Correlithm Object Subtraction via Dimensional Alignment
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Solution Overview
Problem
Conventional computers are limited in comparing data samples due to their reliance on ordinal numbers, which only provide information about sequence order, failing to determine similarity between data samples, leading to complex signal processing challenges and reduced performance in applications like facial recognition and fraud detection.
Innovation Solution
The implementation of a correlithm object processing system that uses categorical numbers and correlithm objects to represent data samples, enabling non-binary comparisons and quantifying similarity between data samples, regardless of their type or format, through a configuration involving sensor, node, and actor tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional computers use ordinal binary integers to represent and manipulate data, then they can perform operations like counting, sorting, indexing, and mathematical calculations, but they cannot determine similarity between different data samples unless there is an exact match
Solution Approach 1:
The patent transforms the numerical representation system from ordinal binary integers to a coordinate-based system in n-dimensional space. Each data sample is represented by coordinates rather than sequential numbers, fundamentally changing how data is encoded and enabling similarity detection through geometric distance measurements instead of exact ordinal matches.
Solution Approach 2:
The patent introduces n-dimensional space as a new framework for data representation. By mapping data samples to points in n-dimensional space with multiple coordinates, the system enables similarity comparison through spatial distance metrics, adding dimensional information that ordinal numbers cannot provide.
2Measurement precision
If conventional computers rely on complex signal processing techniques to determine similarity between data samples, then they can achieve accurate comparison, but processing power is consumed which reduces system speed and performance
Solution Approach 1:
The patent replaces complex signal processing algorithms with geometric distance calculations in n-dimensional space. Instead of using traditional signal processing techniques to compare data samples, the system computes spatial distances between coordinate points, which is computationally more efficient while maintaining similarity determination accuracy.
Solution Approach 2:
The patent changes the computational approach from complex signal processing operations to simple distance metric calculations. By transforming data into coordinate representations, similarity measurement becomes a matter of calculating geometric distances, which requires less processing power and increases system performance.
3Loss of information
If conventional computers use ordinal numbers to represent data samples, then they can store and manipulate information efficiently, but they cannot quantify the degree of similarity between data samples
Solution Approach 1:
The patent introduces n-dimensional coordinate space to represent data samples, where each dimension provides additional information about the data sample's characteristics. This dimensional expansion preserves similarity information that would be lost in ordinal representation, as the spatial relationship between points naturally encodes similarity degrees.
Solution Approach 2:
The patent uses n-dimensional coordinates as an intermediary representation between raw data and similarity comparison. Instead of directly comparing ordinal numbers or raw data samples, the system transforms data into coordinate space where similarity can be quantified through geometric relationships, simplifying the comparison operation.
Data Source
AI summary
A system includes a memory and a node. The memory stores first and second linear string correlithm objects. The node receives first and second real-world numerical values, and identifies a first sub-string correlithm object from the first linear string correlithm object representing the first real-world numerical value and a second sub-string correlithm object from the second linear string correlithm object representing the second real-world numerical value. The node aligns the first and second linear string correlithm objects such that the first sub-string correlithm object aligns with a sub-string correlithm object. The node identifies a sub-string correlithm object from the second linear string correlithm object that represents zero and determines which sub-string correlithm object from the first linear string correlithm object aligns with the identified sub-string correlithm object from the second linear string correlithm object. The node outputs the determined sub-string correlithm object from the first linear string correlithm object.