Google Inc.

Shiblee Imtiaz Hasan

Joseph Edwin Johnson, Jr

Dongeek Shin

Debanjan Mukherjee

David C Lee

Basheer Tome

Chun Yat Frank Li

Juston Payne

Bhaskar Vadathavoor

Jingying Hu

Chris Hluchan

Xiuxiu Yuan

Sarnab Bhattacharya

Kathleen Alexandra Bryan

A first event associated with a first client device of multiple client devices of participants of a video conference is identified. The first event indicates a request to present content in a user interface (UI) including regions each corresponding to one of multiple video streams from the client devices. A first video layer that includes a reframed visual representation of the first participant is generated from the first video segment of a first video stream from the first client device. A first content layer is generated. The first video layer and the first content layer are composited into the first composite video segment in which the reframed visual representation of the first participant is positioned adjacent to the first part of the content. The first composite video segment is provided to the client devices as a real-time video stream for presentation.

Generating a composite video stream having digital content and a participant video for real-time presentation in a user interface of a video conference system

A method including generating two or more audio signals (e.g., by two or more microphones) based on a user interaction with a wearable device, generating an audio signature based on the two or more audio signals, and identifying at least one of a coordinate or a gesture based on an output of a machine-learned model given the audio signature.

Detecting input gestures using onboard microphones

Techniques of wireless charging involve a charging base configured to predict when the user is about to charge a device based on a state of the device. For example, the state of the device may be defined by inertial measurement units (IMUs). The charging base then activates in response to predicting that the user intends to charge the device. In some implementations, the prediction is based on a machine learning (ML) engine trained to classify a movement of a device by a user as being about to charge or not being about to charge. If a movement of a device is classified as being about to charge, the charging base activates before the device is detected by the charging base.

Predictive wireless charging of electronic devices

To provide navigation directions for a scenic route, a machine learning model is trained using (i) characteristics of road segments that have been assigned a scenic metric and (ii) the scenic metrics for the road segments. In response to a request for navigation directions, a set of candidate routes for navigating from the starting location to the destination location is identified. Then for each road segment within each candidate route, characteristics of the road segment are applied to the machine learning model to generate the scenic metric for the road segment. A route is then selected from the set of candidate routes based at least in part on the scenic metrics of the road segments within the route. A set of navigation directions is provided for presentation on a client device for navigating from the starting location to the destination location via the selected route.

Scenic route-based navigation directions

A method that includes employing several sensors associated with a handheld controller, where each of the sensors is made of one of a hover, touch, and force/pressure sensor, and generating, by one or more of the sensors, sensor data associated with the position of a user's hand and finger in relation to the handheld controller. The method continues with combining the sensor data from several sensors to form aggregate sensor data, sending the aggregate sensor data to a processor, and generating an estimated position of the user's hand and fingers based on the aggregate sensor data.

Virtual reality / augmented reality handheld controller sensing

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Santa Clara, CA, United States of America

Shiblee Imtiaz Hasan