Runtime Dashboard Feature Ranking via ML Context Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current dashboard systems lack an efficient method to dynamically rank and display relevant system features to users based on their interactions, preferences, and environmental context, leading to a cluttered and less user-friendly interface.
Innovation Solution
A system that utilizes a machine learning engine to analyze user activity and environmental data to rank system features, associating them with icons indicative of the type of user interface, and displaying an ordered list of shortcuts on a runtime-generated dashboard, allowing users to access features relevant to their current needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If all system features are displayed on the dashboard, then users can access all available features, but the interface becomes cluttered and less user-friendly
Solution Approach 1:
The system segments the complete set of system features into multiple ranked lists based on different criteria (e.g., user preferences, interaction frequency, relevance to current context). Each segment is then displayed separately on the dashboard, allowing users to access all features while maintaining a clean, organized interface structure.
Solution Approach 2:
The dashboard dynamically adjusts the display of system features by reordering them based on real-time user interactions, contextual information, and usage patterns. This dynamic reorganization ensures that the most relevant features are prominently displayed while less relevant features remain accessible but less prominent, maintaining interface clarity.
2Measurement precision
If dashboard displays features based on comprehensive user analysis, then feature relevance is improved, but system complexity increases
Solution Approach 1:
The system implements feedback loops where user interactions with dashboard features are continuously monitored and fed back into the ranking algorithm. This feedback mechanism refines the feature rankings over time, improving accuracy by learning from actual user behavior patterns while using efficient algorithms to manage system complexity.
Solution Approach 2:
The ranking system operates autonomously by automatically analyzing user interactions, contextual data, and feature usage patterns without requiring manual intervention. The system self-adjusts feature rankings based on collected data, achieving high measurement precision through automated analysis while minimizing the operational complexity for users.
Data Source
AI summary
Techniques for displaying a runtime-generated dashboard to a user are disclosed. A system receives user information regarding a user accessing a system and determine a plurality of system features available to the user based on the user information. The system assigns each of the plurality of system features a rank and orders the plurality of system features based on respective assigned ranks. The system displays a dashboard comprising multiple shortcuts corresponding respectively to the plurality of system features. The shortcuts are ordered based on ranks of respective corresponding system features. Concurrently with displaying the shortcuts, the system displays a heterogeneous plurality of interface elements corresponding respectively to the plurality of system features. Each interface element visually indicates a type of interaction associated with the respective system feature. At least two of the plurality of system features are associated with different types of interactions.