Automated Mixed Data Extraction via NLP and Image Processing
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Solution Overview
Problem
Manual processing of large datasets containing both textual and visual data is time-consuming and expensive, as traditional data processing software struggles to adequately handle mixed data types, particularly in complex inspections like gas turbine condition assessment.
Innovation Solution
A computer-implemented method that receives input datasets comprising both textual and visual data, processes the textual data using natural language processing and the visual data using image processing, and outputs a combined dataset, allowing for efficient and reliable extraction and analysis without requiring expert knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual extraction and processing of information from documents is performed, then data accuracy can be maintained through expert review, but time consumption and cost increase significantly
Solution Approach 1:
The patent replaces the manual mechanical process of expert review and data extraction with an automated computer-implemented system. The system uses optical character recognition (OCR) to convert images of text into machine-readable format, natural language processing (NLP) to extract and structure information, and automated validation rules to ensure data accuracy. This substitution eliminates the need for manual human intervention while maintaining reliability through systematic automated verification processes.
Solution Approach 2:
The system enables self-service data extraction and validation by automatically processing documents without requiring expert intervention. The automated validation rules and confidence score thresholds allow the system to self-verify data quality, flagging only uncertain cases for potential review. This self-service capability dramatically reduces time consumption while preserving accuracy through automated quality control mechanisms.
2Ease of manufacture
If traditional data processing software is used to handle mixed data types, then existing tools can be utilized, but processing capability and reliability deteriorate due to inadequate handling of textual and visual data
Solution Approach 1:
The patent implements a multi-functional processing system that can handle multiple data types (text, images, tables) within a single unified platform. The system integrates OCR for image-to-text conversion, NLP for textual data extraction and structuring, and specialized processors for tabular data. This universal approach eliminates the need for separate specialized tools for each data type while maintaining high processing capability and reliability through coordinated multi-functional operations.
Solution Approach 2:
The system employs a composite processing architecture that combines multiple specialized processing components (OCR engines, NLP models, validation rules) into an integrated system. Each component is optimized for its specific function, and their coordinated operation creates a processing capability greater than the sum of individual parts. This composite approach enables reliable handling of mixed data types that traditional single-purpose software cannot process effectively.
3Measurement precision
If expert knowledge is required for data extraction and analysis, then processing accuracy improves, but operational complexity and cost increase
Solution Approach 1:
The patent replaces the need for expert knowledge with automated intelligent systems. NLP algorithms automatically understand and extract information from unstructured text, OCR technology accurately converts images to readable text, and automated validation rules ensure data quality. These systems encode domain knowledge in their algorithms, eliminating the need for human experts to manually interpret documents while maintaining high extraction accuracy through sophisticated pattern recognition and validation mechanisms.
Solution Approach 2:
The system performs self-service data extraction and validation without requiring expert intervention. Automated confidence score calculation and threshold-based validation allow the system to independently assess data quality and flag only uncertain extractions for review. This self-service capability dramatically simplifies operation while maintaining precision through automated quality control, making the system accessible to non-experts.
Data Source
AI summary
The present invention relates to a method for extracting an output data set, wherein the method includes the following steps receiving an input data set; wherein the input data set comprises at least one textual input data set and at least one visual input data set; processing the at least one textual input data set using natural language processing into at least one textual output data set; processing the at least one visual input data set using image processing into at least one visual output data set, and outputting the output data set, including the at least one textual output data set and/or the at least one visual output data set. Further, the present invention is related to a computer program product and system.