Automated Document Revision System Using Semantic Similarity
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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 editors to manually search through multiple documents for similar language, leading to inconsistencies and loss of institutional knowledge.
Innovation Solution
An automated system that tokenizes documents into statements, generates similarity scores, and suggests edits by aligning and modifying these statements based on a database of previously edited documents, ensuring consistency and retaining institutional knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual editing is used to revise electronic documents, then editors can make revisions based on their understanding, but it is time-consuming and burdensome to identify and locate similar language in prior documents
Solution Approach 1:
The patent replaces the manual mechanical process of searching through documents with an automated computer-based system that uses natural language processing and machine learning algorithms to automatically identify similar language and suggest revisions, thereby eliminating the time-consuming manual search process
Solution Approach 2:
The system enables the document revision process to serve itself by automatically analyzing the document, comparing it with prior documents in the database, identifying similar language, and generating revision suggestions without requiring manual intervention for each search and comparison task
2Reliability
If editors manually review many prior documents to find similar language, then they can identify revisions, but previously reviewed documents can be overlooked and institutional knowledge is lost
Solution Approach 1:
The system implements feedback mechanisms where the database of prior documents and their revisions is continuously updated and refined based on analyzed documents, ensuring that institutional knowledge is captured, stored, and made available for future revisions, preventing loss of information
Solution Approach 2:
The system creates and maintains a digital copy of institutional knowledge in the form of a structured database that stores prior documents, their revisions, and metadata, ensuring that this knowledge is preserved and can be efficiently retrieved without relying on human memory or manual documentation
3Measurement precision
If Dealmaker software is used to compare documents, then lexical differences are displayed, but it does not consider semantic similarity or propose revisions
Solution Approach 1:
The patent transitions from simple lexical comparison parameters to advanced semantic analysis parameters by implementing natural language processing and machine learning models that understand meaning, context, and intent, thereby enabling the system to detect semantic similarity rather than just surface-level word differences
Solution Approach 2:
The system automatically generates revision suggestions by analyzing semantic similarities and applying learned patterns from prior documents, eliminating the need for manual revision proposal and enabling full automation of the revision suggestion process
4Adaptability or versatility
If human editors revise documents, then they can apply their understanding of grammar and content, but different editors may revise the same portion differently even with the same past documents
Solution Approach 1:
The patent replaces variable human judgment with a consistent automated system that applies the same natural language processing and machine learning algorithms to all documents, ensuring that revision suggestions are generated based on objective analysis rather than subjective editorial preferences, thereby improving consistency
Solution Approach 2:
The system creates a universal revision suggestion mechanism that can be applied consistently across different documents, editors, and contexts by using learned patterns from the database that capture organizational preferences and best practices, making the revision process independent of individual editor variability
Data Source
AI summary
Disclosed is a method for suggesting revisions to a document-under-analysis (“DUA”) from a seed database, the seed database including a plurality of original texts each respectively associated with one of a plurality of final texts. The method includes tokenizing the DUA into a plurality of statements-under-analysis (“SUAs”), selecting a first SUA of the plurality of SUAs, generating a first similarity score for each of the plurality of the original texts, the similarity score representing a degree of similarity between the first SUA and each of the original texts, selecting a first candidate original text of the plurality of the original texts, and creating an edited SUA (“ESUA”) by modifying a copy of the first SUA consistent with a first candidate final text associated with the first candidate original text.