AI Character Level Evaluation for Training Data Valuation
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Solution Overview
Problem
Existing systems lack a method to evaluate the learning level of artificial intelligence characters based on their accumulated learning in conversation services and to trade training data and provide subscription services based on this evaluation.
Innovation Solution
A method to evaluate the level of an artificial intelligence character based on its learning amount through conversation services, allowing for the trading of training data and subscription to the character based on its level, with additional services and exposure to suitable conversation partners.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI character conversation services are provided to accumulate learning data, then the learning amount and utility of AI characters increase, but there is no evaluation mechanism to assess the value of accumulated learning
Solution Approach 1:
The patent replaces subjective human evaluation of AI character learning quality with an automated evaluation module that objectively measures learning amounts through quantifiable metrics such as conversation volume, vocabulary diversity, and grammatical accuracy. This substitution enables precise measurement of previously intangible learning progress.
Solution Approach 2:
The evaluation module provides continuous feedback to both the AI character and users about learning progress. The system measures conversation data, evaluates it against predefined criteria, and returns level assessments that guide further learning activities, creating a closed-loop feedback mechanism for continuous improvement.
2Adaptability or versatility
If AI character training data is to be traded between users, then the utility and customization capability increase, but no valuation mechanism exists for training data
Solution Approach 1:
The patent transforms the abstract concept of training data quality into measurable parameters including conversation count, learning level, and specificity. By changing the representation of training data from unquantified experience to parameterized metrics, the system enables objective valuation and fair trading between users.
Solution Approach 2:
The evaluation module serves as an intermediary that objectively assesses training data quality before trading occurs. This mediator prevents subjective overvaluation or undervaluation by providing standardized measurements that both parties in a trade can trust, facilitating fair exchanges of AI character training data.
3Productivity
If subscription services are implemented for AI characters, then service revenue and functionality control improve, but user trust and satisfaction may decrease without transparent evaluation criteria
Solution Approach 1:
The system performs preliminary evaluation of AI characters before they are offered for subscription. By pre-assessing learning levels and conversation capabilities, the system provides transparent information to potential subscribers about what they are purchasing, building trust before the transaction occurs rather than relying on post-purchase revelations.
Solution Approach 2:
The patent segments the AI character service into distinct levels or tiers based on evaluated learning amounts. This segmentation creates clear, comparable subscription options with transparent features, allowing users to understand exactly what they are paying for at each level, thereby maintaining trust while enabling differentiated pricing and revenue streams.
Data Source
AI summary
The present invention relates to an artificial intelligence character level evaluation and trading method in which the level of an artificial intelligence character is evaluated according to the learning amount of the character accumulated through a conversation service, and the learning data of the artificial intelligence character is traded on the basis of the evaluated level, and a system therefor.