Context-Based Personalized Emoticon Generation System
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Solution Overview
Problem
Current communication platforms face challenges in presenting suitable emojis and emoticons in real-time or near-real-time communications due to the vast number of options available, limiting users' ability to select desired visual elements that match the emotional and contextual context of the communication scenario.
Innovation Solution
A computer-implemented method and system that generates and presents context-based tailored emoticons using generative adversarial networks (GANs) in combination with long short-term memory (LSTM) and convolutional neural network (CNN) modules for language and visual recognition, personalized emoticon creation, and knowledge graph frameworks to predict user responses and adapt to changing contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a vast number of emoji and emoticon options are provided in communication platforms, then users have more choices for emotional expression, but users face difficulty in selecting desired visual elements that match the emotional and contextual context in real-time
Solution Approach 1:
The system performs preliminary actions by detecting the user's emotion and generating a customized subset of relevant emojis and emoticons before the user needs to select. This pre-filtering process based on emotional context and user profile prepares only the most relevant options, eliminating the need for users to search through vast libraries during real-time communication.
Solution Approach 2:
The system applies local quality by creating personalized emoji sets tailored to each user's emotional state and communication context. Instead of providing a uniform vast library to all users, the system generates context-specific subsets with appropriate emotional tone and style matching the current communication scenario and user preferences.
2Productivity
If generic emoji libraries are used across all users, then implementation is simple and fast, but the emoticons lack personalization and contextual relevance to individual users
Solution Approach 1:
The system performs preliminary actions by detecting the user's emotion and generating a customized subset of relevant emojis and emoticons before the user needs to select. This pre-filtering process based on emotional context and user profile prepares only the most relevant options, eliminating the need for users to search through vast libraries during real-time communication.
Solution Approach 2:
The system applies local quality by creating personalized emoji sets tailored to each user's emotional state and communication context. Instead of providing a uniform vast library to all users, the system generates context-specific subsets with appropriate emotional tone and style matching the current communication scenario and user preferences.
3Adaptability or versatility
If context-based tailored emoticons are generated using advanced AI models, then personalization and contextual relevance are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the complex task of emoji selection into distinct functional modules: emotion detection module, user profile analysis module, emoji generation module, and selection presentation module. Each module handles a specific aspect of the personalization process, making the overall complex system manageable and maintainable while achieving high contextual relevance.
Solution Approach 2:
The system introduces an intermediary AI processing layer between the user and the emoji library. This intermediary layer (comprising GANs, LSTMs, and CNNs) automatically analyzes context and user state, then translates these into personalized emoji recommendations, shielding users from system complexity while delivering highly relevant results.
Data Source
AI summary
A method, system, and computer program product for generating context-based tailored emoticons within a communication scenario are provided. The method receives detects an emotion of a user within a communication stream. A set of candidate emojis are generated. A set of emoticons are generated from the set of candidate emojis and a representation of the user. The set of emoticons are presented to the user in a user interface on a computing device associated with the user. The method incorporates a selected emoticon of the set of emoticons into the communication stream with the selected emoticon being selected by the user from the user interface.