ML Artwork Appraisal With Blockchain Audit Traceability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for verifying the provenance of valuable objects such as art and antiquities are subjective, expensive, and lack comprehensive documentation, necessitating improved technological systems for authentication and appraisal.
Innovation Solution
An automated assessment system using a machine learning model and distributed ledger to analyze images of objects, identify reference features, calculate appraised values, and generate audit reports, with the option to record on a blockchain for immutability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional provenance verification methods (comparative techniques, expert opinions, scientific tests) are used, then authentication accuracy may be maintained, but the process becomes expensive and time-consuming
Solution Approach 1:
The patent creates digital copies of physical artworks through high-resolution imaging and stores them on a blockchain ledger. These digital replicas contain embedded metadata and provenance information that can be verified without handling the original artwork, eliminating the need for time-consuming physical examinations by experts while maintaining authentication reliability through cryptographic verification
Solution Approach 2:
The patent replaces manual expert examination and physical scientific testing with automated machine learning models that analyze digital images. The system uses trained neural networks to authenticate artworks by comparing them against known patterns and provenance data stored on the blockchain, substituting human expertise and laboratory equipment with algorithmic analysis that operates instantly
2Reliability
If conventional provenance verification methods are used, then comprehensive authentication may be achieved, but costs increase significantly
Solution Approach 1:
The patent creates digital copies of physical artworks through high-resolution imaging and stores them on a blockchain ledger. These digital replicas contain embedded metadata and provenance information that can be verified without handling the original artwork, eliminating the need for time-consuming physical examinations by experts while maintaining authentication reliability through cryptographic verification
Solution Approach 2:
The patent implements a self-service authentication system where the blockchain ledger automatically verifies provenance claims through smart contracts. The system autonomously compares submitted artwork data against stored records and cryptographic signatures, eliminating the need for expensive human expert intervention while maintaining high authentication accuracy through decentralized consensus mechanisms
3Reliability
If human experts perform provenance verification, then nuanced judgment may be applied, but subjectivity and inconsistency arise between evaluators
Solution Approach 1:
The patent replaces manual expert examination and physicalscientific testing with automated machine learning models that analyze digital images. The system uses trained neural networks to authenticate artworks by comparing them against known patterns and provenance data stored on the blockchain, substituting human expertise and laboratory equipment with algorithmic analysis that operates instantly
Solution Approach 2:
The patent transforms subjective expert judgment into objective measurable parameters by training machine learning models on large datasets of authenticated artworks. The system converts nuanced artistic evaluation into quantifiable features such as brushstroke patterns, material composition ratios, and stylistic metricsthat can be consistently measured and compared, eliminating human subjectivity while capturing the complexity of artistic authentication
4Measurement precision
If scientific tests are used for provenance verification, then objective data may be obtained, but the process becomes expensive and incomplete
Solution Approach 1:
The patent replaces manual expert examination and physicalscientific testing with automated machine learning models that analyze digital images. The system uses trained neural networks to authenticate artworks by comparing them against known patterns and provenance data stored on the blockchain, substituting human expertise and laboratory equipment with algorithmic analysis that operates instantly
Data Source
AI summary
Embodiments of the disclosure provide a system and method of automated assessment of objections using a machine learning model. A method of the disclosure includes applying an image recognition model to an image of an object of interest to identify at least one reference feature of the object of interest from the image. The reference feature of the object of interest is analyzed via a machine learning model trained on a curated database of identifiable features. The curated database includes a listing of known art or collectibles cross-referenced to appraised values for each known art or collectible in the listing. The method includes calculating an appraised value for the object based on the analyzing and generating an audit report for the object of interest. The audit report includes a record of at least one item in the curated database used by the machine learning model to calculate the appraised value.