Predictive Gaming Insight Platform for Dynamic Device Configuration
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Solution Overview
Problem
The gaming industry faces challenges in developing entertaining and exciting gaming content that requires advanced hardware to handle high-resolution and complex animations, leading to increased costs and technical complexities.
Innovation Solution
A system and method that aggregates gaming data from casino devices connected to a network, uses a machine learning model to predict user behavior, and automatically adjusts device configurations to optimize user experience, thereby enhancing gaming content delivery without the need for constant hardware upgrades.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If gaming content resolution and detail are increased to enhance entertainment value, then gaming content quality is improved, but hardware requirements and system complexity increase
Solution Approach 1:
The patent uses machine learning models to create virtual copies or representations of gaming content that can be processed and delivered more efficiently. The system generates predictive models of player behavior and content preferences, allowing the casino to serve optimized content without requiring players to have high-end hardware for every possible game scenario.
Solution Approach 2:
The system dynamically adjusts content delivery parameters based on predicted player behavior and device capabilities. By changing parameters such as content resolution, format, and delivery method based on real-time data and machine learning predictions, the system optimizes the balance between content quality and hardware requirements.
2Adaptability or versatility
If gaming content complexity increases to provide more sophisticated features, then player engagement is improved, but technical complexity and development costs increase
Solution Approach 1:
The patent implements dynamic content adjustment where gaming features and content are automatically adapted based on predicted player behavior. The system uses machine learning to predict what features each player will engage with and dynamically adjusts the game configuration, content delivery, and feature prioritization in real-time, reducing the need for complex pre-programmed scenarios.
Solution Approach 2:
The system incorporates feedback loops where player behavior data is continuously collected, analyzed by machine learning models, and used to refine future content delivery and feature activation. This feedback mechanism allows the system to learn from player responses and optimize gaming features without requiring complex manual programming for every possible player interaction.
3Reliability
If hardware is continuously upgraded to support advanced gaming content, then gaming quality is maintained, but operational costs increase
Solution Approach 1:
The system uses machine learning models to automatically optimize content delivery and feature activation based on predicted player behavior, reducing the need for manual hardware upgrades and configuration changes. The system self-adjusts to serve players with appropriate content quality matched to their device capabilities, minimizing the quantity of high-performance hardware resources needed.
Data Source
AI summary
A system and method(s) for aggregating gaming data generated by casino devices connected to a casino network. The system accesses a machine learning model trained through exploratory data analysis of the aggregated gaming data, mapping input features to model parameters used for predicting a target output value. The system further predicts, using the machine learning model, user-specific output value that identifies a player behavior by analyzing a portion of the aggregated gaming data associated with a specific user account logged into one of the casino devices. Based on the identified player behavior, the system automatically adjusts a configuration of the casino devices to optimize its operation for the specific user account.