Frame Slicing and Packaging Machine for Image NFT Provenance
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Solution Overview
Problem
Current digital image processing technologies lack efficient methods for extracting individual frames from digital movies and verifying their authenticity and provenance, especially in converting and printing high-resolution images while maintaining their integrity and metadata.
Innovation Solution
A system that includes a frame slicing and packaging machine with AI-enhanced units for metadata assignment, object detection, and cryptographic hashing, which generates unique hashes for each frame and metadata, stored in a blockchain for secure and transparent verification, enabling reliable chain of title and custody.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual frames are extracted from digital movies using conventional methods, then frame extraction is achieved, but authenticity verification and provenance tracking are lacking
Solution Approach 1:
The patent segments the movie into individual frames and processes each frame independently through cryptographic hashing and metadata assignment. Each frame receives a unique digital fingerprint (hash value) that can be individually verified, enabling frame-level authenticity tracking without compromising the overall system
Solution Approach 2:
The patent introduces blockchain technology as an intermediary layer between frame extraction and authenticity verification. The blockchain serves as a decentralized ledger that records and verifies the provenance of extracted frames, providing trustless authentication without requiring complex centralized verification systems
2Manufacturing precision
If high-resolution images are printed from digital movies, then image quality is improved, but maintaining integrity and metadata during conversion is challenging
Solution Approach 1:
The patent performs preliminary assignment of comprehensive metadata and cryptographic hashes to each frame before the actual printing process. This ensures that authenticity information is embedded in advance, protecting against metadata loss during subsequent high-resolution conversion and printing operations
Solution Approach 2:
The patent implements a feedback mechanism where blockchain-verified authenticity data is fed back into the printing system. This allows the printing process to maintain integrity by continuously referencing the original frame's cryptographic fingerprint and metadata, ensuring no information is lost during high-resolution conversion
3Productivity
If conventional frame extraction is used, then processing speed is maintained, but productivity in producing verified image products is reduced
Solution Approach 1:
The patent performs cryptographic hashing and metadata assignment during the frame extraction process itself, rather than as a separate post-processing step. This preliminary action embeds verification data immediately, eliminating dedicated verification time and enabling parallel processing of multiple frames through the blockchain network
Solution Approach 2:
The blockchain network provides self-service verification capabilities where each frame's authenticity can be independently validated without requiring centralized verification resources. This distributed verification approach scales efficiently, maintaining processing speed while increasing overall productivity of verified image products
Data Source
AI summary
Methods and processes for manufacture of an image product from a digital image. An object in the digital image is detected and recognized. Object metadata is assigned to the object, the object metadata linking sound to the object in the digital image which produced the sound. At least one cryptographic hash of the object metadata is generated, and the hash is written to a node of a transaction processing network.