Profile Data Updates Using ML-Based Document Extraction Routing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data management systems face challenges in efficiently processing unstructured documents due to their lack of predefined data models, impacting efficiency, accuracy, and scalability, especially when handling diverse document formats.
Innovation Solution
A computing architecture utilizing machine learning techniques, including large language models and optical character recognition, dynamically selects data extraction engines based on document type, prioritizes data points by confidence scores, and updates profile data structures to maintain accuracy and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data management systems process unstructured documents, then they can handle diverse document formats, but processing efficiency and accuracy deteriorate due to lack of predefined data models
Solution Approach 1:
The system performs preliminary classification of documents into structured and unstructured categories before extraction. This preliminary action enables the selection of appropriate extraction methods, improving both efficiency and accuracy by avoiding the use of complex ML models for structured documents that have simpler extraction requirements.
Solution Approach 2:
The system dynamically adapts its processing approach based on document classification results. For structured documents, it uses rule-based extraction; for unstructured documents, it employs machine learning models. This dynamic adaptation resolves the contradiction by optimizing processing efficiency for each document type while maintaining versatility across diverse formats.
2Measurement precision
If machine learning models are used to extract data from all documents, then extraction accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system classifies documents as structured or unstructured before applying extraction methods. This preliminary classification enables the avoidance of computationally expensive ML models for structured documents, reducing processing time while maintaining high accuracy through appropriate method selection.
Solution Approach 2:
Different extraction approaches are applied to different document types: rule-based extraction for structured documents and ML-based extraction for unstructured documents. This localized quality approach ensures high extraction accuracy for each document type while optimizing processing time by using the simplest effective method for each case.
3Adaptability or versatility
If multiple data extraction engines are used to handle diverse document types, then extraction capability improves, but system complexity increases
Solution Approach 1:
The system segments the extraction process into two distinct pathways: rule-based extraction for structured documents and ML-based extraction for unstructured documents. This segmentation manages complexity by creating clear, separate processing flows for each document type rather than attempting a single unified approach.
Solution Approach 2:
Document classification serves as an intermediary step that bridges the gap between diverse document types and appropriate extraction engines. This intermediary classification mechanism simplifies the overall system architecture by providing a clear decision point that routes documents to the most suitable extraction method, reducing the complexity of managing multiple extraction engines.
4Loss of information
If data extraction is performed on all extracted data points, then data completeness improves, but processing efficiency deteriorates due to lack of prioritization
Solution Approach 1:
The system applies confidence score thresholds to determine which extracted data points require further processing or validation. By performing partial action only on data points below certain confidence thresholds, the system maintains data completeness for critical information while improving processing efficiency by skipping redundant validation of high-confidence extractions.
Data Source
AI summary
A system can receive one or more documents. The system can determine, for a document, using a machine learning model, a classification of a type of the document and a confidence score associated with the classification, where the confidence score indicates a level of performance with which the machine learning model outputs the classification of the type of the document. For the document, the system can select a data extraction engine based on the confidence score, where the data extraction engine extracts data points from the document. The system can prioritize the extracted data points based on the confidence score associated with the document. The system can update a profile data structure in response to aggregating the prioritized extracted data points. The system can input the profile data structure into a payroll processing system to execute an operation in accordance with the updated profile data structure.