Microsoft Technology Licensing, LLC

Jihad Tafas

Sandeep Prabhakar

Iulian Doroftei Calinov

Paresh J Maisuria

Eric Kratzer

Rahul Nair

Ning Zhang

Javier Nisim Flores Assad

M Nashaat Soliman

Ajay Arvind Bhave

Samarth Harish Shah

Tapan Ansel

Gabriel Knezek

Taofiq Ezaz

Zachary Sandburg Holmes

David Maralack

Aditya Dhar

Matt Graves

Eric Timmreck

Mike Weidenbener

Chris McMillan

Shane Patton

Julian Doroftei Calinov

James Kennyth Truckle

Sergiy Dukhopel

Paresh Maisuria

Eric R Kratzer

Usage data of a device is maintained over some time period, such as a couple weeks or a couple months. This usage data reflects an amount of energy that is used or stored for different time durations each having a various combinations of different state values of the device, such as a screen state, a power source state, a power mode state, a battery saver state, a processor consumption state, and so forth. This provides a record of a capacity drain rate or capacity charge rate for the battery for each combination of state values. At any given time, the device can determine the current state values of the device. The capacity drain rates or capacity charge rates for previous time durations having the same state values as the current state values are used to determine an estimated capacity drain rate or capacity charge rate for the device.

Usage data based battery charge or discharge time determination

Techniques for detecting software regression in computing systems are disclosed. One example technique includes fitting data in a dataset into a multiple variable model to obtain a set of estimated coefficients. The dataset having data representing multiple entries each containing an identification of a combination of multiple payloads included in a build of a software product and a corresponding value of a performance metric of executing the build at a computing device. The payloads individually represent a source code change, a feature enablement, or a configuration modification of the software product. The estimated coefficients individually correspond to one of the payloads. The method further includes in response to determining that a corresponding estimated coefficient of one of the payloads has an absolute value that is greater than a preset threshold, indicating that a software defect is likely present in the corresponding payload.

Software regression detection in computing systems

A computing device has an energy storage device system with one or more energy storage devices. A target run-time is obtained, which refers to how long the computing device is to run given the current amount of energy in the energy storage device(s). A predicted power usage over the target run-time is determined, and what, if any, power management actions to take in order to achieve the target run-time are determined. The power management actions are then taken. A target charge-time is also obtained, which refers to how long the computing device is to take to charge the energy storage device(s) to a threshold level (e.g., 100% or fully charged). A predicted power gain over the target charge-time is determined, and what, if any, power management actions to take in order to achieve the target charge-time are determined. The power management actions are then taken.

Target based power management

Consumption of power by device components is profiled on a per-session basis of user interaction. A session engine determines start of a user interaction session with the device (e.g., by detecting screen turn on). The engine generates a session identifier (SID) and broadcasts same to device component(s). In response to the SID, the component(s) record power consumption data. Upon receiving a signal indicating end of the user interaction session (e.g., screen turn off), the session engine broadcasts a notice allowing component(s) to stop recording power consumption data. The components communicate aggregated data to the session engine for storage in a centralized location (e.g., trace buffer). In response to a query posed within a command line prompt, stored data may be parsed on a per-session basis for inclusion in a detailed report of power consumption. Power consumption inefficiencies endemic to components and/or user behaviors may be thus be identified and remediated.

Per-screen on session power consumption profiling

Systems and methods are provided for activation of software that uses digital licenses. In some embodiments, a software vendor may securely deliver at least one digital proof-of-purchase to an OEM (e.g., to avoid piracy of software licenses). The OEM may install an associated software copy on a computer system, and store the at least one digital proof-of-purchase on it. The OEM may communicate the proof(s)-of-purchase stored on the computer system and its system identifier to the software vendor. A user of the computer system may attempt to activate the OEM pre-installed software by sending an activation request to a software vendor, who may verify whether or not the digital proof-of-purchase and system identifier in the activation request match those previously reported by the OEM, and may either grant or deny the activation request.

Software activation using digital licenses

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Redmond, WA, United States of America

Jihad Tafas