Parametric Resource Valuation Using NFT Data and Machine Learning
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Solution Overview
Problem
Existing resource selection analysis methods lack efficiency in optimizing resource valuation models for resource exchange agreements, particularly in handling dynamic requirements and changing market trends.
Innovation Solution
A system utilizing non-fungible tokens (NFTs) and machine learning to determine optimal resource valuation models by categorizing past resource exchange agreements, extracting resource descriptors, and predicting dynamic valuation models based on current requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional resource selection analysis methods are used, then the process is simpler, but the efficiency in optimizing resource valuation models is insufficient and cannot handle dynamic requirements effectively
Solution Approach 1:
The patent implements dynamic requirements handling by making the resource valuation model adaptable to changing conditions. The system uses dynamic parameter extraction from NFTs and applies machine learning techniques that can adjust to new data patterns, enabling the model to respond to market trends and changing requirements in real-time rather than relying on static historical analysis.
Solution Approach 2:
The patent introduces NFTs (non-fungible tokens) as an intermediary layer between historical resource exchange agreements and the valuation model. These NFTs encapsulate structured data from past agreements, serving as a bridge that enables efficient querying and analysis without directly processing raw historical data, thus improving efficiency while managing complexity.
2Measurement precision
If parametric optimization analysis with machine learning is implemented, then the accuracy of resource valuation is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-structuring historical agreement data into NFTs with standardized schemas before analysis. This pre-processing step organizes data in advance with proper categorization and metadata, reducing the computational burden during actual valuation operations and making the machine learning process more efficient while maintaining high accuracy.
Solution Approach 2:
The patent extracts only the necessary parametric features from historical agreements and stores them in NFTs. Rather than processing complete historical agreements, the system extracts relevant parameters (resource types, valuation metrics, exchange conditions) and stores them in a condensed format, reducing computational complexity while preserving the accuracy needed for valuation predictions.
Data Source
AI summary
Systems, computer program products, and methods are described herein for implementing parametric optimization analysis for resource selection. The present invention is configured to determine a first set of requirements associated with a resource exchange agreement; identify one or more non-fungible tokens (NFTs) for one or more categories of past resource exchange agreements based on at least the first set of requirements; extract, from the one or more NFTs, one or more resource descriptors associated with one or more past resource exchange agreements in the one or more categories; predict, using a machine learning subsystem, an optimal resource valuation model for one or more resources that meet the first set of requirements using the one or more resource descriptors and the first set of requirements; and transmit control signals configured to cause a first end-point device to display the optimal resource valuation model.