NFT-Based Real-Time Subject Assessment System
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Solution Overview
Problem
Existing systems for assessing objects and subjects often rely on outdated or incomplete historical data, leading to inaccurate evaluations in transactions such as buying, selling, or insuring, as they fail to incorporate real-time and dynamic information effectively.
Innovation Solution
A network-based system that utilizes a token management computer device to generate and maintain non-fungible tokens (NFTs) by collecting and updating data from various sources, including sensors and reference data, to provide a secure and current assessment of objects and subjects through automated processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional assessment systems use historical data, then data collection is simple, but the assessment accuracy and currency deteriorate
Solution Approach 1:
The system transitions from static historical data collection to dynamic real-time data collection through sensor integration. Sensors continuously monitor object characteristics and automatically update NFT records, enabling the system to adapt to changing conditions while maintaining assessment accuracy without proportional increases in complexity
Solution Approach 2:
The system implements automated assessment processes where the NFT generation and update mechanisms operate autonomously. The trained models automatically process sensor data and generate assessments without manual intervention, reducing the operational complexity while improving assessment currency and accuracy
2Reliability
If real-time data collection is implemented, then assessment currency is improved, but data security and integrity challenges increase
Solution Approach 1:
NFTs serve as cryptographic intermediaries that securely store and verify object history and assessment data. The blockchain technology underlying NFTs provides immutable recording of data transactions, ensuring data integrity and security while enabling real-time tracking and assessment updates without exposing raw sensor data to security risks
Solution Approach 2:
The system replaces traditional centralized data storage and verification mechanisms with decentralized blockchain-based NFT storage. This substitution provides enhanced security through cryptographic verification and distributed consensus, eliminating single points of failure while maintaining real-time data accessibility for assessments
3Productivity
If manual assessment processes are used, then system complexity is low, but productivity and efficiency deteriorate
Solution Approach 1:
The system implements continuous automated assessment processes that operate without interruption. Sensors continuously collect data, trained models continuously process information, and NFTs are continuously updated with new assessments, maximizing productivity through uninterrupted automated operation while the initial setup complexity is offset by long-term efficiency gains
4Loss of information
If comprehensive object history is tracked, then assessment completeness is improved, but information management complexity increases
Solution Approach 1:
The system extracts only the essential assessment-relevant data from comprehensive object history and stores it in NFTs. Rather than managing all raw historical data, the system selectively captures key characteristics and assessment outcomes in immutable NFT records, maintaining information completeness for assessment purposes while reducing data management complexity through selective extraction
Data Source
AI summary
A computer system for generating and maintaining non-fungible tokens for real-time subject assessment is described herein. The computer system includes at least one processor in communication with at least one memory device, the at least one processor programmed to: (i) receive a plurality of data associated with a subject history of a subject; (ii) generate a container file for the subject history to include the plurality of data; (iii) generate a non-fungible token (NFT) for the subject based upon the container file; (iv) store the NFT and the container file for the subject; (v) retrieve the NFT to access the plurality of data; and (vi) input the plurality of data to a trained model to receive an initial subject assessment as output from the trained model.