Wireless App Behavior Modeling From Contextual Usage Sequences
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solutions for analyzing data from wireless devices, such as smartphones, are limited in their ability to provide intelligent, flexible, and adaptive data analysis, often relying on manual labor and focusing on narrow analytical scopes, and do not effectively utilize the rich data streams generated by these devices.
Innovation Solution
A centralized server arrangement with an automated data mining engine that processes and interprets behavioral, contextual, and technical observations from wireless devices, performing data aggregation, correlation, clustering, and factoring to derive meaningful information, which is then distributed through APIs to external systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data analysis methods are used, then analysis depth can be focused on specific aspects, but productivity and scalability are limited
Solution Approach 1:
The system employs automated data mining engines that autonomously process behavioral, contextual, and technical observations from wireless devices without requiring manual intervention. The engine automatically performs data aggregation, correlation analysis, clustering, and pattern recognition, enabling the system to serve itself in analyzing vast amounts of data while maintaining high productivity and scalability
Solution Approach 2:
Manual mechanical data analysis processes are replaced with automated computational systems. The patent substitutes human analysts and manual methods with computer-based data mining engines that use algorithms for automatic pattern recognition, clustering, and correlation analysis, dramatically increasing processing throughput while reducing system operational complexity
2Loss of information
If comprehensive data collection from wireless devices is implemented, then analytical scope and insight quality improve, but data processing complexity and resource requirements increase
Solution Approach 1:
The system segments data processing into distinct functional modules: data collection from multiple sources (behavioral, contextual, technical), data aggregation, correlation analysis, clustering, and pattern recognition. This segmentation allows comprehensive data utilization while managing complexity through modular architecture, where each module handles specific aspects of the data pipeline independently
Solution Approach 2:
The data mining engine is designed as a universal system capable of handling multiple data types (behavioral, contextual, technical) and performing various analytical functions (aggregation, correlation, clustering, pattern recognition) through a single integrated platform, reducing overall system complexity compared to having separate specialized systems for each function
3Productivity
If automated data mining is implemented, then productivity and scalability increase, but system complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary data aggregation and preprocessing before applying complex automated mining algorithms. By pre-processing data into organized structures and identifying initial patterns, the system reduces the computational burden on subsequent automated analysis stages, maintaining high productivity while managing automation complexity
Solution Approach 2:
The patent introduces intermediary processing layers between raw data collection and final automated analysis. These intermediaries organize and structure data before it enters the automated data mining engine, acting as a buffer that simplifies the automation process and reduces computational complexity while maintaining high processing efficiency
Data Source
AI summary
System and method for behavioral and contextual data analytics are disclosed. An example computer system to process observational data received from a wireless device includes a memory including machine readable instructions and a processor to execute the instructions to: process the observational data to identify temporally adjacent applications to generate usage metric data, the observational data including application usage data; build a behavior model based on the identified temporally adjacent applications, the behavior model to describe user behavior associated with the wireless device; and apply the behavior model to predict a usage duration of a second application in response to usage of a first application.