Deduction Claim Parsing Engine for Multi-Format Data Extraction
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Solution Overview
Problem
Current solutions for processing deduction claim documents are inefficient, prone to errors, and require manual adaptation for each new format, lacking a standardized approach and leading to high resource consumption.
Innovation Solution
A machine learning-based system that classifies and extracts data from various document formats, including tabular and non-tabular data, using a fine-tuned transformer model (LayoutLM v1) to generate structured data objects and update itself for improved accuracy, with error assessment and adaptation capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing of deduction claim documents is used, then employees can review and process documents, but it leads to high operational costs, inefficiency, and error-proneness
Solution Approach 1:
The patent replaces the manual mechanical processing system with an automated machine learning-based document processing system. The ML model automatically extracts, classifies, and validates deduction claim data from various document formats, eliminating the need for manual employee review and processing while significantly improving efficiency and reducing errors.
Solution Approach 2:
The system enables self-service processing where the machine learning model autonomously handles document extraction, classification, and validation without requiring manual intervention. The model learns from training data and automatically adapts to different document formats, allowing the system to serve itself in processing deduction claims without human operational involvement.
2Measurement precision
If general-purpose document parsing software or OCR systems are used, then document processing can be automated, but accuracy and processing speed remain low
Solution Approach 1:
The patent changes the fundamental parameters of document processing by transitioning from traditional OCR-based text recognition to machine learning-based semantic understanding. The ML model uses trained parameters and patterns to accurately extract structured data from unstructured documents, achieving both high accuracy and improved processing speed compared to general-purpose parsing software.
Solution Approach 2:
The system performs preliminary action by pre-training the machine learning model on diverse document formats and deduction claim patterns before actual processing. This pre-training enables the model to quickly and accurately process new documents without requiring real-time adaptation, thereby improving both accuracy and processing speed from the outset.
3Reliability
If document parsing systems are customized for each customer or form, then processing accuracy for that specific customer improves, but adaptation time for new customers increases
Solution Approach 1:
The patent implements a universal machine learning model that can process deduction claim documents from multiple customers and various document formats simultaneously. The model is designed with multi-functionality to handle different layouts, styles, and structures through its training on diverse data, eliminating the need for separate customized systems for each customer while maintaining high reliability.
Solution Approach 2:
The system uses feedback mechanisms where the machine learning model continuously learns from processed documents and validation results. This feedback loop enables the model to adapt to new customer formats and patterns over time without requiring manual reconfiguration, reducing adaptation time while maintaining processing reliability through continuous improvement.
4Quantity of substance
If manual processing of extensive datasets is performed, then all documents can be reviewed, but computing and storage resources are inefficiently used
Solution Approach 1:
The patent replaces resource-intensive manual processing with an automated machine learning system that efficiently handles large volumes of documents. The ML model processes extensive datasets using optimized algorithms that consume fewer computing and storage resources compared to manual handling, enabling scalable processing of large quantities of deduction claims.
Data Source
AI summary
The present invention is related to data processing methods and systems thereof. According to an embodiment, the present invention provides a method of processing claim deduction documents using a machine learning model. The process begins by accessing data files and extracting information from them, which is subsequently stored. This document information, along with the machine learning model trained on various document formats, is used to classify the data files and generate tabular data. From this tabular data, data objects are created and included in an output data file. The information from the output file is then used to update the data of the machine learning model, optimizing it for improved future document processing. There are other embodiments as well.