Data Density Visualization for Context-Aware UI Guidance
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Solution Overview
Problem
Conventional application development systems lack accuracy in guiding developers on modifying display windows for improved usability, are inefficient due to excessive client device interactions, and are inflexible in adapting to different application types and functions within display windows.
Innovation Solution
A data density system that determines and provides recommendations for adjusting data density in display windows based on application type and function, reducing user interactions and improving comprehensibility by generating visual representations and suggestions for adding, removing, or modifying data elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional application development systems provide generic development tools without data density analysis, then the systems are simple to implement, but the systems lack accuracy in guiding developers for improving usability
Solution Approach 1:
The system performs preliminary analysis of data density in display windows before the development process completes. By calculating data density metrics upfront and providing guidance on optimal densities, the system enables developers to make informed decisions during the design phase, improving measurement precision without requiring complex post-development analysis tools.
Solution Approach 2:
The patent replaces manual trial-and-error methods with automated data density calculation and analysis. The system automatically computes data density metrics, compares them against optimal ranges, and provides actionable guidance, substituting the mechanical process of manual interface testing and adjustment with an automated analytical system.
2Reliability
If conventional systems require extensive trial and error with multiple client device interactions, then the systems can achieve practicable results, but the systems become slow and computationally expensive
Solution Approach 1:
The system performs preliminary data density analysis during the development phase itself, eliminating the need for multiple iterative client device interactions. By calculating data density metrics and providing guidance upfront, the system ensures practicable results are achieved faster, improving productivity without sacrificing reliability.
Solution Approach 2:
The system implements continuous feedback loops that monitor data density metrics during development and provide real-time guidance. This feedback mechanism allows developers to adjust interfaces based on objective measurements rather than relying on multiple trial iterations, maintaining reliable outcomes while significantly improving development efficiency.
3Adaptability or versatility
If conventional systems use a universal one size fits all approach, then the systems are easy to implement, but the systems lack flexibility to adapt to different application types and functions
Solution Approach 1:
The system applies local quality by providing customized data density guidance based on specific application types and display window functions. Instead of using a universal threshold, the system analyzes the particular context (e.g., social media vs. banking application, registration vs. terms review) and provides tailored recommendations, achieving high adaptability through context-aware analysis rather than complex configurable systems.
Solution Approach 2:
The system dynamically adjusts data density parameters and optimal ranges based on the detected application type and display window function. By changing the reference parameters according to context, the system achieves versatility across different application scenarios without requiring complex manual configuration or multiple specialized tools.
Data Source
AI summary
This application relates to one or more embodiments of methods, non-transitory computer-readable media, and systems provide developer tools for generating and providing visualizations of data densities for various portions of a computer application. For example, the systems can determine a data density that reflects a ratio or an amount of data presented within a (portion of a) display window relative to the entire (portion of the) display window. The systems can further provide a visual representation of a data density for display on a client device, along with indications or suggestions for how to improve (e.g., reduce or increase) the data density for better comprehensibility (e.g., upon distribution of the application). In certain embodiments, the systems can generate suggestions based on an application type of a computer application in development and/or based on a function associated with a particular (portion of a) display window.