Pastel Growth Fund LLC (20240256741). System and Method for Authentication of Rareness of a Digital Asset simplified abstract
Contents
System and Method for Authentication of Rareness of a Digital Asset
Organization Name
Inventor(s)
Jeffrey Emanuel of Brooklyn NY (US)
System and Method for Authentication of Rareness of a Digital Asset - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240256741 titled 'System and Method for Authentication of Rareness of a Digital Asset
Simplified Explanation: The patent application describes systems and methods for asset fingerprinting and authenticating the rarity of a digital asset, particularly non-fungible tokens (NFTs) associated with digital visual artwork recorded on a blockchain. The system includes a fingerprinting engine (FE) and a relative rareness engine (RRE) to generate a rareness score for NFTs based on their digital fingerprint vectors.
- Trained neural network models are used by the FE to create digital fingerprint vectors of NFT images.
- The RRE compares these vectors to a dataset of registered digital fingerprint vectors using correlation measures to determine the rarity of the NFT.
- The RRE can also evaluate the rarity of the NFT relative to images found on the internet.
Key Features and Innovation:
- Asset fingerprinting and rareness authentication for digital assets, specifically NFTs.
- Use of neural network models to generate digital fingerprint vectors.
- Comparison of digital fingerprint vectors to determine the rarity of NFTs.
- Evaluation of rarity relative to images on the internet.
Potential Applications: The technology can be applied in the art industry for securely registering, trading, and collecting digital visual artwork. It can also be used in other industries that deal with digital assets and require authentication of their rarity.
Problems Solved: The technology addresses the need for a secure and reliable method to authenticate the rarity of digital assets, particularly NFTs, in a decentralized and peer-to-peer platform.
Benefits:
- Secure registration, trading, and collection of NFTs.
- Reliable authentication of the rarity of digital assets.
- Enhanced trust and transparency in the digital art market.
Commercial Applications: The technology can be utilized in online art marketplaces, digital asset trading platforms, and blockchain-based authentication services for digital assets.
Questions about Asset Fingerprinting and Rareness Authentication: 1. How does the system ensure the security and integrity of the digital fingerprint vectors? 2. What are the potential implications of this technology for the digital art market?
Frequently Updated Research: Stay updated on advancements in neural network models for generating digital fingerprint vectors and correlation measures for evaluating rarity in digital assets.
Original Abstract Submitted
systems and methods for asset fingerprinting and for authentication of rareness of a digital asset. the solution can be implemented in a peer-to-peer decentralized platform for securely registering, trading, and collecting non-fungible-tokens, which are associated with digital visual artwork and are recorded on a blockchain. the system comprises a fingerprinting engine (fe) and relative rareness engine (rre). the fe processes the digital asset (e.g., nft image) using trained neural network models, which generate a digital fingerprint vector representation of the image. the rre compares the digital fingerprint vectors to a dataset of registered digital fingerprint vectors using multiple correlation measures. the correlation results are analyzed and combined to generate a relative rareness score representing the rarity of the nft. additionally, the rre can evaluate rareness of the image relative to images on the internet.
