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 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 and quantify similarity between data samples, allowing for direct comparison of 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
1Adaptability or versatility
If conventional computers use ordinal binary integers to represent and manipulate data samples, then they can perform basic operations such as counting, sorting, and indexing, but they are unable to determine similarity between different data samples without complex signal processing techniques
Solution Approach 1:
The patent transforms the number system parameter from ordinal binary integers to a custom number system with base greater than 2 (e.g., base 3, base 4, or higher). This parameter change enables the representation of multiple relationships (equality, similarity, dissimilarity) using different digit values at each position, allowing computers to determine similarity between data samples through direct numerical comparison rather than complex signal processing
Solution Approach 2:
The patent adds a new dimensional aspect to data representation by introducing a relationship dimension through the custom number system. Each digit position in the custom number system represents a different relationship dimension (e.g., most significant digit represents equality, less significant digits represent varying degrees of similarity), enabling multi-faceted comparison of data samples without increasing computational complexity
2Measurement precision
If conventional computers rely on complex signal processing techniques to compare data samples, then they can determine similarity, but the processing power is consumed which reduces system speed and performance
Solution Approach 1:
The patent replaces the mechanical signal processing system with a mathematical number system-based comparison mechanism. Instead of using complex algorithms and computational operations to measure similarity, the system uses direct arithmetic comparison of numbers represented in the custom number system, where the relationship between data samples is encoded in the numerical values themselves, enabling fast and accurate comparison through simple arithmetic operations
Solution Approach 2:
The patent performs preliminary encoding of data samples into the custom number system format where similarity relationships are pre-encoded in the numerical representation. This preliminary action transforms the data into a form that can be directly compared using simple arithmetic operations, eliminating the need for complex real-time signal processing and thereby improving system speed while maintaining comparison accuracy
3Reliability
If conventional computers use ordinal numbers to represent data samples, then they can store and manipulate information, but they cannot tell if a data sample matches or is similar to other data samples unless there is an exact match in ordinal number values
Solution Approach 1:
The patent changes the fundamental parameter of the number system from base-2 binary to a custom base (e.g., base 3, base 4, or higher). This parameter change enables the system to represent multiple relationship states (exact match, partial match, no match) through different digit values, providing both reliable matching capability and flexible comparison adaptability across different types of data samples
Data Source
AI summary
A device configured to associate data to a multiple string correlithm object in a correlithm object processing system includes a bidirectional string correlithm object generator, a node, and a memory. The bidirectional string correlithm object generator outputs multiple bidirectional string correlithm objects that intersect at a central sub-string correlithm object. The node receives data elements, including a common data element. The memory stores a node table that associates the central sub-string correlithm object with the common data element. The node table associates sub-string correlithm objects of a first bidirectional string correlithm object with a first plurality of data elements, and associates sub-string correlithm objects of a second bidirectional string correlithm object with a second plurality of data elements.