Hierarchical Product Parameter Classification via Dynamic Sub-Feature Generation
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Solution Overview
Problem
Existing systems for classifying hierarchical product parameters in connected computing environments are inefficient, prone to inconsistencies, and difficult to maintain, especially in the context of rapid software development and deployment.
Innovation Solution
A method that involves generating sub-features from parent features, transforming them into independent features, and merging parent feature properties with sub-features, utilizing a Part Discovery module to classify and reclassify hierarchical product parameters dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual classification methods are used for hierarchical product parameters, then flexibility in customization is maintained, but classification accuracy and consistency deteriorate
Solution Approach 1:
The system enables self-service classification by automatically discovering features, generating sub-features, and performing classification without manual intervention. The automated classification engine processes product parameters independently, maintaining consistency while improving accuracy through algorithmic decision-making rather than manual judgment.
Solution Approach 2:
The system transforms classification from a manual process to an automated one by changing the operational parameters of the classification engine. It uses configurable thresholds, classification rules, and algorithms that can be adjusted to balance automation accuracy with organizational-specific requirements, thereby maintaining flexibility while improving precision.
2Productivity
If automated classification systems are implemented, then classification speed and consistency are improved, but system complexity increases
Solution Approach 1:
The automated classification system is segmented into distinct functional modules: feature discovery module, sub-feature generation module, classification engine, and hierarchy update module. Each module performs a specific task, making the overall complex system manageable through modular design while maintaining high classification speed and consistency.
Solution Approach 2:
The classification system is designed as a universal platform that can handle multiple types of product parameters across different industries and organizations. By creating a multi-functional automated system that adapts to various classification needs through configurable parameters rather than hard-coded logic, the system achieves high productivity without proportionally increasing complexity.
3Measurement precision
If hierarchical product parameters are frequently updated to reflect rapid software development, then real-time accuracy is improved, but maintenance difficulty increases
Solution Approach 1:
The hierarchical product parameter structure is designed to be dynamic rather than static. The system automatically detects changes in product features, generates new sub-features, and updates the classification hierarchy in real-time as software develops. This dynamic approach maintains parameter accuracy without requiring manual maintenance intervention for each update.
Solution Approach 2:
The system performs preliminary actions by continuously monitoring product parameters and preparing classification updates before they are formally required. The automated feature discovery and sub-feature generation occur proactively, so when updates are needed, the system is already prepared, reducing maintenance burden while maintaining accuracy.
4Measurement precision
If detailed sub-features are generated from parent features, then classification granularity is improved, but data processing complexity increases
Solution Approach 1:
The system segments parent features into detailed sub-features automatically based on product parameter analysis. This segmentation increases classification granularity by breaking down high-level features into specific, measurable sub-features. The automated process manages the complexity of handling numerous sub-features through algorithmic organization rather than manual processing.
Solution Approach 2:
The feature hierarchy is structured as nested levels where parent features contain sub-features, which may contain further sub-sub-features. This nesting organization allows the system to maintain detailed classification granularity while managing data processing complexity through hierarchical abstraction, where higher levels provide overview and lower levels provide detail only when needed.
Data Source
AI summary
Techniques for classifying hierarchical product parameters in a hierarchy of operational factors for an offering operating in a unified platform are disclosed. The technique includes obtaining hierarchical product parameters associated with an offering, retrieving features composed of sub-modules, and determining decisive metrics for the sub-modules. The technique identifies specific sub-modules with metrics exceeding predefined thresholds and classifies them as sub-features. Hierarchical links are established between sub-features and features. The technique includes monitoring the performance and utilization of sub-features and reclassifying sub-features by transforming them into independent features or merging them with parent features. A hierarchical product parameter map is updated based on classifications and reclassifications. The invention provides a flexible, efficient approach for automatically classifying and reclassifying hierarchical product parameters based on operational significance. This allows for more accurate, real-time management of the hierarchy of operational factors, providing valuable insights into product usage, customer needs, and areas for improvement.