Token Sky Maps Decode Emoticon Meaning
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Solution Overview
Problem
Existing communication systems struggle to accurately decode non-original-meaning emoticons in electronic communications, as current tools are unable to comprehend these emoticons due to their varied meanings across cultures, geographies, and interests, leading to confusion and misinterpretation.
Innovation Solution
The use of token sky maps, which are undirected and unweighted topological graphs, to represent the neighborhood distribution structures of emoticons and plaintext words, allowing for the identification of candidate meanings through graph convolution networks (GCN) models, enabling the overlap of emoticon token sky maps with plaintext token sky maps to determine the most likely meaning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional decoding tools are used to interpret emoticons, then the decoding process is simple and fast, but the accuracy of decoding non-original-meaning emoticons is poor due to inability to comprehend varied meanings across cultures and contexts
Solution Approach 1:
The patent transforms the decoding problem from traditional text-based analysis to a spatial topological representation using token sky maps. By mapping tokens to spatial coordinates based on their neighborhood distribution structures, the system creates a new dimensional space where semantic relationships are geometrically encoded, enabling more accurate interpretation of non-original-meaning emoticons through spatial pattern recognition rather than conventional lexical matching
Solution Approach 2:
The patent introduces token sky maps as an intermediary representation layer between the raw emoticon data and the decoding process. These topological graphs serve as a mediator that captures the neighborhood distribution structures and spatial relationships of tokens, allowing the GCN model to indirectly analyze emoticon meanings through their topological patterns rather than direct textual analysis
2Measurement precision
If token sky maps with GCN models are used to decode emoticons, then the decoding accuracy improves through spatial distribution matching, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-computing the token sky maps and their neighborhood distribution structures before the actual decoding process. By establishing the topological graphs and spatial relationships in advance, the system prepares the data structure needed for GCN processing, reducing the computational burden during real-time decoding operations and enabling faster inference while maintaining high accuracy
3Loss of information
If token sky maps are used to represent neighborhood distribution structures, then the semantic relationships between emoticons and plaintext words are better captured, but the data structure complexity increases
Solution Approach 1:
The patent creates a universal topological graph structure (token sky map) that can represent multiple types of tokens (emoticons, plaintext words, and their combinations) within a single unified framework. This multi-functional data structure captures neighborhood distribution structures and spatial relationships across different token types, enabling the system to retain rich semantic information while using a consistent representation method that simplifies the overall data structure management
Data Source
AI summary
Embodiments relate to decoding communications with token sky maps. At least one electronic communication including emoticons having a non-original meaning is received. A candidate meaning is determined for the emoticons having the non-original meaning in the at least one electronic communication based at least in part on token neighborhood distribution structures. The candidate meaning for the emoticons having the non-original meaning is caused to be displayed on at least one device.