Messaging Context Relevance Scoring for Message Misdirection
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Solution Overview
Problem
Users often inadvertently send messages to the wrong recipient due to the complexity of managing multiple conversations across messaging applications, where the system fails to accurately determine the relevance of a reply to the intended conversation, leading to potential misdirection of messages.
Innovation Solution
A system generates context data structures for each conversation based on conversation content and correlates replies with these structures to determine a relevance score, preventing message sending to unintended recipients by ensuring the highest relevance score is met before sending, and displaying warnings if the score is not met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manage multiple conversations simultaneously in messaging applications, then communication efficiency and productivity are improved, but the risk of sending messages to unintended recipients increases
Solution Approach 1:
The system performs preliminary analysis of conversation context and message content before the user sends a message. It generates context data structures, compares them with the intended recipient's conversation history, and provides warnings or corrections in advance, preventing misdirection before it occurs.
Solution Approach 2:
The system provides feedback to users by displaying relevance scores and context comparisons. When a message might be sent to the wrong recipient, the system alerts the user with information about the mismatch, allowing the user to correct the error before sending.
2Reliability
If the system performs comprehensive relevance checking for every message, then message delivery accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The system performs partial relevance checking by focusing on key contextual elements rather than analyzing every aspect of each conversation. It generates context data structures that capture essential conversation themes and compares these with the intended message, providing sufficient accuracy without exhaustive analysis.
Solution Approach 2:
The system segments the relevance checking process into distinct components: generating context data structures from conversation history, comparing these structures with the intended message, calculating relevance scores, and providing feedback. This modular approach manages complexity while maintaining comprehensive checking.
3Reliability
If the system displays warnings for potential misdirection, then message delivery accuracy is improved, but user operation time and convenience are reduced
Solution Approach 1:
The system displays warnings selectively rather than for every message. It triggers warnings only when the relevance score falls below a threshold or when significant contextual mismatches are detected, allowing most messages to be sent without interruption while still catching potential errors.
Data Source
AI summary
Methods and systems are described for preventing a message from being sent to an unintended recipient. A first context data structure is generated for a first conversation, a second context data structure is created for a second conversation, and then a message input is received as intended for the first conversation. The message is correlated with each of the first context data structures and the second context data structure to determine a relevance score corresponding to each of the first and second conversation. The relevance scores are compared and if the relevance score for the first conversation is greater than the relevance score for the second conversation, the message is sent; however, if the relevance score for the second conversation is greater than the relevance score for the first conversation, the message is not sent and the message is prevented from being sent to, for example, an unintended recipient.