Abstract: 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.

Abstract: 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.