Reverse Mapping Form Filling via User Profile Matching
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Solution Overview
Problem
Current form-filling technologies face challenges in maintaining high accuracy and cost-effectiveness across a large number of websites, particularly due to the complexity of natural language understanding and image analysis, leading to inefficiencies in recognizing profile attributes and requiring significant trial-and-error for accuracy improvements.
Innovation Solution
The reverse mapping method leverages user intelligence by matching submitted form data with stored user profiles, using a combination of hashing and Bayesian theory to achieve high accuracy and self-correction, allowing for automatic mapping and extension to international sites, while reducing maintenance costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hard-coded mapping is used to achieve accurate form filling, then mapping precision is improved, but maintenance cost and device complexity increase significantly
Solution Approach 1:
Instead of mapping form fields to profile attributes forward (site profiling approach), the patent inverts the process by matching submitted profile attributes back to form fields (reverse mapping). This inversion eliminates the need for maintaining forward mapping tables for thousands of sites, as the system learns field meanings indirectly from user submissions.
Solution Approach 2:
The system enables self-service by allowing user submissions to automatically contribute to the mapping knowledge base. Each submitted form automatically trains the system, eliminating the need for manual maintenance of mapping tables by developers or administrators.
2Device complexity
If intelligent mapping with rule-based approaches is used to reduce maintenance cost, then device complexity is reduced, but mapping precision deteriorates to just above 85%
Solution Approach 1:
The system implements feedback by using actual user submissions to continuously improve and refine the reverse mapping model. The accumulated submission data provides feedback signals that enhance mapping precision over time without requiring manual intervention or dictionary updates.
Solution Approach 2:
The system performs preliminary action by pre-processing and hashing profile attributes before submission, preparing them in advance for efficient matching. This preliminary preparation enables the system to achieve high precision quickly without requiring extensive trial-and-error tuning during deployment.
3Measurement precision
If site profiling is implemented to achieve high accuracy for specific domains, then mapping precision is improved, but the cost to collect and maintain profile information for large sets of domains increases prohibitively
Solution Approach 1:
The reverse mapping system achieves universality by creating a single system that can handle multiple domains and languages simultaneously. Instead of requiring separate profile collections for each domain, the universal submission-based approach serves all domains with a single unified mechanism.
Solution Approach 2:
The system uses copying by replicating the reverse mapping mechanism across different domains and languages without requiring domain-specific customization. The same core algorithm processes submissions from various sources, copying the successful approach across multiple contexts.
4Ease of operation
If intelligent mapping with dictionaries and patterns is used to achieve form filling, then ease of operation is improved, but productivity deteriorates due to significant trial-and-error and fine-tuning requirements
Solution Approach 1:
The system enables self-service by automatically learning from user submissions without requiring external tuning or maintenance. The system serves itself by using its own operational data to improve performance, eliminating the need for continuous human intervention.
Solution Approach 2:
The system maintains continuity of useful action by continuously learning from every user submission. Rather than requiring periodic updates or maintenance cycles, the learning process operates continuously in the background, constantly improving productivity without interrupting service.
Data Source
AI summary
In the presently preferred embodiment of the invention, every time a user submits a form the client software tries to match the submitted information with the stored profile of that user. If a match is discovered, the program tags the field of the recognized data with a corresponding type. The resulting profile can be used after that to help all subsequent users to fill the same form.