Correlithm Object Processing for Data Similarity
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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
1Measurement precision
If conventional computers use ordinal binary integers to represent and manipulate data, then they can perform basic operations such as counting, sorting, and indexing, but they fail to determine similarity between different data samples, requiring complex signal processing techniques that reduce system speed and performance
Solution Approach 1:
The patent transforms the numerical representation system from ordinal binary integers to a correlithm object-based system using n-dimensional space coordinates. This parameter change enables direct similarity measurement through coordinate comparison rather than requiring complex signal processing, thus improving both measurement precision and maintaining productivity
Solution Approach 2:
The patent replaces the mechanical signal processing approach with a mathematical coordinate-based comparison system. Instead of using complex algorithms to determine similarity, the system directly compares n-dimensional coordinates of correlithm objects, substituting mechanical processing with a more efficient mathematical approach
2Measurement precision
If conventional computers rely on complex signal processing techniques to compare data samples, then they can determine similarity, but the complex processes consume processing power which reduces the speed and performance of the system
Solution Approach 1:
The patent changes the fundamental parameters of data representation from ordinal numbers to correlithm objects in n-dimensional space. This enables direct coordinate-based comparison that achieves accurate similarity determination without complex processing algorithms, thereby reducing device complexity while maintaining measurement precision
Solution Approach 2:
The patent extracts the essential similarity determination capability from complex signal processing algorithms and encapsulates it in the correlithm object coordinate system. By taking out the core function and representing it through simple coordinate comparison, the system achieves accurate measurement with reduced complexity
3Adaptability or versatility
If conventional computers use ordinal numbers to represent data samples, then they can store and manipulate information, but they are unable to tell if a data sample matches or is similar to any other data samples unless there is an exact match
Solution Approach 1:
The patent introduces n-dimensional space coordinates to represent correlithm objects, adding dimensional information that captures similarity relationships. This dimensional expansion allows the system to determine both exact matches and partial similarities, preventing loss of similarity information while enhancing adaptability for various comparison scenarios
Solution Approach 2:
The patent creates a universal correlithm object representation system that can handle both exact matching and similarity determination through its coordinate-based approach. This multi-functional system can adapt to different comparison needs (exact match, partial match, similarity degree) without requiring separate processing mechanisms, thus improving versatility while preserving information
Data Source
AI summary
A system includes a memory and a node. The memory stores first and second log string correlithm objects. The node receives first and second real-world numerical values, and identifies a first sub-string correlithm object from the first log string correlithm object representing the first real-world numerical value and a second sub-string correlithm object from the second log string correlithm object representing the second real-world numerical value. The node aligns the first and second log string correlithm objects such that the first sub-string correlithm object aligns with the second sub-string correlithm object. The node identifies a sub-string correlithm object from the second log string correlithm object representing the logarithmic value of one. The node determines which sub-string correlithm object from the first log string correlithm object aligns with the identified sub-string correlithm object from the second log string correlithm object. The node outputs the determined sub-string correlithm object.