Microsoft Technology Licensing, LLC

Microsoft Technology Licenisng, LLC

Martin Ellis

Ross Garrett Cutler

Ryan S Menezes

Juri Reitel

Andrei Birjukov

ZhiCheng Miao

Joyce S Fang

David Yuheng Zhao

Thang Duong

Ju Hee Cho

Ganesh Ananthanarayanan

Yu Yan

Satya Sasikanth Bendapudi

Jizhe Jin

Ramiro Gonzalez Monroy

Harpreet Kaur

Michael Harrison Rudow

Jose Mario Carranza Rojas

Zhicheng Miao

Disclosed in some examples are methods, systems, and machine-readable mediums which customizes application feature settings using ranked clusters from an unsupervised modelling algorithm that clusters similar computing platforms and feature settings templates that map these ranks to feature settings. In some examples, a model may is periodically built using a first set of computing platform properties observed from computing platforms that the application is executing on. These clusters are then ranked using a second set of computing platform properties observed from other computing platforms that the application is executing on and performance data that describes performance of the application on those platforms.

Machine capability model for self-configurable applications

An agent interfaces with a sending computing device and a receiving computing device to automatically adjust one-way or two-way real-time audio and real-time video transmission parameters responsive to changing network conditions and/or application requirements. The agent incorporates a reinforcement learning model that adjusts transmission parameters to maximize an expected value of a sum of future rewards; the expected value of the sum of future rewards is based on a current state of the sending computing, a current action (e.g. a current set of transmission parameters) at the sending computing device and a reward provided by the receiving computing device. The reward is representative of a user-perceived quality of experience at the receiving computing device.

Reinforcement learning in real-time communications

A system is provided herein that can correlate service issues with system telemetry associated with the software session associated with those service issues. Using a statistical approach, the system can evaluate data across numerous software sessions to rank the importance of the reported service issues. To accomplish the ranking, the system can parse the reports of service issues on a periodic basis, can extract telemetry identifiers (IDs) from the logs, can query the telemetry, may compute the relative importance of detected issues (in the context of calls going on for that day), and then can report this impact hack to the service issue database.

Service issue prioritisation based on impact using software telemetry

Identifying, by a sender and for each frame i of a plurality of frames of a video stream, a partition of a set of video data symbols D[i] into a first set of video data symbols U[i] and a second set of video data symbols V[i]. Generating, by the sender and for each frame i, a set of one or more streaming forward error correction (FEC) code parity symbols P[i] based on the symbols: V[i&#x2212;&#x3c4;] through V[i&#x2212;1], U[i&#x2212;&#x3c4;], and the symbols D[i], wherein &#x3c4; is a function of a maximum tolerable latency of the video stream expressed as a whole number of frames. Encoding, by the sender and for each frame i, packets carrying the symbols D[i], and P[i]. Transmitting, by the sender, each frame i of encoded packets in frame order to one or more receivers.

Loss recovery using streaming codes in forward error correction

A system is provided herein that can correlate service issues with system telemetry associated with the software session associated with those service issues. Using a statistical approach, the system can evaluate data across numerous software sessions to rank the importance of the reported service issues. To accomplish the ranking, the system can parse the reports of service issues on a periodic basis, can extract telemetry identifiers (IDs) from the logs, can query the telemetry, may compute the relative importance of detected issues (in the context of calls going on for that day), and then can report this impact back to the service issue database.

Computer-aided identification of a potential problem node in a network. As part of data collection, a computing system gathers telemetric and trace route data for communications that occur within a network. Upon detecting that a performance problem has occurred within the network, multiple communications that occurred around the time of the performance problem are identified. Then, the trace route data for these multiple communications are aggregated. The aggregated data may then be used to identify a potential problem network node within the network. The identity of the potential problem network node (and potentially observed telemetry for that network node) may then be communicated to an entity that is affected by the performance problem. This may allow the entity to understand which network node is causing the problem and thus take appropriate remedial measures.

Network problem node identification using traceroute aggregation

Within a communication system, packets are received for a video channel defining a shared scene and a surface reconstruction channel associated with surface reconstruction data of image data. The image data and intrinsic and extrinsic capture device data are from an image capture device and are encoded and packetized within the video channel. The surface reconstruction data is encoded and packetized within the surface reconstruction channel. The image data and the intrinsic and extrinsic capture device data are extracted from within the video channel. The surface reconstruction data is extracted from within the surface reconstruction channel. The shared scene is rendered using the image data, the intrinsic and extrinsic capture device data, and the surface reconstruction data.

Shared scene mesh data synchronization

A user device within a communication architecture, the user device comprising: an image capture device configured to determine image data and intrinsic/extrinsic capture device data for the creation of a video channel defining a shared scene; a surface reconstruction entity configured to determine surface reconstruction data associated with the image data from the image capture device; a video channel configured to encode and packetize the image data and intrinsic/extrinsic capture device data; a surface reconstruction channel configured to encode and packetize the surface reconstruction data; a transmitter configured to transmit the video and surface reconstruction channel packets; and a bandwidth controller configured to control the bandwidth allocated to the video channel and the surface reconstruction channel.

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Pittsburgh, PA, United States of America

Martin Ellis