NLP System Categorizes Image Documents via ML Concept Extraction
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Solution Overview
Problem
Existing technologies struggle to efficiently process and analyze unsearchable or partially legible image documents, particularly in the oil and gas industry, due to variations in document quality, organization, and terminology.
Innovation Solution
A natural language processing system and method that uses machine learning to categorize and subcategorize text from image documents, generating a user interface with navigable document images and lists of concepts, allowing users to efficiently navigate and analyze documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image documents are processed without post-processing, then processing time is reduced, but searchability and analysis capability deteriorate
Solution Approach 1:
The system performs preliminary OCR text extraction and machine learning-based concept categorization on document images during the ingestion phase. This preliminary processing creates searchable concept tags and categories before the documents are viewed or analyzed, enabling rapid full-text search and concept-based navigation without requiring post-processing when documents are retrieved.
2Measurement precision
If manual review of numerous documents is performed, then analysis accuracy is improved, but time consumption increases
Solution Approach 1:
The system replaces manual mechanical review with automated machine learning models that perform concept extraction, categorization, and document analysis. The ML models automatically identify key concepts, assign categories, and generate summaries, eliminating the need for manual review while maintaining high accuracy through trained algorithms that can process thousands of documents with consistent precision.
Solution Approach 2:
The system introduces an intermediary layer of concept tags and categories between the raw document text and the user analysis process. This intermediary representation allows users to search and analyze documents by concept rather than manually reviewing text, significantly reducing review time while preserving analysis accuracy through the structured concept hierarchy.
3Adaptability or versatility
If documents are organized with unique terms and structures, then document specificity is improved, but system complexity increases
Solution Approach 1:
The system employs a universal machine learning-based categorization framework that can handle diverse document types, unique terms, and varying structures through a single unified approach. The ML models are trained to recognize concepts across different document formats and terminologies, mapping them to a standardized category system, thereby managing document variety without increasing processing complexity.
Solution Approach 2:
The system transforms the variable parameters of unique document terms and structures into standardized concept categories through machine learning. By changing the representation parameters from original document-specific terminology to universal concept tags, the system maintains adaptability to various document types while simplifying the processing complexity through consistent categorical mapping.
4Loss of information
If concept categorization is applied to all documents, then searchability is improved, but computational resources increase
Solution Approach 1:
The system applies concept categorization selectively rather than uniformly to all documents. It prioritizes categorization for frequently accessed documents, documents with high search probability, or those containing critical information, while using lighter processing for less important documents. This partial application of full categorization maintains searchability for critical searches while reducing overall computational energy consumption.
Data Source
AI summary
In various embodiments, the disclosed systems and methods may receive documents, analyze the documents, categorize portions of the analyzed documents, and present the images of the documents and at least a portion of the categories. The analysis may include identification of categories and the presentation may include indicia of the portion of the image of the document related to the category. The systems and methods disclosed may allow querying and/or reporting of a plurality of documents to facilitate processing.