ML Claim Document Classification Across Diverse Formats
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Solution Overview
Problem
Existing claim document processing systems face challenges due to lack of standardization, requiring multiple bots for varied formats, leading to high operational costs, inefficiencies, and errors, and traditional OCR tools fail to achieve accuracy for complex documents.
Innovation Solution
A machine-learning based system that classifies and processes claim documents by obtaining, pre-processing, and classifying email contents and attached documents using an ML model with prompts, extracting relevant information, and providing outputs through user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If rule-based and template-specific bots are used to automate claim document processing, then automation extent is improved, but device complexity increases due to needing multiple bots for varied formats
Solution Approach 1:
The patent applies universality by creating a single unified bot that can process multiple claim document formats from different buyers. Instead of maintaining separate template-specific bots for each buyer's format, the system uses a general-purpose bot with capabilities to handle various formats (PDF, Word, Excel, images) and adapt to different templates, thereby reducing device complexity while maintaining high automation extent
Solution Approach 2:
The patent applies dynamics by implementing a flexible, adaptive bot system that can dynamically adjust to different claim document formats and templates. The bot uses machine learning and natural language processing to automatically adapt to new formats without requiring manual reconfiguration, allowing the system to remain simple while handling diverse document types
2Measurement precision
If rule-based systems are customized for specific buyer formats, then measurement precision is improved, but ease of manufacture deteriorates due to frequent modifications required
Solution Approach 1:
The patent applies self-service by enabling the bot to automatically adapt to new claim document formats without requiring manual customization. The system uses self-learning capabilities where the bot can independently analyze new document formats, learn their structures, and adjust its extraction rules automatically, thereby maintaining high measurement precision while eliminating the need for costly and time-consuming manual modifications
Solution Approach 2:
The patent applies parameter changes by using a flexible configuration system where the bot can dynamically adjust its processing parameters based on the detected document format. Instead of hardcoding rules for each format, the system changes its operational parameters adaptively, allowing it to maintain high extraction accuracy across different formats without requiring manual reconfiguration for each new buyer template
3Ease of operation
If traditional OCR tools are used to extract data from claim documents, then ease of operation is improved, but measurement precision deteriorates for complex or handwritten documents
Solution Approach 1:
The patent applies composite materials by combining multiple data extraction techniques within a single unified system. Instead of relying solely on traditional OCR, the bot integrates OCR with machine learning-based extraction, natural language processing, and format-specific parsing algorithms. This composite approach maintains ease of operation while significantly improving measurement precision for complex and handwritten documents by leveraging the strengths of multiple extraction methods
4Adaptability or versatility
If manual processing of claim documents is performed, then adaptability to various formats is improved, but productivity deteriorates due to labor intensity and delays
Solution Approach 1:
The patent applies mechanics substitution by replacing manual mechanical processing with an automated intelligent system. The bot uses machine learning and natural language processing to automatically adapt to various claim document formats, eliminating the need for manual intervention while maintaining high adaptability. This substitution dramatically improves productivity by processing documents at machine speed without sacrificing the ability to handle diverse formats
Data Source
AI summary
A machine-learning based (ML-based) system and method for automatically processing one or more documents are disclosed. Initially, data associated with at least one of: first contents and second contents are obtained from data sources. The data are pre-processed to generate pre-processed data. The first contents are classified are classified as at least one of: claim-based contents and non-claim-based contents, using a machine learning (ML) model with prompts. Additionally, the second contents are classified as at least one of: the claim-based contents and the non-claim-based contents, based on intents of the second contents, using the ML model with the prompts. At least one of: the claim-based contents and the non-claim-based contents, are provided as an output, to users on user interfaces associated with electronic devices associated with the users.