Directed Graph Smart Contract Representation
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Solution Overview
Problem
Existing smart contracts and contract information models rely on program instructions or industry-specific data structures, making them difficult to generalize, compare, or reuse due to minor differences in contract details, limiting their application beyond natural language documents.
Innovation Solution
A process and system that construct, interpret, enforce, analyze, and reuse smart contract terms in a systematic and unambiguous way using a directed graph representing the smart contract state, where vertices encode norms and edges represent relationships between them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If smart contracts use program instructions or industry-specific data structures, then they can be implemented with specific functionality, but they become difficult to generalize, compare, or reuse due to minor differences in contract details
Solution Approach 1:
The patent applies universality by creating a unified graph-based data structure that can represent multiple types of smart contracts across different domains. The directed graph with standardized vertices (encoding norms, conditions, actions) and edges (representing relationships) serves as a universal representation that handles diverse contract types while maintaining structural consistency, enabling generalization and reuse across different smart contract applications
Solution Approach 2:
The patent employs parameter changes by transforming contract representations from traditional program instructions or industry-specific data structures into a standardized graph format with specific parameters (vertices with norm encodings, edge relationships, category types). This parameter transformation allows contracts to be represented in a consistent manner that facilitates comparison and reuse while preserving their functional characteristics
2Manufacturing precision
If smart contracts are customized for specific domains with detailed program instructions, then they achieve functional precision, but they cannot be easily compared or reused across different domains
Solution Approach 1:
The patent applies segmentation by breaking down smart contracts into discrete, standardized components represented as vertices in a directed graph. Each vertex encodes specific norms, conditions, or actions as distinct elements with standardized attributes. This segmentation allows precise representation of functional details while enabling modular comparison and reuse across different contract domains through the standardized vertex structure
Solution Approach 2:
The patent transitions from traditional one-dimensional linear program instructions to a multi-dimensional graph structure where vertices and edges create additional dimensions for representing relationships. This dimensional change allows contracts to maintain functional precision through detailed vertex properties while simultaneously enabling cross-domain comparison and reuse through the standardized graph topology and relationship patterns
3Ease of manufacture
If traditional data structures are used to represent smart contracts, then implementation is straightforward, but systematic construction, interpretation, and enforcement across various domains is difficult
Solution Approach 1:
The patent introduces a graph-based data structure as an intermediary layer between traditional implementation approaches and systematic cross-domain construction. The directed graph with standardized vertices and edges serves as a mediating representation that maintains implementation simplicity while enabling systematic construction, interpretation, and enforcement across various domains through its structured relationships and norm encodings
Data Source
AI summary
A method includes determining a set of features associated with a set of vertices of a directed graph, obtaining a set of feature values associated with the set of vertices, where each respective vertex of set of vertices is associated with a respective subset of feature values. The method includes determining updatable features based on the set of features, selecting a first subset of features based on the set of updatable features. Selecting the first subset of features includes determining candidate subsets of features, determining feature subset scores associated with the candidate subsets of features based on a category label, and selecting the first subset of features based on the feature subset scores. The method includes performing a first operation to determine extracted feature values by determining feature extraction input values.