Machine Learning Document Inconsistency Detection System
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Solution Overview
Problem
Current systems for identifying inconsistencies in large volumes of financial documents, such as billing documents from multiple vendors, are inefficient due to the time-consuming nature of manual transcription and the limitations of optical character recognition (OCR) systems, which struggle to understand and analyze document content across different formats and layouts.
Innovation Solution
A computer-implemented system using a machine learning model that receives and analyzes financial documents, identifies logical inconsistencies, and generates visualizations for user input to improve accuracy, with features like confidence scoring, training datasets, and rule generation for resolving inconsistencies, and reporting changes over intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual text transcription is used, then accuracy of document content understanding is improved, but time consumption and labor requirements increase significantly
Solution Approach 1:
The patent replaces manual mechanical text transcription with an automated machine learning-based system that uses optical character recognition (OCR) combined with natural language processing to extract and understand document content automatically, eliminating the need for manual typing while maintaining accuracy
Solution Approach 2:
The system performs self-service by automatically transcribing, analyzing, and identifying inconsistencies in financial documents without requiring human intervention for the core processing tasks, though human review is needed for final validation of identified issues
2Loss of time
If optical character recognition (OCR) systems are used, then time for text transcription is reduced, but the ability to understand and analyze document content is limited
Solution Approach 1:
The patent merges OCR technology with machine learning models and natural language processing capabilities into a unified system that not only transcribes text but also understands context, relationships between elements, and logical inconsistencies within financial documents
Solution Approach 2:
The system incorporates feedback mechanisms where users can correct identified inconsistencies, and these corrections are fed back into the machine learning model to improve its accuracy over time, enabling the system to learn from real-world data and enhance its analytical capabilities
3Extent of automation
If automated text recognition systems are used, then labor requirements are decreased, but reliability of inconsistency identification is reduced due to inability to understand document context
Solution Approach 1:
The patent changes the operational parameters of the system by introducing machine learning models that analyze multiple document attributes simultaneously (such as amounts, dates, line items, and their relationships) rather than processing text in isolation, enabling reliable detection of contextual inconsistencies
Solution Approach 2:
The system adds new dimensions to document analysis by examining not just the text content but also the structural relationships between elements, temporal patterns, and logical connections across different parts of the document, enabling detection of inconsistencies that would be invisible to simple OCR systems
Data Source
AI summary
Disclosed herein are exemplary implementations of systems and methods for utilizing a machine learning model to identify logical inconsistencies. An aspect of the disclosed embodiments includes a method comprises receiving, via a network, a document related to a user; receiving, from the machine learning model, a predetermined label and a value associated with the predetermined label wherein the value is associated with a location on the document; storing the predetermined label and the value associated with the predetermined label in a structured data object; identifying, based a consistency rule, a logical inconsistency in the structured data object; generating a visualization based on the logical inconsistency and providing, at a display device, the structured data object, wherein the structured data object includes the visualization of the logical inconsistency.