Semantic Message Deletion for Time-Sensitive Communication
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Solution Overview
Problem
Existing communication systems often fail to efficiently manage time-sensitive messages, leading to inefficient communication due to missed or expired messages being retained unnecessarily.
Innovation Solution
Implementing a system that classifies messages as application-to-person (A2P) or person-to-person (P2P) and uses machine learning models to analyze message content for timeout or action completion, automatically marking or deleting messages based on predetermined periods or user actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If messages are retained in the messaging system, then communication history is preserved, but storage space is wasted and communication efficiency decreases due to expired messages
Solution Approach 1:
The system performs preliminary classification of messages into A2P and P2P categories upon receipt, and pre-establishes timeout periods based on message type before the messages are needed. This allows the system to automatically manage message retention without requiring user intervention, resolving the contradiction by proactively organizing messages according to their expiration characteristics.
Solution Approach 2:
The messaging system automatically manages its own message retention by using machine learning models to classify messages and determine timeout periods without user intervention. The system self-regulates which messages to retain and which to delete based on their content and type, eliminating the need for manual message management while optimizing storage space.
2Ease of operation
If manual message management is performed, then user control over message retention is maintained, but user time and effort are consumed
Solution Approach 1:
The system performs automatic message classification and timeout determination using machine learning models, eliminating the need for users to manually manage messages. The system autonomously determines which messages to retain and which to delete based on their content and inferred timeout periods, saving user time while maintaining appropriate message management control.
Solution Approach 2:
The system uses machine learning models that continuously learn from user behavior patterns to improve message classification accuracy. This feedback mechanism allows the system to adapt to user preferences over time, providing increasingly accurate automatic message management that reduces the need for manual intervention while maintaining user control.
3Measurement precision
If machine learning semantic analysis is performed on all messages, then message timeout detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system segments the message management process into distinct stages: first classifying messages into A2P or P2P categories using a classification model, then applying semantic analysis only to messages that require timeout determination. This segmentation reduces unnecessary processing by applying complex semantic analysis only when needed, improving accuracy while reducing processing time.
Solution Approach 2:
The system applies semantic analysis selectively rather than universally - using it partially on messages where timeout detection is necessary while skipping it for messages where simple classification suffices. This partial application of the more resource-intensive semantic analysis reduces overall processing time while maintaining sufficient accuracy for timeout detection.
Data Source
AI summary
A received message is classified as originating from a person or from an application. For a message originated from a person, a machine learning model is used to semantically analyze content of the message for a timeout for the message or an action corresponding to the message. For a message originated from an application, either a message characteristic is used to determine the timeout for the message, or the machine learning model is used to semantically analyze content of the message for the timeout for the message or the action corresponding to the message. When the timeout has elapsed or the action corresponding to the message has been completed, the message is marked for automatic deletion following a period of N days. Management of time-sensitive messages and communication efficiency are improved.