Electronic Form Validation via Sender Feedback Loop
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Solution Overview
Problem
Current form processing systems face errors in data extraction and categorization due to corrupt input data and incorrect categorization, particularly in industries where automatic processing is desirable but lacks scalable solutions, leading to redundant data entry and transfer issues.
Innovation Solution
An electronic contact address-based system that automatically extracts and validates data from documents using OCR and template recognition, allowing direct upload and validation of electronically generated forms, reducing human intervention and errors through a notification server, OCR software, template generator, and data extraction engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If OCR and specialized machines are used to automatically extract and categorize data from documents, then processing speed is improved, but categorization accuracy deteriorates due to errors in associating data with correct fields
Solution Approach 1:
The system implements feedback by sending extracted form data back to the sender entity for validation. The sender reviews the OCRed and categorized data, correcting any errors in data association or categorization. This feedback loop ensures high accuracy while maintaining automated processing speed, as the sender only needs to review and correct exceptions rather than manually process every document.
Solution Approach 2:
The system introduces an intermediary validation step where the sender entity acts as a mediator between automated OCR extraction and final data storage. The sender receives the extracted data, validates categorization accuracy, and confirms correct field associations before the data is finalized. This intermediary process resolves the contradiction by ensuring accuracy without requiring full manual processing.
2Measurement precision
If third party validation is used to correct OCR and categorization errors, then data accuracy is improved, but system complexity and redundant data entry increase
Solution Approach 1:
The system enables self-service by allowing the sender entity to validate and correct their own form data through a web interface. Instead of requiring a separate third party validation system, the sender receives the extracted data, reviews it, and makes corrections directly. This eliminates the need for complex third-party integration and redundant data entry, as the sender is already familiar with their own document formats and data requirements.
Solution Approach 2:
The system makes the sender entity multi-functional by combining their existing role as document creator with the additional function of data validation. The same entity that generates the invoice or form also validates the extracted data, eliminating the need for separate validation personnel or systems. This universal approach reduces system complexity while maintaining high data accuracy.
3Measurement precision
If manual review and correction of extracted data is performed, then categorization accuracy is improved, but processing time and productivity decrease
Solution Approach 1:
The system applies partial action by having the sender review and correct only the extracted data that contains errors or exceptions, rather than manually processing every single document. The automated OCR and extraction handle the majority of documents correctly, requiring minimal human intervention. This partial review approach maintains high productivity while ensuring accuracy for documents that need correction.
Solution Approach 2:
The feedback mechanism allows senders to review extracted data and make corrections only when needed. The system presents extracted data for validation, and the sender provides feedback by correcting errors. This feedback-driven approach ensures high categorization accuracy for documents requiring attention while maintaining automated processing for the majority of documents, thus preserving overall productivity.
Data Source
AI summary
A computer-implemented method comprises determining, that a first document corresponds to a particular template that provides field specification data for identifying one or more fields from a document; generating a first electronic validation form as a graphical user interface from the first document using the particular template; sending a second notification of the first electronic validation form via email to the sender device, the notification comprising a first option for confirming invoice data in the first electronic validation form, at least some of the invoice data inline or as an attachment, and a second option for updating the invoice data in the first electronic validation form; uploading, in response to a selection of the first option, the invoice data into an enterprise resource planning (ERP) system; causing, in response to a selection of the second option, presentation of the first electronic validation form by the sender device.