Dynamic Incentive Awarding via Segmented Player Play Tracking
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Solution Overview
Problem
Current player incentive systems in gaming machines are inflexible and do not fully utilize advanced technologies to provide maximum benefits to casino operators, failing to effectively leverage data for awarding incentives based on player play patterns and preferences.
Innovation Solution
A system and method that tracks player play across multiple gaming machines and games, using a computer and database to award incentives based on predetermined criteria, allowing for flexible and dynamic awarding of bonuses as a function of tracked play, enabling personalized and data-driven incentive programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional player tracking systems are used to award incentives, then player identification and basic tracking are achieved, but the system lacks flexibility and fails to maximize benefits from available data
Solution Approach 1:
The system segments player tracking data by game type (first playable game vs. second playable game) and applies different award criteria to each segment. This allows flexible, tailored incentive structures without requiring a completely new tracking infrastructure, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The award criteria are made dynamic and adjustable based on tracked play patterns. The system can responsively modify award parameters such as credit amounts, bonus triggers, and eligibility conditions based on real-time player behavior data, enabling flexibility without permanent system redesign.
2Loss of information
If player tracking data is collected across multiple games and machines, then comprehensive player behavior information is obtained, but data processing and award determination complexity increases
Solution Approach 1:
The system applies different processing rules and award criteria to different game types (first game vs. second game) based on their specific characteristics. This localized approach to data processing reduces overall complexity by handling each game type according to its own requirements rather than implementing a single complex universal processor.
Solution Approach 2:
Award criteria and thresholds are pre-configured for different game types before player play occurs. The system pre-establishes the rules for evaluating play data, eliminating the need for complex real-time decision algorithms and simplifying the data processing burden during actual gameplay.
3Productivity
If incentive awards are awarded based on comprehensive play tracking, then player engagement is enhanced, but the computational resources and processing time required increase
Solution Approach 1:
The system pre-calculates and stores award thresholds, credit amounts, and eligibility criteria for different game types before players begin playing. This preliminary configuration allows rapid real-time evaluation of player play against pre-set rules, maintaining high player engagement while minimizing computational processing time during gameplay.
Solution Approach 2:
The system implements award evaluation at selective checkpoints during gameplay rather than continuously monitoring every action. By evaluating play data at key intervals or trigger events rather than every single player input, the system maintains responsive player engagement while reducing overall computational resource consumption.
Data Source
AI summary
System and system award a player of a gaming machine an incentive award. The gaming machine has first and second playable games. A computer is connected to the machine. The player chooses to play one of the first and second games and play on the gaming machine is tracked. The incentive award is awarded as a function of the tracked play on the gaming machine and a set of predetermined award criteria.