Correlithm Object Processing for 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 their reliance on ordinal numbers, which only provide information about sequence order, unable to quantify similarity or dissimilarity between data samples unless there is an exact match.
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 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 or dissimilarity between data samples unless there is an exact match
Solution Approach 1:
The patent transforms the numerical representation system from ordinal binary integers to a distance metric-based system. Data samples are represented by their distances to reference points in a multidimensional space, allowing direct computation of similarity through distance comparisons rather than complex signal processing. This parameter transformation enables computers to naturally quantify similarity while maintaining computational efficiency.
2Measurement precision
If conventional computers rely on complex signal processing techniques to compare data samples, then they can determine similarity between data samples, but processing speed and system performance are reduced due to high processing power consumption
Solution Approach 1:
The patent pre-computes and stores distance metrics between data samples and reference points during data ingestion or preprocessing stages. When similarity comparison is needed, the system directly retrieves and compares these pre-computed distances rather than performing complex real-time signal processing. This preliminary action significantly speeds up comparison operations while maintaining accurate similarity determination.
Solution Approach 2:
The patent creates simplified representations of data samples in the form of distance vectors or distance matrices that capture the essential similarity information. These copied representations can be quickly compared using simple distance metrics without needing to process the original complex data samples, thereby improving processing speed while preserving similarity determination accuracy.
3Loss of information
If conventional computers use ordinal numbers to represent data samples, then they can store and manipulate information efficiently, but they lose information about relationships such as similarity between different data samples
Solution Approach 1:
The patent introduces a new dimensional framework for data representation by mapping data samples into a multidimensional space where distance corresponds to dissimilarity. Instead of using single-dimensional ordinal values, the system uses multidimensional distance vectors that encode similarity relationships. This dimensional transformation preserves relationship information while maintaining a structured representation suitable for efficient computation.
Data Source
AI summary
A device configured to emulate a correlithm object processing system includes a memory that stores a node table that identifies a plurality of source correlithm objects and a plurality of corresponding target correlithm objects. The system further includes a node coupled to the memory and configured to receive an input correlithm object, identify a source correlithm object from the node table with the shortest n-dimensional distance to the input correlithm object, and identify a first target correlithm object from the node table linked with the identified source correlithm object. The node further generates a second target correlithm object that is offset in n-dimensional space from the first target correlithm object by the distance between the input correlithm object and the identified source correlithm object. The node outputs the second target correlithm object.