Hierarchical Segmentation Classification for Data Structures
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Solution Overview
Problem
Conventional machine learning architectures fail to efficiently classify objects with hierarchical data structures as they do not utilize the hierarchical nature of the data, leading to computational inefficiencies and errors by starting at the highest level with limited knowledge of lower levels.
Innovation Solution
A hierarchical segmentation and classification system that begins classification at the lowest level of the hierarchical data structure and ascends to higher levels, using consolidated classifications from lower levels to improve computational performance and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional machine learning architectures classify objects starting at the highest level of hierarchical data structure, then the classification process is simpler to implement, but computational efficiency deteriorates and classification accuracy decreases
Solution Approach 1:
The patent inverts the conventional classification approach by starting at the lowest level of the hierarchical data structure and progressing upward to the highest level, rather than starting at the top. This inversion allows the system to build knowledge from granular details before making higher-level classification decisions, improving computational efficiency and accuracy while maintaining implementation simplicity through the systematic bottom-up approach
2Ease of manufacture
If conventional machine learning architectures classify objects starting at the highest level of hierarchical data structure, then the classification process is simpler to implement, but classification accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by performing classification at the lowest levels of the hierarchical data structure before proceeding to higher levels. This preliminary classification at the granular level provides foundational knowledge that improves the accuracy of subsequent higher-level classification decisions, while the systematic approach maintains ease of implementation
3Measurement precision
If hierarchical segmentation and classification is performed from lowest level to highest level, then classification accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the classification process into distinct hierarchical levels, processing each level separately from lowest to highest. This segmentation breaks down the complex task into manageable stages, where each level handles specific granularities of data. The modular segmented approach improves accuracy by treating each level independently while systematically building toward the final classification, without requiring overwhelming system complexity
Data Source
AI summary
Systems and methods for hierarchical segmentation and classification may include receiving an input object having a hierarchical data structure. The input object may be segmented at each hierarchical level in a descending manner. The components of each hierarchical level may be classified in an ascending manner. Classification at higher levels may depend on the results from classification at the lower levels. After classification, a hierarchically-classified object may be outputted.