Google Inc.

Miroslav Bojic

Henry Owen Newton-Dunn

Devin Mancuso

Golden Gopal Krishna

Daniel June Hyung Park

Mindy Pereira

Carl Magnus Borg

Jacob M Klinker

Lily Sin

Mikkel Crone Koser

Henry Holland

Ding Xu

Mårten Jönsson

Rohan Dhing

Zhengnan Zhao

Michael DelGaudio

Ben Bashford

Matt Webb

Michael Delgaudio

Mårten JÖNSSON

In general, the subject matter described in this disclosure can be embodied in methods, devices, and program products for modifying interactions between a mobile device and a peripheral device based on proximity. The mobile device monitors a wireless communication channel and receives a transmission from the peripheral device over the channel indicating that the mobile device is located within a first zone of proximity to the peripheral device. The mobile device determines distance values between the devices. When the mobile device determines that a distance value satisfies a threshold value, the mobile device visually indicates that it is within a second zone of proximity to the peripheral device. As a result of the mobile device receiving data from the peripheral device using a second communication protocol, the mobile device determines that it is located within a third zone of proximity to the peripheral device, and performs an action.

Modifying the type of interaction between a mobile computing device and a peripheral device based on proximity

This document describes techniques for suggesting actions based on machine learning. These techniques determine a task that a user desires to perform, and presents a user interface through which to perform the task. To determine this task, the techniques can analyze content displayed on the user device or analyze contexts of the user and user device. With this determined task, the techniques determine an action that may assist the user in performing the task. This action is further determined to be performable through analysis of functionalities of an application, which may or may not be executing or installed on the user device. With some subset of the application's functionalities determined, the techniques presents the subset of functionalities via the user interface. By so doing, the techniques enable a user to complete a task more easily, quickly, or using fewer computing resources.

Suggesting actions based on machine learning

A method includes receiving, by a first computing device () and from a second computing device (), using a short-range wireless communication unit of the first computing device (), data including state information of the second computing device () and identification information for the second computing device (). The method also includes, responsive to a distance between the first computing device () and the second computing device () satisfying a threshold distance, determining a particular template graphical user interface from a plurality of template graphical user interfaces. The method also includes determining one or more operations the second computing device () is configured to perform. The method further includes outputting a graphical user interface () associated with the remote computing device (). The method includes receiving a user input selecting a particular graphical element () of the graphical user interface () and transmitting, to the second computing device (), an indication of an operation corresponding to the particular graphical element.

Controlling remote devices using user interface templates

This document describes techniques and devices for a refined search with machine learning. These techniques improve computer-aided searches through enabling selection of search criteria used in a prior search and providing a refined search result based on that selection. Furthermore, a machine-learning component of a search engine can be altered to improve future search results based on the selection and an indication of the desirability of the refined search result.

Refined search with machine learning

This document describes techniques and devices for task-related sorting, application discovery, and unified bookmarking for application managers. Through use of an application manager, multiple applications (including standalone applications, instant applications, websites, and other content) that a person can use to accomplish a single task, or multiple related tasks, are sorted into discrete groups for display in the application manager. The application manager can automatically recognize relationships between activities performed with the applications and recognize user actions with the applications that are related to the activities. Based on the relationships and user actions, the application manager can automatically determine that the activities and actions represent a task and display a task group that includes the applications that represent the task. The task groups may be visually displayed as a stack, strip, or pile of windows or thumbnails representing each application or other content the person could use for the task.

Task-related sorting, application discovery, and unified bookmarking for application managers

Apparatus and methods related to hop navigation on mobile devices are provided. A computing device can generate a first visible display displaying a first screen. The computing device can receive a request to activate movement between screens. After receiving the request to activate movement between screens, the computing device can: generate a second visible display of an expanded screen plane, where the second visible display includes a display of the first screen and at least a portion of a neighboring screen within the expanded screen plane, enable interaction with the expanded screen plane using one or more movements of the computing device to navigate to the neighboring screen, and receive a request to deactivate movement between screens. After receiving the request to deactivate movement between screens, the computing device can generate a third visible display displaying the neighboring screen.

Hop navigation

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San Francisco, CA, United States of America

Miroslav Bojic