Animated Emoji Mashup Generation via Machine Learning
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Solution Overview
Problem
Current user device communication systems are limited by predefined emojis that often fail to accurately represent the context or sentiment of a message, leading users to resort to stickers or GIFs for better expression.
Innovation Solution
A system and method using machine learning to generate animated emoji mashups by combining emojis with other digital media, such as images, GIFs, and videos, to create more comprehensive representations of contextual information through word vectorization, sentiment analysis, and matchmaking logic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predefined emojis are used for message representation, then the system is simple and easy to operate, but the emoji accuracy in representing context and sentiment deteriorates
Solution Approach 1:
The system transforms static emojis into dynamic animated mashups by changing their state parameters. Machine learning models analyze message context, sentiment, and tone to dynamically select and animate appropriate emoji combinations, transforming the parameter of emoji expressiveness from fixed to variable based on message characteristics
Solution Approach 2:
The system creates composite emoji representations by combining multiple emojis into animated mashups. Instead of using single predefined emojis, the system composes multiple emoji elements into unified animated sequences that better capture nuanced sentiments and contextual information
2Adaptability or versatility
If single emojis are used for expression, then the device complexity is low, but the expressiveness and versatility of communication deteriorates
Solution Approach 1:
The system merges multiple individual emojis into unified animated mashups that convey complex sentiments. By combining emoji elements into coordinated animated sequences, the system achieves greater communicative versatility while managing complexity through automated generation rather than manual selection of multiple separate emojis
Solution Approach 2:
The system introduces dynamics to emoji expression by creating animated sequences that evolve over time. Instead of static single emojis, animated mashups display temporal variations in expression, allowing the communication medium to adapt its expressiveness to match the evolving sentiment and context of the message
Data Source
AI summary
Aspects of the present disclosure involve systems, methods, devices, and the like for animated emoji mashup generation. The system and method introduce a method and model that can generate animated emoji mashups representative of contextual information received by a user at an application. The animated emoji mashup may come in the form of emojis coherently combined with one or more images to represent the contextual idea or emotion being conveyed.