Google Inc.

Jacopo Sannazzaro Natta

Victor Carbune

Luv Kothari

Matthew Nirvan Sharifi

Behshad Behzadi

Zaheed Sabur

Balint Miklos

Bogdan Prisacari

Nicolo D'Ercole

Julia Proskurnia

Domenico Carbotta

Jae Seo

Fo Lee

Magali Boizot-Roche

Alvin Abdagic

Matthew Sharifi

Felix Weissenberger

Thomas Qian

Matthew Miao

Neha Garg

Ray Chen

Kevin Fu

Mucun Lu

Barbara Poblocka

Justin Lu

Krishna Sapkota

Srikanth Pandiri

Márius Sajgalík

Viesturs Zarins

Krzysztof Andrzej Goj

Implementations described herein relate to an application and/or automated assistant that can identify arrangement operations to perform for arranging text during speech-to-text operations—without a user having to expressly identify the arrangement operations. In some instances, a user that is dictating a document (e.g., an email, a text message, etc.) can provide a spoken utterance to an application in order to incorporate textual content. However, in some of these instances, certain corresponding arrangements are needed for the textual content in the document. The textual content that is derived from the spoken utterance can be arranged by the application based on an intent, vocalization features, and/or contextual features associated with the spoken utterance and/or a type of the application associated with the document, without the user expressly identifying the corresponding arrangements. In this way, the application can infer content arrangement operations from a spoken utterance that only specifies the textual content.

Arranging and/or clearing speech-to-text content without a user providing express instructions

Systems and methods described herein relate to determining whether to incorporate recognized text, that corresponds to a spoken utterance of a user of a client device, into a transcription displayed at the client device, or to cause an assistant command, that is associated with the transcription and that is based on the recognized text, to be performed by an automated assistant implemented by the client device. The spoken utterance is received during a dictation session between the user and the automated assistant. Implementations can process, using automatic speech recognition model(s), audio data that captures the spoken utterance to generate the recognized text. Further, implementations can determine whether to incorporate the recognized text into the transcription or cause the assistant command to be performed based on touch input being directed to the transcription, a state of the transcription, and/or audio-based characteristic(s) of the spoken utterance.

Voice commands for an automated assistant utilized in smart dictation

Implementations set forth herein relate to an automated assistant that can control a camera according to one or more conditions specified by a user. A condition can be satisfied when, for example, the automated assistant detects a particular environment feature is apparent. In this way, the user can rely on the automated assistant to identify and capture certain moments without necessarily requiring the user to constantly monitor a viewing window of the camera. In some implementations, a condition for the automated assistant to capture media data can be based on application data and/or other contextual data that is associated with the automated assistant. For instance, a relationship between content in a camera viewing window and other content of an application interface can be a condition upon which the automated assistant captures certain media data using a camera.

Conditional camera control via automated assistant commands

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Berkeley, CA, United States of America

Jacopo Sannazzaro Natta