Message Queue Emoji Reaction System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing applications face challenges in accurately determining appropriate emoji reaction icons for user messages that are grammatically incomplete or incorrect, leading to inconsistent sentiment representation.
Innovation Solution
A system that preprocesses user messages to identify triggering conditions, combines incomplete messages, and uses a machine learning model trained with natural language processing to determine the appropriate emoji reaction icons, incorporating a preprocessing engine and a machine learning module to analyze and correct grammatical errors and associate correct emojis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system processes individual messages separately, then the response time is fast, but the accuracy of emoji reaction determination is low for incomplete or incorrect messages
Solution Approach 1:
The system performs preliminary actions by combining multiple messages into a unified context before processing. The message queue stores and buffers incoming messages, allowing the system to accumulate complete thought sequences before determining emoji reactions, thus improving accuracy without significant time loss.
Solution Approach 2:
The patent merges multiple individual messages into a single combined message context. By combining messages that form a complete thought sequence, the system can accurately determine emoji reactions for incomplete or incorrect individual messages, resolving the contradiction between speed and accuracy.
2Adaptability or versatility
If the system uses simple emoji reaction assignment, then the processing is simple and fast, but it cannot handle grammatically incomplete or incorrect messages
Solution Approach 1:
The system performs preliminary message combination and context building before emoji reaction determination. This preliminary processing allows the system to handle grammatically incomplete or incorrect messages by waiting for and combining subsequent messages that complete the thought, thereby increasing adaptability without excessive complexity.
Solution Approach 2:
The message queue acts as an intermediary component that buffers, stores, and manages message flow. This intermediary structure enables the system to collect complete message sequences before processing, allowing accurate emoji reaction determination for incomplete or incorrect individual messages while maintaining manageable system complexity.
3Reliability
If the system processes messages in real-time individually, then the user experience is responsive, but the sentiment representation becomes inconsistent for corrected messages
Solution Approach 1:
The system merges multiple messages into a unified context before determining emoji reactions. This merging process ensures that corrected messages are processed together with their predecessors, maintaining consistent sentiment representation while preserving responsive user experience through efficient message queue processing.
Solution Approach 2:
The system implements feedback mechanisms where corrected messages trigger re-processing of the combined message sequence. This feedback loop ensures that emoji reactions are updated to reflect the corrected sentiment, maintaining reliability and consistency in sentiment representation while adapting to user corrections naturally.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for providing an untrained machine learning model with a combined message and an appropriate emoji reaction icon. An embodiment operates by receiving a first and second user message. The first and second user comprises a first and second user text or a first and second user-inserted emoji reaction icon, respectively. The first or second user text is associated with a first or second system-specified emoji reaction icon, respectively. Thereafter, the first user message is determined to be associated with the second user message, the combined message is created based on the first and second messages, and the appropriate emoji reaction is identified as associated with the combined message. The combined message and associated appropriate emoji reaction icon are then sent to an untrained machine learning model configured to for training.