Google Inc.

Tomer Amarilio

Jeremy Drew Payne

Benjamin Margolin

John Jaesung Lee

Andrew Shivam Wickham

Brett Rolston Lider

Yuzhao Ni

Alok Chandel

Daniel George Koulomzin

Ronghui Zhu

Nathan Dickerson Beach

Jeffrey Hoefs

Nathaniel Nesiba

Bryan Allen

Anurag Jain

Jacky Cheung

Norbert Tydingco

Chunya Hua

Gregory Kielian

Will Donnelly

Andrew Swerdlow

Dan Fredinburg

Michael W Brandt

Travis Harrison Kroll Green

Umesh Shankar

Tilke Mary Judd

Jonathan McPhie

Lars Fabian Krüger

Brandon Kyle Trew

Mor Miller

Kyle Garner Harrison

Feng Yuan

Sara Lee Su

Hristo Stefanov Stefanov

Gregory Dardyk

Peter Klein

Christoph Urs Oehler

Mark Russell Thomas

Marlo James McGriff, Ii

Etienne DeGuine

Si-Wai Yan Lai

Giora Unger

Itay Maman

Jose Javier Zuniga, Jr

Max Michiel Loubser

Joseph Adam Taylor

Alison Michelle Huml

Bao Lam

Geva Rechav

Lauren A Schmidt

Oren Naim

Dennis Ai

In one example, a method includes receiving, by a computing system, context information associated with a computing device; inferring, by the computing system and based on the context information, an action of a user of the computing device, the action associated with at least one entity; determining, by the computing system and based on stored attribute information associated with the at least one entity, and based on a stored set of rules associated with the inferred action, that the inferred action is not advisable; and responsive to determining that the inferred action is not advisable, outputting, by the computing system and for display on the computing device, notification data indicating that the inferred action is not advisable.

Inferred user intention notifications

In response to a user interacting with a tangible peripheral assistant control device (e.g., depressing a button of the device), causing an automated assistant to perform one or more actions. The action(s) performed can be based on input previously provided by the user in configuring the peripheral assistant control device. The action(s) performed in response to interaction with the peripheral assistant control device can vary based on one or more conditions, such as which user is currently active, where the peripheral assistant control device is currently located (which can optionally be inferred based on which of multiple assistant computing devices the button is paired with), and/or the current state of one or more smart devices and/or other devices (e.g., as determined based on a device topology). A utility of the peripheral assistant control device can be automatically extended beyond what was specifically requested by a user during configuration.

Conditionally assigning various automated assistant function(s) to interaction with a peripheral assistant control device

The present disclosure is generally directed a system to detect activation phrases within input audio signals transmitted over a low-bandwidth network. The system can use a two-stage activation phrase detection process. First a sensing device, which can include a plurality of microphones for detecting an input audio signal, can detect an input audio signal that includes a candidate activation phrase. Second, the sensing device can transmit the recordings of the input audio signal to a client device for confirmation that the input audio signal includes the activation phrase.

Audio processing in a low-bandwidth networked system

Systems and methods for prioritizing audio announcements are provided. In one embodiment, a method can include obtaining, by one or more processors associated with a computing device, an audio notification that comprises a message and a preferred priority associated with the message. The method can include determining, by the one or more processors, an assigned priority associated with the message based at least in part on a content of the message and the preferred priority associated with the message. The method can include generating, by the one or more processors, an output indicative of the message and the assigned priority associated with the message. The method can include providing, by the one or more processors, the output to an audio device to audibly produce the output based at least in part on the priority associated with the message.

Audio announcement prioritization system

A computing device is described that receives, at a first particular time, first notification data. Responsive to receiving the first notification data, the computing device outputs, at a first intensity level, a first alert associated with the first notification data. The computing device receives, at a second particular time after the first particular time, second notification data. Responsive to the computing device receiving an indication of user interaction with the computing device between the first particular time and the second particular time, the computing device outputs, at the first intensity level, a second alert associated with the second notification data. Conversely, responsive to the computing device not receiving the indication of user interaction with the computing device, the computing device either outputs, at a second intensity level that is less than the first intensity level, the second alert, or refrains from outputting the second alert entirely.

Limiting alerts on a computing device

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining the relevancy of entities. One of the methods includes obtaining a first entity identifier, wherein the first entity identifier identifies a first entity associated with a query; identifying one or more second entities associated with the first entity; and for each of the one or more second entities, determining a relevancy score for the second entity from a measure of the popularity of the second entity and a measure of the freshness of the second entity, wherein the relevancy score represents a relevance of the second entity to the query; determining that the relevancy score satisfies a threshold; and providing the second entity in response to the query.

Determining the relevancy of entities

A method includes gathering a plurality of instances of online activity associated with a user, analyzing the plurality of instances of online activity to determine a characteristic that is likely to correspond to a profile attribute of the user and generating a profile enrichment suggestion for the user based on the determined characteristic.

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Palo Alto, CA, United States of America

Tomer Amarilio