Google Inc.

Lucas Mirelmann

Zaheed Sabur

Behshad Behzadi

Daniel Cotting

Denis Burakov

Sergey Nazarov

Michael Golikov

Steve Cheng

Mario Bertschler

Bohdan Vlasyuk

Adrian Zumbrunnen

Bryan Christopher Horling

Ibrahim Badr

Vladimir Vuskovic

Dhruv Bakshi

Andrea Terwisscha Van Scheltinga

Yariv Adan

Luv Kothari

Tilke Mary Judd

Jan Lamecki

Lan Huo

Mikhail Reutov

Marcin M Nowak-Przygodzki

Jonathan David Lee

Okan Kolak

Vinh Quoc Ly

Mugurel Ionut Andreica

Yan Zhong

Radu Voroneanu

Lucia Terrenghi

Marie Patriarche Bledowski

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 can reduce the time required to obtain responses from an automated assistant by, for example, obviating the need to provide an explicit invocation to the automated assistant, such as by saying a hot-word/phrase or performing a specific user input, prior to speaking a command or query. In addition, the automated assistant can optionally receive, understand, and/or respond to the command or query without communicating with a server, thereby further reducing the time in which a response can be provided. Implementations only selectively initiate on-device speech recognition responsive to determining one or more condition(s) are satisfied. Further, in some implementations, on-device NLU, on-device fulfillment, and/or resulting execution occur only responsive to determining, based on recognized text form the on-device speech recognition, that such further processing should occur. Thus, through selective activation of on-device speech processing, and/or selective activation of on-device NLU and/or on-device fulfillment, various client device resources are conserved.

Selectively activating on-device speech recognition, and using recognized text in selectively activating on-device NLU and/or on-device fulfillment

Implementations can reduce the time required to obtain responses from an automated assistant through proactive caching, locally at a client device, of proactive assistant cache entries—and through on-device utilization of the proactive assistant cache entries. Different proactive cache entries can be provided to different client devices, and various implementations relate to technique(s) utilized in determining which proactive cache entries to provide to which client devices. In some of those implementations, in determining which proactive cache entries to provide (proactively or in response to a request) to a given client device, a remote system selects, from a superset of candidate proactive cache entries, a subset of the cache entries for providing to the given client device.

Proactive caching of assistant action content to enable resolution of spoken or typed utterances

Methods, apparatus, and computer readable media are described related to automated assistants that proactively incorporate, into human-to-computer dialog sessions, unsolicited content of potential interest to a user. In various implementations, in an existing human-to-computer dialog session between a user and an automated assistant, it may be determined that the automated assistant has responded to all natural language input received from the user. Based on characteristic(s) of the user, information of potential interest to the user or action(s) of potential interest to the user may be identified. Unsolicited content indicative of the information of potential interest to the user or the action(s) may be generated and incorporated by the automated assistant into the existing human-to-computer dialog session. In various implementations, the incorporating may be performed in response to the determining that the automated assistant has responded to all natural language input received from the user during the human-to-computer dialog session.

Proactive incorporation of unsolicited content into human-to-computer dialogs

Implementations set forth herein allow a user to access a first application in a foreground of a graphical interface, and simultaneously employ an automated assistant to respond to notifications arising from a second application. The user can provide an input, such as a spoken utterance, while viewing the first application in the foreground in order to respond to notifications from the second application without performing certain intervening steps that can arise under certain circumstances. Such intervening steps can include providing a user confirmation, which can be bypassed, and/or time-limited according to a timer, which can be displayed in response to the user providing a responsive input directed at the notification. A period for the timer can be set according to one or more characteristics that are associated with the notification, the user, and/or any other information that can be associated with the user receiving the notification.

Dynamically delaying execution of automated assistant actions and/or background application requests

Implementations relate to receiving natural language input that requests an automated assistant to provide information and processing the natural language input to identify the requested information and to identify one or more predicted actions. Those implementations further cause a computing device, at which the natural language input is received, to render the requested information and the one or more predicted actions in response to the natural language input. Yet further, those implementations, in response to the user confirming a rendered predicted action, cause the automated assistant to initialize the predicted action.

Performing subtask(s) for a predicted action in response to a separate user interaction with an automated assistant prior to performance of the predicted action

Implementations set forth herein relate to an automated assistant that can be invoked while a user is interfacing with a foreground application in order to retrieve data from one or more different applications, and then provide the retrieved data to the foreground application. A user can invoke the automated assistant while operating the foreground application by providing a spoken utterance, and the automated assistant can select one or more other applications to query based on content of the spoken utterance. Application data collected by the automated assistant from the one or more other applications can then be used to provide an input to the foreground application. In this way, the user can bypass switching between applications in the foreground in order to retrieve data that has been generated by other applications.

Automated assistant for generating, in response to a request from a user, application input content using application data from other sources

Implementations herein relate to pre-caching data, corresponding to predicted interactions between a user and an automated assistant, using data characterizing previous interactions between the user and the automated assistant. An interaction can be predicted based on details of a current interaction between the user and an automated assistant. One or more predicted interactions can be initialized, and/or any corresponding data pre-cached, prior to the user commanding the automated assistant in furtherance of the predicted interaction. Interaction predictions can be generated using a user-parameterized machine learning model, which can be used when processing input(s) that characterize a recent user interaction with the automated assistant. The predicted interaction(s) can include action(s) to be performed by third-party application(s).

Implementations can reduce the time required to obtain responses from an automated assistant through proactive caching, locally at a client device, of proactive assistant cache entries&#x2014;and through on-device utilization of the proactive assistant cache entries. Different proactive cache entries can be provided to different client devices, and various implementations relate to technique(s) utilized in determining which proactive cache entries to provide to which client devices. In some of those implementations, in determining which proactive cache entries to provide (proactively or in response to a request) to a given client device, a remote system selects, from a superset of candidate proactive cache entries, a subset of the cache entries for providing to the given client device.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for pairing a speech-enabled device with a display device. A determination may be made to pair a speech-enabled device with a display device of a particular type. A set of display devices that are associated with the speech-enabled device may be identified in response to determining to pair the speech-enabled device with the display device of the particular type. An instruction may be provided to each of the display devices. The instruction may cause the display device to determine (i) whether the display device is of the particular type and (ii) whether the display device and the speech-enabled device both share a local area network and display on the display device an indication regarding pairing with the speech-enabled device.

Pairing a voice-enabled device with a display device

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Zurich, Switzerland

Lucas Mirelmann