Apple Inc.

Sanket V Bedare

Amit K Vyas

Abhinav Pathak

Michael S Ferris

Anand Ramadurai

David S Choi

Kevin C Milden

Drew A Schmitt

Sathish K Narayanaswamy

Christopher R D'Angelo

Hari Gorak

Sean Shi

Anca Andreea Chandra

Rohit Mundra

Anshul Dawra

Anthony R Newnam

Yue Li

Andreas C Bergen

Steve Brashear

Ritika Kalia

Vincent B Yeh

Embodiments of the present disclosure include methods and systems for generating and processing energy consumption reports received from a plurality of devices. The reports may include a plurality of call stacks captured and stored by remote device as a result of an energy consumption of the corresponding device. For each energy consumption report, the plurality of call stacks may be analyzed to determine a representative call stack of the energy consumption report. The energy consumption reports may be assigned to one or more of a plurality of energy consumption categories based on distances between the representative call stacks of the energy consumption reports and corresponding representative call stacks of the energy consumption categories. A request may be received for energy consumption reports for a particular energy consumption category. Upon receiving the request, providing a representative set of energy consumption reports assigned to the particular energy consumption category.

Detection and analysis of energy consumption of software applications

Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for measuring operational performance metrics, and presenting these metrics through an application programming interface (API) for developers to access for optimizing their applications. Exemplary metrics can include central processing unit or graphics processing unit time, foreground/background time, networking bytes (per application), location activity, display average picture luminance, cellular networking condition, peak memory, number of logical writes, launch and resume time, frame rates, and hang time. Regional markers can also be used to measure specific metrics for in application tasks. The techniques provide multiple user interfaces to help developers recognize the important metrics to optimize the performance of their applications. The data can be normalized over various different devices having different battery size, screen size, and processing requirements. The user interfaces can provide an intelligent method for visualizing performance changes for significant changes in application versions.

Clustering techniques for third party application data

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Milpitas, CA, United States of America

Sanket V Bedare