Smart Contract Machine Learning on Distributed Ledger
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Solution Overview
Problem
Current financial systems face inefficiencies in asset transfers due to reliance on intermediaries, high reconciliation costs, and difficulties in tracking asset ownership and conditional transfers, which are exacerbated by the complexity of centralized ledgers and limited capabilities of existing blockchain technologies like Bitcoin and Ethereum.
Innovation Solution
Implementing distributed ledger technology in a cloud-based computing environment with intelligent consensus models, smart contracts, and machine learning to facilitate efficient, secure, and scalable asset management, enabling conditional transfers and improved fraud prevention within a permissioned blockchain network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If centralized ledgers are used for asset management, then asset ownership tracking is centralized and control is simplified, but reconciliation costs increase and system complexity increases
Solution Approach 1:
The patent segments the centralized ledger system into multiple distributed nodes, each maintaining a copy of the ledger. This segmentation eliminates the need for a single centralized authority while distributing the tracking function across multiple independent entities, thereby reducing system complexity and reconciliation costs.
Solution Approach 2:
The patent introduces smart contracts as intermediary automated agreements that facilitate asset transfers and ownership tracking between parties. These self-executing contracts embedded in the blockchain eliminate the need for manual reconciliation processes, reducing both costs and system complexity.
2Reliability
If existing blockchain technologies like Bitcoin and Ethereum are used, then decentralized digital cash and basic smart contracts are enabled, but transaction throughput is limited and scalability is constrained
Solution Approach 1:
The patent implements a layered blockchain architecture that segments the network into different levels (e.g., settlement layer and execution layer). This segmentation allows high-value transactions to be settled on the main chain while lower-value transactions are processed on side chains or state channels, thereby increasing overall transaction throughput without compromising security.
Solution Approach 2:
The patent employs partial consensus mechanisms where not all nodes need to validate every transaction. Instead, a subset of trusted nodes or validators can process and confirm transactions, reducing the computational overhead and increasing transaction throughput while maintaining adequate security through selective verification.
3Reliability
If machine learning models are integrated into smart contracts, then conditional transfers and fraud prevention capabilities are enhanced, but computational requirements and energy consumption increase
Solution Approach 1:
The patent introduces off-chain computation intermediaries that handle machine learning model execution. Instead of running complex ML models directly on the blockchain, the system uses off-chain computational environments to process data and generate results, which are then verified and recorded on-chain. This intermediary approach reduces energy consumption while maintaining fraud prevention capabilities.
Solution Approach 2:
The patent implements selective ML model deployment where machine learning is applied only to specific high-risk transactions or particular fraud detection scenarios rather than all transactions. This partial application of ML reduces overall computational requirements and energy consumption while still providing enhanced fraud prevention where most needed.
Data Source
AI summary
Systems, methods, and apparatuses for implementing machine learning models for smart contracts using distributed ledger technologies in a cloud based computing environment are described herein. For example, according to one embodiment there is a system having at least a processor and a memory therein executing within a host organization and having therein: means for operating a blockchain interface to a blockchain on behalf of a plurality of tenants of the host organization, in which each one of the plurality of tenants operate as a participating node with access to the blockchain; receiving historical data from each of the participating nodes on the blockchain; generating a new machine learning model at the host organization by inputting the historical data received from the participating nodes into a neural network of a machine learning platform operating at the host organization; receiving a consensus agreement from the plurality of participating nodes; deploying the new machine learning model to the participating nodes as a component of a smart contract to be executed in fulfillment of the smart contract transactions; receiving a transaction at the blockchain and responsively triggering the smart contract to process the transaction onto the blockchain; and executing the smart contract which includes executing the new machine learning model as part of the smart contract. Other related embodiments are disclosed.