Correlithm Object Processing System for Data Similarity Detection
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Solution Overview
Problem
Conventional computers are limited in comparing and determining similarity between data samples due to their reliance on ordinal numbers, which only provide information about sequence order, making it difficult to identify similarities or matches without exact matches, especially in applications like face recognition and fraud detection.
Innovation Solution
The implementation of a correlithm object processing system that uses categorical numbers and correlithm objects, enabling non-binary comparisons and quantifying similarity between data samples, regardless of their type or format, through a combination of sensor, node, and actor tables that transform data between ordinal and categorical representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional computers use ordinal binary integers to represent and manipulate data, then they can perform operations such as counting, sorting, indexing, and mathematical calculations, but they are unable to determine similarity between different data samples without exact matches
Solution Approach 1:
The patent changes the fundamental parameter of number representation from ordinal binary integers to a new number system that encodes similarity relationships. This allows the system to directly determine similarity between data samples without complex signal processing, as the numeric values themselves contain information about relationships between data samples.
Solution Approach 2:
The patent replaces the mechanical signal processing techniques traditionally used for similarity comparison with a mathematical approach using a new number system. Instead of performing complex computational operations to determine similarity, the system uses properties of the new number representation to directly identify similar data samples.
2Reliability
If conventional computers rely on complex signal processing techniques to compare data samples, then they can determine similarity, but processing speed and performance are reduced due to consumption of processing power
Solution Approach 1:
By changing the parameter of data representation to a new number system that inherently encodes similarity information, the patent eliminates the need for complex signal processing operations. This allows for rapid determination of data sample similarity using simple numeric comparisons, significantly improving processing speed while maintaining accuracy.
3Loss of information
If conventional computers use ordinal numbers to represent data samples, then they can store and manipulate information, but they cannot quantify the degree of similarity between different data samples
Solution Approach 1:
The patent creates a universal number system that can represent multiple types of data relationships (similarity, dissimilarity, degree of match) within a single framework. This allows the system to work with different data types and formats while maintaining the ability to quantify relationships, providing both information preservation and adaptability.
Data Source
AI summary
A device configured to emulate a correlithm object system includes a memory that stores a sensor table. The sensor table identifies a plurality of real-world value entries and a plurality of corresponding input correlithm objects. A sensor receives a first input signal associated with a first timestamp, the first input signal representing a first real-world value entry in the sensor table. The sensor identifies a first input correlithm object in the sensor table linked with the first real-world value entry and outputs the first input correlithm object. The memory further stores a sensor output table that identifies the first real-world value entry associated with the first input correlithm object and the first timestamp.