Document Metadata Generation via Logical Segmentation and NLP
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Solution Overview
Problem
Traditional document analysis methods in specialized fields like finance and law are labor-intensive and inefficient due to lack of context awareness, limited scalability, and inability to handle unstructured data, leading to inaccuracies and increased costs.
Innovation Solution
A computer-implemented method using large language models to generate metadata for documents by segmenting text into logical segments, creating structured metadata summaries, and storing them in libraries, enabling efficient document analysis and generation of new documents based on analyzed metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual human analysis is used for document management, then accuracy in understanding complex documents is improved, but productivity and efficiency deteriorate due to labor-intensive processes
Solution Approach 1:
The patent segments documents into logical sections and processes each section independently through multiple NLP passes. This allows the system to maintain high accuracy by analyzing each segment with appropriate context while improving productivity through parallel processing and automated pipeline operations.
Solution Approach 2:
The patent introduces an intermediary NLP processing layer that acts as a mediator between raw documents and final analysis results. This intermediary layer performs context-aware disambiguation, entity recognition, and relationship extraction, enabling automated processing to achieve accuracy previously only attainable through human expertise.
2Ease of operation
If traditional NLP methods are used, then ease of operation is improved, but measurement precision deteriorates due to lack of context awareness
Solution Approach 1:
The patent performs preliminary actions by first segmenting documents into logical sections and performing initial entity recognition before conducting detailed relationship extraction. This preliminary structuring enables subsequent processing to maintain high context awareness while keeping the overall system easy to operate through automated pipeline execution.
Solution Approach 2:
The patent implements continuous useful action through multi-pass NLP processing where each pass builds upon previous results. The system continuously refines entity recognition, relationship extraction, and context disambiguation across multiple processing stages, maintaining high measurement precision while operating as an automated continuous pipeline.
3Ease of manufacture
If rule-based techniques are used for metadata extraction, then ease of manufacture is improved, but adaptability deteriorates due to inability to handle variations in text formats
Solution Approach 1:
The patent implements dynamic processing by using NLP models that can adapt to different document types, formats, and structures. The system dynamically adjusts its analysis approach based on the specific characteristics of each document while maintaining a consistent automated pipeline, achieving both ease of operation and high adaptability.
Solution Approach 2:
The patent changes processing parameters dynamically based on document characteristics. The NLP pipeline adjusts entity recognition thresholds, relationship extraction criteria, and segmentation strategies according to the specific document type and structure, enabling high adaptability while maintaining automated ease of operation.
4Device complexity
If prior NLP methods are used, then device complexity is reduced, but reliability deteriorates due to inability to disambiguate phrases and words
Solution Approach 1:
The patent segments documents into logical sections and processes entities within each segment with full contextual information. This segmentation approach improves reliability of disambiguation by maintaining local context while keeping system complexity manageable through modular processing stages.
Solution Approach 2:
The patent implements feedback mechanisms where NLP processing results from one pass inform and refine subsequent processing passes. The system uses feedback from entity recognition to improve relationship extraction, and from relationship extraction to refine context disambiguation, enhancing reliability while maintaining automated operation.
Data Source
AI summary
Provided is a system and a computer implemented method for generating metadata for a document and documents therefrom. A document is received for analysis via a communication interface a document for analysis. Text is extracted from the document and using a structural analyser model a plurality of segment titles therein are identified and regular expressions derived therefrom. A segmented document is generated comprising extracted text in logical segments with corresponding segment titles by analysing the generated regular expressions. For at least some of the plurality of the logical segments of the segmented document structured metadata summaries of that segment are generated using a metadata creator model. Document metadata thereof is also generated using a metadata creator model. When a new document is requested, using a library of documents and corresponding metadata that document may be generated.