Metabolic Data Organization via Hybrid Groups and Tags
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Solution Overview
Problem
Current methods for categorizing and organizing metabolic-related clinical data, such as those from continuous glucose monitoring, face challenges in balancing broad categorization that overlooks subtle differences with precise categorization that becomes overly complex, and existing data management systems struggle with flexible yet meaningful tagging that leads to data loss and mislabeling.
Innovation Solution
A computer research tool that combines hierarchical Groups with flexible Tags to organize and visualize metabolic-related clinical data, allowing users to define, label, and search discrete time periods (Data Memos) within a structured filing system, using Boolean queries and interactive graphs to facilitate pattern recognition and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If hierarchical data organization is used, then data structure and navigation are improved, but data flexibility and searchability deteriorate
Solution Approach 1:
The patent combines hierarchical Groups with flat Tags to create a hybrid data organization system. Groups provide stable hierarchical structure while Tags add flexible tagging capability, allowing data to be organized both structurally and semantically without the limitations of either approach alone.
2Ease of operation
If broad data categorization is used, then data overview is improved, but data precision and detail recognition deteriorate
Solution Approach 1:
The patent segments data organization into two levels: broad hierarchical Groups for overall structure and overview, and specific Tags for precise categorization and detail recognition. This segmentation allows users to navigate from general to specific views without losing either the big picture or important details.
3Measurement precision
If precise data categorization is used, then data accuracy is improved, but system complexity deteriorates
Solution Approach 1:
The patent introduces Tags as an intermediary layer between data and hierarchical Groups. Tags provide precise categorization without requiring complex hierarchical restructuring, simplifying the system while maintaining data accuracy through flexible keyword-based classification.
4Adaptability or versatility
If flexible tagging system is used, then data adaptability is improved, but data consistency and reliability deteriorate
Solution Approach 1:
The patent makes Tags universal by allowing them to be applied across multiple Groups and data types consistently. This universality ensures that the same Tag maintains the same meaning throughout the system, improving data consistency and reliability while preserving flexibility.
Data Source
AI summary
A computer research tool for inputting, searching, displaying, and analyzing metabolic-related clinical data utilizing a novel graphical user interface (GUI) for visual-statistical data analysis and insight generation and method thereof are disclosed.