Fractional NFT Quantity Modeling for Content Exchange Valuation
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Solution Overview
Problem
Content providers face challenges in monetizing their content through NFTs due to limited scalability of current marketplaces, inability to value their content, limited liquidation options, and inefficient use of computing resources in identifying and quantifying NFTs, leading to erroneous transactions.
Innovation Solution
An f-NFT system that utilizes a parameter unification model and multi-level linear regression machine learning models to calculate an optimum quantity of fractional NFTs (f-NFTs) and divestment ratios, creating a unique reference and NFTs on a blockchain for content, enabling semi-fungible token trading with smart contracts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional NFT marketplaces are used for content monetization, then content providers can sell and trade NFTs, but the marketplaces lack scalability and efficiency in identifying and quantifying NFTs
Solution Approach 1:
The system enables automated self-service through machine learning models that automatically identify content candidates, calculate content scores, determine optimal f-NFT quantities, and execute transactions without manual intervention, thereby improving productivity while managing complexity through automation
Solution Approach 2:
The patent replaces manual mechanical processes of NFT identification and valuation with automated machine learning systems (parameter unification model, multi-level linear regression model), substituting human-driven mechanical operations with intelligent automated systems that improve efficiency
2Measurement precision
If content providers create NFTs without automated valuation, then they maintain ownership control, but they lack the ability to accurately value their content
Solution Approach 1:
The system introduces an intermediary automated valuation mechanism that uses machine learning models (parameter unification model, multi-level linear regression model) to objectively assess content value based on multiple parameters, providing accurate valuation without requiring content providers to manually determine worth
Solution Approach 2:
The system implements feedback loops where content performance data, transaction history, and market conditions are continuously fed back into the machine learning models to refine and improve valuation accuracy over time, creating a self-improving automated valuation system
3Productivity
If fractional NFTs are generated without optimized quantity calculation, then content can be divided for trading, but computing resources are inefficiently used and transaction errors occur
Solution Approach 1:
The system performs preliminary actions by pre-calculating optimal f-NFT quantities and divestment ratios using machine learning models before actual token generation and trading occurs, ensuring that resource allocation and transaction parameters are optimized in advance to prevent errors
Solution Approach 2:
The patent replaces manual or heuristic-based f-NFT quantity determination with automated machine learning systems that calculate optimal quantities based on content scores and market parameters, substituting imprecise mechanical processes with intelligent automated calculation to improve both efficiency and accuracy
Data Source
AI summary
A device may identify standard parameters and real-time parameters associated with content of a content type, and may process the content type, the standard parameters, and the real-time parameters, with a parameter unification model, to generate derived parameters for the content. The device may process the derived parameters and the content type, with a multi-level linear regression machine learning model, to calculate a content score for the content, and may process the derived parameters and the content score, with a linear regression machine learning model, to calculate a quantity of f-NFTs to generate for the content and a divestment ratio. The device may create a unique reference to the content, and may create an NFT for the content based on the unique reference. The device may generate the quantity of f-NFTs for the content based on the NFT, and may provide the quantity of f-NFTs to a content exchange.