Google Inc.

Radu Voroneanu

Behshad Behzadi

Marcin M Nowak-Przygodzki

Zaheed Sabur

Daniel Cotting

Lucas Mirelmann

Denis Burakov

Sergey Nazarov

Michael Golikov

Steve Cheng

Mugurel Ionut Andreica

Mario Bertschler

Bohdan Vlasyuk

Cliff Kuang

Mugurel-Ionut Andreica

Andre Elisseeff

Dana Ritter

Diana Avram

Alice Liang

Adam Coimbra

Yariv Adan

Sharon Stovezky

Sneha Ashok

Ragnar Groot Koerkamp

Kyunghoon Lee

Deepak Goyal

Anton Berezin

Andrea D'olimpio

Florian Hasler

Implementations set forth herein relate to a system that employs an automated assistant to further interactions between a user and another application, which can provide the automated assistant with permission to initialize relevant application actions simultaneous to the user interacting with the other application. Furthermore, the system can allow the automated assistant to initialize actions of different applications, despite being actively operating a particular application. Available actions can be gleaned by the automated assistant using various application-specific schemas, which can be compared with incoming requests from a user to the automated assistant. Additional data, such as context and historical interactions, can also be used to rank and identify a suitable application action to be initialized via the automated assistant.

Initializing non-assistant background actions, via an automated assistant, while accessing a non-assistant application

Implementations relate to identifying actions performed by a user while the user is interacting with multiple applications and providing a routine suggestion to the user based on the identified actions. While a user is interacting with one or more applications, the user actions are determined. The user actions are compared to one or more template routines and a template routine is selected that matches the actions of the user and provided to the user as a suggested routine. The suggested routine can be implemented by an automated assistant to perform the actions of the template by providing a corresponding command.

Automatically suggesting routines based on detected user actions via multiple applications

Implementations relate to determining a rendering type for an application that is executing automatically. Based on user interactions with an application that is associated with specified input from the user while the user is interacting with the application, a confidence metric is generated for each specified input and a rendering type is determined based on the confidence metrics. Subsequently, when the user requests that a sequence of actions be performed, the application will be displayed according to the rendering type.

Automatically executing application routines with user inputs

Implementations set forth herein relate to an automated assistant that operates according to a variety of different location-based biasing modes for rendering responsive content for a user and/or proactively suggesting content for the user. The user can provide condensed spoken utterances to the automated assistant, when the automated assistant is operating according to one or more location-based biasing modes, but nonetheless receive accurate responsive outputs from the automated assistant. A responsive output generated by biasing toward a subset of location characteristic data that has been prioritized over other subsets of location characteristic data. The biasing allows the automated assistant to compensate for any details that may be missing from a spoken utterance, but allows the user to provide shorter spoken utterances, thereby reducing an amount of language processing when processing inputs from the user.

Location-based mode(s) for biasing provisioning of content when an automated assistant is responding to condensed natural language inputs

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Zurich, Switzerland

Radu Voroneanu