Automated Asset Ownership Detection via Location Pattern Analysis
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Solution Overview
Problem
In IoT/M2M solutions, particularly for moving assets like vehicles and heavy machinery, there is a need to automatically detect changes in ownership, which is crucial for Original Equipment Manufacturers (OEMs) and financiers to prevent non-payment of loans and manage service subscriptions effectively.
Innovation Solution
A computer-implemented method and system that learns and stores location information, detects stationary locations, classifies them as points of interest, and analyzes driving and usage patterns to predict ownership changes by comparing new data against a data model, using machine learning to determine a prediction score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated detection systems are implemented to monitor asset location and usage patterns, then ownership change detection accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the ownership detection problem into multiple independent analysis modules: location pattern analysis, usage pattern analysis, and behavioral analysis. Each module processes specific data types and generates separate scores that are combined to produce the final ownership change prediction, reducing overall system complexity while maintaining high detection accuracy
Solution Approach 2:
The system transitions from traditional single-dimensional ownership verification to multi-dimensional analysis by incorporating spatial dimensions (location patterns), temporal dimensions (usage patterns over time), and behavioral dimensions (operational characteristics). This dimensional expansion enables more accurate detection while distributing computational complexity across multiple analysis layers
2Reliability
If continuous monitoring of asset location and usage patterns is performed, then detection reliability is improved, but energy consumption and data processing load increase
Solution Approach 1:
The system implements periodic monitoring intervals rather than continuous monitoring, analyzing asset location and usage patterns at predetermined time intervals. This periodic approach maintains detection reliability by capturing sufficient behavioral data while significantly reducing energy consumption and data processing requirements compared to continuous monitoring
Solution Approach 2:
The system replaces heavy computational mechanics with optimized algorithms that process monitoring data more efficiently. By using streamlined data processing techniques and selective analysis of key behavioral parameters, the system maintains high detection reliability while reducing the computational energy burden on mobile assets
3Measurement precision
If detailed location information and behavioral patterns are collected and analyzed, then prediction accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system extracts and focuses on the most discriminative behavioral features from the collected data, such as frequent location changes, unusual usage patterns, and deviations from established baselines. By concentrating analysis on these key indicators rather than processing all raw data equally, the system achieves high prediction accuracy while minimizing data processing time
Solution Approach 2:
The system performs preliminary data processing and pattern recognition during data collection phases, pre-computing behavioral baselines and location patterns before ownership change detection is needed. This preliminary analysis reduces the computational burden during actual detection events, enabling fast and accurate predictions without extensive real-time processing
Data Source
AI summary
In one example embodiment, a computer-implemented method and system for automated detection of change of ownership of one or more assets are disclosed. The method includes learning and storing location information of at least one asset; detecting a location where no movement of the at least one asset has occurred over a pre-determined duration of time; determining whether the detected location is classified as a location of interest based on a pre-defined criteria; preparing a data model for the at least one asset to learn and analyze determined location of interest; classifying the determined locations of interests based on frequency of occurrence of pre-determined events; comparing new data to the data model; and determining probability of change of ownership of the at least one asset as a prediction score as a result of the comparison.