Triangle Lattice Correlithm Generation for Similarity-Based Data Comparison

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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 and reduced performance 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 like counting, sorting, and indexing, but they cannot 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 the number system parameter from ordinal binary integers to a hybrid system incorporating categorical numbers and geometric object representations. This parameter change enables direct similarity determination through geometric relationships (distance, angle, orientation) between represented data samples, eliminating the need for complex signal processing while maintaining computational capability.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent introduces geometric objects as intermediary representations between raw data samples and similarity measurements. These geometric objects encode data characteristics in spatial dimensions, allowing similarity to be determined through geometric operations (distance calculation, angular comparison) rather than complex signal processing, thus resolving the contradiction between measurement precision and device complexity.

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 speed and system performance are reduced due to high computational burden

Engineering Contradiction:

Improvesimilarity determination accuracyVSAvoidprocessing speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces the mechanical/computational signal processing system with a geometric representation system. Similarity determination is achieved through geometric operations (distance, angle, orientation calculations) on geometric objects rather than through complex iterative signal processing algorithms, significantly reducing computational burden while maintaining accuracy and improving processing speed.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent maps data samples into geometric space with multiple dimensions, where similarity is determined by spatial relationships (distance, angle, orientation) rather than numerical comparison. This dimensional transformation converts complex multi-parameter signal processing into simpler geometric operations, enhancing both accuracy and processing speed.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

3Loss of information

If conventional computers use ordinal numbers to represent data samples, then they can store and manipulate information, but they lose information about relationships between data samples such as similarity

Engineering Contradiction:

Improverelationship information retentionVSAvoidnumber system flexibility

Core Design Contradiction:

Loss of informationVSAdaptability or versatility

Solution Approach 1:

The patent creates a composite number system that combines ordinal binary integers with categorical numbers and geometric object representations. This composite system retains the computational advantages of binary integers while incorporating categorical properties that preserve relationship information (similarity, classification), and geometric representations that encode spatial relationships, thus preventing information loss while maintaining system versatility.

Inventive Principle:

Principle #40Composite materials

Solution Approach 2:

The patent designs a multi-functional number system where the same representation framework (geometric objects) serves multiple purposes: storing data samples, representing categorical relationships, encoding spatial relationships, and enabling similarity determination. This universal representation system eliminates information loss about relationships while maintaining adaptability across different data types and operations.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11107003B2Computer architecture for emulating a triangle lattice correlithm object generator in a correlithm object processing system

Publication Date: 2021.08.31 BANK OF AMERICA CORP

AI summary

A device configured to emulate a triangle lattice correlithm object generator includes multiple processing stages that operate together to output a triangle lattice correlithm object. A triangle lattice correlithm object has a generally triangular shape and is formed by a first sub-lattice correlithm object, a second sub-lattice correlithm object that is some number of bits away from the first sub-lattice correlithm object in n-dimensional space, and a third sub-lattice correlithm object that is some number of bits away from the second sub-lattice correlithm object in n-dimensional space.