Google Inc.

David Kogan

Bryan Christopher Horling

Wangqing Yuan

Wei Chen

Michael Itz

Maryam Garrett

Wan Fen Nicole Quah

Ruijie He

Daniel Kunkle

Vincent Lacey

Guanglei Wang

Yossi Matias

Vladimir Vuskovic

Michael Fink

Richard D Borovoy

Arthur Edmond Blume

Idan Szpektor

Evangelos Kokkevis

Eran Ofek

Asaf Revach

Won S Chun

Deborah Cohen

Rachel L Petterson

Shimon Or Salant

Andrew Callahan

Andrew Richardson

Daniel George Koulomzin

Shaun Michael Post

Roni Fischman Zeiger

Carl Elkin

Michael Anthony Schuler

Jonathan Berant

Shaun Post

Warren Wang

Carlos David Correa Ocampo

David Robert Black

Ameesh Makadia

Gabriel Wolosin

Andrew Logan Harp

Ehud Rivlin

Diego Ariel Gertzenstein

Emily Jane Johnston

Anthony Gerald Francis

George M N Ruban

Sarah J Bell

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

Implementations relate to determining a secondary language proficiency measure, for a user in a secondary language (i.e., a language other than a primary language specified for the user), where determining the secondary language proficiency measure is based on past interactions of the user that are related to the secondary language. Those implementations further relate to utilizing the determined secondary language proficiency measure to increase efficiency of user interaction(s), such as interaction(s) with a language learning application and/or an automated assistant. Some of those implementations utilize the secondary language proficiency measure in automatically setting value(s), biasing automatic speech recognition, and/or determining how to render natural language output.

Determining and utilizing secondary language proficiency measure

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

Generating expanded responses that guide continuance of a human-to computer dialog that is facilitated by a client device and that is between at least one user and an automated assistant. The expanded responses are generated by the automated assistant in response to user interface input provided by the user via the client device, and are caused to be rendered to the user via the client device, as a response, by the automated assistant, to the user interface input of the user. An expanded response is generated based on at least one entity of interest determined based on the user interface input, and is generated to incorporate content related to one or more additional entities that are related to the entity of interest, but that are not explicitly referenced by the user interface input.

Generating a composite response to natural language input using a trained generative model and based on responsive content from disparate content agents

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

Selectively generating expanded responses that guide continuance of a human-to-computer dialog

Techniques are described herein for chatbots to achieve greater social grace by tracking users' states and providing corresponding dialog. In various implementations, input may be received from a user at a client device operating a chatbot, e.g., during a first session between the user and the chatbot. The input may be semantically processed to determine a state expressed by the user to the chatbot. An indication of the state expressed by the user may be stored in memory for future use by the chatbot. It may then be determined, e.g., by the chatbot based on various signals, that a second session between the user and the chatbot is underway. In various implementations, as part of the second session, the chatbot may output a statement formed from a plurality of candidate words, phrases, and/or statements based on the stored indication of the state expressed by the user.

Forming chatbot output based on user state

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Natick, MA, United States of America

David Kogan