Correlithm Object Processing for Data Similarity

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Solution Overview

Problem

Conventional computers are limited in comparing data samples due to their reliance on ordinal numbers, which only provide information about sequence order, failing to determine similarity between data samples, leading to complex signal processing challenges that reduce system speed and performance, especially in applications like face recognition and fraud detection.

Innovation Solution

The implementation of a correlithm object processing system that uses categorical numbers and geometric objects to enable non-binary comparisons and quantify similarity between data samples, employing a combination of sensor, node, and actor tables to transform data samples between ordinal and correlithm object domains.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional computers use ordinal numbers to represent data samples, then they can perform basic operations like counting and sorting, but they cannot determine similarity between different data samples

Engineering Contradiction:

Improvesimilarity determination capabilityVSAvoidsignal processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent transforms the number system parameter from ordinal to categorical, enabling similarity determination. Categorical numbers provide information about relationships between data samples rather than just sequence order, allowing the system to quantify similarity without complex signal processing techniques.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces complex mechanical signal processing techniques with a mathematical transformation approach using categorical numbers and correlithm objects. This substitution simplifies the system by using direct mathematical comparisons instead of iterative signal processing algorithms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

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

Engineering Contradiction:

Improvedata sample comparison accuracyVSAvoidsystem processing speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent performs preliminary transformation of data samples into correlithm objects represented by categorical numbers before comparison. This pre-processing step enables direct mathematical comparisons that are computationally efficient, avoiding the need for complex signal processing during the actual comparison operation and thus improving processing speed.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent creates correlithm objects as mathematical representations (copies) of data samples. These correlithm objects preserve the essential characteristics needed for similarity determination while enabling faster mathematical operations compared to processing the original data samples directly with complex signal processing techniques.

Inventive Principle:

Principle #26Copying

3Measurement precision

If conventional computers use ordinal binary integers to represent information, then they can perform mathematical calculations, but they cannot quantify the degree of similarity between data samples

Engineering Contradiction:

Improvesimilarity quantification capabilityVSAvoiddata type flexibility

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent creates a universal representation system using correlithm objects that can represent multiple data types (images, audio, text, etc.) in a unified manner. Categorical numbers provide a common language for expressing relationships across different data types, enabling the system to perform similarity determination and quantification universally across diverse data formats without requiring type-specific processing.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11354533B2Computer architecture for identifying data clusters using correlithm objects and machine learning in a correlithm object processing system

Publication Date: 2022.06.07 BANK OF AMERICA CORP

AI summary

A device that includes a model training engine implemented by a processor. The model training engine is configured to obtain a set of data values associated with a feature vector. The model training engine is further configured to transform a first data value and a second data value from the set of data value into sub-string correlithm objects. The model training engine is further configured to compute a Hamming distance between the first sub-string correlithm object and the second sub-string correlithm object and to identify a boundary in response to determining that the Hamming distance exceeds a bit difference threshold value. The model training engine is further configured to determine a number of identified boundaries, to determine a number of clusters based on the number of identified boundaries, and to train the machine learning model to associate the determined number of clusters with the feature vector.