Correlithm Object Processing System for Distributed Similarity Detection
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Solution Overview
Problem
Conventional computers are limited in comparing and determining similarity between data samples due to their reliance on ordinal numbers, which only provide information about sequence order, making it difficult to identify similarities or matches without exact matches, 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 between data samples, regardless of their type or format, through the use of sensor, node, and actor tables that transform data between ordinal and correlithm object domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computers use ordinal numbers to represent data samples, then they can perform basic operations like counting and sorting, but they cannot determine similarity between data samples without exact matches
Solution Approach 1:
The patent transforms the numerical representation system from ordinal to categorical. Correlithm objects use categorical numbers where each dimension represents a specific feature, and the value in each dimension is determined by the relationship between the data sample and that feature rather than its position in a sequence. This parameter change enables direct similarity measurement through correlation calculations without requiring complex signal processing.
Solution Approach 2:
The patent introduces correlithm objects as an intermediary representation layer between raw data samples and comparison operations. These correlithm objects encode data samples in a transformed domain where similarity is inherently represented by the correlation between the correlithm vectors. This intermediary structure allows direct similarity measurement through simple correlation calculations rather than complex processing of the original data.
2Measurement precision
If conventional computers rely on complex signal processing techniques to compare data samples, then they can determine similarity, but processing speed and system performance are reduced
Solution Approach 1:
The patent performs preliminary transformation of data samples into correlithm object representations during data ingestion or preprocessing. This preliminary action encodes the essential features and relationships of each data sample into the correlithm structure, so that subsequent similarity comparisons can be performed directly through correlation calculations without requiring complex signal processing at query time. The computationally intensive transformation is done once, enabling fast comparisons thereafter.
Solution Approach 2:
The patent replaces complex mechanical signal processing operations with simpler mathematical correlation calculations. Instead of using traditional signal processing techniques that involve multiple stages of filtering, transformation, and comparison, the correlithm object representation allows direct computation of similarity through correlation between the correlithm vectors, significantly reducing computational complexity and improving processing speed.
3Productivity
If conventional computers use ordinal binary integers to represent information, then they can manipulate data efficiently, but they lose information about relationships such as similarity between data samples
Solution Approach 1:
The patent transitions from one-dimensional ordinal representation to multi-dimensional categorical representation. Each correlithm object is a vector in n-dimensional space where each dimension corresponds to a specific feature or attribute. This dimensional expansion allows the representation to simultaneously encode both the identity of the data sample and its relationships to various features, preserving relationship information that would be lost in pure ordinal representation while maintaining efficient manipulation through vector operations.
Solution Approach 2:
The correlithm object representation serves multiple functions simultaneously: it uniquely identifies each data sample, encodes relationships to multiple features across different dimensions, and enables direct similarity measurement through correlation. This multi-functional representation eliminates the need for separate structures to handle identification and relationship encoding, allowing efficient data manipulation while preserving relationship information in a unified framework.
Data Source
AI summary
A distributed node network to emulate a correlithm object processing system includes a distribution node, first and second intermediate calculation nodes, and first and second final calculation nodes. The distribution node stores a correlithm object mapping table that comprises a plurality of source correlithm objects and a plurality of corresponding target correlithm objects. It divides each source correlithm object into at least first and second portions. It further divides the correlithm object mapping table into at least first and second portions. The first and second intermediate calculation nodes store the first and second portions of each source correlithm object, respectively. The first and second final calculation nodes stores the first and second portions of the mapping table, respectively.