NFT Assessment Prediction Using Vectorized Attribute Aggregation
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Solution Overview
Problem
Determining the precise value of non-fungible tokens (NFTs) is difficult due to their novelty, as tangible artifacts have defined values, making the assessment of NFTs tedious and subjective.
Innovation Solution
A system utilizing machine learning (ML) and artificial intelligence (AI) techniques for dynamic data aggregation and prediction, capturing attributes such as security status, metadata, community information, creator details, scarcity, and engagement terminology to generate a vector array for training an ML model, which predicts and tunes assessments of NFTs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment methods are used for NFT valuation, then human judgment can be applied, but the process becomes tedious and subjective
Solution Approach 1:
The patent replaces manual human assessment with an automated machine learning system that processes NFT attributes through vectorization and predictive modeling. The system captures NFT attributes, converts them to vector arrays, trains ML models, and generates assessments automatically, eliminating the need for tedious human judgment while improving consistency and speed
Solution Approach 2:
The system enables self-service assessment where the ML model independently evaluates NFTs without human intervention. The automated pipeline captures attributes from multiple sources, processes them through vectorization engines, and generates assessments autonomously, freeing human operators from repetitive valuation tasks
2Measurement precision
If comprehensive data aggregation is performed for NFT assessment, then prediction accuracy improves, but computing resource consumption increases
Solution Approach 1:
The system extracts only the most relevant NFT attributes from available data sources using targeted capture mechanisms. The vectorization engine selectively processes specific attribute types (ownership history, scarcity metrics, community engagement) rather than aggregating all possible data, reducing computational overhead while maintaining assessment accuracy
Solution Approach 2:
The system implements a balanced data aggregation strategy that captures sufficient attributes for accurate prediction without overwhelming computational resources. The ML model is trained on a curated set of essential NFT attributes, providing adequate prediction accuracy while avoiding the excessive resource consumption that would result from processing all available data
Data Source
AI summary
Systems, computer program products, and methods are described herein for dynamic data aggregation and prediction for assessment of electronic non-fungible resources. The present invention is configured to receive, from a user input device, a request to predict an assessment of an NFT for a resource at a first time; capture, using a ML subsystem, one or more attributes associated with the NFT; trigger a vectorization engine to map the one or more attributes represented in the one or more data formats into a vector array; train, using the ML subsystem, an ML model using the vector array of the one or more attributes; generate, using the ML subsystem, a trained ML model based on at least the training; predict, using the trained ML model, the assessment of the NFT at the first time; and store the predicted assessment of the NFT at the first time in an assessment repository.