Independent Random Activators for Full and Partial Gaming Features
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Solution Overview
Problem
Existing gaming systems lack the ability to dynamically generate and display different full and partial features associated with activators in a way that enhances player engagement and reward variability.
Innovation Solution
A gaming system that independently generates and displays full and partial features through activators, where each activator determines the activation of a pot, with full features including multiple enhancements and partial features including at least one but not all enhancements, allowing for dynamic feature activation and display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If gaming systems use static feature activation mechanisms, then the system complexity is low, but player engagement and reward variability are reduced
Solution Approach 1:
The feature activation system is segmented into multiple independent components: activators (triggering elements), pots (feature containers), full features (complete feature sets), and partial features (subsets of features). This segmentation allows the system to manage complexity through modular organization while enabling diverse reward combinations. Each activator can independently trigger different pots, creating variability without requiring a monolithic complex system.
Solution Approach 2:
The system transitions from static feature activation to dynamic activation through random generation of activators and pots during gameplay. The activators and pots are randomly determined for each play, creating dynamic feature combinations that adapt to each gameplay instance. This dynamic approach increases reward variability while managing complexity through algorithmic generation rather than pre-configured static mechanisms.
2Loss of information
If the system displays all possible features and activators, then information completeness is high, but player confusion and reduced engagement occur
Solution Approach 1:
The system extracts and displays only the relevant activators and features that are actually activated during each play, rather than displaying all possible features. The display shows activators and pots that have been randomly determined for the current play, filtering out irrelevant information. This extraction approach maintains information completeness for the active features while preventing player confusion by excluding inactive features from the display.
Solution Approach 2:
The display presents different information quality at different locations: activated features receive prominent display with full details, while non-activated features are either omitted or displayed with reduced prominence. The system applies local quality by highlighting only the active activators and pots, creating a focused display that maintains completeness for relevant information while improving ease of operation through selective presentation.
3Productivity
If the system provides only full features, then feature activation is simple, but player excitement and interaction are reduced
Solution Approach 1:
The system implements partial features that provide subsets of the complete feature set, rather than always providing full features. This partial action approach maintains activation efficiency by still delivering meaningful rewards while increasing player interaction through varied and unexpected feature combinations. The partial features create a sense of progression and discovery, enhancing player excitement without significantly complicating the activation mechanism.
Solution Approach 2:
The system changes the parameter of feature completeness by randomly determining whether to activate full features or partial features for each play. This parameter variation creates dynamic gameplay experiences where the extent of feature activation fluctuates, maintaining efficiency through algorithmic control while enhancing player interaction through variability. The random determination of feature activation scope introduces unpredictability that boosts player engagement.
Data Source
AI summary
Gaming systems and methods configured to independently generate, display, and provide different full and partial features in association with different activators during plays of a game. If the gaming system determines not to provide the full feature associated with a pot based on an occurrence of the activator associated with that pot, the gaming system determines whether to provide a partial feature associated with that pot. The gaming system is also configured to, for each partial feature to determine how many plays of the game to provide the partial feature for.