Form Field Mapping Engine for Ambiguous Electronic Forms
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional form filler applications fail to consistently locate and identify electronic form fields due to ambiguous, meaningless, or misleading field attributes, leading to inefficient data entry in electronic documents and forms.
Innovation Solution
The use of a mapping engine that identifies form fields using multiple field terms and machine learning algorithms to analyze context and structure, enabling accurate identification and mapping of form fields, even in 'formless' forms, by generating human-readable labels and dynamic identifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional form filler applications use static form field attributes for identification, then the identification process is simple, but the accuracy of form field identification deteriorates due to ambiguous, meaningless, or misleading field attributes
Solution Approach 1:
The system transforms static form field attributes into dynamic identifiers by generating multiple candidate identifiers for each form field and selecting the most appropriate one based on context analysis. This allows the system to adapt to varying form structures and attribute qualities, significantly improving identification accuracy without requiring a complete overhaul of the identification process
Solution Approach 2:
The patent introduces an intermediary layer between the form field attributes and the identification process. This intermediary generates and evaluates multiple candidate identifiers, using context analysis and machine learning to select the best match. This mediator handles the complexity of ambiguous attributes while presenting a clean identification interface
2Adaptability or versatility
If form filler applications rely on exact field name matches, then the mapping process is straightforward, but the adaptability to different form designs deteriorates when websites implement design changes
Solution Approach 1:
The system creates a universal identification framework that can handle multiple form field identification strategies (exact matching, partial matching, context-based matching, machine learning-based matching) within a single unified process. This multi-functional approach allows the system to adapt to various form designs and attribute qualities without requiring separate identification mechanisms for each case
Solution Approach 2:
The patent implements dynamic identifier generation that adapts to the specific characteristics of each form field. Instead of using static exact-matching rules, the system generates multiple candidate identifiers and dynamically selects the most appropriate one based on context analysis and machine learning predictions, enabling seamless adaptation to different form designs
3Reliability
If conventional form fillers use a single field term for identification, then the identification process is fast, but the reliability of form field identification deteriorates when field names are ambiguous or identical across multiple fields
Solution Approach 1:
The system performs preliminary analysis by generating multiple candidate identifiers for each form field before the actual identification process. This pre-computation of candidate identifiers and their associated confidence scores allows the system to quickly retrieve and evaluate options during form filling, maintaining speed while improving reliability through multi-candidate evaluation
Solution Approach 2:
The patent implements a feedback mechanism where the system learns from successful and unsuccessful identification attempts. Machine learning models are trained on identification outcomes, continuously improving the system's ability to select the correct identifier from multiple candidates. This feedback loop enhances reliability over time while the system caches learned patterns to maintain fast identification speeds
Data Source
AI summary
Systems and methods for locating, identifying, mapping and completing electronic form fields are provided herein. A mapping engine is configured to identify form fields using a variety of similar field names through one or more algorithms configured to identify and match similar field names and combinations of field names. A form field mapping and identification engine identifies a form category using a machine learning classification algorithm, then determines and maps form labels to form fields using seeded values and optical scanning in order to produce a human readable label for each form field. The field labels are used to generate a set of terms for each form field that are used to identify content to be filled in the form with a high degree of accuracy. Additional embodiments are directed toward locating form fields in an electronic form known as a formless form.