User Bet Tracking Through Dynamic Threshold Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wagering platforms lack effective methods to identify and prevent users from cheating by ensuring compliance with betting rules, as relying on a single threshold can be easily circumvented by users adjusting their betting strategies.
Innovation Solution
A system that tracks user wager history and patterns, compares current activity with historical data, and adjusts winning thresholds dynamically to detect deviations, incorporating modules for user behavior analysis, threshold determination, and cheating prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single fixed threshold is used to identify cheating, then the system is simple to operate, but users can easily circumvent the threshold by adjusting their betting strategies
Solution Approach 1:
The patent applies dynamics by transitioning from a static, fixed threshold to a dynamic, adaptive threshold that automatically adjusts based on real-time analysis of user betting patterns. The system continuously monitors wager amounts, frequencies, and outcomes to recalculate and update thresholds, making them responsive to changing user behaviors and betting strategies.
Solution Approach 2:
The patent implements feedback mechanisms where the system analyzes user betting data, compares it against established patterns, and uses this information to automatically adjust thresholds. The feedback loop involves collecting betting data, analyzing deviations from normal patterns, and modifying the threshold accordingly, thereby continuously improving detection accuracy.
2Reliability
If multiple variables and dynamic analysis are used to detect cheating, then the reliability of cheating detection improves, but the device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the cheating detection system into distinct functional modules: data collection module, pattern analysis module, threshold calculation module, and decision module. Each module handles specific aspects of the analysis process, making the overall complex system more manageable and maintainable through modular architecture.
Solution Approach 2:
The system implements self-service by automatically performing pattern recognition, threshold calculation, and adjustment without requiring manual intervention. The system self-calibrates by continuously learning from user betting data and automatically updating its detection parameters, reducing the need for complex manual configuration and monitoring.
Data Source
AI summary
A method of monitoring user behavior to adjust betting odds based upon that behavior or to identify users who may be cheating, where users are subscribers of a proprietary data management and analytic software system/wagering platform. The system monitors users' behavior for deviations from their normal betting habits or patterns to detect cheating and lower the threshold for winning when user patterns change.