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 management, leading to inefficiencies and reliance on legal experts, especially in contract creation and negotiation.
Innovation Solution
Modeling documents as knowledge graphs enables machine understandable semantic documents, allowing for automated retrieval and exchange of information, verification of legal and logical rules compliance, and determination of aggregate statistics, facilitating automated contract negotiation and management through symbolic reasoning.
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 maintained, but machine understandability and automated information extraction capability deteriorate
Solution Approach 1:
The patent segments document information into structured entities (parties, terms, clauses, obligations) represented as nodes in a knowledge graph, with relationships between them represented as edges. This segmentation transforms unstructured text into machine-understandable components while preserving the original document's editable nature.
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary layer between the unstructured document and the automated processing system. The knowledge graph serves as a mediator that enables machine understanding without requiring changes to the original document format, allowing both ease of operation and automation to coexist.
2Productivity
If machine learning models are used to generate document text, then document generation speed is improved, but legal compliance validation and reliability deteriorate due to lack of rule-based verification
Solution Approach 1:
The patent implements feedback loops where generated documents are automatically validated against stored legal rules and constraints. The system provides feedback on compliance status and iteratively refines the generated text to meet legal requirements, ensuring both speed and reliability.
Solution Approach 2:
The patent performs preliminary actions by pre-defining legal rules, constraints, and validation criteria in the knowledge graph before document generation. This allows the system to verify compliance during the generation process itself rather than as a separate post-processing step, maintaining both speed and reliability.
3Reliability
If contract terms are negotiated manually by legal teams, then compliance and accuracy are maintained, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The patent enables contracts to serve themselves by automatically negotiating terms based on pre-defined legal rules and constraints stored in the knowledge graph. The system can autonomously validate compliance and resolve conflicts without requiring manual legal team intervention for routine negotiations, dramatically reducing time while maintaining reliability.
Solution Approach 2:
The patent introduces dynamics by allowing contract terms to be automatically adjusted and negotiated within legally defined boundaries. The system can dynamically explore different term combinations and validate them against legal rules in real-time, enabling fast automated negotiation while maintaining compliance.
4Extent of automation
If documents are created with expected final state structure, then searchability and automated processing are improved, but flexibility for modifications and reversibility during negotiation deteriorate
Solution Approach 1:
The patent adds another dimension by representing documents not just as static text but as dynamic knowledge graphs with multiple layers of structure (entities, relationships, constraints). This dimensional transformation enables both improved searchability through structured data and enhanced flexibility by allowing modifications at different levels of the knowledge graph representation.
Data Source
AI summary
A semantic document generation and search system is described. The semantic document extraction system generates a knowledge graph representing a collection of documents, each document being represented as a sub-graph of the knowledge graph being linked to each other by common terms of a plurality of document terms. The system extracts a first filter criterion based on the plurality of terms of the sub-graphs representing the collection of documents, receives a first search value for the first filter criterion, and identifies a subset of sub-graphs, of the knowledge graph, that include a term corresponding to the first filter criterion and having a term value corresponding to the first search value. The system prunes the knowledge graph to include only the identified subset of sub-graphs, and extracts and outputs a subset of the collection of documents corresponding to the subset of sub-graphs included in the pruned knowledge graph.