Microsoft Technology Licensing, LLC

Frederick Douglass Campbell

Paul Luo Li

Minsuk Kang

Chan Chaiyochlarb

Abhinav Mishra

Raymond Duane Parsons

Alexander Oot

Nicolas Justin Lavigne

Cheng Wu

Yutong Liao

Brandon H Paddock

James H Dooley, Iv

Xiaoyu Chai

Jilong Liao

Neeraja Abburu

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

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Bothell, WA, United States of America

Frederick Douglass Campbell