AI Field Mapping for Completing Multiple Electronic Forms
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Solution Overview
Problem
Existing form automation technologies struggle with converting paper or electronic forms with undefined form fields or inconsistent field definitions, leading to inaccuracies and inefficiencies in data collection and processing, especially when dealing with forms from different vendors.
Innovation Solution
A system and method that imports fillable electronic forms, presents selectable form fields, receives user input, and deploys it across multiple forms with shared fields, utilizing artificial intelligence for data inference and automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing form automation techniques are used to convert paper or electronic forms with undefined or inconsistent field definitions, then the conversion process can be automated, but the accuracy and reliability of data collection deteriorates due to field definition inconsistencies
Solution Approach 1:
The system changes the parameter of field definition representation by using natural language processing to interpret and standardize field definitions across different forms. Instead of requiring explicit structured definitions, the system processes unstructured or semi-structured field descriptions and transforms them into consistent, machine-readable formats, thereby maintaining automation while improving reliability.
Solution Approach 2:
The system introduces an intermediary layer of AI-based form field analysis that sits between the source forms and the conversion process. This intermediary analyzes field definitions, identifies inconsistencies, and standardizes them before conversion, resolving the contradiction by enabling automation without sacrificing accuracy.
2Measurement precision
If visual verification of form accuracy is required to ensure forms match original documents, then data accuracy improves, but the time and complexity of the process increases
Solution Approach 1:
The system enables self-service form verification through AI-based automatic comparison and validation. The system autonomously verifies that converted forms match original documents by comparing field definitions, data types, and structural elements, eliminating the need for manual visual verification while maintaining high accuracy standards.
Solution Approach 2:
The system implements automated feedback mechanisms that compare converted forms against source documents and provide immediate validation results. This feedback loop ensures accuracy without requiring human intervention, as the system automatically detects and flags any discrepancies for review.
3Adaptability or versatility
If manual form completion is used to handle forms from different vendors with inconsistent field definitions, then adaptability improves, but productivity and efficiency deteriorate
Solution Approach 1:
The system achieves universality by creating a vendor-agnostic form processing platform that can handle forms from different sources with varying field definitions. The AI-based field analysis and standardization capabilities enable the system to adapt to any form format while maintaining consistent data extraction and processing, thereby providing both adaptability and high productivity.
Solution Approach 2:
The system performs preliminary analysis and standardization of field definitions before the actual form processing begins. By pre-processing and normalizing field structures from different vendors, the system eliminates the need for manual adaptation during form completion, significantly improving productivity while maintaining versatility.
4Extent of automation
If explicit field definitions are required in source forms for automation to work, then automation capability improves, but the ease of manufacture and deployment of forms deteriorates
Solution Approach 1:
The system inverts the traditional approach by not requiring explicit field definitions in source forms. Instead of forcing forms to conform to a predefined structure, the system analyzes and extracts field definitions automatically from the source forms themselves, thereby maintaining automation capability while simplifying form creation and deployment.
Data Source
AI summary
A system and method for completing fillable electronic forms. One exemplary technique involves automatically importing one or more fillable electronic forms from a database, receiving, at a user interface component via a network, a request to select the one or more fillable electronic forms, presenting a set of selectable fillable form fields from the one or more fillable electronic forms, presenting the one or more fillable electronic forms, receiving a user response for populating one or more fillable form fields from the set of selectable fillable form fields, and automatically deploying the user response in a second and subsequent fillable electronic form from the one or more fillable electronic forms, the second fillable electronic form having one or more fillable form fields shared in common with the one or more fillable form fields from the set of selectable fillable form fields.