Form Fragment Recommendation Engine Using Analytics Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Form designers face challenges in efficiently creating new forms due to the tedious process of manually searching and selecting form fields and fragments from existing forms, as existing tools lack a systematic way to recommend high-performing form fragments based on analytics data.
Innovation Solution
A system that collects analytics data on form fragments, scores them based on performance metrics, and recommends top-scoring fragments to designers during the form creation process, allowing for easy selection and integration of preexisting form elements with optimized attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If form designers manually search and select form fields and fragments from existing forms, then they can create custom forms with specific requirements, but the process becomes tedious and time-consuming
Solution Approach 1:
The system collects analytics data from form usage and feeds it back to the form designer through automated recommendations. The feedback loop analyzes performance metrics (completion rates, error rates, time to complete) and suggests optimized form fragments that have proven effective in similar contexts, eliminating manual searching and trial-and-error approaches.
Solution Approach 2:
The system enables form designers to automatically obtain recommended form fragments without manual intervention. The analytics engine self-services by continuously monitoring form performance and autonomously generating recommendations based on collected data, allowing designers to quickly access optimized fragments without time-consuming manual analysis.
2Productivity
If designers use preexisting forms as a starting point, then form creation is faster, but they may not capture all desired information or formatting for the new form
Solution Approach 1:
The system allows designers to selectively apply recommendations to specific portions of the form rather than accepting entire preexisting forms. Analytics data identifies which specific form fragments (fields, sections, validation rules) perform well, enabling designers to incorporate only the locally optimized elements that match their specific requirements while maintaining overall form customization.
Solution Approach 2:
The system breaks down forms into discrete, reusable fragments (fields, sections, validation rules) that can be independently selected and combined. This segmentation allows designers to mix and match optimized fragments from different sources to create highly customized forms that meet specific information and formatting requirements while leveraging proven design patterns.
3Adaptability or versatility
If designers create many forms from scratch or using preexisting templates, then they can meet diverse requirements, but it becomes impractical to create new fields or select appropriate fragments consistently
Solution Approach 1:
The system creates a universal recommendation engine that serves multiple form design scenarios simultaneously. The same analytics infrastructure and recommendation algorithms work across diverse form types (surveys, applications, registrations), providing consistent guidance regardless of the specific form domain, thereby simplifying the design process while maintaining adaptability to different requirements.
Solution Approach 2:
The system dynamically adjusts recommendations based on changing parameters such as form purpose, target audience, and performance metrics. As analytics data accumulates, the recommendation parameters evolve to reflect emerging best practices, allowing the system to adapt to new form types and requirements while maintaining a consistent underlying process.
Data Source
AI summary
The present invention provides recommendation of top scoring form fragments to a form designer. A plurality of form fragments may be stored in a form repository, each form fragment including user-defined form fragment attributes, and analytics data for the form fragments may be collected over a period of time to calculate a performance score for each of the form fragments. When an author searches for a form fragment, at least one matching form fragment from the form repository may be obtained based on the search query or criteria inputted by the author using natural language processing (NLP). The matching form fragments may be ordered based on the performance score, where higher performing form fragments are listed first. The ordered form fragments may be displayed on a device associated with the author so that the form fragments may be used by the author when authoring forms.