Correlithm Object Converter for Efficient Data Similarity
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Solution Overview
Problem
Conventional computers are limited in comparing and determining similarity between data samples, relying on complex signal processing techniques due to the ordinal nature of their number systems, which consumes processing power and reduces speed and performance, especially 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, with the aid of sensor, node, and actor tables for data transformation and comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computers use ordinal binary integers to represent and manipulate data samples, then the system can perform basic operations such as counting, sorting, and indexing, but the system is unable to efficiently determine similarity between different data samples and must rely on complex signal processing techniques that consume processing power and reduce system speed and performance
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 fundamental parameter change enables the representation of multiple data samples using the same ordinal value, thereby encoding similarity information directly in the numeric representation without requiring complex signal processing techniques.
Solution Approach 2:
Instead of using different numeric values to represent different data samples (conventional approach), the patent inverts the approach by using the same numeric value to represent multiple related data samples. This inversion allows the system to inherently encode similarity relationships, where samples with the same representation are by definition similar, eliminating the need for complex comparison algorithms.
2Productivity
If conventional computers use ordinal binary integers to represent data samples, then the system can store and manipulate information efficiently, but the system consumes excessive processing power and experiences reduced speed when performing similarity comparisons between data samples
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 more efficient encoding of data samples where similarity relationships are directly represented by numeric values, allowing for faster comparison operations that require minimal processing power.
Solution Approach 2:
The patent creates multiple representations or copies of the same data sample using different custom number representations. By pre-computing and storing these representations, the system can quickly compare new data samples against stored representations without performing complex real-time processing, thereby increasing speed while reducing processing power consumption.
3Measurement precision
If conventional computers rely on complex signal processing techniques to determine data sample similarity, then the system can achieve accurate similarity determination, but the system complexity and processing requirements increase significantly
Solution Approach 1:
The patent fundamentally changes the numeric representation parameter from ordinal binary to a custom number system where the base reflects the dimensionality or feature space of the data. This parameter change transforms similarity determination from a complex computational problem into a simple numeric comparison, maintaining accuracy while dramatically simplifying system operation.
Solution Approach 2:
The patent replaces the mechanical signal processing system (involving complex algorithms and computations) with a mathematical number system-based approach. Instead of using mechanical or computational signal processing techniques to determine similarity, the system uses inherent mathematical properties of the custom number system, where similarity is directly encoded in the numeric representations, thereby simplifying operations.
Data Source
AI summary
A device that includes a converter engine configured to receive an input signal at one of a first input and a second input. In response to receiving the input signal at the first input, the device is configured to identify a real world value in a converter table based on the input signal, fetch a correlithm object linked with the real world value, and to output the identified correlithm object as the first output signal. In response to receiving the input signal at the second input, the device is configured to identify a correlithm object from the converter table with the shortest distance, to fetch a real world value from the converter table linked with the identified correlithm object, and to output the identified real world value as the second output signal.