Correlithm Object Processing for Distributed Similarity Comparison
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Solution Overview
Problem
Conventional computers are limited in comparing and determining similarity between data samples, relying on binary comparisons that require exact matches, which is inefficient and consumes significant processing power, especially in applications like face recognition and fraud detection.
Innovation Solution
Implementing a correlithm object processing system that uses categorical numbers and correlithm objects to enable non-binary comparisons and quantify similarity between data samples, allowing for the comparison of data samples regardless of their type or format through a configuration involving sensor, node, and actor tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computers use ordinal binary integers to represent and compare data samples, then the system can perform basic counting, sorting, and indexing operations, but the system is unable to efficiently determine similarity between different data samples and must rely on complex signal processing techniques that consume significant processing power
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 both measurement precision for similarity determination and productivity by reducing processing power consumption
Solution Approach 2:
The patent introduces correlithm objects as an intermediary data structure that bridges the gap between conventional ordinal number representation and similarity comparison needs. These correlithm objects encode data samples in a format that naturally supports similarity operations, eliminating the need for complex processing while maintaining high accuracy
2Measurement precision
If conventional computers use complex signal processing techniques to determine similarity between data samples, then the system can achieve accurate similarity determination, but the processing power consumption increases and system performance decreases
Solution Approach 1:
The patent changes the fundamental parameter of data representation from ordinal binary to categorical numbers, which inherently supports similarity operations. This parameter change eliminates the need for energy-intensive signal processing techniques while maintaining accurate similarity determination
Solution Approach 2:
The patent extracts and removes the complex signal processing component from the system by replacing it with direct correlithm object comparison operations. This extraction eliminates unnecessary processing power consumption while preserving the essential similarity determination function
Data Source
AI summary
A distributed node network to emulate a correlithm object processing system includes a distribution node, a first calculation node, and a second calculation node communicatively coupled to each other. The distribution node is configured to divide each source correlithm object of a correlithm object mapping table into at least a first portion that comprises a first subset of the binary values in that source correlithm object and a second portion that comprises a second subset of the binary values in that source correlithm object. The first calculation node stores the first portion of each source correlithm object. The second calculation node stores the second portion of each source correlithm object.