Cultural Symbol Concept Vector Generation via Multimodal Fusion
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Solution Overview
Problem
Current machine learning solutions fail to effectively represent the cultural meaning and interrelationships of cultural symbols, such as hieroglyphs, across multiple modalities like images, texts, and sounds, limiting their application in AI systems.
Innovation Solution
A machine learning method that generates concept vectors by analyzing and fusing feature vectors from multiple materials including images, pronunciations, and videos of cultural symbols, enabling the characterization of cultural symbols with visual, auditory, and grammatical features, and their meanings, and relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing machine learning models (FastText, VisualRank) are used to process cultural symbols, then text classification or image-text correlation analysis can be achieved, but the cultural meaning and interrelationships of symbols cannot be represented
Solution Approach 1:
The patent segments the cultural symbol processing into multiple independent feature extraction modules: visual feature extraction from images/videos, auditory feature extraction from pronunciations, and linguistic feature extraction from texts. Each module processes one type of material independently, then their outputs are integrated to form a comprehensive concept vector that preserves cultural meaning while maintaining adaptability across different symbol systems.
2Loss of information
If digital codes are generated from single modality (image or text only), then direct and consistent association with the symbol can be achieved, but the cultural meaning and interrelationships between symbols cannot be captured
Solution Approach 1:
The patent merges multiple modality-specific feature extraction processes into a unified concept vector generation system. Visual features from images/videos, auditory features from pronunciations, and linguistic features from texts are combined through a fusion mechanism that integrates them into a single comprehensive digital representation. This merging preserves cultural meaning while the modular architecture manages complexity through organized feature processing pipelines.
Solution Approach 2:
The concept vector is constructed as a composite digital representation that integrates features from multiple material types (images, videos, pronunciations, texts). Each material contributes specific feature dimensions to the composite vector, analogous to how composite materials combine different substances to achieve superior properties. This composite approach ensures comprehensive cultural meaning representation while the structured fusion process manages the complexity of processing diverse input types.
3Adaptability or versatility
If machine learning models process only single modality data, then processing speed and simplicity are maintained, but the ability to characterize cultural symbols across multiple modalities is limited
Solution Approach 1:
The patent implements a universal concept vector framework that can process multiple modality types (visual, auditory, linguistic) through a single integrated system. The feature extraction and fusion mechanism is designed to handle different input types uniformly, extracting relevant features from images, videos, pronunciations, and texts, then integrating them into a common representation space. This multi-functional approach enables versatile cultural symbol characterization while the standardized processing pipeline manages the complexity of handling diverse modalities.
Data Source
AI summary
The present disclosure proposes a method for characterizing cultural symbols by using a machine learning model, including: receiving multiple materials about a symbol unit, wherein the multiple materials at least include a picture drawing the symbol unit, pronunciation of the symbol unit, and an image or a video showing cultural meaning of the symbol unit, and the symbol unit is a single cultural symbol or a combination of multiple cultural symbols; for each material of the multiple materials, analyzing and learning the material to extract features of the material to form a set of feature vectors; fusing all of the formed feature vectors into a tensor; and analyzing and learning the tensor to generate a concept vector characterizing the symbol unit, wherein the concept vector is directly and consistently associated with the symbol unit. The method can characterize a broader range of cultural symbols, and can be used for multimodal information retrieval.