Analysing Effects of Programs on Mobile Devices

摘要

A method for analysing the effect of installed applications (A1-An) on battery usage of mobile communications devices (D1-Dm). Each device has monitoring software which monitors battery usage at frequent intervals throughout the day. The monitoring software calculates the average battery discharge and at less frequent intervals, such as once a day, communicates remotely to a server (105) the details of which applications are installed on the device and the details of the state of the battery. Using data from many devices, the server estimates the effect of each application on battery usage. The estimated effects of the applications are updated each time that a further report is received, by revising the existing estimate. Greater weight is given to new data if the effect on battery usage has been consistently over- or under-estimated. It is not necessary to determine whether a particular application has been used on a device. The effect of all applications installed on a device is taken into account jointly. The effect on properties other than battery usage can be determined using this method.

主权项

1. A method of analysing the effect of programs on the battery usage of a plurality of mobile, battery powered, data processing devices, wherein:
on each of those mobile devices there is installed a monitoring application which (i) analyses events relating to battery usage of the device (ii) determines the programs installed on the device and (iii) at intervals transmits monitoring data to a server over a wireless communications network, the monitoring data including identifiers identifying programs installed on the device, and information concerning the battery usage of the device; on the server, the monitoring data from the plurality of devices is aggregated and estimates are obtained of the contribution of each program to the effect on battery usage of the devices; subsequent monitoring data from the plurality of devices is aggregated and revised estimates are obtained, by machine learning techniques, of the contribution of each program to the effect on battery usage of the devices; wherein, when a new observation is received by the server concerning the effect on battery usage of a particular device and a revised estimate is obtained of the contribution of each program to the effect on the battery usage of the devices, all past observations are not used equally, but instead the last estimate is updated by the new observation.