Neural Network Document Classification for Vehicle Settlement
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Solution Overview
Problem
Manual review and processing of documents for vehicle settlement processes are time-consuming and error-prone, especially when dealing with handwritten information, irregular document formats, and varying local regulations, which complicates the verification and transfer of ownership.
Innovation Solution
A computer-implemented method and system using a neural network model to classify and extract data from scanned documents, generating electronic documents for identified vehicles, and automatically verifying and correcting data, thereby streamlining the workflow and reducing manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual review and data entry processes are used for document processing, then flexibility in handling various document formats is maintained, but processing time and labor intensity increase significantly
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated optical character recognition (OCR) system that uses machine learning algorithms to extract data from documents. This substitution dramatically increases processing speed while the system handles complexity through automated classification and validation rules.
Solution Approach 2:
The system performs self-service by automatically classifying documents into categories, extracting relevant data fields, and validating information without requiring manual intervention for routine processing. The automated workflow manages itself through predefined business rules and exception handling protocols.
2Productivity
If automated data extraction systems are implemented, then processing efficiency increases, but accuracy may decrease due to errors in recognizing handwritten or irregularly formatted information
Solution Approach 1:
The system incorporates feedback mechanisms where extracted data is validated against known patterns and rules. When accuracy is uncertain, the system can flag items for manual review or use confidence scoring to prioritize processing, thereby maintaining high overall accuracy while preserving automated throughput for clear cases.
Solution Approach 2:
The system performs preliminary classification and preprocessing of documents before full data extraction. By categorizing documents into known types first, the system can apply specialized extraction algorithms optimized for each document type, improving accuracy for handwritten and irregular formats while maintaining automated efficiency.
3Adaptability or versatility
If multiple document formats and local regulations are accommodated, then system adaptability improves, but processing complexity and time requirements increase
Solution Approach 1:
The patent implements a universal document processing framework that can handle multiple document formats and regulatory requirements through a single system. The system uses format-agnostic OCR technology combined with configurable extraction rules that can be adjusted for different document types and local regulations, eliminating the need for separate processing systems for each format.
Solution Approach 2:
The processing system segments the document handling into distinct modular stages: initial classification, format-specific preprocessing, data extraction, and validation. This segmentation allows parallel processing of different document types simultaneously, reducing overall processing time while maintaining comprehensive format support.
4Measurement precision
If manual verification of extracted data is performed, then data accuracy improves, but processing time and operational costs increase
Solution Approach 1:
The system applies partial verification by focusing manual review only on low-confidence extractions or critical data fields rather than verifying every piece of data. This selective approach maintains high accuracy for important information while minimizing the time loss associated with comprehensive manual verification of all documents.
Data Source
AI summary
A computer-implemented method of generating electronic documents is described. The method comprises receiving a plurality of scanned documents for a plurality of vehicles; providing the plurality of scanned documents to a neural network model that outputs respective class identifiers of the plurality of scanned documents; for each scanned document of the plurality of scanned documents, extracting data from the scanned document according to a corresponding class identifier, and associating the scanned document and the extracted data with an identified vehicle of the plurality of vehicles, wherein the identified vehicle is identified by the extracted data; and generating an electronic document for the identified vehicle using the extracted data.