Data Parsing and Verification for Electronic Form Pre-population
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Solution Overview
Problem
Filling out electronic forms is time-consuming and prone to human error, especially when users need to refer to multiple documents, and is particularly challenging on mobile devices with small displays.
Innovation Solution
A data parsing and verification system that extracts and verifies relevant data from user-submitted documents, such as CVs, and pre-populates electronic forms by matching extracted information against a database of known entities, reducing the need for manual data entry and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data entry is used to fill electronic forms, then users can complete forms with custom information, but the process becomes time-consuming and prone to human error
Solution Approach 1:
The system automatically extracts data from user-uploaded documents and pre-populates form fields without requiring manual data entry. The system serves itself by performing OCR, parsing, and form filling operations autonomously, eliminating the need for users to manually type information while maintaining accuracy through automated verification processes.
Solution Approach 2:
The system performs data extraction, validation, and form pre-population before the user submits the form. By completing these actions in advance, the system reduces the user's workload to merely reviewing and confirming pre-filled data, significantly improving both speed and accuracy while allowing users to catch any errors before final submission.
2Ease of operation
If users manually fill out forms by referring to multiple documents, then accurate data can be entered, but the process becomes particularly challenging on mobile devices with small displays
Solution Approach 1:
The system replaces the mechanical process of manually reading documents and typing data with an automated optical character recognition (OCR) and data parsing system. This substitution eliminates the need for users to physically handle multiple documents on small mobile screens, as the system automatically extracts and structures the data from uploaded files.
Solution Approach 2:
The system handles multiple document types and formats through a single unified interface, accepting various file formats and automatically detecting the appropriate parsing method. This multi-functional approach works consistently across different devices including mobile phones, tablets, and desktop computers, providing universal access without requiring device-specific manual procedures.
3Extent of automation
If OCR is used to convert scanned documents into machine-readable text, then data extraction can be automated, but errors may occur in the conversion process
Solution Approach 1:
The system implements a feedback mechanism where extracted data is validated against known formats, patterns, and databases. The system compares OCR results with expected data structures, identifies anomalies or low-confidence extractions, and either automatically corrects them or flags them for user review. This feedback loop continuously improves accuracy by learning from corrections and user validations.
Solution Approach 2:
The system performs preliminary validation and verification steps immediately after OCR extraction but before final form submission. This includes checking data formats, validating against databases, and identifying potential errors early in the process, allowing for automatic correction or user confirmation before the data is committed to the form.
Data Source
AI summary
Systems and methods are provided for parsing, verifying, and supplementing user-submitted data and pre-populating the information detected in the user-submitted data into a user-fillable electronic form. For example, the user may upload a cumulative vitae (CV) onto a system, and the system may analyze the CV to extract the date ranges and the corresponding entities enumerated in the CV. The system may confirm the information about the extracted entities in a database of entities, and pre-populate an electronic form using the confirmed information. Upon viewing the electronic form, the user may make any changes as needed before submitting the electronic form. Based on the changes made by the user, the system can update its data parsing and/or verification techniques using machine learning.