Correlithm Object Processing for Data Similarity
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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 requirements and reduced performance in applications like face recognition and fraud detection.
Innovation Solution
The implementation of a correlithm object processing system using 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.
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 efficiently, but they cannot determine similarity between different data samples without complex signal processing techniques
Solution Approach 1:
The patent transforms data representation from ordinal binary integers to categorical numbers. This parameter change enables direct similarity comparison by treating data as categorical entities rather than ordered values, eliminating the need for complex signal processing while preserving the ability to determine similarity between data samples.
Solution Approach 2:
The patent replaces the mechanical signal processing system with a categorical number-based comparison system. Instead of using complex algorithms to analyze data samples, the system directly compares categorical representations, substituting the mechanical processing approach with a simpler mathematical comparison approach.
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 which reduces system speed and performance
Solution Approach 1:
By changing the fundamental parameter of data representation from ordinal to categorical, the system enables O(1) similarity comparisons instead of requiring complex processing. This parameter change directly improves productivity by eliminating computational overhead while maintaining accurate similarity determination.
3Adaptability or versatility
If conventional computers use ordinal numbers to represent data samples, then they can store and manipulate information efficiently, but they are unable to tell if a data sample matches or is similar to any other data samples unless there is an exact match
Solution Approach 1:
The patent changes the number system parameter from ordinal to categorical, which fundamentally alters how data is compared. Categorical numbers preserve similarity information by treating data as members of categories rather than ordered values, enabling the system to determine both exact matches and similarities without losing information.
Data Source
AI summary
A distributed node network to emulate a correlithm object processing system includes a distribution node, a first calculation node, and a second calculation node. The distribution node stores a correlithm object mapping table that comprises a plurality of first source correlithm objects, a plurality of second source correlithm objects, and a plurality of target correlithm objects that each corresponds to a first source correlithm object and a second source correlithm object. Each source correlithm object comprises an n-bit digital word of binary values, and each target correlithm object comprises an n-bit digital word of binary values. The first calculation node stores the plurality of first source correlithm objects. The second calculation node stores the plurality of second source correlithm objects.