Smart Contract Billing Manifests for Distributed Trust Services
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Solution Overview
Problem
Existing edge computing environments face challenges in accurately tracking and billing for trust services provided across distributed nodes, leading to inefficiencies and potential regulatory compliance issues.
Innovation Solution
Implementing a Data Confidence Fabric (DCF) with smart contracts to define and deploy a DCF billing manifest that includes smart contract billing logic, vendor and data owner IDs, trust annotation and billing ledgers, and time ranges, enabling precise tracking and billing for trust services across multiple nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If distributed trust services are provided across multiple edge computing nodes, then service coverage and reliability are improved, but tracking and billing accuracy deteriorate
Solution Approach 1:
The patent combines multiple tracking and billing records from distributed edge nodes into a unified blockchain ledger. Smart contracts aggregate service usage data from various nodes and vendors, merging fragmented information into a single source of truth that maintains both distributed service delivery and centralized billing accuracy.
Solution Approach 2:
The system implements feedback mechanisms where smart contracts continuously monitor and verify service usage across distributed nodes, comparing actual usage against billing records. This closed-loop feedback ensures that tracking accuracy is maintained even as service coverage expands across multiple edge computing environments.
2Productivity
If smart contracts are deployed across distributed edge nodes, then automation and efficiency are improved, but system complexity increases
Solution Approach 1:
The patent implements universal smart contract templates that can be deployed across different edge computing nodes and vendors with consistent functionality. These multi-functional contracts handle various trust service types (data annotation, confidence scoring, etc.) using standardized logic, reducing the need for custom implementations and simplifying system management despite distributed deployment.
Solution Approach 2:
The billing system is segmented into independent smart contract modules that can be deployed and managed separately on different edge nodes. Each node runs specific billing logic for its local services, and the blockchain network coordinates these segments, allowing high automation through distribution while managing complexity through modularization.
3Measurement precision
If comprehensive billing manifests are implemented across all nodes, then billing accuracy is improved, but data processing overhead increases
Solution Approach 1:
The system performs preliminary actions by pre-generating and caching billing manifests at edge nodes before actual service delivery. These pre-computed billing templates contain anticipated service parameters and pricing logic, allowing nodes to quickly match actual service usage against pre-prepared billing data, reducing real-time processing overhead while maintaining accurate billing.
Data Source
AI summary
One method includes obtaining a snapshot of a data confidence fabric billing manifest, broadcasting the snapshot to one or more nodes of the data confidence fabric, deploying, at the nodes, billing logic included in the data confidence fabric billing manifest, and executing the billing logic to obtain information concerning a trust service provided regarding data associated with the nodes. The trust service may include trust annotating the data with an assessment as to the relative trustworthiness of the data.