Digital Token Indexing for Duplicate NFT Prevention
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Solution Overview
Problem
Existing technologies fail to effectively manage and prevent the creation of duplicate or similar digital tokens, leading to issues such as copyright violations and digital fraud, as they do not adequately restrict or manage the similarity of digital assets represented by non-fungible tokens (NFTs) on notarized ledgers.
Innovation Solution
A token management platform (TMP) that assesses the similarity of digital tokens before and during their generation, using an indexing engine, scoring engine, and smart contract engine to manage the lifecycle of digital tokens, ensuring compliance with existing tokens and enforcing restrictions through program codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing notarized ledger technology is used to create digital tokens, then digital tokens can be created and stored, but the technology fails to prevent creation of duplicate or similar tokens representing the same digital asset
Solution Approach 1:
The patent implements preliminary action by performing similarity assessment and uniqueness verification before a digital token is created and stored on the blockchain. The system compares the digital asset against existing tokens in the database, calculates similarity metrics, and prevents token creation if duplication is detected, thereby ensuring uniqueness before the transaction is committed to the blockchain.
Solution Approach 2:
The patent introduces an intermediary layer between the blockchain and digital assets in the form of a centralized database that stores metadata and similarity information. This intermediary database acts as a mediator that performs similarity comparisons and uniqueness verification, allowing the decentralized blockchain to maintain its integrity while adding centralized verification capabilities to prevent duplicate tokens.
2Reliability
If similarity assessment is performed for all digital tokens, then duplicate tokens can be prevented, but computational resources and time are significantly consumed
Solution Approach 1:
The patent applies partial action by performing similarity assessment selectively rather than for every single token creation attempt. The system uses metadata filtering and hashing to quickly identify potentially duplicate tokens before performing full similarity comparison, thereby reducing the number of computationally intensive operations while still preventing duplicates effectively.
Solution Approach 2:
The patent segments the similarity assessment process into multiple stages: initial metadata comparison, hashing verification, and detailed similarity measurement. This segmentation allows the system to quickly filter out obviously different tokens using simple comparisons before investing computational resources in more sophisticated similarity algorithms, thereby reducing overall processing time.
3Reliability
If comprehensive management of digital tokens is implemented, then copyright violations and digital fraud are reduced, but the complexity of managing multiple tokens increases
Solution Approach 1:
The patent implements universality by designing a single centralized database and similarity assessment system that performs multiple functions: storing metadata, calculating similarity metrics, verifying uniqueness, preventing duplicates, and providing audit trails. This multi-functional system replaces what would otherwise require multiple separate systems for each function, thereby reducing overall system complexity while comprehensively addressing copyright protection and fraud prevention.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors token creation attempts, compares them against the database, and provides real-time verification results. The feedback loop includes similarity scores, uniqueness confirmation, and rejection notifications, allowing the system to adapt and improve its detection capabilities while maintaining straightforward management through automated decision-making.
Data Source
AI summary
Techniques include minting a genesis token on a notarized ledger, the genesis token including a first pointer to an off-chain location. The techniques further include receiving new digital content related to the genesis token. The techniques further include generating a new digital token representing the new digital content. The techniques further include storing new digital token data related to the new digital token at the off-chain location. The techniques further include updating the off-chain location to include a second pointer to the new digital token. The techniques further include forming an expandable token set by linking the new digital token with the genesis token.