Historical Data Gaming Payouts Using Performance Benchmarks
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Solution Overview
Problem
Existing gaming systems lack the ability to utilize historical performance data to enhance gameplay by incorporating skill-based and chance-based components, leading to a lack of engagement and variety.
Innovation Solution
A gaming system that uses historical performance data to determine payouts based on entities meeting performance benchmarks, allowing users to select entities from organized groups with temporal constraints and providing a mix of skill-based and chance-based components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional chance-based gaming systems are used, then the gaming system is simple to operate, but the gaming experience lacks engagement and user knowledge is not utilized
Solution Approach 1:
The patent transforms the gaming system from pure chance to a hybrid model by changing the outcome determination parameters. Instead of relying solely on random outcomes, the system incorporates historical performance data and user-selected entities as determining factors for payouts, thereby enhancing engagement while maintaining operational simplicity
Solution Approach 2:
The gaming system is segmented into distinct functional components: entity selection interface, historical data retrieval module, performance benchmark comparison engine, and payout determination system. This segmentation allows each component to handle specific tasks efficiently, maintaining ease of operation while incorporating complex analytical functions for enhanced engagement
2Adaptability or versatility
If historical performance data is incorporated into gaming outcomes, then user knowledge and historical data accuracy enhance the gaming experience, but the system complexity increases
Solution Approach 1:
The system employs a universal data processing framework that handles multiple functions: retrieving historical performance data, comparing against benchmarks, determining payouts, and managing user selections. This multi-functional approach consolidates complexity into a single integrated system rather than requiring separate mechanisms for each function
Solution Approach 2:
The patent introduces an intermediary processing layer between user input and outcome determination. This intermediary module automatically retrieves historical data, performs benchmark comparisons, and calculates payouts based on pre-established criteria, shielding users from system complexity while enabling sophisticated gameplay mechanics
3Reliability
If payouts are based on entity performance benchmarks rather than chance, then gaming fairness and user skill utilization improve, but the gaming system requires more complex data processing
Solution Approach 1:
The system performs preliminary actions by pre-establishing performance benchmarks for multiple entities across different time periods before gaming sessions begin. Historical performance data is retrieved and stored in ready-to-use formats, so that during actual gameplay, the system only needs to compare user selections against pre-computed benchmarks, reducing real-time processing complexity while maintaining fairness
Solution Approach 2:
The system implements feedback mechanisms where historical performance data continuously informs payout determinations. User selections are compared against verified historical benchmarks, and results feed back into the gaming system to adjust future outcomes. This feedback loop ensures fairness by consistently applying objective historical performance criteria rather than arbitrary chance-based outcomes
Data Source
AI summary
Methods and systems are described herein for gaming using historical performance data. A listing of entities is provided to a user. A selection of entities is received from the user. Historical performance data for each of the selected entities during a given performance period is compared to a performance benchmark. A payout is provided based on the number of selected entities that met the performance benchmark during their respective performance period.