Correlithm Object Processing for Non-Binary Data Similarity
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Solution Overview
Problem
Conventional computers are limited in comparing and determining similarity between data samples, relying on binary comparisons that require exact matches, which is inefficient and consumes significant processing power, 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 enable non-binary comparisons and quantify similarity between data samples, allowing for the comparison of data samples regardless of their data type or format through a combination of sensor, node, and actor tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional computers use ordinal numbers to represent data samples, then data can be stored and manipulated using standard binary integers, but the system cannot determine similarity between data samples and requires complex signal processing techniques
Solution Approach 1:
The patent changes the fundamental parameter of number representation from ordinal binary integers to categorical numbers. This parameter change enables the system to represent data samples in a way that inherently encodes similarity information, allowing direct comparison without complex signal processing techniques.
Solution Approach 2:
The patent replaces the mechanical/conventional binary integer comparison system with a categorical number system. This substitution allows the computer to perform similarity determination through straightforward categorical comparisons rather than complex signal processing operations.
2Productivity
If conventional computers rely on exact matches in ordinal numbers for comparison, then standard binary operations can be used, but processing power is consumed and speed is reduced
Solution Approach 1:
By changing from ordinal to categorical number representation, the system enables faster comparison operations that consume less processing power. Categorical numbers allow direct similarity assessment without the iterative complex calculations required by conventional ordinal-based approaches.
3Measurement precision
If conventional computers use ordinal numbers for data representation, then standard computational operations are simplified, but the system cannot quantify the degree of similarity between data samples
Solution Approach 1:
The patent introduces categorical numbers as a new parameter for data representation. This parameter change enables the system to quantify similarity degrees directly through categorical comparisons, providing measurement precision without increasing operational complexity.
Data Source
AI summary
A device that includes a sensor engine and a node engine. The sensor engine is configured to receive an input signal representing a data sample and identify a real world value entry in a sensor table based on the input signal. The sensor engine is further configured to fetch an input correlithm object in the sensor table linked with the real world value entry and send the input correlithm object to a node engine. The node engine is configured to determine distances between the input correlithm object and each of the child correlithm objects in a node table in response to receiving the input correlithm object and identify a child correlithm object from the node table with the shortest distance. The node engine is further configured to fetch a parent correlithm object from the node table linked with the identified child correlithm object and output the identified parent correlithm object.