Mobile Device Life Change Detection via Behavioral Abnormality
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Solution Overview
Problem
Current systems lack the capability to effectively identify life changes in mobile device users based on behavior patterns, location changes, and group associations, which are crucial for understanding preference changes and predicting user behavior.
Innovation Solution
A method and system that analyze historic data to establish normal behavior patterns of mobile devices, using location and group association data to detect deviations, and determine life changes by monitoring changes in routine patterns, with confidence levels and thresholds to verify these changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional advertising methods are used to reach users, then broad coverage is achieved, but the ability to identify specific life changes and preferences is lost
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing behavioral data (location, group associations, device usage patterns) before life changes occur. This historical data is then analyzed to detect deviations that indicate life changes, enabling early identification and targeted advertising before users are exposed to broad, untargeted campaigns.
Solution Approach 2:
The system implements feedback by continuously monitoring current behavioral patterns against historical baselines and adjusting predictions of user preferences based on detected deviations. This feedback loop enables the system to refine its identification of life changes and update advertising strategies in real-time based on observed behavioral abnormalities.
2Measurement precision
If comprehensive behavioral data is collected to identify life changes, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the comprehensive behavioral data into distinct categories (location data, group association data, device usage patterns) and analyzes each segment separately using specialized algorithms. This segmentation reduces the complexity of analyzing the entire dataset at once while maintaining the ability to detect life changes across multiple behavioral dimensions.
Solution Approach 2:
The system introduces intermediary components including behavioral baselines (historical averages) and deviation metrics that mediate between raw comprehensive data and final life change predictions. These intermediaries simplify the analysis by transforming complex multi-dimensional data into interpretable metrics that indicate significant behavioral changes.
Data Source
AI summary
Method of determining user's life change based on behavioral abnormality starts with processor receiving first location data and first proximity information from first mobile device. First proximity information includes identification of mobile devices within proximity sensitivity radius of first mobile device. Processor determines whether first location data and first proximity information are included in historical location data and historical proximity information, respectively, associated with first mobile device. When first location data and first proximity information is not included, processor determines whether subsequent location data and subsequent proximity information received from first mobile device over predetermined time period is included. Processor signals to monitor life change of user of first mobile device when subsequent location data and subsequent proximity information received from first mobile device over predetermined time period is not included in historical location data and historical proximity information, respectively, associated with first mobile device. Other embodiments are described.