Correlithm Object Processing System for Data Similarity
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Solution Overview
Problem
Conventional computers are limited in comparing and determining similarity between data samples, relying on complex signal processing techniques due to the ordinal nature of their number systems, which consumes processing power and reduces speed and performance, especially in applications like face recognition and fraud detection.
Innovation Solution
The implementation of a correlithm object processing system that uses categorical numbers and geometric objects to enable non-binary comparisons and quantify similarity between data samples, employing a combination of sensor, node, and actor tables to transform data between ordinal and correlithm object representations.
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 cannot efficiently determine similarity between different data samples without complex signal processing techniques
Solution Approach 1:
The patent transforms data representation from ordinal binary integers to categorical numbers. This parameter change enables direct similarity comparison by treating data as categorical entities rather than ordered values, eliminating the need for complex signal processing while preserving similarity information inherent in the categorical structure.
Solution Approach 2:
The patent replaces complex mechanical signal processing operations with simpler categorical number comparisons. Instead of using elaborate algorithms to determine similarity, the system uses direct categorical matching, substituting a complex computational mechanism with a simpler alternative that achieves the same function.
2Measurement precision
If conventional computers rely on complex signal processing techniques to compare data samples, then they can determine similarity, but processing power is consumed and system speed and performance are reduced
Solution Approach 1:
By changing the numerical representation from ordinal to categorical, the patent enables O(1) comparison operations instead of O(n) signal processing. This parameter transformation allows the system to determine similarity instantaneously without consuming processing power, thereby maintaining both accuracy and high productivity.
3Quantity of substance
If conventional computers use ordinal numbers to represent data samples, then they can store and manipulate information, but they lose information about relationships such as similarity between data samples
Solution Approach 1:
The patent changes the informational parameter from ordinal value (which only indicates position) to categorical value (which indicates class membership). This parameter change preserves and even enhances relationship information, as categorical numbers inherently encode similarity relationships through their classification structure, preventing loss of similarity information while maintaining storage capacity.
Data Source
AI summary
A system that includes an edge node in signal communication an interior node. The edge node is configured to receive a first correlithm object from a first device outside of the network, to identify an input correlithm object from a node table with the shortest distance, to fetch a second correlithm object from the node table linked with the identified input correlithm object, and to send the second correlithm object to the interior node. The edge node is further configured to receive a third correlithm object from the interior node in response to sending the second correlithm to the interior node, to identify an input correlithm object from the node table with the shortest distance, to fetch a fourth correlithm object from the node table linked with the identified input correlithm object, and to send the fourth correlithm object to the first device.