Correlithm Object Processing for Non-Binary Data Similarity

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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 facial recognition and fraud detection.

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, node, and actor tables.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

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 determine similarity between different data samples without complex signal processing techniques

Engineering Contradiction:

Improvesimilarity determination capabilityVSAvoidsignal processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent transforms data representation from ordinal binary integers to categorical numbers. This parameter change enables direct similarity comparison by treating data samples as categories rather than ordered values, eliminating the need for complex signal processing techniques while maintaining the ability to determine similarity between data samples.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent introduces correlithm objects as an intermediary representation layer between raw data samples and comparison operations. These correlithm objects encode data samples in a format that naturally supports similarity determination through bitwise operations, serving as a mediator that simplifies the comparison process without losing essential information.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

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

Engineering Contradiction:

Improvesimilarity determination capabilityVSAvoidprocessing speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces complex mechanical signal processing operations with simple bitwise logical operations on correlithm objects. This substitution dramatically reduces computational complexity and processing time, as bitwise operations can be executed efficiently by standard computer hardware without requiring intensive signal processing algorithms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

By changing the representation parameter from ordinal to categorical, the patent enables direct comparison operations that do not require iterative signal processing. This parameter transformation allows similarity determination to be performed through single-pass bitwise operations, significantly improving processing speed and system productivity.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If conventional computers use ordinal numbers to represent data samples, then they can store and manipulate information, but they cannot perform non-binary comparisons to identify similar data samples without exact matches

Engineering Contradiction:

Improvecomparison flexibilityVSAvoidmatch accuracy

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent changes the number system parameter from ordinal to categorical, enabling the system to perform non-binary comparisons. This allows data samples to be compared for similarity rather than requiring exact matches, increasing comparison flexibility while maintaining reliability through the structured correlithm object representation that preserves data integrity.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent introduces dynamic similarity thresholds and adjustable comparison parameters through the correlithm object framework. This allows the system to adaptively determine match criteria based on application requirements, providing both flexible comparison capabilities and reliable match identification by adjusting the stringency of similarity requirements.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS10936349B2Computer architecture for performing addition using correlithm objects in a correlithm object processing system

Publication Date: 2021.03.02 BANK OF AMERICA CORP

AI summary

A system includes a memory and a node. The memory stores first and second linear string correlithm objects. The node receives first and second real-world numerical values, and identifies a first sub-string correlithm object from the first linear string correlithm object that corresponds to the first real-world numerical value. The node aligns the first and second linear string correlithm objects such that the first sub-string correlithm object aligns with a sub-string correlithm object from the second linear string correlithm object that corresponds to zero. The node identifies a second sub-string correlithm object from the second linear string correlithm object that corresponds to the second real-world numerical value, and determines which sub-string correlithm object from the first linear string correlithm object aligns with the second sub-string correlithm. The node outputs the determined sub-string correlithm object from the first linear string correlithm object.