Message Sticker Suggestion via Semantic Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges in providing timely and relevant responses in messaging conversations, especially when attention is divided or device limitations restrict text input, leading to inefficiencies in using message stickers for communication.
Innovation Solution
Implementing a computer-implemented method that analyzes messages for semantic concepts and suggests relevant message stickers by comparing descriptors with stored responses, allowing users to select and send these stickers with reduced user input, leveraging server-based analysis and local storage for efficient message processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually select and send message stickers, then communication expressiveness is improved, but response time and user effort increase
Solution Approach 1:
The system performs preliminary analysis of the received message to determine semantic concepts before the user needs to respond. Suggested message stickers are pre-selected and presented to the user, so the user does not need to manually search or select stickers from scratch, thereby reducing response time while maintaining communication expressiveness
Solution Approach 2:
The system automatically analyzes incoming messages, identifies relevant semantic concepts, and generates suggested message sticker responses without requiring manual user input for each sticker selection. This self-service approach reduces user effort and accelerates the messaging process while preserving the ability to convey nuanced emotions and meanings through stickers
2Measurement precision
If comprehensive message analysis is performed to suggest relevant stickers, then suggestion accuracy is improved, but computational resources increase
Solution Approach 1:
The system extracts only the essential semantic concepts from incoming messages that are most relevant for sticker suggestion, rather than performing exhaustive analysis of all message attributes. This selective extraction maintains suggestion accuracy by focusing on key semantic elements while reducing the overall computational burden
Solution Approach 2:
The system dynamically adjusts the depth and scope of message analysis based on message characteristics, user context, and device capabilities. By changing analysis parameters adaptively, the system achieves high suggestion accuracy when needed while conserving computational resources during routine interactions, effectively balancing precision with energy consumption
Data Source
AI summary
Implementations relate to automatic suggested responses based on message stickers provided in a messaging application. In some implementations, a computer-implemented method to provide message suggestions in a messaging application includes detecting a first message sent by a first user device to a second user device over a communication network, programmatically analyzing the first message to determine a semantic concept associated with the first message, identifying one or more message stickers based at least in part on the semantic concept, and transmitting instructions to cause the one or more message stickers to be displayed in a user interface displayed on the second user device.