Semantic Mapping for Electronic Form Generation
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Solution Overview
Problem
Current solutions for converting paper forms to electronic forms fail to provide users with a rich data capture experience, as they require manual modification of data fields and business rules, making the process time-consuming and error-prone.
Innovation Solution
The automatic generation of electronic forms using a data elements dictionary that includes user experience constructs and business rules, where data fields from paper forms are semantically mapped to corresponding digital data capture constructs, enabling the creation of interactive and error-checked fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual modification of data fields and business rules is used to convert paper forms to electronic forms, then the data capture experience can be improved, but the conversion process becomes time-consuming and error-prone
Solution Approach 1:
The system enables self-service conversion by automatically generating electronic forms from paper forms using optical character recognition and semantic mapping technology. The conversion process autonomously extracts form fields, identifies data elements, and populates corresponding fields without requiring manual intervention, thus resolving the contradiction between improving data capture experience and reducing conversion time
Solution Approach 2:
The patent replaces the mechanical manual modification process with an automated digital system. Optical character recognition technology converts paper form images into machine-readable text, and semantic mapping algorithms automatically match form fields to data elements, substituting human manual work with automated computational processes to eliminate time consumption and human error
2Ease of operation
If manual modification of data fields is performed during paper form conversion, then user experience constructs can be applied, but the process becomes complex and error-prone
Solution Approach 1:
The system automatically applies user experience constructs during the conversion process by using semantic mapping to identify form field purposes and characteristics. The technology self-determines appropriate data element types, validation rules, and display formats based on the analyzed paper form structure, eliminating the need for manual configuration while maintaining high user experience standards
Solution Approach 2:
The conversion system performs multiple functions simultaneously: it recognizes text from paper forms, extracts form field structures, determines semantic meanings, maps to data elements, and applies user experience constructs all in one automated process. This multi-functionality reduces operational complexity by consolidating what would otherwise require separate manual steps into a single unified system
3Ease of manufacture
If current conversion solutions are used, then the conversion process is simple, but the generated electronic forms lack rich data capture capabilities
Solution Approach 1:
The patent replaces simple but inadequate conversion methods with advanced automated technology. Optical character recognition and semantic mapping algorithms automatically analyze paper form structures, extract field information, and generate electronic forms with proper data capture capabilities, maintaining process simplicity while dramatically improving output quality and reliability
Solution Approach 2:
The system autonomously enhances data capture capabilities by automatically identifying form field semantics and applying appropriate digital data capture constructs. The conversion process self-adapts to different form types and structures, generating electronic forms with built-in validation, formatting, and user experience features without requiring manual enhancement, thus maintaining simplicity while improving reliability
Data Source
AI summary
Electronic form generation techniques are disclosed which automatically generate an electronic form based on a paper form by semantically mapping form fields. An example method may include identifying a data field in a paper form, extracting a semantic meaning of the data field, searching multiple data elements to identify a data element that semantically matches the data field, and, responsive to identifying a semantically matching data element, generating a data field in the electronic form based on the semantically matched data element and the associated one or more digital data capture constructs. Each data element may have associated therewith one or more digital data capture constructs. The generated data field in the electronic form corresponds to the data field in the paper form.