Industry-Specific NLP Models for Smart Contract Conversion
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Solution Overview
Problem
Current CRM and CPQ systems face challenges in accurately processing and enforcing contracts across various industries due to the complexity of natural language contracts, requiring improved natural language processing capabilities to identify terms and conditions effectively.
Innovation Solution
The implementation of industry-specific natural language processing models within a cloud computing platform that utilize machine learning and blockchain technology to analyze and convert natural language contracts into smart contracts, enabling automated contract processing and enforcement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language contracts are processed using traditional methods, then contract processing can be performed, but accuracy in identifying terms and conditions is insufficient
Solution Approach 1:
The patent segments the contract processing task into multiple specialized NLP models tailored to different industries (e.g., healthcare, finance, legal). Each industry-specific model is trained on domain-specific contract data to accurately identify and extract relevant terms, conditions, and clauses. This segmentation allows the system to handle the complexity of diverse contract types while maintaining high accuracy in each specific domain.
Solution Approach 2:
The patent introduces an intermediary layer of industry-specific NLP processing models that act as mediators between the raw natural language contract text and the final contract enforcement logic. These intermediary models translate unstructured legal text into structured data representations that can be reliably processed by the smart contract execution engine, thereby improving term identification accuracy without overwhelming the core processing system.
2Measurement precision
If industry-specific NLP models are implemented, then accuracy in contract term identification is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal NLP processing framework that can handle multiple industries and contract types through a common architecture. The system uses a standardized pipeline for text preprocessing, entity recognition, and relationship extraction that works across different domains. Industry-specific adaptations are achieved through configurable parameters and trained models rather than fundamentally different system structures, allowing the same core system to serve multiple functions across healthcare, finance, legal, and other sectors.
Solution Approach 2:
The patent manages system complexity by allowing dynamic parameter changes in the NLP models based on industry requirements. Instead of building entirely separate systems for each industry, the framework adjusts model parameters, training data, and processing thresholds to optimize performance for specific domains. This parameter-based adaptability enables high accuracy in term identification across diverse industries while maintaining a unified system architecture.
3Productivity
If natural language contracts are converted to smart contracts, then automated enforcement is achieved, but handling of nuanced legal language becomes difficult
Solution Approach 1:
The patent applies preliminary NLP processing actions to analyze and interpret natural language contracts before converting them to smart contracts. Industry-specific models pre-process the legal text to identify ambiguities, extract contextual meanings, and resolve linguistic nuances before the conversion process. This preliminary interpretation ensures that the automated smart contract enforcement accurately reflects the intended legal meaning, maintaining precision while achieving automation.
Solution Approach 2:
The patent incorporates feedback mechanisms where the NLP models continuously learn from contract interpretation results and enforcement outcomes. The system analyzes discrepancies between natural language intent and smart contract execution, using this feedback to refine its interpretation algorithms. This iterative feedback loop improves the accuracy of legal language translation over time, ensuring that automated enforcement progressively becomes more precise in handling nuanced legal concepts.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for processing a written-language contract using an industry-specific natural language processing model to determine flows or actions to undertake in a Customer Relationship Management (CRM) solution. A CRM solution may include the ability to receive or create a binding natural-language contract. The CPQ or CRM system may use natural language processing (NLP) to determine terms and conditions included in a natural-language contract. The NLP may further use an industry-specific model that may be determined based on information in the CRM solution to more efficiently and accurately analyze the natural-language contract. The CRM solution may further receive a legal language construct and convert the terms of the legal language construct into a smart contract.