Network-Modeled Data Assets for Blockchain Data Liquidity
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Solution Overview
Problem
Current data management and utilization technologies are limited by oligopolistic data structures, leading to restricted data liquidity, lack of agility, and inefficiencies in data utilization, particularly in the context of Web3 decentralized ecosystems.
Innovation Solution
A novel framework for creating, managing, and trading data as a tangible asset through data factorization, standardization, and monetization, using blockchain-based smart contracts to enable secure and efficient data transfer and liquidity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in static data structures through centralized data warehousing, then data collection and storage capability is improved, but data liquidity and operational agility deteriorate
Solution Approach 1:
The patent segments data from static warehouse storage into dynamic, tradable data assets that can be independently transferred and licensed. Data is divided into discrete units that can be moved across the network, transforming centralized static storage into distributed dynamic data flows that maintain both quantity and liquidity.
Solution Approach 2:
The system transforms static data structures into dynamic data assets that can change state and be transferred in real-time. Data moves from fixed warehouse positions to fluid tradable units that can be bought, sold, and licensed dynamically, enabling both large-scale collection and high liquidity simultaneously.
2Measurement precision
If conventional ML/AI technologies process static data structures, then analysis capability is improved, but computational efficiency and real-time processing deteriorate
Solution Approach 1:
The patent replaces traditional mechanical data processing pipelines with a digital asset-based system where data is tokenized and traded automatically. This substitution enables real-time processing and transfer of data assets, dramatically improving computational efficiency while maintaining analysis capability through smart contract automation.
3Quantity of substance
If data is treated as intangible asset in traditional frameworks, then data storage is improved, but data monetization and operational efficiency deteriorate
Solution Approach 1:
The system changes the fundamental parameter of data from static storage capacity to dynamic tradable asset value. By implementing tokenization and creating liquid data markets, data transforms from a stored resource into an actively traded commodity, enabling both large-scale storage and high operational efficiency through automated licensing and transfer mechanisms.
Data Source
AI summary
Disclosed are systems and methods that provide a novel framework for the creation, management and electronic transfer of electronic, machine-readable data as tangible digital assets accessible from a network storage (e.g., blockchain, for example), which can be utilized by entities via native and/or third party software. The disclosed framework can operate by transforming data in a raw static format to a liquid format through standardization and factorization. The data can be standardized via tags associated with governance and policy frameworks, as well as infrastructure requirements. In some embodiments, the (tagged) data can be factorized to create data (or containers, data models or collections of containers/models) as labeled, intelligent data. And, the framework can then electronically transfer the labeled data. As a result, capabilities of data liquidity can be achieved via a web-agnostic storage and framework accessible through smart contracts.