Knowledge Graph Document Generation and Search
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Solution Overview
Problem
Current document generation and contract negotiation processes are hindered by the unstructured nature of word processing documents, making them non-machine understandable, which requires user intervention for information extraction and validation, leading to inefficiencies and reliance on legal teams, especially in contract management and negotiation.
Innovation Solution
A system that models documents as knowledge graphs using language models to generate and reconcile terms, enabling machine understandable semantic documents, automating document generation, negotiation, and management through symbolic reasoning, allowing for self-aware contract management and automated alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If documents are stored in unstructured word processing format, then ease of creation and editing is improved, but machine understandability deteriorates
Solution Approach 1:
The patent segments document information into structured data fields ( parties, terms, clauses, dates, amounts) within the knowledge graph, separating the unstructured text from the structured semantic meaning. This allows the document to maintain readable text format while simultaneously providing machine-understandable structured data extraction.
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary layer between the unstructured document text and machine processing requirements. The knowledge graph serves as a mediator that captures semantic relationships and structured information from unstructured text, enabling both human readability and machine understandability simultaneously.
2Measurement precision
If manual user intervention is used for information extraction and validation, then accuracy is improved, but productivity deteriorates
Solution Approach 1:
The patent implements self-service through automated language models and symbolic reasoning systems that extract and validate document information without human intervention. The system autonomously processes documents, extracts structured data, validates consistency through logical reasoning, and generates outputs, eliminating the need for manual user intervention while maintaining high accuracy through multiple validation layers.
Solution Approach 2:
The patent incorporates feedback mechanisms where the symbolic reasoning system validates extracted information against logical constraints and consistency rules. The system provides feedback loops that verify extraction accuracy, reconcile conflicting information, and correct errors automatically, ensuring high precision while maintaining automated processing speed.
3Adaptability or versatility
If traditional document formats are used, then compatibility with existing systems is improved, but automated information exchange deteriorates
Solution Approach 1:
The patent implements multi-functionality by designing the knowledge graph structure to serve multiple purposes simultaneously: it maintains compatibility with existing document formats for human reading, provides structured data for machine processing, enables automated information exchange between systems, and supports various document types and workflows through a universal semantic framework.
Data Source
AI summary
A semantic document generation and search system is described. A collection of documents is received and a categorization of the contents of documents is generated using a first language model. The categorization is input into a second language model to extract terms from each document of the collection of documents. A knowledge graph is generated for each document, each knowledge graph having a plurality of nodes corresponding to the extracted terms from each document. The knowledge graphs are linked to each other by common terms to form a collection of knowledge graphs. When a new document is received, the terms from the new document are extracted using the first categorization as input to the second language model. A new knowledge graph is generated for the new document, and linked to the knowledge graphs in the collection of knowledge graphs using common terms.