Graphical Units and Intersections for AI-Prioritized Targets
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Solution Overview
Problem
Existing software user interfaces for business data analysis overwhelm users with too many options and variables, making it difficult to display important KPIs efficiently and link them to intelligent analysis, leading to chaotic and disorganized interfaces.
Innovation Solution
A system leveraging statistical analysis and AI algorithms to identify and prioritize targets for emphasis, allowing manual selection and presenting user interfaces that focus on specific strategies and KPIs, using a graphical user interface to optimize resource allocation based on health scores and predictive prescriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing software user interfaces present all business data and KPIs to users, then complete information is provided, but the interface becomes overwhelming and chaotic
Solution Approach 1:
The patent divides the user interface into modular graphical units, each representing a specific KPI or data element. These units are organized into hierarchical groups and subsets, allowing users to view comprehensive data while maintaining interface clarity through structured segmentation. Each graphical unit can be independently manipulated, selected, or collapsed, enabling users to navigate large datasets without being overwhelmed by a chaotic monolithic interface.
2Adaptability or versatility
If user interfaces display many KPIs and variables, then comprehensive analysis is enabled, but the interface becomes complex and disorganized
Solution Approach 1:
The patent introduces hierarchical organization as an additional dimension for structuring data presentation. KPIs are arranged in multiple levels of abstraction, from high-level summaries to detailed individual metrics. Users can navigate this hierarchical dimension to access comprehensive analysis capabilities while maintaining a clean, organized interface at each level. The hierarchical structure allows the interface to scale from simple to complex views without becoming disorganized.
3Adaptability or versatility
If traditional interfaces show all data options, then users have complete control, but it becomes difficult to identify important KPIs
Solution Approach 1:
The patent applies local quality by providing different levels of detail and interaction for different graphical units based on their importance and characteristics. Frequently accessed or critical KPIs are positioned prominently or given enhanced visual characteristics, while less critical data elements are organized in collapsible groups or lower-priority locations. This differentiated approach maintains user control over all data while making important KPIs easily identifiable through their local positioning and visual properties within the hierarchical structure.
Data Source
AI summary
The present disclosure uses statistical analysis and an artificial intelligence (AI) algorithm to help identify a plurality of targets for emphasis. An emphasis is a real-world activity that is designed to lead to a desired behavior by a target. A user interface is presented that allows for a selection of targets in a manner that improves the health of weak strategies and indicators as predicted by the AI algorithm instead of focusing on a single overall metric for all targets being analyzed. A separate user interface provides at least two arcs that are each associated with separate types of statistical analysis (or indicators), where intersecting lines are associated with a subset of targets. At the intersections are graphical units that are color coded based on the trend of those indicators over time.