response. Then, the one or more servers communicate the search results to the client device.

Generating query answers from a user's history

Implementations relate to determining multilingual content to render at an interface in response to a user submitted query. Those implementations further relate to determining a first language response and a second language response to a query that is submitted to an automated assistant. Some of those implementations relate to determining multilingual content that includes a response to the query in both the first and second languages. Other implementations relate to determining multilingual content that includes a query suggestion in the first language and a query suggestion in a second language. Some of those implementations relate to pre-fetching results for the query suggestions prior to rendering the multilingual content.

Determining multilingual content in responses to a query

Methods, apparatus, systems, and computer-readable media are provided for automatically augmenting message exchange threads based on a detected tone of messages exchanged between participants. In various implementations, a message contributed to a message exchange thread involving one or more message exchange clients by a participant may be determined. In various implementations, an idle chatter score associated with the message may be calculated. In various implementations, either a conversational response to the message or content responsive to a search query generated based on the message may be selectively incorporated into the message exchange thread based at least in part on the idle chatter score. In some implementations, a search query suitability score associated with the message may also be calculated.

Automatically augmenting message exchange threads based on tone of message

Implementations relate to determining whether and/or how to implement a user request to prevent a particular search result from being provided in response to a search query. Some of those implementations grant or deny the request based on processing of the particular search result, the search query, and/or account information for a user submitting the user request. For example, some implementations process such information utilizing a classifier in determining whether to automatically deny the request, automatically approve the request, or to provide the request for manual review. Some additional or alternative implementations at least selectively automatically expand (or suggest for automatic expansion) an approval of a request to search result(s) and/or to one or more search queries that are not specified in the request.

Determining whether and/or how to implement request to prevent provision of search result(s)

Implementations set forth herein relate to conditionally caching responses to automated assistant queries according to certain contextual data that may be associated with each automated assistant query. Each query can be identified based on historical interactions between a user and an automated assistant, and—depending on the query, fulfillment data can be cached according to certain contextual data that influences the query response. Depending on how the contextual data changes, a cached response stored at a client device can be discarded and/or replaced with an updated cached response. For example, a query that users commonly ask prior to leaving for work can have a corresponding assistant response that depends on features of an environment of the users. This unique assistant response can be cached, before the users provide the query, to minimize latency that can occur when network or processing bandwidth is unpredictable.

Conditional response fulfillment cache for locally responding to automated assistant inputs

The various implementations described herein include methods, devices, and systems for monitoring activity in a home environment. In one aspect, a method performed at a voice-assistant device includes: transitioning to operating in a second mode from a first mode; while operating in the second mode, detecting a sound; obtaining a determination as to whether the sound meets one or more monitoring criteria; and in accordance with a determination that the sound meets the one or more monitoring criteria, generating an alert or notification.

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Sunnyvale, CA, United States of America

Bryan Christopher Horling