Microsoft Technology Licensing, LLC

Paul Luo Li

Frederick Douglass Campbell

Minsuk Kang

Baskar Sridharan

Stephan Doll

Chan Chaiyochlarb

Abdelsalam Heddaya

Abhinav Mishra

Andrew J Lagattuta

Raymond Duane Parsons

Alexander Oot

Nicolas Justin Lavigne

Cheng Wu

Yutong Liao

Joshua Stanley Allen

Bolin Ding

Matthew Jeremiah Eason

Brandon H Paddock

James H Dooley, Iv

Xiaoyu Chai

Harsha Prasad Nori

Jilong Liao

Neeraja Abburu

Matt Eason

Ho Jeannie Chung

Irina Ioana Niculescu

The present disclosure is directed to automated generation and management of update estimates relative to application of an update to a computing device. One or more updates to be applied to a computing device are identified. A trained artificial intelligence (AI) model is applied that is adapted to generate an update estimate predicting an amount of time that is required to apply an update to the computing device. An update estimate is generated based on a contextual analysis that evaluates one or more of: parameters associated with the update; device characteristics of the computing device to be updated; a state of current user activity on the computing device; historical predictions relating to prior update estimates for one or more computing devices (e.g., that comprise the computing device); or a combination thereof. A notification of the update estimate is then automatically generated and caused to be rendered.

Intelligent generation and management of estimates for application of updates to a computing device

A server computing device generates training data based upon an identifier for a device, a timestamp, and a label received from a developer computing device. The server computing device trains a computer-implemented machine learning (ML) model based upon the training data. The server computing device also generates client configuration data for the ML model that specifies transformations that are to be applied to values in order to generate input values for the ML model. The server computing device deploys ML assets to client computing devices, the ML assets comprising the ML model and the client configuration data. The client computing devices execute the ML model using input values derived via transformations of (local) values produced by the client computing devices and transmit telemetry data to the server computing device. The server computing device updates the ML assets based upon the telemetry data.

Computing system for training, deploying, executing, and updating machine learning models

Methods, systems, apparatuses, and computer-readable storage medium are described herein for remotely analyzing testing results based on LDP-based data obtained from client devices in order to determine an effect of a software application with respect to its features and/or the population in which the application is tested. The analysis is based on a series of statistical computations for conducting hypothesis tests to compare population means, while ensuring LDP for each user. For example, an LDP scheme is used on the client-side that privatizes a measured value corresponding to a usage of a resource of the client. A data collector receives the privatized data from two sets of populations. Each population's clients have a software application that may differ in terms of features or user group. The privatized data received from each population is analyzed to determine an effect of the difference between the software applications of the different populations.

Remote testing analysis for software optimization based on client-side local differential privacy-based data

A method of detecting a network hang and restoring an application that communicates on a connection giving rise to the network hang. A user experience may be improved by providing the user with an option to restore the hung application without losing unsaved data or state information. The network hang may be detected when the user tries to terminate the application. The method may include determining whether the network hang is recoverable, which may involve diagnosing a type of the hang. If recoverable, a network connection reset mechanism may be triggered by instructing a network stack of the computer to terminate the network connection.

Network hang recovery

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Redmond, WA, United States of America

Paul Luo Li