Mobile Device Performance Estimator Using Usage Data
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Solution Overview
Problem
Users face challenges in selecting the most suitable mobile device for their needs due to varying technical specifications and limited information from reviews and sales personnel, leading to costly trial-and-error purchases.
Innovation Solution
A method and system that estimate mobile device performance by accessing device, application, and usage information from a database, allowing users to select a device and applications, and providing resource usage impact estimates based on user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users purchase mobile devices without accurate performance prediction tools, then they can buy devices quickly, but they may make incorrect purchasing decisions leading to wasted money and time
Solution Approach 1:
The system performs preliminary analysis by collecting and storing device information, application information, and usage information from multiple mobile devices before the user makes a purchasing decision. This pre-computed data enables accurate performance predictions to be generated quickly when the user inputs their device type and application needs, resolving the contradiction between prediction accuracy and decision time.
2Loss of information
If users rely on printed and online reviews for device selection, then they can gather information without direct device testing, but the reviews may not address all user concerns and provide insufficient detail
Solution Approach 1:
The system automatically collects, stores, and processes device information, application information, and usage information from multiple mobile devices without requiring manual intervention. When a user inputs their device type and application needs, the system self-services by retrieving relevant data and generating personalized performance predictions, providing comprehensive information without increasing user burden.
3Reliability
If users conduct trial-and-error purchases to ensure device suitability, then they can make accurate purchasing decisions, but it is an expensive endeavor
Solution Approach 1:
The system uses feedback from actual device performance data collected from multiple mobile devices to generate accurate performance predictions. By analyzing real usage patterns, application resource consumption, and device specifications, the system provides reliable predictions that help users make informed purchasing decisions without needing to perform costly trial-and-error purchases.
Data Source
AI summary
A method for estimating mobile device performance is provided. The method includes accessing device information, application information and usage information from a plurality of mobile devices and receiving a user selection that indicates a type of mobile device and one or more applications. The method includes determining an impact the one or more applications cause to the selected type of mobile device, in terms of resources of the selected type of mobile device, based on the user selection and based on the device information, application information and usage information from the plurality of mobile devices. The method includes communicating information about the impact, in terms of the resources of the selected type of mobile device. A computer readable media and a system are also provided.