LLM-Based Document Revision System for Semantic Consistency
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Solution Overview
Problem
Current methods for revising electronic documents, such as legal contracts, are inefficient as they rely on manual editing and lack automated suggestions for consistency, often requiring human editors to manually search through multiple documents for similar language, leading to time-consuming and inconsistent results, and existing software like Dealmaker only provides lexical comparisons without semantic or syntactic analysis.
Innovation Solution
An automated system using Large Language Models (LLMs) to parse and compare documents, suggesting edits by creating a database of previously edited documents, tokenizing text into statements, generating similarity scores, and aligning them to suggest revisions based on semantic and syntactic analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual editing is used to revise electronic documents, then editors can make revisions based on understanding of grammar and content, but the process is time-consuming and inconsistent across different editors
Solution Approach 1:
The patent introduces an automated editing system as an intermediary between the original document and the final revised document. This system uses natural language processing and machine learning models to analyze document content, identify inconsistencies, and suggest revisions, thereby reducing the time burden on human editors while maintaining revision quality through automated precision
Solution Approach 2:
The system creates and maintains a database of previously edited documents and revisions. By copying and analyzing patterns from historical editing data, the system learns consistent revision approaches and applies them to new documents, ensuring uniformity across different editing tasks and reducing reliance on individual editor expertise
2Loss of information
If editors manually search through multiple documents to find similar language, then they can identify relevant prior revisions, but the process is burdensome and may overlook previously reviewed documents
Solution Approach 1:
The patent creates a universal database system that consolidates multiple document sources and revision histories into a single accessible repository. This database can be queried using natural language or keyword searches, allowing editors to efficiently find relevant prior revisions without manually browsing through numerous documents, thereby preventing information loss while improving search ease
Solution Approach 2:
The system implements feedback mechanisms where the database of previously reviewed documents continuously grows and improves based on new editing work. As editors interact with the system and review documents, the feedback is captured and used to enhance future search results and revision suggestions, ensuring that institutional knowledge is retained and progressively improved
3Measurement precision
If Dealmaker software is used to compare documents, then lexical differences can be identified, but semantic and syntactic similarities are not considered leading to false differences
Solution Approach 1:
The patent transitions from simple lexical parameter comparison to multi-dimensional parameter analysis including semantic meaning, syntactic structure, and contextual relationships. By changing the parameters of comparison from basic word-matching to sophisticated linguistic feature analysis, the system achieves higher precision in detecting actual differences while filtering out false positives caused by synonymous or structurally different but semantically equivalent expressions
Data Source
AI summary
Aspects of the present disclosure relate to systems, methods, and computer program products for revising electronic documents, and more particularly, to systems, methods, and computer program products for suggesting edits to an electronic document using large language models (LLMs).