Conversation Correction Service for Messaging Apps
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Solution Overview
Problem
Users of messaging systems often accidentally send messages to the wrong person, leading to undesirable consequences such as embarrassment or inadvertent disclosure of confidential information, due to the ease of switching between conversations.
Innovation Solution
Implementing a conversation correction service that uses mathematical models, specifically conversation encoding and message encoding models, along with natural language processing to determine if a message is intended for the correct conversation, and providing warnings to the user before sending, utilizing neural networks and classifiers to compute match scores and assess potential harm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a messaging system allows users to switch between multiple conversations easily, then the ease of operation is improved, but the reliability deteriorates due to accidental messages sent to wrong people
Solution Approach 1:
The system performs preliminary analysis of the message content and conversation context before the user sends the message. The natural language processing model evaluates whether the message is appropriate for the current conversation based on keywords, entities, and contextual relevance, providing a warning or correction suggestion before the message is actually sent.
Solution Approach 2:
The system provides immediate feedback to the user by analyzing the message in real-time and presenting warnings or suggestions. The feedback mechanism compares the message content against the conversation context and notifies the user of potential mismatches, allowing the user to correct the error before sending.
2Reliability
If natural language processing is implemented to analyze message content, then the reliability is improved by detecting incorrect conversations, but the device complexity increases
Solution Approach 1:
The patent introduces a natural language processing model as an intermediary component between the user and the messaging system. This model acts as a mediator that analyzes message content and conversation context without requiring complex changes to the core messaging infrastructure. The NLP model processes text through encoding and comparison operations, providing intelligent analysis while maintaining system modularity.
Solution Approach 2:
The system replaces manual user review and mechanical checking processes with automated natural language processing. Instead of requiring users to manually verify each message or implementing simple keyword-based filtering, the patent employs neural network-based NLP models that perform semantic analysis, entity recognition, and contextual understanding to automatically detect potential errors.
3Reliability
If real-time analysis of message content is performed, then the reliability is improved, but the loss of time increases due to processing delays
Solution Approach 1:
The system performs partial analysis by focusing on the most critical aspects of message content and context. Rather than analyzing every single word and aspect of the conversation in equal detail, the NLP model prioritizes key entities, keywords, and contextual signals that are most indicative of potential mismatches. This selective analysis reduces processing time while maintaining high detection accuracy.
Solution Approach 2:
The analysis process operates continuously in the background as the user composes the message, rather than as a separate batch process. The system continuously monitors message content as it is being typed, maintaining readiness to provide immediate feedback without requiring a separate processing step that would interrupt the user workflow.
Data Source
AI summary
A user using a messaging application may be in conversations with multiple people and may inadvertently send a message intended for a first person to a second person. The user may be warned before making such mistakes by processing the text of an entered message and/or the text of the conversations with a mathematical model. A match score may be computed that indicates the match between the entered message and the conversation in which it was entered. Where the match score indicates a possible mistake, a warning may be presented to the user. In some implementations, a match score may be computed using a conversation encoding vector and a message encoding vector. In some implementations, a match score may be computed by processing a sequence of tokens for the conversation and the entered message that includes special token separators.