Correlithm Object Processing System for Efficient Data Similarity Detection
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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 performance, especially in applications like face recognition and fraud detection.
Innovation Solution
Implementing a correlithm object processing system that uses categorical numbers and geometric objects to enable non-binary comparisons and quantify similarity between data samples, allowing for direct comparison of data samples regardless of their type or format through the use of correlithm objects, sensor tables, node tables, and actor tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
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 efficiently determine similarity between different data samples without complex signal processing techniques
Solution Approach 1:
The patent transforms the number system parameter from ordinal binary integers to categorical numbers. This fundamental parameter change enables direct similarity comparison operations without requiring complex signal processing, thereby improving processing speed while reducing operational complexity. The categorical number system allows data samples to be represented in a form where similarity can be determined through simple bitwise operations rather than complex mathematical computations.
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 and system performance is reduced
Solution Approach 1:
The patent replaces complex mechanical signal processing operations with simpler categorical number-based comparisons. Instead of using traditional signal processing algorithms that require extensive computational resources, the system uses categorical numbers where similarity can be determined through straightforward bitwise operations, significantly reducing processing power consumption while maintaining comparison accuracy.
3Adaptability or versatility
If conventional computers use ordinal numbers to represent data samples, then they can store and manipulate numerical values, but they cannot directly compare data samples for similarity unless there is an exact match in ordinal number values
Solution Approach 1:
The patent implements a universal correlithm object framework that can represent and compare diverse data types (images, audio, text, etc.) using a unified categorical number system. This multi-functional approach allows the same comparison mechanism to handle different data types without requiring type-specific processing, thereby enhancing adaptability while maintaining operational simplicity through consistent bitwise comparison operations.
Data Source
AI summary
A correlithm object processing system includes a reference table that stores a plurality of correlithm objects, and a first node communicatively coupled to a second node by a communication channel. The first node is configured to receive a particular one of the plurality of correlithm objects from the second node over the communication channel. The first node determines distances between the received correlithm object and each of the plurality of correlithm objects stored in the reference table. The first node further identifies one of the plurality of correlithm objects from the reference table with the shortest distance, and outputs the identified correlithm object.