Typing Feedback Indicator for Context Awareness
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Solution Overview
Problem
Messaging applications do not provide recipients with context awareness of incoming messages, only indicating that a message is being typed without revealing the subject matter.
Innovation Solution
A computer-implemented method that detects user input in a messaging application, analyzes the text to determine sentiment, emotion, and topic, and generates a typing feedback indicator reflecting the message content, which is displayed on the recipient's device if the confidence in the indication exceeds a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional typing indicators are used, then the system is simple and fast, but the recipient receives no context awareness of the incoming message content
Solution Approach 1:
The system performs preliminary analysis of the message content (sentiment, emotion, topic) while the user is still composing it, before the message is sent. This allows the typing indicator to display contextual information in advance, giving the recipient context awareness before the actual message arrives.
Solution Approach 2:
The patent introduces an intermediary processing layer that analyzes the composed text and generates contextual metadata (sentiment, emotion, topic). This intermediary layer bridges the gap between the simple typing indicator and the rich message content, providing context without requiring the recipient to read the full message.
2Loss of information
If message content is analyzed in real-time, then context awareness is provided, but processing time and computational resources increase
Solution Approach 1:
The analysis is performed preliminarily while the user composes the message, utilizing idle processing time before message transmission. This avoids adding processing time to the critical message delivery path, as the analysis completes before the send action occurs.
Solution Approach 2:
The system performs partial analysis by focusing on key aspects (sentiment, emotion, topic) rather than comprehensive text processing. This selective analysis provides sufficient context awareness while minimizing computational overhead and processing time.
3Measurement precision
If detailed text analysis is performed, then accurate context indication is achieved, but the system becomes more complex and resource-intensive
Solution Approach 1:
The text analysis is segmented into three distinct components: sentiment analysis, emotion detection, and topic identification. Each component focuses on a specific aspect of context, making the overall system more manageable and less complex while maintaining comprehensive context coverage.
Solution Approach 2:
The system applies different analysis depths to different aspects of the message. Rather than uniformly analyzing all text elements in detail, it focuses computational resources on extracting key contextual features (sentiment polarity, emotion type, topic category) that provide the most valuable context awareness.
Data Source
AI summary
A computer-implemented method, system and computer program product for dynamically providing a typing feedback indicator. Text entered in a body of a message that is to be sent to a recipient is detected. Prior to the user (author) sending the message to the recipient, the text of the message being composed by the user is analyzed to determine a sentiment and an emotion as well as to determine a topic of the content of the text. Based on such analyses, as well as possibly based on other criteria, a typing feedback indicator is generated to reflect an indication of the content of the message to be sent to the recipient. The typing feedback indicator may then be displayed on a computing device of the recipient while the user (author) is composing the message thereby providing the recipient context awareness of the message prior to receipt of such a message.