AI-Based Message Routing for Messenger Conversation Rooms
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Solution Overview
Problem
Conventional messenger applications often lead to embarrassing situations when a user mistakenly inputs a message into an incorrect conversation room, as they lack effective methods to determine the most appropriate conversation room for a given message based on the context of the conversation.
Innovation Solution
An electronic device and method that analyze the characteristics of conversation rooms using AI models to identify the most suitable room for a user-input sentence by calculating similarity and relevance scores, ensuring the message is sent to the correct conversation room.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional messenger applications allow users to freely transmit messages in multiple conversation rooms, then users can communicate with different groups efficiently, but users may mistakenly input messages into incorrect conversation rooms causing embarrassing situations
Solution Approach 1:
The system provides feedback by analyzing the input message against conversation room characteristics and presenting multiple candidate rooms with similarity scores to the user. This feedback loop allows users to confirm or correct the suggested routing before final message transmission, preventing mistakes while maintaining efficient communication.
Solution Approach 2:
The message routing system acts as an intermediary between the user's message input and the conversation room selection. It introduces an analysis layer that evaluates message content against room characteristics and provides recommended routing options, mediating the decision process to improve accuracy without significantly increasing user effort.
2Reliability
If the system analyzes message content and conversation room characteristics to determine the most appropriate room, then message routing accuracy improves, but system complexity increases due to multiple AI models and analysis processes
Solution Approach 1:
The system segments the message routing task into distinct functional modules: message analysis, conversation room characteristic analysis, similarity calculation, and candidate generation. Each module is handled by a specific AI model or processing component, making the complex system more manageable and maintainable while achieving high routing accuracy.
Solution Approach 2:
The AI models are designed to perform multiple functions: analyzing message content, extracting conversation room characteristics, calculating similarity scores, and generating candidate room recommendations. This multi-functionality reduces the need for separate specialized components, managing system complexity while maintaining high routing accuracy.
3Measurement precision
If the system calculates similarity scores and relevance scores using multiple AI models, then the precision of conversation room identification improves, but the time required for message routing increases
Solution Approach 1:
The system performs partial analysis by focusing on the most relevant features of messages and conversation rooms. It calculates similarity scores for key characteristics and generates a limited number of top candidate rooms rather than exhaustively analyzing all possible rooms, maintaining high precision while reducing processing time.
Solution Approach 2:
The system performs preliminary analysis of conversation room characteristics and message content before final routing decision. By pre-processing and storing key features of conversation rooms and analyzing message patterns in advance, it reduces the computational burden during actual message routing, maintaining precision while minimizing time loss.
Data Source
AI summary
An electronic device and a method for controlling thereof are provided. The electronic device may include: a memory and a processor configured to: input sentences input into a plurality of conversation rooms generated through a messenger application into a first model and acquire characteristic information of conversations conducted in the plurality of respective conversation rooms, based on a first sentence being input, input the first sentence and the characteristic information into a second model and acquire first scores indicating a similarity between the first sentence and the characteristics of the conversations conducted in the plurality of respective conversation rooms, input the first sentence and the sentences input into the plurality of conversation rooms into a third model and acquire second scores indicating relevance between the first sentence and the sentences input into the plurality of conversation rooms, identify a conversation room as a conversation room corresponding to the first sentence among the plurality of conversation rooms based on the characteristic information, the first scores, and the second scores, and input the first sentence into the identified conversation room.