Google Inc.

Brandon Charles Barbello

Leonardo Giusti

Ivan Poupyrev

Patrick M Amihood

Vignesh Sachidanandam

Tyler Reed Kugler

Tim Phillip Wantland

Alok Chandel

Johan Prag

Selim Flavio Cinek

Lucas Dupin Moreira Costa

Artur Tsurkan

Blaise Hilary Aguera-Arcas

John David Jacobs

Devon James O'Reilley Stern

Jung Ook Hong

Abel Seleshi Mengistu

Matthew Nirvan Sharifi

Dominik Roblek

Yossi Matias

Gabriel Taubman

Julian James Odell

Thomas Weedon Hume

Christopher Thornton

Beat Gfeller

Nadav Bar

Jan Heinrich Althaus

Kevin I Kilgour

Benjamin Schlesinger

Noam Etzion-Rosenberg

Robert Berry

Katsiaryna Naliuka

Shavit Matias

Ori Kabeli

Marvin Karl Ritter

James David Lyon

Dongeek Shin

Shwetak N Patel

Adarsh Prakash Murthy Kowdle

Eiji Hayashi

Michael John Schoenberg

Jan Piotr Jedrzejowicz

Christopher Max Breithaupt

Bryan Woods

Mihajlo Velimirović

Rebecca Chiou

Aditi Bhargava

Usman Abdullah

Alex Agranovich

Eric Erfanian

Anshuman Kumar

Paul Dunlop

Kyungmin Youn

David Petrou

Michael D Dixon

David Lloyd Jones

Anupam Pathak

Kyle Pedersen

Nir Shemy

Michelle Tadmor

Devon James O'Reilly Stern

Various arrangements for performing ballistocardiography using a mobile device are presented. A radar integrated circuit of a mobile device may emit frequency-modulated continuous-wave (FMCW) radar. Reflected radio waves based on the FMCW radar being reflected off objects may be received and used to create a raw radar waterfall. The raw radar waterfall may be analyzed to create a ballistocardiography waveform. Data based on the ballistocardiography waveform may be output, such as to a machine-learning application installed on the mobile device.

Heart beat measurements using a mobile device

Systems and methods for determining identifying semantic entities in audio signals are provided. A method can include obtaining, by a computing device comprising one or more processors and one or more memory devices, an audio signal concurrently heard by a user. The method can further include analyzing, by a machine-learned model stored on the computing device, at least a portion of the audio signal in a background of the computing device to determine one or more semantic entities. The method can further include displaying the one or more semantic entities on a display screen of the computing device.

Systems and methods for identifying and providing information about semantic entities in audio signals

A computing device is described that accepts, a telephone call, from another device, initiated by a caller. Prior to establishing a telephone user interface that receives spoken input from the user and outputs spoken audio from the caller, the computing device executes a call screening service that outputs an audio user interface, to the other device and as part of the telephone call. The audio user interface interrogates the caller for additional information including a purpose of the telephone call, which allows the user to have more context of the telephone call before deciding whether to accept the call or hang up. The computing device outputs a graphical user interface associated with telephone call. The graphical user interface includes an indication of the additional information obtained via the audio user interface that interrogates the caller.

Synchronous communication using voice and text

This document describes techniques and systems for authentication management through IMU and radar. The techniques and systems use inertial sensor data from an inertial measurement unit (IMU) and/or radar data to manage authentication for a computing device. By so doing, the techniques conserve power, improve accuracy, or reduce latency relative to many common techniques and systems for computing-device authentication.

Authentication management through IMU and radar

Various arrangements for performing radar-based measurement of vital signs. Waveform data may be received then filtered of data indicative of static objects to obtain motion-indicative waveform data. The motion-indicative waveform data may be analyzed to determine one or more frequencies of movement present within the motion-indicative waveform data. A spectral analysis may be performed on the motion-indicative waveform data to determine a spectral-analysis state of a monitored region. The spectral-analysis state of the monitored region may be determined to match a predefined spectral-analysis state during which vital sign monitoring is permitted. One or more vital signs of a monitored user present within the monitored region may be determined and output based on analyzing the motion-indicative waveform data.

Sleep tracking and vital sign monitoring using low power radio waves

A computing system can be configured to input model input that includes context data into a machine-learned model and receive model output that describes one or more semantic entities referenced by the context data. The computing system can be configured to provide data descriptive of the semantic entity or entities to the computer application(s) and receive application output(s) respectively from the computing application(s) in response to providing the data descriptive of semantic entity or entities to the computer application(s). The application output(s) received from each computer application can describe available action(s) of the corresponding computer application with respect to the semantic entity or entities. The computing system can be configured to provide at least one indicator to a user that describes the available action(s) of the corresponding computer applications with respect to the semantic entity or entities.

System and method for selecting and providing available actions from one or more computer applications to a user

This document describes techniques and systems for radar-based gesture-recognition with context-sensitive gating and other context-sensitive controls. Sensor data from a proximity sensor and/or a movement sensor produces a context of a user equipment. The techniques and systems enable the user equipment to recognize contexts when a radar system can be unreliable and should not be used for gesture-recognition, enabling the user equipment to automatically disable or 'gate' the output from the radar system according to context. The user equipment prevents the radar system from transitioning to a high-power state to perform gesture-recognition in contexts where radar data detected by the radar system is likely due to unintentional input. By so doing, the techniques conserve power, improve accuracy, or reduce latency relative to many common techniques and systems for radar-based gesture-recognition.

Context-sensitive control of radar-based gesture-recognition

Systems and techniques are described for robust radar-based gesture-recognition. A radar system detects radar-based gestures on behalf of application subscribers. A state machine transitions between multiple states based on inertial sensor data. A no-gating state enables the radar system to output radar-based gestures to application subscribers. The state machine also includes a soft-gating state that prevents the radar system from outputting the radar-based gestures to the application subscribers. A hard-gating state prevents the radar system from detecting radar-based gestures altogether. The techniques and systems enable the radar system to determine when not to perform gesture-recognition, enabling user equipment to automatically reconfigure the radar system to meet user demand. By so doing, the techniques conserve power, improve accuracy, or reduce latency relative to many common techniques and systems for radar-based gesture-recognition.

Robust radar-based gesture-recognition by user equipment

This document describes techniques and systems that enable radar-based authentication status feedback. A radar field is used to enable an electronic device to account for the user's distal physical cues to determine and maintain an awareness of the user's location and movements around the device. This awareness allows the device to anticipate some of the user's intended interactions and provide functionality in a timely and seamless manner, such as preparing an authentication system to authenticate the user before the user touches or speaks to the device. These features also allow the device to provide visual feedback that can help the user understand that the device is aware of the user's location and movements. In some cases, the feedback is provided using visual elements presented on a display.

Radar-based authentication status feedback

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving, at a mobile computing device that is associated with a called user, a call from a calling computing device that is associated with a calling user; in response to receiving the call, determining, by the mobile computing device, that data associated with the called user indicates that the called user will not respond to the call; in response to determining that the called user will not respond to the call, inferring, by the mobile computing device, an informational need of the calling user; and automatically providing, from the mobile computing device to the calling computing device, information associated with the called user and that satisfies the inferred informational need of the calling user.

Automatically performing actions by a mobile computing device

The methods and systems described herein provide for depth-aware image editing and interactive features. In particular, a computer application may provide image-related features that utilize a combination of a (a) the depth map, and (b) segmentation data to process one or more images, and generate an edited version of the one or more images.

Depth-aware photo editing

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Mountain View, CA, United States of America

Brandon Charles Barbello