2D Super-Character Symbol for Ideogram Learning
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Solution Overview
Problem
Current machine learning technologies face challenges in effectively learning the combined meaning of multiple ideograms in natural languages, particularly in representing and processing complex scripts like logosyllabic characters, which are essential for understanding written texts.
Innovation Solution
A multi-layer two-dimensional symbol is created, comprising a matrix of pixels that represents a 'super-character' formed by combining ideograms, using image processing techniques like convolutional neural networks to classify and determine the meaning, facilitating the learning process in a CNN-based computing system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional machine learning methods are used to process natural language texts, then the processing of individual characters is straightforward, but the system cannot effectively learn the combined meaning of multiple ideograms in complex scripts
Solution Approach 1:
The patent transforms one-dimensional character sequences into two-dimensional symbol representations. Each 2-D symbol contains multiple ideograms arranged in a grid pattern, allowing the CNN to process spatial relationships between characters. This dimensional transformation enables the system to capture combined meanings of multiple ideograms simultaneously rather than processing them sequentially, directly resolving the limitation of traditional methods while maintaining manageable system complexity through leveraging existing CNN architecture capabilities.
2Productivity
If a CNN-based system is designed to process multiple ideograms, then the learning capability improves, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent merges multiple ideograms into a single 2-D symbol structure, consolidating what would otherwise require multiple separate processing operations into one unified input unit. The CNN processes the entire 2-D symbol in a single forward pass, extracting features from all ideograms simultaneously. This merging approach improves learning efficiency by capturing combined meanings in one operation while reducing processing time compared to sequential analysis of individual characters.
Solution Approach 2:
The 2-D symbol is segmented into a grid of sub-matrices, where each sub-matrix corresponds to a specific ideogram position. This segmentation allows the CNN to apply localized filters to different regions, efficiently extracting features from multiple ideograms in parallel. The segmented structure maintains organizational clarity while enabling simultaneous processing, thus improving productivity without proportionally increasing processing time.
3Adaptability or versatility
If the system processes complex logosyllabic characters, then the natural language processing capability improves, but the difficulty of detecting and measuring the meaning increases
Solution Approach 1:
The patent assigns different visual representations (analogous to color channels) to different aspects of ideogram processing. Each ideogram in the 2-D symbol can be represented with distinct visual features that the CNN learns to interpret. This visual differentiation simplifies the detection and measurement of meaning by transforming abstract semantic relationships into detectable visual patterns that the neural network can process efficiently, thereby reducing the difficulty of determining combined meanings in complex scripts.
Data Source
AI summary
A string of natural language texts is received and formed a multi-layer 2-D symbol in a first computing system. The 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 with each sub-matrix containing (N/M)×(N/M) pixels. N and M are positive integers, and N is preferably a multiple of M. Each sub-matrix represents one ideogram defined in an ideogram collection set. “Super-character” represents a meaning formed from a specific combination of a plurality of ideograms. The meaning of the “super-character” is learned in a second computing system by using an image processing technique to classify the 2-D symbol, which is formed in the first computing system and transmitted to the second computing system. Image process technique includes predefining a set of categories and determining a probability for associating each of the predefined categories with the meaning of the “super-character”.