Dynamic Form Generation System for Personalized Data Capture
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional data capture experiences face challenges in efficiently capturing user information due to the complexity of forms, incompatibility with various computing devices, and frequent changes in required user information, leading to frustrated users and suboptimal conversion rates.
Innovation Solution
A dynamic form generation system that uses automation objects, including an annotated schema, machine learning models, and layout templates, to automatically construct, personalize, and optimize data capture forms based on form generation parameters such as user activity data and analytics, ensuring relevant fields and sections are presented to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional static forms are used for data capture, then form structure is simple and easy to implement, but user experience deteriorates due to manual development cycles and inability to adapt to different devices
Solution Approach 1:
The patent implements dynamic form generation where forms are automatically constructed based on user profiles, device characteristics, and real-time data. The system transitions from static pre-defined forms to dynamic forms that adapt their structure, fields, and layout automatically, resolving the contradiction between adaptability and complexity by automating the adaptation process
Solution Approach 2:
The form generation system performs self-service by automatically generating optimized forms without manual intervention. The system uses machine learning models and automation objects to autonomously determine form structure, select relevant fields, and optimize layout for different devices, eliminating the need for manual form development while achieving high adaptability
2Productivity
If manual form development is used, then form structure is stable and predictable, but productivity deteriorates due to lengthy development cycles and frequent updates required
Solution Approach 1:
The system performs preliminary action by pre-defining automation objects, schemas, and layout templates that can be automatically assembled. Machine learning models are pre-trained on user behavior data to predict optimal form configurations. This preliminary preparation enables rapid form generation without manual development, dramatically improving productivity while reducing time loss
Solution Approach 2:
The patent replaces the mechanical manual form development process with an automated computer-based system. Machine learning models and automation objects substitute for manual form design, construction, and optimization activities. This substitution eliminates tedious manual work while maintaining form quality, resolving the contradiction between productivity and time consumption
3Ease of operation
If generic forms are presented to all users, then implementation is simple and consistent, but user experience deteriorates due to lack of personalization and relevance
Solution Approach 1:
The system applies local quality by customizing specific portions of forms based on user characteristics, device type, and context. Different users receive personalized form sections, field arrangements, and input requirements tailored to their needs. This localized personalization maintains overall system simplicity while enhancing user experience through relevance
Solution Approach 2:
The patent dynamically changes form parameters such as displayed fields, layout configuration, input validation rules, and section visibility based on user profiles and device characteristics. The system adjusts these parameters automatically without changing the underlying form structure, enabling personalization while maintaining ease of operation through consistent automated generation
Data Source
AI summary
Various embodiments, methods and systems for implementing a form generation system are provided. Generating forms includes dynamic generation, personalization, and optimization of the forms based on automation objects that instruct on how to construct, structure and present forms for personalized data capture experiences. In operation, a form generator engine receives a request from a computing device to access a form. The form generator engine accesses form generation automation rules that are based on form generation parameters and automation objects. Using form generation automation rules, form generation parameters are used to generate automation objects including an annotated schema, a machine learning model, and a layout. Based on the form generation automation rules the automation objects are used to generate the form such that at least a field or a section of the form is selected based on a relevance score associated with field or section. The form is communicated for display.