Correlithm Object Processing for Data Similarity Detection

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Solution Overview

Problem

Conventional computers rely on ordinal numbers for data processing, which only provide information about sequence order and fail 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

Implementing a correlithm object processing system that uses categorical numbers and geometric objects to enable non-binary comparisons and quantify similarity between data samples, allowing for efficient comparison of data samples regardless of their type or format.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If conventional computers use ordinal numbers for data processing, then data can be stored and manipulated, but similarity between data samples cannot be determined

Engineering Contradiction:

Improvesimilarity informationVSAvoidprocessing system

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent changes the fundamental parameter of data representation from ordinal numbers to correlithm objects that encode categorical information. This parameter change enables the system to represent similarity relationships directly in the data structure itself, eliminating the need for complex similarity computation algorithms while maintaining the ability to store and manipulate data.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent introduces correlithm objects as an intermediary representation layer between raw data and processing operations. These correlithm objects serve as mediators that transform data into a format where similarity is inherently encoded, allowing the system to determine similarity without requiring complex external processing algorithms.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If conventional computers rely on complex signal processing techniques to determine similarity, then similarity can be assessed, but processing speed and performance are reduced

Engineering Contradiction:

Improvesimilarity determinationVSAvoidprocessing speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent performs the similarity encoding action preliminarily during data representation. Instead of computing similarity during processing, the system pre-encodes similarity information into the correlithm object structure itself. This preliminary action eliminates the need for time-consuming similarity computation during actual processing operations, dramatically improving throughput while maintaining measurement precision.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces the mechanical computational process of signal processing with a structural encoding approach. Rather than using algorithms to calculate similarity, the system uses the inherent structure of correlithm objects to represent similarity relationships, substituting computational mechanics with structural representation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If conventional computers use ordinal numbers for data representation, then data can be stored, but flexibility to work with different data types is limited

Engineering Contradiction:

Improvedata type flexibilityVSAvoidprocessing system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent creates a universal data representation framework using correlithm objects that can handle multiple data types uniformly. The same correlithm object structure can represent different data types (images, audio, text) while maintaining the ability to encode similarity relationships, providing multi-functionality without requiring type-specific processing logic.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent segments the data representation function into distinct components: the correlithm object structure handles universal representation, while separate stimulus condition tables handle type-specific processing. This segmentation allows the system to maintain flexibility across data types while keeping the core processing system relatively simple.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11055120B2Computer architecture for emulating a control node in conjunction with stimulus conditions in a correlithm object processing system

Publication Date: 2021.07.06 BANK OF AMERICA CORP

AI summary

A device configured to emulate a correlithm object processing system includes a stimulus sensor, a memory and a control node. The stimulus sensor outputs an input stimulus correlithm object comprising an n-bit digital word. The memory stores a control table that comprises control correlithm objects and stimulus correlithm objects corresponding to various stimulus conditions. The control node is communicatively coupled to the stimulus sensor and the memory, and is configured to receive the input stimulus correlithm object and determine n-dimensional distances between the input stimulus correlithm object and each of the corresponding control correlithm objects in control table. The control node identifies the control correlithm object that has the smallest n-dimensional distance to the input stimulus correlithm object and determines that it is within a predetermined n-dimensional distance threshold. The control node outputs an output stimulus correlithm object corresponding to the identified control correlithm object.