Blockchain Smart Contract Oracles for Reliable Trigger Detection
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Solution Overview
Problem
Existing blockchain smart contracts lack a reliable and authoritative source for determining trigger events, leading to potential trustworthiness issues and inefficiencies in executing associated actions.
Innovation Solution
A computing system that processes blockchain data using machine-learned models and knowledge graphs to determine trigger events, generating queries to search databases and transmit notifications to blockchain systems for executing corresponding actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blockchain smart contracts use existing sources for trigger event determination, then the system can operate, but the reliability and trustworthiness of trigger event detection is insufficient
Solution Approach 1:
The patent introduces an intermediary system consisting of oracle nodes, knowledge graphs, and machine-learned models that mediate between blockchain smart contracts and external trigger events. These intermediaries verify and validate trigger events through multiple sources and cross-checking mechanisms, enhancing reliability without requiring direct integration of complex authoritative sources into the blockchain itself.
Solution Approach 2:
The system implements feedback mechanisms where oracle nodes continuously monitor trigger events, validate them against knowledge graphs, and provide verification results back to the blockchain. This feedback loop ensures that only authenticated trigger events are processed, improving trustworthiness while maintaining system efficiency through automated validation.
2Measurement precision
If blockchain smart contracts implement comprehensive trigger event monitoring, then accuracy of event detection improves, but processing time and computational resources increase
Solution Approach 1:
The patent pre-loads and structures trigger event criteria into knowledge graphs before runtime. These pre-organized knowledge structures enable rapid matching and validation of trigger events without requiring complex real-time analysis, thus maintaining high detection accuracy while reducing processing time during actual smart contract execution.
Solution Approach 2:
The system divides trigger event monitoring into separate oracle nodes, each responsible for specific types of events or data sources. This segmentation allows parallel processing of different trigger events, improving both detection accuracy through specialized monitoring and reducing overall processing time through concurrent operations.
3Reliability
If blockchain smart contracts use multiple knowledge graphs for trigger event verification, then reliability of event determination improves, but system complexity increases
Solution Approach 1:
The patent designs knowledge graphs with universal schemas and standardized data structures that can accommodate multiple types of trigger events and data sources. This universal framework allows the system to verify diverse events using consistent validation logic, improving reliability without proportionally increasing system complexity through repeated specialized structures.
Data Source
AI summary
Systems and methods for trigger event determination can include processing blockchain data to determine a trigger event. Data associated with the trigger event can be processed to determine a query. The query can then be utilized to recursively search a database for data descriptive of the trigger event occurring. A notification can then be provided to instruct a resulting action to be performed.