Super-Character Ideogram Matrix for Latin Text Learning
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Solution Overview
Problem
Current machine learning technologies face challenges in effectively learning the meaning of written Latin-alphabet based languages, as they are not well-equipped to handle the complex combinations of ideograms present in these languages, limiting their ability to understand and process the nuanced meanings conveyed by multiple characters.
Innovation Solution
A multi-layer two-dimensional symbol is created, where a string of Latin-alphabet based language texts is transformed into a matrix of pixels representing a super-character, divided into sub-matrices that represent individual ideograms, allowing image processing techniques like convolutional neural networks to classify and learn the combined meaning of these ideograms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning approaches are used to process Latin-alphabet based languages, then the system can handle individual characters, but it cannot effectively learn the combined meaning of multiple ideograms
Solution Approach 1:
The patent segments a Latin-alphabet word into multiple ideogram components, where each ideogram is represented as a sub-matrix within a larger 2-D symbol. This segmentation allows the machine learning system to process and understand the combined meaning of multiple ideograms by treating them as distinct yet related units within the super-character structure.
Solution Approach 2:
The patent implements a nested structure where multiple ideogram sub-matrices are contained within a larger 2-D symbol (super-character). Each sub-matrix represents an individual ideogram, and these are nested within the overall 2-D symbol structure, allowing the system to learn both individual ideogram meanings and their combined meanings simultaneously.
2Loss of information
If Latin-alphabet text is processed as traditional text strings, then processing is straightforward, but the system cannot capture nuanced meanings conveyed by multiple characters
Solution Approach 1:
The patent transforms traditional 1-D text string representation into a 2-D symbol structure. By arranging ideogram sub-matrices in a two-dimensional grid format, the system can preserve and convey nuanced meanings that are lost in linear text representation, while the structured 2-D format actually simplifies the processing compared to handling complex combinatorial relationships in 1-D strings.
3Measurement precision
If the system uses detailed 2-D symbol representation with multiple sub-matrices, then it can learn combined meaning of ideograms, but the processing complexity increases
Solution Approach 1:
The patent creates a universal 2-D symbol structure that can represent multiple ideograms in a standardized format. This multi-functional representation allows the same image processing techniques and machine learning algorithms to handle various Latin-alphabet words and languages uniformly, reducing the need for specialized processing logic despite the increased representational detail.
Data Source
AI summary
A string of Latin-alphabet based language texts is received and formed a multi-layer 2-D symbol in a computing system. The received string contains at least one word with each word containing at least one letter of the Latin-alphabet based language. 2-D symbol comprises a matrix of N×N pixels of data representing a super-character. The matrix is divided into M×M sub-matrices. Each sub-matrix represents one ideogram formed from the at least one letter contained in a corresponding word in the received string. Ideogram has a square format with a dimension EL letters by EL letters (i.e., row and column). EL is determined from the total number of letters (LL) contained in the corresponding word. EL, LL, N and M are positive integers. Super-character represents a meaning formed from a specific combination of at least one ideogram. Meaning of the super-character is learned with image classification of the 2-D symbol.