Microsoft Technology Licensing, LLC

Chan Chaiyochlarb

Abhinav Mishra

Paul Luo Li

Raymond Duane Parsons

Sean P Ryan

Minsuk Kang

Frederick Douglass Campbell

Alexander Oot

Nicolas Justin Lavigne

Cheng Wu

Ashfaque Tumbi

Yutong Liao

Marcelo Medeiros De Barros

Murali R Krishnan

Susan Chory

Hui Shi

Shriram Nanjundaiah

Kenton Gidewall

Robert Irwin Voightmann

Christina Ruth Dhanaraj

Lin Jiang

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 computing device for media connection channel quality testing is described, including communications hardware; non-volatile memory storing a test initiation program; and a processor configured to execute the test initiation program to: transmit, via the communications hardware, a plurality of comparative testing media streams to a test server, each of the comparative testing media streams sent over one of a plurality of media connection channels; receive, via the communications hardware, a plurality of objective quality scores from the test server, each objective quality score corresponding to one of the plurality of media connection channels and are determined by the test server using an objective scoring test comparing the comparative testing media stream to reference testing data associated with the one of the comparative testing media streams; and select a recommended operational media connection channel from the plurality of media connection channels based on the objective quality scores.

Media connection channel quality testing system

A context-aware personalization system operable with a device such as a smartphone, tablet, personal computer (PC), game console, etc. extracts user interaction patterns from a graph, which is generated using telemetry data points, of a device user's behaviors and interactions. The telemetry data is mined from instrumented applications, operating system, and other components executing on the device. A machine learning pattern recognition algorithm is applied to the behavior and interaction graph to generate a dataset that can include a prioritized list of activities. The list is used to automatically implement personalization of the local device that are tailored to the user while also enabling background agents and processes associated with lower priority applications to be suspended to preserve device resources such as processor cycles, memory, battery power, etc. and increase device performance.

User interaction pattern extraction for device personalization

A method and a processing device may be provided for creating a shadow database, having a size being approximately a desired percentage of a size of a relational database. Referential integrity of tables of the relational database may be preserved in the created shadow database. A representation of a connected graph may be created, based on a schema description of the relational database, and may be used to find driving tables and related tables of the relational database. Portions of driving tables of the relational database may be copied to corresponding tables of the shadow database and rows of tables of the relational database, related to the copied portions of the driving tables, may be copied to corresponding tables of the shadow database in a number of iterations until the size of the shadow database is approximately the desired percentage of the size of the relational database.

Slicing of relational databases

Growing community of inventors

Redmond, WA, United States of America

Chan Chaiyochlarb