Event-Based Entity Scoring with Directed-Graph Contract Models
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Solution Overview
Problem
Existing smart contracts and contract information models often rely on program instructions or industry-specific data structures, making them difficult to generalize, compare, or reuse across different contexts, leading to issues with ambiguity and resource wastage in contract enforcement.
Innovation Solution
A directed graph-based system is used to represent smart contracts, where vertices encode norms with conditional statements, allowing for systematic construction, interpretation, and enforcement of contract terms across various fields, utilizing deontic logic models and symbolic AI systems for verification and predictive techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If program instructions or industry-specific data structures are used to represent smart contracts, then the contracts can be enforced, but they become difficult to generalize, compare, or reuse across different contexts
Solution Approach 1:
The patent applies universality by creating a standardized graph-based representation framework that can represent multiple types of smart contracts across different domains (financial, supply chain, healthcare, etc.) using the same vertex and edge structure. This allows a single graph representation system to serve multiple contract types and enforcement contexts, enabling reuse without domain-specific customization.
Solution Approach 2:
The patent segments smart contracts into discrete graph components: vertices representing contractual elements (parties, obligations, rights) and edges representing relationships between them. This segmentation transforms monolithic, hard-to-compare contract instructions into modular, comparable graph structures that can be systematically analyzed and reused across different contexts.
2Reliability
If traditional data structures are used for smart contracts, then enforcement is possible, but ambiguity and resource wastage occur
Solution Approach 1:
The patent substitutes traditional procedural enforcement mechanisms with a declarative graph-based representation system. Instead of executing complex program instructions sequentially, the system uses graph matching and comparison operations to determine contract enforcement, reducing computational overhead and eliminating ambiguity inherent in traditional data structure interpretation.
3Measurement precision
If more memory is allocated for contract representation, then enforcement accuracy improves, but the number of concurrent executions decreases
Solution Approach 1:
The patent changes the fundamental parameters of contract representation from traditional data structures to graph structures with specific vertex and edge attributes. This parameter change enables a more compact representation that requires less memory while maintaining interpretation precision, thereby allowing increased concurrent executions without sacrificing enforcement accuracy.
Data Source
AI summary
A method includes obtaining a directed graph of a self-executing protocol, the directed graph including a set of vertices associated with mutually exclusive category labels, where the self-executing protocol identifies a first entity. The method may include obtaining a first graph portion template that includes a vertex template and an edge template. The vertex template is associated with a category of the mutually exclusive category labels. The method may include determining whether the first graph portion template matches a graph portion in the directed graph and an edge of the directed graph matching the edge template. The method may include determining an outcome score based on the graph portion template matching the graph portion, determining whether the outcome score satisfies an outcome score threshold, and storing a value indicating that the outcome score satisfies the outcome score threshold.