Google Inc.

Wei Chen

David Kogan

Wangqing Yuan

Bryan Christopher Horling

Michael Itz

Maryam Garrett

Wan Fen Nicole Quah

Ruijie He

Daniel Kunkle

Vincent Lacey

Guanglei Wang

Angel L Rodriguez

Shaun Michael Post

Divya Sharma

Molly Murphy

Shaun Post

Onur Ozdemir

Ron Eden

Sean Jordan

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, 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)

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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Belmont, MA, United States of America

Wei Chen