Google Inc.

Jaclyn Konzelmann

Tuan Anh Nguyen

Diego Melendo Casado

Caroline Rebecca Pantofaru

Vinay Bettadapura

Ruxandra Georgiana Davies

Trevor D Strohman

Raunaq Shah

Andrew C Gallagher

Utsav Prabhu

Lisa Takehana

Adrian Diaconu

Gustavo Moura

Tanya Kraljic

Michael Andrew Goodman

Valerie Nygaard

Dan Rasin

Rafael Goldfarb

Lior Alon

Dekel Auster

Nino Tasca

Johan Schalkwyk

Kenneth Edward Mixter

Hideaki Matsui

Joseph Robert Smarr

Sourish Chaudhuri

Jonathan Bloom

Andrew Gallagher

Caroline Pantofaru

Techniques are described herein for enabling the use of 'dynamic' or “context-specific” hot words for an automated assistant. In various implementations, an automated assistant may be operated at least in part on a computing device. Audio data captured by a microphone may be monitored for default hot word(s). Detection of one or more of the default hot words may trigger transition of the automated assistant from a limited hot word listening state into a speech recognition state. Transition of the computing device into a given state may be detected, and in response, the audio data captured by the microphone may be monitored for context-specific hot word(s), in addition to or instead of the default hot word(s). Detection of the context-specific hot word(s) may trigger the automated assistant to perform a responsive action associated with the given state, without requiring detection of default hot word(s).

Dynamic and/or context-specific hot words to invoke automated assistant

Implementations relate to an automated assistant that provides and manages output from one or more elements of output hardware of a computing device. The automated assistant manages dynamic adjustment of access permissions to the computing device according to, for example, a detected presence of one or more users. An active-user queue can be established each time a unique user enters a viewing window of a camera of the computing device when, up to that point, no user was considered active. Multiple image frames can be captured via the camera and processed to determine whether an initial user remains in the viewing window and/or whether another user has entered the viewing window. The initial user can be considered active as long as they are exclusively detected in the viewing window. Restricted content associated with the user may be rendered by the computing device whilst the user is active.

Assigning priority for an automated assistant according to a dynamic user queue and/or multi-modality presence detection

Implementations set forth herein relate to employing dynamic regulations for governing responsiveness of multiple automated assistant devices, and specifically the responsiveness an automated assistant to a given spoken utterance that has been acknowledged by two or more of the assistant devices. The dynamic regulations can be context-dependent and adapted over time in order that the automated assistant can accommodate assistant interaction preferences that may vary from user to user. For instance, a spoken utterance such as 'stop,' may be intended to affect different assistant actions based on a context in which the user provided the spoken utterance. The context can refer to a location of the user relative to other rooms in a home, a time of day, a user providing the spoken utterance, an arrangement of the assistant devices within a home, and/or a state of each device in the home.

Regulating assistant responsiveness according to characteristics of a multi-assistant environment

Techniques are described herein for dialog-based enrollment of individual users for single- and/or multi-modal recognition by an automated assistant, as well as determining how to respond to a particular user's request based on the particular user being enrolled and/or recognized. Rather than requiring operation of a graphical user interface for individual enrollment, dialog-based enrollment enables users to enroll themselves (or others) by way of a human-to-computer dialog with the automated assistant.

Enrollment with an automated assistant

Implementations described herein relate to reducing latency in automated assistant interactions. In some implementations, a client device can receive audio data that captures a spoken utterance of a user. The audio data can be processed to determine an assistant command to be performed by an automated assistant. The assistant command can be processed, using a latency prediction model, to generate a predicted latency to fulfill the assistant command. Further, the client device (or the automated assistant) can determine, based on the predicted latency, whether to audibly render pre-cached content for presentation to the user prior to audibly rendering content that is responsive to the spoken utterance. The pre-cached content can be tailored to the assistant command and audibly rendered for presentation to the user while the content is being obtained, and the content can be audibly rendered for presentation to the user subsequent to the pre-cached content.

Methods and systems for reducing latency in automated assistant interactions

As part of a dialog session between a user and an automated assistant, implementations can process, using a streaming ASR model, a stream of audio data that captures a portion of a spoken utterance to generate ASR output, process, using an NLU model, the ASR output to generate NLU output, and cause, based on the NLU output, a stream of fulfillment data to be generated. Further, implementations can further determine, based on processing the stream of audio data, audio-based characteristics associated with the portion of the spoken utterance captured in the stream of audio data. Based on the audio-based characteristics and/the stream of NLU output, implementations can determine whether the user has paused in providing the spoken utterance or has completed providing of the spoken utterance. If the user has paused, implementations can cause natural conversation output to be provided for presentation to the user.

Enabling natural conversations with soft endpointing for an automated assistant

Assigning priority for an automated assistant according to a dynamic user queue and/or multimodality presence detection

Hot-word free adaptation of one or more function(s) of an automated assistant. Sensor data, from one or more sensor components of an assistant device that provides an automated assistant interface (graphical and/or audible), is processed to determine occurrence and/or confidence metric(s) of various attributes of a user that is proximal to the assistant device. Whether to adapt each of one or more of the function(s) of the automated assistant is based on the occurrence and/or the confidence of one or more of the various attributes. For example, certain processing of at least some of the sensor data can be initiated, such as initiating previously dormant local processing of at least some of the sensor data and/or initiating transmission of at least some of the audio data to remote automated assistant component(s).

Hot-word free adaptation of automated assistant function(s)

Techniques are described herein for enabling the use of 'dynamic' or 'context-specific' hot words for an automated assistant. In various implementations, an automated assistant may be operated at least in part on a computing device. Audio data captured by a microphone may be monitored for default hot word(s). Detection of one or more of the default hot words may trigger transition of the automated assistant from a limited hot word listening state into a speech recognition state. Transition of the computing device into a given state may be detected, and in response, the audio data captured by the microphone may be monitored for context-specific hot word(s), in addition to or instead of the default hot word(s). Detection of the context-specific hot word(s) may trigger the automated assistant to perform a responsive action associated with the given state, without requiring detection of default hot word(s).

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Mountain View, CA, United States of America

Jaclyn Konzelmann