Correlithm Object Processing System for Data Similarity
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Solution Overview
Problem
Conventional computers rely on ordinal numbers for data processing, which limits their ability to compare and determine similarity between data samples, leading to complex processes that consume processing power and reduce speed, 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 represent data samples, enabling non-binary comparisons and quantifying similarity between data samples, regardless of their type or format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computers use ordinal binary integers to represent and manipulate data, then they can perform operations like counting, sorting, and mathematical calculations efficiently, but they are unable to determine similarity between different data samples without complex signal processing techniques
Solution Approach 1:
The patent transforms the parameter system from ordinal binary integers to n-dimensional binary vectors (correlithm objects). This parameter change enables direct similarity determination through bitwise operations, eliminating the need for complex signal processing while maintaining the ability to represent diverse data types including images, audio, and text.
2Measurement precision
If conventional computers rely on complex signal processing techniques to compare data samples, then they can determine similarity between data samples, but processing speed and system performance are reduced due to consumption of processing power
Solution Approach 1:
The patent replaces complex mechanical signal processing operations with simple bitwise logical operations on binary vectors. This substitution dramatically reduces processing power consumption and increases processing speed while maintaining accurate similarity determination capability through the n-dimensional binary vector representation.
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 data samples unless there is an exact match
Solution Approach 1:
The patent transitions from one-dimensional ordinal numbers to n-dimensional binary vectors, adding dimensional complexity that enables similarity quantification. Each dimension represents a feature or attribute, allowing the system to retain and measure similarity information across multiple dimensions while maintaining simple bitwise comparison operations.
Data Source
AI summary
A correlithm object processing system that includes a trainer configured to send a node entry request to a node engine that triggers the node engine to generate an entry in a node table. The trainer is further configured to receive a source correlithm object and a target correlithm object in response to sending the node entry request. The trainer is further configured to send a real world input value and the source correlithm object to a sensor engine which triggers the sensor engine to generate an entry in a sensor table linking the real world input value and the source correlithm object. The trainer is further configured to send a real world output value and the target correlithm object to an actor engine which triggers the actor engine to generate an entry in an actor table linking the real world output value and the target correlithm object.