Microsoft Technology Licensing, LLC

Christopher John Tector

Aaron William Ogus

Matthew L Bronder

James Andrew Goossen

Hamze M Kalache

Christopher Michael McAllen

Nicholas P Sagall

Orest B Zborowski

David E Glerok

Enhanced user input devices and user input interfacing systems are provided herein which can reduce perceived interaction latency. In one example, a method of operating a user input interface on a host system includes identifying a target pace for delivery of user input state to an application, and determining, based at least on the target pace, one or more timing parameters for transfer of the user input state from a user input device. The method also includes indicating the one or more timing parameters to the user input device, wherein the user input device responsively transfers the user input state according to the one or more timing parameters.

Latency adjustment of user input devices

Techniques for GPU self throttling are described. In one or more embodiments, timing information for GPU frame processing is obtained using a timeline for the GPU. This may occur by inserting callbacks into the GPU processing timeline. An elapsed time for unpredictable work that is inserted into the GPU workload is determined based on the obtained timing information. A decision is then made regarding whether to 'throttle' designated optional/non-critical portions of the work for a frame based on the amount of elapsed time. In one approach the elapsed time is compared to a configurable timing threshold. If the elapsed time exceeds the threshold, work is throttled by performing light or no processing for one or more optional portions of a frame. If the elapsed time is less than the threshold, heavy processing (e.g., 'normal' work) is performed for the frame.

GPU self throttling

Multiple vehicles being simulated at multiple devices are coordinated across a network as part of a shared gaming experience. Participating devices initially synchronize a time base using a designated host device. During game play, local devices simulate local vehicles. Local devices also produce predicted vehicle states for the local vehicles and transmit the predicted vehicle states to remote devices with regard to network latency. Remote devices in turn produce interpolated vehicle states from the predicted vehicles states and then render the vehicles using the interpolated vehicle states. Interpolation values may also be employed to maintain the synchronized time base. Additionally, the interpolations may be tuned, especially with respect to collisions, by a glancing collisions handler, a dragging collisions effectuator, and/or a remote vehicle damage agent.

Multi-vehicle cross-network coordination

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Sammamish, WA, United States of America

Christopher John Tector