Behavior Modeling Engine for Wireless App Usage Prediction
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Solution Overview
Problem
Existing solutions for analyzing data from wireless devices, such as smartphones, are limited in their ability to perform comprehensive data analysis, rely heavily on manual labor, and lack adaptive and scalable methods for processing behavioral, contextual, and technical observations.
Innovation Solution
A centralized server arrangement with an automated data mining engine that processes and interprets data from multiple wireless devices, performing data aggregation, correlation, clustering, and factoring to derive actionable insights, and provides these insights through APIs to external systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data analysis methods are used, then implementation simplicity is maintained, but productivity and analysis capability are insufficient
Solution Approach 1:
The system employs automated data mining engines that autonomously collect, process, and analyze behavioral data from wireless devices without requiring manual intervention. The engine self-configures data collection parameters, automatically processes incoming data streams, and generates analytics reports, enabling the system to serve itself and eliminating dependency on manual analysis operations.
Solution Approach 2:
The patent replaces manual mechanical data analysis processes with automated computational systems. The data mining engine uses algorithmic processing, statistical analysis, and pattern recognition techniques to substitute human analysts, dramatically increasing processing speed and productivity while reducing operational complexity through standardization.
2Measurement precision
If comprehensive data collection from wireless devices is implemented, then data quality and analytical value improve, but loss of time and processing overhead increase
Solution Approach 1:
The system pre-configures data collection parameters and processing pipelines before data arrives from wireless devices. Data validation rules, aggregation strategies, and analysis algorithms are predetermined and cached, enabling immediate processing upon data receipt. This preliminary preparation minimizes latency and reduces the time required for comprehensive data analysis.
Solution Approach 2:
The data mining engine operates continuously to collect, process, and analyze data streams from wireless devices without interruption. The system maintains persistent data collection processes and continuous analytics computation, eliminating idle time and ensuring that data processing occurs in real-time as data becomes available, thereby reducing overall processing time while maintaining comprehensive data quality.
3Adaptability or versatility
If automated data mining engine is deployed, then productivity and adaptability improve, but device complexity increases
Solution Approach 1:
The data mining engine is designed as a universal platform capable of performing multiple analytical functions across different data types and application scenarios. It simultaneously supports behavioral analysis, pattern recognition, statistical modeling, and predictive analytics, adapting to various analytical needs through configurable parameters rather than requiring separate specialized systems, thereby improving versatility without proportionally increasing complexity.
Solution Approach 2:
The system achieves adaptability through dynamic parameter adjustment rather than structural complexity. The data mining engine modifies analysis parameters, data collection frequencies, and processing thresholds based on incoming data characteristics and analytical requirements. This parameter-based flexibility allows the system to adapt to different scenarios while maintaining a consistent underlying architecture, reducing the complexity-adaptability trade-off.
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.