Correlithm Object Processing for Data Similarity Detection
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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 the use of sensor tables, node tables, and actor tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computers use ordinal numbers to represent data samples, then they can perform basic operations like counting and sorting, but they cannot determine similarity between different data samples
Solution Approach 1:
The patent transforms the number system parameter from ordinal to categorical, enabling similarity detection. Categorical numbers provide information about relationships between data samples rather than just sequence order, allowing the system to determine similarity without complex signal processing techniques
Solution Approach 2:
The patent replaces complex mechanical signal processing operations with simpler categorical number comparisons. Instead of using complex algorithms to analyze data sample relationships, the system uses inherent properties of categorical numbers to directly determine similarity, reducing computational complexity
2Measurement precision
If conventional computers use complex signal processing techniques to compare data samples, then they can determine similarity, but processing power is consumed and performance is reduced
Solution Approach 1:
By changing the numerical representation parameter from ordinal to categorical, the system achieves both accurate similarity detection and high processing speed. Categorical numbers inherently encode relationship information, allowing direct comparison without iterative processing or complex algorithms
Solution Approach 2:
The patent extracts the essential similarity information directly from categorical number representations, eliminating the need for complex signal processing intermediate steps. This extraction approach provides both accuracy and speed by working with the fundamental properties of the data representation
3Adaptability or versatility
If conventional computers use ordinal binary integers to represent information, then they can perform mathematical calculations, but they cannot efficiently compare data samples with different formats
Solution Approach 1:
The patent creates a universal comparison mechanism using categorical numbers that works across different data formats. Categorical numbers provide a common framework for representing and comparing diverse data types, eliminating the need for format-specific comparison logic and reducing overall system complexity
Data Source
AI summary
A system configured to emulate a correlithm object processing system includes an input node, a first output node, and a second output node. The input node receives a real-world numeric value comprising a mantissa value and an exponent value. The first output node receives the mantissa value and generates a first correlithm object associated with the mantissa value. The second output node receives the exponent value and generates a second correlithm object associated with the exponent value. A string correlithm object engine maps the first correlithm object to a first sub-string correlithm object of a string correlithm object, and maps the second correlithm object to a second sub-string correlithm object of the string correlithm object.