Correlithm Object Processing System for 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 their reliance on ordinal numbers, which only provide information about sequence order, unable to quantify similarity or dissimilarity between data samples without exact matches.
Innovation Solution
Implementing a correlithm object processing system that uses categorical numbers and correlithm objects, enabling non-binary comparisons and quantifying similarity between data samples, regardless of their type or format, through a configuration involving sensor, node, and actor tables that transform data samples between ordinal and correlithm object domains.
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 basic operations such as counting, sorting, and indexing, but they are unable to determine similarity between data samples without exact matches
Solution Approach 1:
The patent transforms data representation from ordinal binary integers to categorical numbers. This parameter change enables the system to determine similarity between data samples by comparing their categorical values directly, rather than requiring complex signal processing techniques. The categorical number system provides inherent similarity information through its structure, allowing efficient comparison operations.
Solution Approach 2:
The patent replaces the mechanical signal processing approach with a mathematical categorical number system. Instead of using complex algorithms and processing mechanisms to determine similarity, the system uses the inherent properties of categorical numbers to directly quantify similarity, thereby reducing computational complexity and improving efficiency.
2Measurement precision
If conventional computers rely on complex signal processing techniques to determine similarity, then they can compare data samples, but processing speed and system performance are reduced
Solution Approach 1:
By changing the numerical representation from ordinal to categorical, the system enables direct comparison operations that are computationally efficient. Categorical numbers allow for quick determination of similarity through simple value comparisons, eliminating the need for time-consuming signal processing algorithms and thereby increasing processing speed.
Solution Approach 2:
The patent extracts the essential similarity information directly from the categorical number representation itself, rather than requiring additional processing steps. The categorical system inherently encodes similarity relationships, allowing the system to extract and utilize this information immediately without invoking complex external processing routines.
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 different data samples
Solution Approach 1:
The patent changes the fundamental parameter of data representation from ordinal to categorical numbers. This transformation preserves all necessary information for similarity determination within the number system itself, eliminating information loss while avoiding the need for complex comparison processes. The categorical structure inherently maintains similarity relationships.
Data Source
AI summary
A device configured to emulate a correlithm object system includes a memory that stores a node table. The node table identifies a plurality of source correlithm objects and a corresponding plurality of target correlithm objects. A node receives a first input correlithm object associated with a first timestamp, computes distances between the first input correlithm object and each of the source correlithm objects in the node table, and identifies a first source correlithm object from the node table with the shortest distance. The node identifies a first target correlithm object from the node table linked with the identified first source correlithm object, and outputs the first target correlithm object. The memory stores a node output table that identifies the first target correlithm object associated with the first source correlithm object, the first timestamp, and the computed distance between the first input correlithm object and the first source correlithm object.