Google Inc.

Julia Proskurnia

Balint Miklos

Nicolo D'Ercole

Victor Carbune

Behshad Behzadi

Zaheed Sabur

Luis Garcia Pueyo

Luv Kothari

Tobias Kaufmann

Marc-Allen Cartright

Jacopo Sannazzaro Natta

Ivo Krka

Alvin Abdagic

Justin Lu

Krishna Sapkota

Magali Boizot-Roche

Márius Sajgalík

Viesturs Zarins

Timothy Youngjin Sohn

Bogdan Prisacari

Akshay Kannan

Paul Roland Lambert

Matthew Vincent Dierker

Jacqueline Amy Tsay

Thijs Van As

Srikanth Pandiri

Adrien Olczak

Victor Anchidin

Mehek Sharma

Krzysztof Andrzej Goj

Bryan Kenneth Rea

Shumin Zhai

Swante Scholz

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

Implementations described herein generally relate to generating a modification selectable element that may be provided for presentation to a user in a smart dictation session with an automated assistant. The modification selectable element may, when selected, cause a transcription, that includes textual data generated based on processing audio data that captures a spoken utterance and that is automatically arranged, to be modified. The transcription may be automatically arranged to include spacing, punctuation, capitalization, indentations, paragraph breaks, and/or other arrangement operations that are not specified by the user in providing the spoken utterance. Accordingly, a subsequent selection of the modification selectable element may cause these automatic arrangement operation(s), and/or the textual data locationally proximate to these automatic arrangement operation(s), to be modified. Implementations described herein also relate to generating the transcription and/or the modification selectable element on behalf of a third-party software application.

System(s) and method(s) to enable modification of an automatically arranged transcription in smart dictation

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

An example method includes receiving, by a computing system, an identification of a recipient of an electronic message being composed from a message account associated with a user; predicting, by the computing system and based on text contained in previous electronic messages sent from the message account, text for a body of the electronic message; and outputting, for display, the predicted text for optional selection and insertion into the body of the electronic message.

Recipient based text prediction for electronic messaging

Methods, apparatus, systems, and computer-readable media are provided for generating and applying outgoing communication templates. In various implementations a corpus of outgoing communications sent by a user may be grouped into a plurality of clusters based on one or more attributes of a context of the user. One or more segments of each outgoing communication of a particular cluster may be classified as fixed in response to a determination that a count of occurrences of the one or more segments across the particular cluster satisfies a criterion. One or more remaining segments of each communication of the particular cluster may or may not be classified as transient. Based on sequences of classified segments associated with each communication of the particular cluster, an outgoing communication template may be generated to automatically populate at least a portion of a draft outgoing communication being prepared by the user.

Generating and applying outgoing communication templates

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Lausanne, Switzerland

Julia Proskurnia