Smart Contract Configuration from Unstructured Data Inputs

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Solution Overview

Problem

The configuration of smart contracts is complex, time-consuming, and error-prone, hindering their widespread adoption.

Innovation Solution

A computer system utilizing unstructured data to identify intent and parameters for data transfer, leveraging a Large Language Model (LLM) to configure and deploy smart contracts on a blockchain network, with optional client confirmation and data retrieval.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If traditional manual configuration methods are used for smart contracts, then configuration accuracy can be maintained through careful review, but the process becomes complex and time-consuming

Engineering Contradiction:

Improveconfiguration accuracyVSAvoidconfiguration time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical configuration processes with an automated system that uses natural language processing and large language models to generate smart contract code. Users provide unstructured natural language descriptions of desired contract functionality, and the system automatically translates this into structured, verified smart contract code, eliminating the need for manual coding and review processes.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces an intermediary system comprising multiple large language models that act as translators between natural language requirements and formal smart contract code. This intermediary layer processes user intent, generates code, and performs verification, serving as a bridge that automates the configuration process while maintaining accuracy through multi-model validation.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Ease of manufacture

If manual configuration processes are used, then detailed control over contract parameters is possible, but the process becomes error-prone

Engineering Contradiction:

Improveease of configurationVSAvoiderror rate

Core Design Contradiction:

Ease of manufactureVSReliability

Solution Approach 1:

The patent implements feedback mechanisms where multiple large language models review and validate each other's generated code. The system provides feedback loops that check for logical consistency, verify parameter correctness, and identify potential errors before deployment. This multi-layered feedback process significantly reduces error rates while maintaining ease of use.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent performs preliminary validation and verification steps before finalizing smart contract configuration. The system cushions against potential errors by having multiple models review requirements, generate alternative interpretations, and validate code logic in advance, preventing errors from propagating to the deployed contract.

Inventive Principle:

Principle #11Beforehand cushioning (Prior cushioning)

3Productivity

If automated code generation is implemented, then configuration speed increases, but complexity of the configuration system increases

Engineering Contradiction:

Improveconfiguration speedVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the automated configuration system into distinct functional modules: natural language processing module, code generation module, verification module, and deployment module. Each module is handled by specialized large language models with specific functions, reducing overall system complexity through modular design while maintaining high configuration speed.

Inventive Principle:

Principle #1Segmentation

4Ease of operation

If unstructured natural language input is accepted, then ease of operation improves, but precision of parameter extraction becomes challenging

Engineering Contradiction:

Improveease of inputVSAvoidparameter extraction accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent performs preliminary processing of unstructured natural language input using large language models that identify and extract key parameters, intents, and conditions before code generation. The system pre-processes user input to structure the information, extract relevant entities, and validate requirements, ensuring accurate parameter extraction while maintaining ease of operation.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12547777B2Smart contract configuration using unstructured data

Publication Date: 2026.02.10 THE TORONTO DOMINION BANK

AI summary

A system for configuring a smart contract may receive unstructured data and identifying, based on the unstructured data, an intent to perform a transfer of data. The system may then identify, based on the unstructured data, parameters associated with the transfer of data. The parameters may include an amount of data to be transferred, a transferee, and at least one condition associated with the transfer of data. The system may then retrieve identifier data from a storage module, the identifier data including a first unique identifier. The system may then send, to a Large Language Model (LLM) via a first prompt engine module and an LLM Application Programming Interface (API), a prompt based on the unstructured data and the identifier data. The system may then receive LLM output from the LLM, and based on the LLM output, configure a smart contract. The LLM may be an artificial intelligence model.