Correlithm Object Processing for Analog 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 challenges that reduce system speed and performance in applications like face recognition and fraud detection.
Innovation Solution
The implementation of 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, through a configuration involving sensor, node, and actor tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional computers use ordinal binary integers to represent and manipulate data, then they can perform operations such as counting, sorting, indexing, and mathematical calculations, but they cannot determine similarity between different data samples without complex signal processing techniques
Solution Approach 1:
The patent transforms the numerical representation system from ordinal binary integers to a hybrid system incorporating analog values and categorical identifiers. This parameter change enables direct similarity determination by comparing analog values continuously, eliminating the need for complex discrete signal processing while maintaining computational efficiency.
Solution Approach 2:
The patent introduces analog values as an intermediary representation between conventional binary integers and similarity comparison operations. These analog values serve as a mediator that preserves continuity information, allowing direct mathematical comparison for similarity determination without requiring complex processing algorithms.
2Reliability
If conventional computers rely on exact match in ordinal number values to determine data sample similarity, then the comparison process is simple, but the system is unable to identify similar data samples that do not have exact matches
Solution Approach 1:
The patent creates a universal data representation framework where analog values can represent various types of data (images, audio, text, etc.) in a unified continuous space. This multi-functional approach allows the same comparison mechanism to handle different data types and formats effectively, improving both reliability and adaptability.
Solution Approach 2:
The patent transitions from discrete ordinal dimensions to continuous analog dimensions, adding a dimensional aspect that captures gradual variations in data. This dimensional change enables the system to detect similarities along continuous scales rather than only at discrete integer points, significantly improving similarity detection accuracy.
Data Source
AI summary
A string correlithm object generator is configured to output a string correlithm object comprising a plurality of sub-string correlithm objects. A node is configured to receive a plurality of data values. A memory is configured to store a node table that associates sub-string correlithm objects with the data values such that a first sub-string correlithm object is associated with a first data value and a second sub-string correlithm object is associated with a second data value. A processor is configured to receive a third data value that is between the first data value and the second data value, determine a third sub-string correlithm object that is interpolated between the first sub-string correlithm object and the second sub-string correlithm object, and associate the third sub-string correlithm object with the third data value.