T-Based Confidence Interval for Wager Anomaly Detection
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Solution Overview
Problem
Electronic gaming devices face challenges in detecting and preventing cheating, particularly in systems that combine live and virtual elements, due to the complexity of managing and processing large amounts of data.
Innovation Solution
A system that calculates a t-based confidence interval for hold percentages of wagers, identifying subsets with a predetermined minimum number and transmitting anomaly alerts when the hold percentage falls below the lower bound of the confidence interval, allowing operators to detect cheating in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical analysis of all wagers is performed to detect cheating, then detection accuracy improves, but data processing complexity and time increase
Solution Approach 1:
The patent segments the large dataset of all wagers into smaller subsets based on specific criteria (time periods, game types, player groups). This allows statistical analysis to be performed on manageable subsets rather than the entire dataset, reducing processing complexity while maintaining detection accuracy through targeted analysis of anomaly-prone segments.
Solution Approach 2:
The patent extracts specific subsets of wagers that are most likely to contain anomalies based on predetermined criteria. By taking out and analyzing only these relevant subsets rather than processing all wager data, the system achieves effective anomaly detection with reduced computational burden and complexity.
2Reliability
If real-time anomaly detection is implemented, then cheating prevention effectiveness improves, but computational load increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining subsets of wagers based on criteria that make them susceptible to anomalies. This preliminary segmentation is done before real-time analysis, so that during actual gameplay, the system only needs to analyze pre-identified subsets rather than processing all wagers in real-time, reducing computational load while maintaining prevention effectiveness.
Solution Approach 2:
The patent applies partial action by analyzing only specific subsets of wagers rather than all wagers. This selective analysis focuses computational resources on the most critical portions of data where anomalies are most likely to occur, achieving effective cheating prevention with reduced computational load compared to comprehensive analysis.
3Measurement precision
If granular analysis of wager subsets is performed, then anomaly detection precision improves, but processing time increases
Solution Approach 1:
The patent segments wager data into granular subsets based on specific criteria, allowing precise anomaly detection within each subset. By organizing data into meaningful segments beforehand, the system can perform focused statistical analysis on each subset independently, achieving high detection precision without the need to process the entire dataset sequentially, thus reducing overall processing time.
Data Source
AI summary
Operations of electronic gaming devices or other gaming devices or systems may include determining wagering data information representative of a plurality of wagers for a wagering game and calculating, based on the plurality of wagers, a t-based confidence interval that defines a range of expected hold percentages, and a predetermined percentage confidence level that is indicative of a likelihood that a calculated hold percentage for any subset of wagers including a predetermined minimum number of wagers will be within the range of expected hold percentages. Operations may also include determining whether the hold percentage for the subset of wagers is outside of the t-based confidence interval. If the hold percentage is below the lower bound of the t-based confidence interval, an anomaly alert may be transmitted that causes an alert indication to be displayed to an operator.