Semantic Contract Maps from Neural Text Extraction
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Solution Overview
Problem
Complex legal contracts with vast inter-related assertions in unstructured natural language pose challenges for efficient understanding and decision-making, leading to inefficiencies and potential misinterpretation due to high cognitive load and lack of automated semantic representation.
Innovation Solution
A process to extract and represent contract clauses as structured semantic triples, enabling visualization, search, and reasoning capabilities through neural models and directed graphs, facilitating the generation of semantic contract maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If legal contracts are represented in unstructured natural language, then the document maintains its natural readability and completeness, but understanding and analysis become inefficient and error-prone due to high cognitive load
Solution Approach 1:
The patent segments the unstructured natural language contract into structured semantic triples (subject, predicate, object) that represent discrete legal assertions. This segmentation transforms the monolithic text into manageable units that can be independently analyzed, reducing cognitive load and enabling efficient navigation through contract provisions.
Solution Approach 2:
The patent introduces an intermediary semantic representation layer between the natural language contract and the analysis system. This intermediary structure (semantic triples stored in graph databases) mediates between human-readable text and machine analysis, enabling both natural language processing and efficient querying without losing the original document's completeness.
2Reliability
If legal contracts are analyzed manually to ensure accurate interpretation, then interpretability and understanding are maintained, but errors and inefficiencies increase due to high cognitive load
Solution Approach 1:
The patent replaces the mechanical cognitive process of manual legal analysis with an automated computational system. Neural models and graph database queries automatically extract, store, and analyze semantic relationships from contract text, eliminating human cognitive limitations while maintaining interpretability through structured semantic representations.
Solution Approach 2:
The patent implements feedback mechanisms where the structured semantic representation enables verification and validation of contract interpretations. The graph database structure allows for consistent querying and cross-referencing of legal assertions, providing feedback that ensures accurate interpretation while accelerating the analysis process.
3Productivity
If automated systems are used to process and analyze legal contracts, then efficiency and productivity are improved, but the system complexity and difficulty of implementation increase
Solution Approach 1:
The patent transitions from analyzing contracts in one dimension (linear text) to multiple dimensions by creating structured semantic triples with subject, predicate, and object components. This dimensional transformation enables efficient multi-criteria querying and analysis while the modular architecture keeps system complexity manageable through standardized processing pipelines.
4Ease of operation
If semantic triples are extracted and stored in structured format, then navigation and search capabilities are enhanced, but the processing and extraction complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-processing contract text into structured semantic triples during the initial ingestion phase. This preliminary structuring creates an optimized representation that enables fast navigation and search operations later, as the semantic relationships are already established in the graph database rather than being computed in real-time during queries.
Data Source
AI summary
Techniques include obtaining, with a computer system, a natural-language-text document comprising unstructured text; generating, with the computer system, based on a first set of machine learning model parameters, a neural representation of the unstructured text; identifying, with the computer system, based on the neural representation, a trigger word located within the unstructured text and associated with a first category; determining, with the computer system, based on the trigger word, a region within the unstructured text comprising descriptors associated with the first category; determining, with the computer system, from the region based on a second set of machine learning model parameters, a descriptor describing an action or condition of the first category; generating, with the computer system, a data model object comprising the descriptor defining an action or condition of the first category; and storing, with the computer system, the data model object in memory.