Leukocyte Profile Identification via 3D Pattern Recognition
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Solution Overview
Problem
Current methods for identifying altered leukocyte profiles are prone to errors due to dichotomization, delayed information, and information loss, particularly in distinguishing between infected and non-infected individuals, and fail to provide real-time, interpretable data.
Innovation Solution
A method utilizing pattern recognition in three-dimensional space to analyze leukocyte numbers and subtypes, generating combinations of data points and plotting them to identify altered profiles, which allows for the discrimination of false positives and negatives based on spatial contrasts and temporal stages of infection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If dichotomization is used to classify leukocyte data into infected/non-infected categories, then classification simplicity is improved, but measurement precision deteriorates due to false positives and false negatives
Solution Approach 1:
The patent transitions from one-dimensional dichotomous classification to multi-dimensional pattern recognition by analyzing multiple leukocyte parameters (neutrophil count, lymphocyte count, monocyte count, eosinophil count, basophil count) simultaneously in three-dimensional space, allowing more nuanced differentiation of infection states without arbitrary cut-offs
Solution Approach 2:
The patent changes the analytical parameters from discrete binary categories to continuous multi-parameter profiles, enabling the detection of subtle patterns and transitions in leukocyte responses that indicate different stages and types of infections
2Productivity
If cut-off values are used for binary classification, then decision-making speed is improved, but reliability deteriorates due to overlapping data ranges
Solution Approach 1:
The patent adds spatial dimensions to the classification process by plotting leukocyte parameters in three-dimensional space, creating distinct clusters for different infection states that can be identified through spatial patterns rather than single threshold values
Solution Approach 2:
The patent creates visual representations (plots and graphs) of leukocyte data patterns that serve as reference copies for comparing against new samples, enabling rapid identification of infection states through pattern matching rather than complex calculations
3Device complexity
If traditional leukocyte analysis methods are used, then data processing simplicity is improved, but information loss increases due to non-interpretable overlapping data
Solution Approach 1:
The patent introduces three-dimensional spatial visualization of leukocyte parameter relationships, transforming overlapping one-dimensional data into separable three-dimensional patterns that preserve and reveal information about infection states, timing, and progression
Solution Approach 2:
The patent segments the continuous leukocyte data space into distinct regional patterns corresponding to different infection states (acute infection, chronic infection, recovery, non-infection), making the information contained in overlapping data accessible through spatial segmentation
Data Source
AI summary
Methods of identifying altered leukocyte profiles are disclosed. In one embodiment, counts or relative percentages of leukocyte cell types are received and constitute input data points. Combinations of input data points are generated. Pairs of input data points and combinations are generated. Secondary data values are generated from the pairs. Three-dimensional plots are then constructed and selected as useful for identifying an altered leukocyte profile through pattern recognition of perpendicular data inflection, data bifurcation, non-overlapping data clusters, or combinations thereof. Such a strategy results in partially or totally non-overlapping data subsets, which are then interpreted.