Correlithm Object Processing System for Efficient Data Similarity

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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 data type or format through the use of sensor, node, and actor tables.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If conventional computers use ordinal binary integers to represent and manipulate data, then they can perform basic operations such as counting, sorting, and indexing, but they are unable to determine similarity between different data samples without exact matches

Engineering Contradiction:

Improveability to determine similarityVSAvoidcomplexity of processing system

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The patent transforms the numerical representation system from ordinal binary integers to categorical numbers. This parameter change in the data representation system enables the computer to directly determine similarity between data samples without requiring exact matches, while maintaining compatibility with conventional computer operations.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent introduces correlithm objects as an intermediary representation layer between conventional binary data and similarity comparison operations. These correlithm objects enable similarity determination by serving as a mediator that translates categorical number relationships into computable forms for conventional systems.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Ease of operation

If conventional computers rely on complex signal processing techniques to compare data samples, then they can determine similarity, but processing power consumption increases and system performance decreases

Engineering Contradiction:

Improveability to compare data samplesVSAvoidprocessing power consumption

Core Design Contradiction:

Ease of operationVSUse of energy by moving object

Solution Approach 1:

The patent replaces complex signal processing mechanisms with a categorical number-based comparison system. By substituting the mechanical signal processing approach with a mathematical categorization system, the patent achieves similarity comparison with significantly reduced processing power consumption and improved system performance.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent changes the fundamental parameter of data representation from ordinal to categorical numbers, which fundamentally alters how similarity comparison is performed. This parameter change eliminates the need for complex signal processing techniques and enables efficient similarity determination through direct categorical comparison.

Inventive Principle:

Principle #35Parameter changes

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

Engineering Contradiction:

Improveinformation about similarity relationshipsVSAvoidcomplexity of number system

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent changes the number system parameter from ordinal to categorical, which preserves all information about similarity relationships. Categorical numbers inherently encode similarity information through their structure, eliminating information loss while maintaining a relatively simple computational framework.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS10896052B2Computer architecture for emulating a correlithm object processing system that uses portions of a mapping table in a distributed node network

Publication Date: 2021.01.19 BANK OF AMERICA CORP

AI summary

A distributed node network to emulate a correlithm object processing system includes a resolution node, a first calculation node, and a second calculation node. The first and second calculation nodes determine n-dimensional distances between an input correlithm object and each of the source correlithm objects that are stored in respective portions of a correlithm object mapping table. The resolution node compares the determined n-dimensional distances from the first calculation node with the determined n-dimensional distance associated with the identified source correlithm object from the second calculation node to identify the smallest determined n-dimensional distance based on the comparison. The resolution node then identifies the target correlithm object associated with the smallest determined n-dimensional distance, and outputs the identified target correlithm object.