Correlithm Object Architecture for Fast Similarity-Based Data Processing
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Solution Overview
Problem
Conventional computers rely on ordinal numbers for data processing, which only provide information about sequence order and fail to determine similarity between data samples, leading to complex signal processing techniques that consume processing power and reduce system speed and performance, especially in applications like face recognition and fraud detection.
Innovation Solution
Implementing 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 tables, node tables, 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 basic operations like counting, sorting, and indexing, but they cannot efficiently determine similarity between different data samples
Solution Approach 1:
The patent transforms data representation from ordinal binary integers to a new parameter system based on fundamental relationships and similarity metrics. By changing the numerical representation parameters, the system can directly compute similarity between data samples without complex signal processing, thus improving measurement precision for similarity detection while reducing process complexity
Solution Approach 2:
The patent replaces the conventional mechanical approach of exact matching with a new system based on similarity computation. Instead of relying on precise ordinal comparisons, the system uses transformed numerical representations that inherently encode similarity information, substituting complex mechanical comparison processes with simpler mathematical operations
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 applies preliminary transformation to data samples, converting them into a new representation format that pre-encodes similarity information. This preliminary action eliminates the need for subsequent complex signal processing steps, allowing rapid similarity comparison while maintaining detection accuracy, thus improving productivity without sacrificing measurement precision
3Adaptability or versatility
If conventional computers use exact match comparisons, then they can determine whether data samples are identical, but they cannot identify similar data samples that do not match exactly
Solution Approach 1:
The patent changes the comparison parameter from exact equality to similarity-based metrics. By transforming data into representations where similarity is encoded in the numerical values themselves, the system gains adaptability to identify similar data samples while preserving similarity information that would be lost in exact matching approaches
Data Source
AI summary
A device that includes a node engine configured to emulate a first node, a second node, and a third node. The first node is configured to receive a first correlithm object, fetch a second correlithm object based on the first correlithm object, and output the second correlithm object to the second node and the third node. Each correlithm object is a point in an n-dimensional space represented by a binary string. The second node is configured to receive the second correlithm object, fetch a third correlithm object based on the second correlithm object, and output the third correlithm object to the third node. The third node is configured to receive the second correlithm object, receive the third correlithm object, fetch a fourth correlithm object based on the second correlithm object and the third correlithm object, and output the fourth correlithm object.