Dynamic Missing Field Detection in Data Submission UIs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Software applications face challenges in dynamically determining required fields, especially in scenarios where field requirements vary based on user input, processing type, or sequential UI interfaces, leading to incomplete data submissions.
Innovation Solution
A dynamic required field determination system utilizing machine learning techniques to assess user interactions, analyze session data, and customize UI notifications by integrating a missing data wizard, rule engine, and ML system to identify and highlight required fields dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional static field requirement methods are used, then implementation is simple, but field requirements cannot adapt to varying user scenarios leading to incomplete data submissions
Solution Approach 1:
The patent implements dynamic field requirement determination by transitioning from static configuration to dynamic assessment. The system evaluates user input, processing type, and sequential UI context in real-time to determine which fields are actually required, allowing field requirements to adapt flexibly to different user scenarios while maintaining manageable complexity through modular architecture
Solution Approach 2:
The system changes the parameter of field requirement status from fixed to variable. By introducing machine learning models that assess multiple parameters (user input patterns, processing type, UI sequence), the system dynamically adjusts field requirement parameters based on actual usage context, enabling adaptability without proportionally increasing system complexity
2Reliability
If machine learning techniques are integrated to dynamically determine required fields, then data submission completeness improves, but system complexity and computational resources increase
Solution Approach 1:
The patent introduces an intermediary layer between the user interface and data processing systems. This intermediary consists of machine learning models that assess field requirements based on user behavior patterns and contextual information. The intermediary translates complex ML computations into simple field requirement determinations, improving data completeness while managing system complexity through layered architecture
Solution Approach 2:
The system implements feedback loops where user interactions and submission outcomes are continuously fed back to the machine learning models. This feedback mechanism allows the system to learn from actual data submission patterns and refine field requirement predictions, improving reliability over time while the feedback infrastructure adds manageable complexity through established ML pipelines
3Measurement precision
If the system dynamically assesses each user session to determine required fields, then user-specific accuracy improves, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on historical user session data before actual use. During user sessions, the pre-trained models quickly assess field requirements based on learned patterns rather than analyzing every parameter from scratch. This preliminary training phase separates heavy computational work from real-time processing, achieving high accuracy while minimizing per-session processing time
Data Source
AI summary
A processor may receive a data submission made through the UI. The data submission may lack data in at least one blank field provided in the UI for entering information in which no information has been entered. The processor may classify the data submission as having at least one data submission type using at least one machine learning (ML) process. The processor may determine at least one required field for the at least one data submission type. The at least one required field may require information to be entered therein. The processor may determine that the at least one blank field is also the at least one required field. The processor may cause the UI to indicate that the at least one blank field is also the at least one required field.