Dynamic Data Entry Screen Sequencing for Redundant Input
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Solution Overview
Problem
Sequential data entry screens are typically hard-coded and static, leading to users having to enter the same information multiple times and manually input information that could be inferred from previously provided data.
Innovation Solution
Implementing dynamic sequences of dynamically populated data entry screens, where the sequence and content are determined based on metrics such as data quality, inferability, and availability, to minimize the number of manual data entry points and streamline the data collection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static sequences of data entry screens are used, then the system structure is simple and easy to implement, but users have to enter the same information multiple times and manually input information that could be inferred
Solution Approach 1:
The patent implements dynamic screen sequencing where the order and content of data entry screens are determined algorithmically based on user responses and data metrics rather than being statically predefined. This allows the system to adapt the screen sequence in real-time, presenting screens in an optimized order that reduces redundant data entry while managing complexity through automated decision-making.
Solution Approach 2:
The system continuously analyzes user responses and data metrics to dynamically adjust the screen sequence. By incorporating feedback loops that evaluate data quality, inferability, and availability metrics, the system learns from user interactions and optimizes the data collection pathway, reducing the burden of repeated data entry while maintaining systematic control.
2Productivity
If dynamic sequences of data entry screens are implemented, then the number of manual data entry points is minimized, but the system complexity and computational requirements increase
Solution Approach 1:
The system pre-calculates and ranks potential screen sequences based on data metrics before actual data collection begins. By performing preliminary analysis of data quality, inferability, and availability metrics, the system prepares optimized screen sequences in advance, reducing real-time computational complexity while maintaining high productivity through pre-planned efficient data collection pathways.
Solution Approach 2:
The system automatically generates and optimizes its own screen sequence without requiring manual configuration or intervention. Through self-service mechanisms where the algorithm independently evaluates metrics and determines the optimal screen order, the system achieves high data collection efficiency while managing complexity through autonomous decision-making rather than human oversight.
3Adaptability or versatility
If data entry screens are hard-coded in sequence, then the implementation is straightforward and maintainable, but the system cannot adapt to optimize data collection based on data metrics
Solution Approach 1:
The system changes key parameters such as screen sequence order, content prioritization, and presentation timing based on data metrics including quality, inferability, and availability. By dynamically adjusting these parameters rather than maintaining fixed hard-coded sequences, the system achieves adaptability while managing implementation complexity through parameter-driven flexibility rather than structural redesign.
Data Source
AI summary
A sequence of data entry screens are configured to collect the data from a user. The method and system receive data entered by a user into a data entry screen. The method and system then determine metrics of the collected data, and ranks the collected data and the data entry screens based on the determined metrics. The ranking is then used to display the next best screen in the sequence for collecting data.