N-Dimensional Space Hidden Feature Detection via Minimum Spanning Tree
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Solution Overview
Problem
Complex phenomena in space, especially those with many variables, are difficult to fully understand due to non-linearity and complexity, making it challenging for AI systems to automatically analyze and predict consequences without human heuristic skills.
Innovation Solution
A method that uses a point distribution in an n-dimensional space to identify hidden features by iteratively adding points that reduce the length of the minimum spanning tree, allowing for the representation of additional parameters or locations relevant to the phenomenon, with the aid of quantized space and Euclidean distance calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional analysis methods are used to study complex phenomena, then current conditions can be determined, but hidden features and additional parameters cannot be discovered
Solution Approach 1:
The method uses the data itself to reveal hidden features through automatic MST construction and analysis, without requiring external human heuristic skills or complex pre-processing. The system serves itself by extracting insights directly from the measured data distribution.
Solution Approach 2:
The patent replaces complex human analytical processes with an automated computational system that uses mathematical algorithms (MST construction, centroid calculation, distance measurement) to discover hidden features, substituting mechanical human reasoning with digital computation.
2Reliability
If more parameters and locations are considered to fully understand complex phenomena, then analysis completeness improves, but computational complexity increases
Solution Approach 1:
The method segments the analysis into discrete steps: constructing the MST, identifying centroids of point clusters, calculating distances, and determining hidden features. This segmentation allows complex phenomenon analysis to be broken down into manageable computational operations.
Solution Approach 2:
The patent transitions from analyzing only measured parameters to adding hidden features as new dimensions in the parameter space. By projecting data into higher-dimensional space and using MST structures, the system reveals additional parameters that were not originally measured but are implied by the data distribution.
3Extent of automation
If automated analysis is implemented without human heuristic skills, then objectivity improves, but ability to handle non-linear complex relationships deteriorates
Solution Approach 1:
The patent replaces human heuristic reasoning with automated computational algorithms that objectively analyze data patterns. The MST construction and centroid calculation provide deterministic, reproducible results without human intervention, achieving both automation and accurate handling of non-linear relationships through mathematical formulation.
Data Source
AI summary
A method of determining implicit hidden features of phenomena, representable by a point distribution in a space, includes the following steps: defining a set of first parameters describing effects of a phenomenon such as an event or process; defining a n-dimensional space, wherein the first parameters are represented by entity points; determining, as a function of measured values of the first parameters, additional geometrical points in the n-dimensional space, which are expected to provide additional characteristic parameters describing the phenomenon or additional locations where the phenomenon will produce its effects; adding the additional parameters or points, in recurrent sequence, to the first parameters or points, to define at each iterative step a shorter minimum spanning tree than at the preceding step; and displaying or printing the n-dimensional space, wherein the additional characteristic parameters or points are shown together with the first parameters and the geometrical point.