Microsoft Technology Licensing, LLC

Adam Edwin Behringer

Julia Schwarz

Sophie Stellmach

Stephen David Bader

Hsiao-Lan Wang

Mathew Julian Lamb

Johnny Hsienchow Lee

Johnson T Apacible

Nicholas Ferianc Kamuda

David William Baumert

Sheng Kai Tang

Joseph Ygona Laurino

Joshua Kyle Neff

Kenneth Mitchell Jakubzak

Jenny Kam

Amy Mun Hong

Michael Harley Notter

Erian Vazquez

Michelle Li

Kristian Jose Davila

Andrew David Wilson

Thomas Matthew Gable

Jason Michael Ray

Jonathan Palmer

Examples are disclosed that relate to hand gesture-based emojis. One example provides, on a display device, a method comprising receiving hand tracking data representing a pose of a hand in a coordinate system, based on the hand tracking data, recognizing a hand gesture, and identifying an emoji corresponding to the hand gesture. The method further comprises presenting the emoji on the display device, and sending an instruction to one or more other display devices to present the emoji.

Hand gesture-based emojis

The techniques disclosed herein provide systems that can control the invocation of precision input mode. A system can initially utilize a first input device, such as a head-mounted display device monitoring the eye gaze direction of a user to control the location of an input target. When one or more predetermined input gestures are detected, the system can then invoke a precision mode that transitions the control of the input target from the first input device to a second input device. The second device can include another input device utilizing different input modalities, such as a sensor detecting one or more hand gestures of the user. The predetermined input gestures can include a fixation input gesture, voice commands, or other gestures that may include the use of a user's hands or head. By controlling the invocation of precision input mode using specific gestures, a system can mitigate device coordination issues.

Controlled invocation of a precision input mode

A computer-implemented method for determining focus of a user is provided. User input is received. An intention image of a scene including a plurality of interactive objects is generated. The intention image includes pixels encoded with intention values determined based on the user input. An intention value indicates a likelihood that the user intends to focus on the pixel. An intention score is determined for each interactive object based on the intention values of pixels that correspond to the interactive object. An interactive object of the plurality of interactive objects is determined to be a focused object that has the user's focus based on the intention scores of the plurality of interactive objects.

Intention image analysis for determining user focus

A machine intelligent communication and control system is able to dynamically predict and adapt information presented to various users. The system provides a personalized experience with its ability to identify users, become contextually aware of the user's location and environment, identify objects and actions, and present customized information tailored for the user and the current environment. The system is further able to control connected items within the environment based upon various user preference considerations.

Machine intelligent predictive communications and control system

Machine intelligent predictive communication and control system

Techniques for maintaining display of a portion of a video as the video continues to be displayed in a background are described herein. In some instances, a video may be captured at a first location and transmitted to a second location, where the video is output in real-time. As the video is displayed, a user may select a portion of the video to be paused. Based on the selection, the portion of the video may be paused while the rest of the video continues in a background. The background video may be displayed according to a different level of focus, image resolution, transparency, etc. than the portion of the video.

Video pinning

Techniques for augmenting video content to enhance context of the video content are described herein. In some instances, a video may be captured at a first location and transmitted to a second location, where the video is output in real-time. A context surrounding a user that is capturing the video and/or a user that is viewing the video may be used to augment the video with additional content. For example, the techniques may process speech or other input associated with either user, a gaze associated with either user, a previous conversation for either user, an area of interest identified by either user, a level of understanding of either user, an environmental condition, and so on. Based on the processing, the techniques may determine augmentation content. The augmentation content may be displayed with the video in an overlaid manner to enhance the experience of the user viewing the video.

Shared experience with contextual augmentation

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Seattle, WA, United States of America

Adam Edwin Behringer