Hierarchical Product Parameter Classification via API Path Analysis
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Solution Overview
Problem
Existing methods for classifying hierarchical product parameters in connected computing environments are prone to inconsistencies and inefficiencies, particularly due to manual or rule-based approaches that fail to adapt to the evolving nature of complex offerings.
Innovation Solution
A bottom-up classification technique that uses lower-tier hierarchical product parameters to infer and construct higher-tier parameters, integrated within a unified platform that unifies distinct ecosystems and enables data-driven adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual or rule-based classification methods are used for hierarchical product parameters, then implementation simplicity is maintained, but classification accuracy and adaptability deteriorate due to inconsistencies and inability to adapt to evolving offerings
Solution Approach 1:
The system enables self-service classification where the classification model automatically categorizes new hierarchical product parameters without manual intervention. The model learns from historical classification data and autonomously assigns parameters to appropriate categories, eliminating the need for continuous manual rule updates while maintaining high classification accuracy.
Solution Approach 2:
The patent replaces manual classification mechanisms with an automated machine learning-based classification model. This substitution transforms the mechanical process of manual parameter classification into an intelligent automated system that continuously adapts to evolving product offerings while maintaining consistency and accuracy.
2Device complexity
If manual or rule-based classification methods are used, then system complexity is reduced, but adaptability to evolving complex offerings deteriorates
Solution Approach 1:
The classification model is designed to be dynamic rather than static. It continuously learns from new data and adapts its classification rules automatically as product offerings evolve. This dynamic capability allows the system to handle complex, changing product structures without requiring proportional increases in system complexity or manual intervention.
Solution Approach 2:
The system incorporates feedback mechanisms where classification results are continuously evaluated and used to refine the classification model. This feedback loop enables the system to adapt to evolving offerings by learning from actual classification performance and adjusting its parameters automatically, maintaining high adaptability without increasing overall system complexity.
3Measurement precision
If automated classification is implemented, then adaptability and accuracy improve, but loss of time for processing and implementing classification increases
Solution Approach 1:
The system performs preliminary classification actions by pre-processing and pre-categorizing hierarchical product parameters as they are generated. This preliminary action reduces the time required for subsequent classification operations, as the model has already performed initial categorization work before formal classification is needed, thereby maintaining accuracy while reducing overall processing time.
4Loss of information
If comprehensive hierarchical classification is implemented, then information completeness improves, but device complexity increases due to multiple tiers and relationships
Solution Approach 1:
The patent applies segmentation by breaking down the complex hierarchical classification into manageable segments or tiers. Each tier handles specific aspects of parameter classification, allowing the system to maintain comprehensive information coverage while managing complexity through modular organization. This segmented approach enables independent processing at each level, reducing overall system complexity.
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. Hierarchical product parameters including lower-tier parameters are obtained. An identifier associated with each lower-tier parameter is retrieved, providing information about its structure and location. An address associated with the identifier is determined. The lower-tier parameter is classified under an appropriate parameter based on the address. A hierarchical link is established between the classified lower-tier parameter and the appropriate parameter. A hierarchical product parameter map is updated based on the classification. The technique improves classification accuracy by extracting host information from API paths and classifying lower-tier parameters under appropriate higher-tier parameters. This provides an efficient approach for automatically classifying and updating the hierarchy while maintaining data integrity and relationships, offering significant benefits in management, development, and communication of the offering.