Master-Slave Correlithm Processing for Similarity-Based Data Matching
Find Innovative SolutionsGenerate Solutions
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, leading to complex processes that consume processing power and reduce system speed.
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 to transform data between ordinal and correlithm object domains.
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 transforms data representation from ordinal binary integers to categorical numbers (correlithm objects). This parameter change enables the system to determine similarity between data samples by comparing categorical values directly, rather than requiring complex signal processing techniques to find patterns in ordinal data.
Solution Approach 2:
The patent replaces the mechanical approach of complex signal processing with a direct categorical comparison mechanism. Instead of using algorithms to analyze patterns in ordinal data, the system uses correlithm object tables to directly compare categorical representations, significantly simplifying the computational process.
2Reliability
If conventional computers rely on complex signal processing techniques to determine data sample similarity, then they can identify matches, but processing power is consumed and system speed is reduced
Solution Approach 1:
The patent performs preliminary transformation of data samples into correlithm objects before comparison. By pre-processing data into categorical form and organizing it in correlithm object tables, the system eliminates the need for complex signal processing during actual comparison operations, thereby increasing processing speed while maintaining matching accuracy.
Solution Approach 2:
The patent creates categorical copies (correlithm objects) of original data samples. These correlithm objects serve as simplified representations that retain similarity information, allowing rapid comparison without processing the original complex data formats, thus improving processing speed.
3Loss of information
If conventional computers use ordinal numbers to represent data samples, then they can store information, but they cannot quantify the degree of similarity between different data samples
Solution Approach 1:
The patent changes the numerical parameter system from ordinal to categorical. This transformation preserves similarity information that is inherently lost in ordinal representations, while simultaneously simplifying comparison operations to direct categorical matching, making the system both more informative and easier to operate.
Data Source
AI summary
A device that includes a master boss and a slave boss. The slave boss is configured to iteratively send execute and output commands to a first plurality of nodes implemented by a node engine identified in a first boss table in response to receiving an execute command from the master boss. The master boss is configured to iteratively send execute and output commands to the slave boss and a second plurality of nodes implemented by the node engine identified in a second boss table. Each node is configured to receive a first correlithm object, fetch a second correlithm object based on the first correlithm object in response to receiving an execute command, and output the second correlithm object in response to receiving an output command.