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 that reduce system speed and performance, especially 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, 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 samples, then they can perform basic operations such as counting, sorting, and indexing, but they are unable to determine similarity between different data samples without complex signal processing techniques
Solution Approach 1:
The patent transforms the representation parameter of data from ordinal binary integers to correlithm objects in n-dimensional space. This parameter change enables direct similarity measurement through geometric distance calculations, eliminating the need for complex signal processing while providing precise similarity determination capability.
Solution Approach 2:
The patent introduces correlithm objects as an intermediary representation between raw data and similarity comparison. These correlithm objects serve as mediators that encode data characteristics in a form suitable for efficient geometric comparison, resolving the contradiction between simple representation and accurate similarity measurement.
2Measurement precision
If conventional computers rely on complex signal processing techniques to compare data samples, then they can determine similarity, but system processing speed and performance are reduced
Solution Approach 1:
The patent replaces complex mechanical signal processing operations with geometric calculations in n-dimensional space. Distance computations between correlithm objects provide accurate similarity measurements while executing much faster than traditional signal processing algorithms, thus improving both accuracy and processing speed.
Solution Approach 2:
The patent moves data representation from 1-dimensional binary values to n-dimensional correlithm objects. This dimensional expansion enables direct geometric similarity measurement through distance calculations, achieving accurate comparison results with significantly reduced computational complexity and improved processing speed.
3Loss of information
If conventional computers use ordinal numbers to represent data, then they can manipulate sequence information, but they lose information about other relationships such as similarity between data samples
Solution Approach 1:
The patent embeds data in n-dimensional correlithm space where multiple relationship types coexist. The geometric structure preserves similarity information through distance metrics while maintaining sequence information, enabling simultaneous analysis of multiple data relationships without information loss.
Solution Approach 2:
The correlithm object representation serves multiple functions simultaneously: it preserves sequence order information, encodes similarity relationships through geometric distance, and enables various types of data relationship analysis. This universal representation eliminates the need to choose between different analysis capabilities.
Data Source
AI summary
A correlithm object processing system includes a memory that stores a first string correlithm object comprising a first plurality of sub-string correlithm objects, and a second string correlithm object comprising a second plurality of sub-string correlithm objects. A string correlithm object engine communicatively coupled to the memory determines the anti-Hamming distances between each of the sub-string correlithm objects of the first string correlithm object pairwise with each corresponding sub-string correlithm object of the second string correlithm object, and stores the determined anti-Hamming distances in a distance table. The engine identifies a group of neighboring anti-Hamming distances stored in the distance table that are greater than a predetermined number of standard deviations beyond a standard distance and, in response, determines the corresponding sub-string correlithm objects of the first and second string correlithm objects to be a match.