Correlithm Object Processing System for Data Similarity Detection
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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 geometric objects to enable non-binary comparisons, allowing for the quantification of similarity between data samples, regardless of their type or format, through the use of correlithm objects and a combination of sensor, node, and actor tables.
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 indexing, but they cannot determine similarity between different data samples
Solution Approach 1:
The patent transforms the number system parameter from ordinal binary integers to categorical numbers. This fundamental parameter change enables the system to represent data in a way that preserves similarity information, allowing direct comparison operations without complex signal processing while maintaining the ability to perform various data manipulation tasks
2Measurement precision
If conventional computers rely on complex signal processing techniques to compare data samples, then they can determine similarity, but processing power is consumed and system speed is reduced
Solution Approach 1:
The patent replaces the mechanical signal processing system with a categorical number-based comparison system. Instead of using complex algorithms and computations to determine similarity, the system uses direct categorical number comparisons that are computationally efficient, thereby maintaining measurement precision while significantly improving processing speed and reducing power consumption
3Adaptability or versatility
If conventional computers use ordinal numbers to represent data samples, then they can store and manipulate information, but they cannot tell if data samples match or are similar unless there is an exact match
Solution Approach 1:
The patent changes the numerical representation parameter from ordinal to categorical. This allows the system to maintain data representation capabilities while simultaneously enabling similarity detection, as categorical numbers inherently encode similarity relationships that ordinal numbers do not capture
Data Source
AI summary
A device configured to link correlithm objects in a correlithm object processing system, includes a link node and a memory. The link node receives a first string correlithm object comprising a first plurality of sub-string correlithm objects and a second string correlithm object comprising a second plurality of sub-string correlithm objects. Each of the second plurality of sub-string correlithm objects are unrelated to each of the first plurality of sub-string correlithm objects in n-dimensional space. The memory is communicatively coupled to the link node and stores a node table that associates at least one of the first plurality of sub-string correlithm objects with at least one of the second plurality of sub-string correlithm objects.