Google Inc.

Microsoft Technology Licensing, LLC

Benjamin Vanik

Aaron Michael Donsbach

Blaise Hilary Aguera-Arcas

Daniel Ramage

Krzysztof Duleba

Jon Gabriel Clapper

Juston Payne

Alison Lentz

Jess Holbrook

Joshua Denali Lovejoy

Emily Anne Fortuna

Oliver Fritz Lange

Iwona Bialynicka-Birula

Li Zhang

Hugh Brendan McMahan

Robert Douglas Fritz, Iii

Brett David Brewer

Daniel Joseph Giambalvo

Timothy R Sullivan

Benjamin J McMahan

Divya Tyamagundlu

Kristine Kohlhepp

Leonard M Kawell

Oscar E Murillo

Lutz Gerhard

The present disclosure provides an image capture, curation, and editing system that includes a resource-efficient mobile image capture device that continuously captures images. In particular, the present disclosure provides low power frameworks for controlling image sensor mode in a mobile image capture device. On example low power frame work includes a scene analyzer that analyzes a scene depicted by a first image and, based at least in part on such analysis, causes an image sensor control signal to be provided to an image sensor to adjust at least one of the frame rate and the resolution of the image sensor.

Low power framework for controlling image sensor mode in a mobile image capture device

Concepts and technologies are described herein for interacting with contextual and task-focused computing environments. Tasks associated with applications are described by task data. Tasks and/or batches of tasks relevant to activities occurring at a client are identified, and a UI for presenting the tasks is generated. The UIs can include tasks and workflows corresponding to batches of tasks. Workflows can be executed, interrupted, and resumed on demand. Interrupted workflows are stored with data indicating progress, contextual information, UI information, and other information. The workflow is stored and/or shared. When execution of the workflow is resumed, the same or a different UI can be provided, based upon the device used to resume execution of the workflow. Thus, multiple devices and users can access workflows in parallel to provide collaborative task execution.

Interactions with contextual and task-based computing environments

The present disclosure provides an image capture, curation, and editing system that includes a resource-efficient mobile image capture device that continuously captures images. The mobile image capture device is operable to input an image into at least one neural network and to receive at least one descriptor of the desirability of a scene depicted by the image as an output of the at least one neural network. The mobile image capture device is operable to determine, based at least in part on the at least one descriptor of the desirability of the scene of the image, whether to store a second copy of such image and/or one or more contemporaneously captured images in a non-volatile memory of the mobile image capture device or to discard a first copy of such image from a temporary image buffer without storing the second copy of such image in the non-volatile memory.

Systems and methods for selective retention and editing of images captured by mobile image capture device

Optimizing data rendering includes, in one embodiment, using templates to express a data request as a set of queries where each query in the set is prioritized based on, among other things, context factors such as capabilities of the requesting device, network factors, and user context. After generating the set of queries, each query in the set of queries is then executed to progressively populate the template with the requested data. As the queries are executing, the templates are progressively rendered.

Data rendering optimization

The present disclosure provides an image capture, curation, and editing system that includes a resource-efficient mobile image capture device that continuously captures images. The mobile image capture device is operable to input an image into at least one neural network and to receive at least one descriptor of the desirability of a scene depicted by the image as an output of the at least one neural network. The mobile image capture device is operable to determine, based at least in part on the at least one descriptor of the desirability of the scene of the image, whether to store a second copy of such image in a non-volatile memory of the mobile image capture device or to discard a first copy of such image from a temporary image buffer without storing the second copy of such image in the non-volatile memory.

Systems and methods that leverage deep learning to selectively store images at a mobile image capture device

Images and/or collections of images may be rendered in a way that adapts to ambient circumstances, and that may enhance a user's perception of experience quality. When an image is requested for display, it is determined whether a rendered version of the image exists. If no rendered version of the image exists, then an imposter image is displayed. The imposter image may include arbitrary colors and/or patterns. If an appropriate rendered version of the image exists, then the rendered version may be displayed in place of the imposter version. While the imposter or rendered image is being displayed, the image may be retrieved, and may be rendered at other resolutions (e.g., successively higher resolutions). The choice of resolutions may be adapted to available transmission bandwidth, display speed, or other considerations.

Adaptive image rendering and use of imposter

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Seattle, WA, United States of America

Benjamin Vanik