Hierarchical Feature Vector Comparison for Variable-Length Data
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Solution Overview
Problem
Existing pattern recognition systems face inefficiencies when dealing with variable-length feature vectors, particularly in heterogeneous data domains like natural language text, where normalization to a fixed-length reference is not feasible, leading to information loss and computational inefficiencies.
Innovation Solution
A method and system that derive pattern class prototypes and similarity measures for both existing and new data objects, allowing for the identification of pattern classes without requiring normalization to a global reference vector, and enabling the processing of nested feature vectors in their natural state, thus avoiding truncation or padding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If variable-length feature vectors are normalized to a fixed-length reference vector, then computational efficiency is improved, but information loss occurs
Solution Approach 1:
The patent segments the feature vector comparison process into hierarchical levels. Instead of comparing entire variable-length vectors directly or normalizing to a fixed reference, the method divides comparison into element-level similarities (computing similarity between corresponding elements) and vector-level similarities (aggregating element similarities). This segmentation allows processing of variable-length vectors without information loss while maintaining computational efficiency through the hierarchical structure.
Solution Approach 2:
The patent introduces a hierarchical dimension to the comparison process by computing element similarities first, then aggregating to vector similarities. This dimensional transformation from direct vector comparison to hierarchical element-to-vector comparison enables efficient processing of variable-length feature vectors without requiring normalization to a fixed reference, thereby preserving information while improving computational efficiency.
2Productivity
If variable-length feature vectors are padded to a fixed length, then computational efficiency is improved, but loss of substance occurs
Solution Approach 1:
The patent segments the feature vector into elements and computes similarities hierarchically. By dividing the comparison into element-level and vector-level stages, the method processes variable-length vectors without padding, thereby avoiding loss of substance while maintaining computational efficiency through the structured hierarchical approach.
Solution Approach 2:
The patent transforms the comparison problem by introducing a hierarchical dimension—first computing element similarities, then aggregating to vector similarities. This dimensional change enables efficient processing of variable-length feature vectors without padding, preserving all original information and avoiding loss of substance.
3Loss of information
If variable-length feature vectors are processed without normalization, then information is preserved, but computational complexity increases
Solution Approach 1:
The patent reduces computational complexity by segmenting the comparison into hierarchical levels. Instead of computing complex distances between entire variable-length vectors, the method computes simpler element-level similarities first, then aggregates them to vector-level similarities. This segmentation preserves information while reducing overall computational complexity through the divide-and-conquer hierarchical structure.
Solution Approach 2:
The patent introduces a hierarchical dimension to transform the computational problem. By computing element similarities first and then aggregating to vector similarities, the method preserves all information from variable-length vectors while reducing computational complexity compared to direct vector-level comparison methods.
4Device complexity
If fixed-length feature vectors are used, then similarity computation is simplified, but adaptability to variable-length data is reduced
Solution Approach 1:
The patent segments the feature vector comparison into hierarchical levels, allowing the system to maintain simplicity in computation while adapting to variable-length data. By computing element-level similarities first and then aggregating to vector-level similarities, the method preserves adaptability to variable-length inputs while keeping the computational steps simple and structured.
Solution Approach 2:
The patent introduces a hierarchical dimension that enables the system to handle variable-length feature vectors while maintaining computational simplicity. The two-stage process (element similarities followed by vector similarities) provides adaptability to variable-length data without sacrificing the simplicity of the computational approach.
Data Source
AI summary
Methods and systems for identifying data patterns from data comprising at least one data object, said data having at least one existing pattern class associated therewith, said data object being represented by a base feature vector, at least one of said base feature vectors having a structure of higher-level and lower-level feature vectors such that at least one element of a higher-level feature vector is itself a lower-level feature vector.