AI Gaming Interface for Personalized Query Trials
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Solution Overview
Problem
User entities and service providers lack effective methods to engage consumers through interactive and educational gaming experiences that leverage high-level data patterns, particularly in the context of digital transactions and machine learning.
Innovation Solution
A system utilizing an AI algorithm to generate a gaming graphical user interface that conducts query and reply trials, where players receive unique questions based on their ratings, with correct answers earning tokens and limited sessions to enhance engagement and education.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional data storage and digital transactions are used to replace paper flow handling, then opportunities for informatics through machine learning and AI are opened, but user entities and service providers remain unaware of high-level patterns in their data flows
Solution Approach 1:
The patent introduces an AI-based gaming system as an intermediary between raw data flows and user understanding. The system processes data flows through machine learning algorithms to identify high-level patterns, then presents these patterns in an engaging gaming interface. This intermediary transforms complex data analysis into accessible, interactive experiences that reveal patterns to users who would otherwise remain unaware of them.
Solution Approach 2:
The gaming system performs self-service by automatically analyzing data flows and generating personalized gaming content without requiring manual configuration. The machine learning algorithms autonomously identify patterns in data flows and translate them into adaptive gaming scenarios, allowing the system to serve itself in creating engaging experiences while simultaneously revealing data patterns to users.
2Adaptability or versatility
If AI algorithms are used to generate personalized gaming experiences, then consumer engagement and education are enhanced, but the system requires sophisticated machine learning techniques and complex query generation
Solution Approach 1:
The patent segments the complex AI system into distinct functional modules: data flow analysis components, pattern recognition algorithms, query generation engines, and gaming interface elements. Each module handles a specific aspect of the personalization process, making the overall system more manageable and maintainable while still delivering sophisticated adaptive experiences.
Solution Approach 2:
The system performs preliminary actions by pre-processing data flows and pre-generating potential gaming queries before user interaction. Machine learning models are trained in advance on historical data patterns, and query templates are prepared beforehand. This preliminary work reduces the computational complexity required during actual user gameplay, enabling rapid personalization without overwhelming system complexity.
3Productivity
If limited trial sessions are implemented with token-based rewards, then player engagement is enhanced through scarcity and reward mechanisms, but the system must track and manage token counts and trial limits
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors player performance, token accumulation, and trial completion. This feedback is used to dynamically adjust difficulty levels, generate personalized follow-up queries, and manage reward distribution. The feedback loop creates a self-regulating system that maintains engagement without requiring complex manual management of trial limits and token economies.
Data Source
AI summary
A gaming graphical user interface is generated for conducting query and reply trials by use of AI. A computing system of a first entity is operated to conduct a session of multiple trials, in succession, in each of which the computing system is operated to: procure, using an AI algorithm trained by a machine learning technique, a trial-unique query prompt based at least in part on the player rating by at least one of automatically generating the query prompt and selecting the query prompt; send the trial-unique query prompt to the player device; receive a reply signal from the player device; compare the reply signal to a stored key to confirm only one of affirmation and refutation; upon confirming affirmation, incrementing a token count associated with the user device; toll a trial count; and upon the trial count reaching a predetermined condition, terminating the session of multiple trials.