On-Demand AI Game Generation via Natural Language Processing
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Solution Overview
Problem
Casinos face challenges in maintaining player engagement with static, pre-defined games of chance, leading to a need for on-demand generation and customization of electronic games to increase interest and excitement among players.
Innovation Solution
The use of natural language processing and generative artificial intelligence to receive audio streams describing game parameters, extract relevant information, and generate configuration settings for electronic games of chance in real-time, allowing for on-demand customization and modification based on player input, regulatory restrictions, and game type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static, pre-defined games are provided, then game implementation is simple and reliable, but player engagement and excitement decrease over time
Solution Approach 1:
The system enables players to directly create and customize their own games by providing intuitive tools and interfaces. Players can define game parameters, select themes, configure payout structures, and generate unique games without requiring complex technical knowledge or assistance from operators
Solution Approach 2:
The system pre-provides a library of validated game templates, themes, and components that have been prepared in advance. These pre-configured elements can be quickly assembled and customized by players, eliminating the need to create games from scratch while maintaining simplicity
2Productivity
If new games are generated regularly to maintain player interest, then player engagement increases, but system complexity and resource requirements increase
Solution Approach 1:
The system uses template-based generation where new games are created by copying and adapting pre-existing validated game structures. Players can replicate successful game formats with different themes and parameters, ensuring rapid generation while maintaining reliability through proven templates
Solution Approach 2:
The generative system is nested within a structured framework of pre-defined game architectures, themes, and components. This layered approach allows complex game generation to occur within constrained, validated boundaries, reducing the overall system complexity while enabling diverse game creation
3Ease of operation
If players can easily customize games on-demand, then player satisfaction and engagement increase, but processing time and computational resources increase
Solution Approach 1:
Game templates, themes, and configuration options are prepared and validated in advance. When players initiate game creation, they are presented with pre-organized choices that can be quickly selected and assembled, dramatically reducing the time required for game configuration
Solution Approach 2:
The game customization process is divided into discrete, manageable steps with pre-configured options at each stage. Players can make selections from segmented categories (themes, mechanics, payouts) without needing to understand the entire game configuration process, speeding up decision-making
4Reliability
If games are customized based on player input and regulatory restrictions, then compliance and player satisfaction improve, but processing complexity increases
Solution Approach 1:
The system continuously validates player input against regulatory requirements and game parameters, providing immediate feedback on what is acceptable and what is not. This real-time validation ensures compliance while guiding players toward valid configurations, reducing the need for complex post-processing
Solution Approach 2:
A validation layer acts as an intermediary between player input and game generation. This intermediary automatically checks requests against regulatory constraints and game rules, translating player intent into compliant configurations without requiring complex manual review processes
Data Source
AI summary
Embodiments of the present disclosure use generative Artificial Intelligence (AI) and Natural Language Processing (NLP) to adjust and/or create an electronic game of chance. For example, a slot player can ask a generative AI, in plain language, to generate a slot game of their design. Embodiments can generate the entire game on-the-fly, in real-time based on the player's request. Additionally, or alternatively, the game can be modified by player, on demand, by giving additional and/or adjusting input.


