AI Document Processor Using Segmented ML Models for Contextual Extraction
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Solution Overview
Problem
Current AI and machine learning technologies struggle with tasks requiring contextual understanding and complex communication, leading to inefficiencies in processing documents with structured and unstructured data, especially in automating processes like insurance claims and inventory management.
Innovation Solution
An AI-based document processing system utilizing multiple machine learning models trained on labeled data to extract responsive data, which preprocesses requests, identifies tasks, and generates outputs based on guidelines, ensuring accurate data extraction and automation of document processing tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional AI and machine learning technologies are used for document processing, then automation capability is improved, but accuracy in tasks requiring contextual understanding deteriorates
Solution Approach 1:
The system segments document processing into distinct stages: optical character recognition (OCR) to convert images to text, natural language processing (NLP) to extract meaningful information, and machine learning classification to categorize documents. This segmentation allows each component to specialize, improving overall accuracy while maintaining automation.
Solution Approach 2:
The patent introduces intermediary components between raw document input and final processing decisions, including preprocessing modules that clean and normalize data, and postprocessing modules that refine extracted information. These intermediaries bridge the gap between automated processing and contextual accuracy by adding layers of validation and refinement.
2Measurement precision
If multiple processing stages are implemented to improve accuracy, then data extraction precision is improved, but system complexity increases
Solution Approach 1:
The system employs universal components that perform multiple functions. For example, the NLP module simultaneously handles text extraction, entity recognition, and relationship mapping. The machine learning classifier serves both as a document categorizer and a quality filter. This multi-functionality reduces overall system complexity while maintaining high extraction accuracy.
Solution Approach 2:
The system performs preliminary actions in the form of preprocessing steps before main processing. Documents are preprocessed to normalize formatting, remove artifacts, and extract basic metadata before entering the main processing pipeline. This preliminary action simplifies subsequent processing stages by presenting cleaner, more standardized input data.
3Productivity
If traditional automation is applied to complex tasks, then efficiency of repetitive tasks is improved, but performance on complex tasks requiring contextual understanding deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where processing results are continuously evaluated and used to refine future processing. Confidence scores from machine learning models trigger feedback loops that determine whether additional verification is needed. This feedback ensures high reliability for complex tasks while maintaining efficiency for straightforward repetitive tasks through adaptive decision-making.
Solution Approach 2:
The system applies partial automation selectively based on task complexity. For simple repetitive tasks, full automation is applied to maximize efficiency. For complex tasks requiring contextual understanding, the system applies automation partially, using AI for information extraction but retaining human review for final validation. This selective approach optimizes the balance between efficiency and reliability.
Data Source
AI summary
An Artificial Intelligence (AI) based document processing system receives a request including one or more of a message and documents related to a process to be automatically executed. A process identifier is extracted and used for retrieving guidelines for the automatic execution of the document processing task. Machine Learning (ML) models, each corresponding to a guideline, are used to extract data responsive to the guidelines. Based on the responsive data meeting the approval threshold and the automatic document processing task executed, one or more of a recommendation to accept or reject the request, and a corresponding letter can be automatically generated.