Filter Node Spam Reporting Metadata Association
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Solution Overview
Problem
Current methods for filtering and reporting spam mobile messages are cumbersome, inefficient, and often require users to manually input spam message details, leading to errors and incomplete data for identifying spam sources, especially in networks where users are charged for received messages and across various mobile platforms.
Innovation Solution
Implementing a method where mobile messages pass through a filter node that creates records with metadata and content, allowing users to report spam with a single message, which is then associated with the stored metadata to identify the spam source, without requiring additional software or smartphone capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually input spam message details to report spam, then the reporting process can be completed, but user experience deteriorates and errors increase
Solution Approach 1:
The filter node extracts and stores metadata (calling number, message content, timestamp) from spam messages before users report them. When a user forwards a spam message to the short code, the system automatically retrieves the pre-stored metadata and associates it with the report, eliminating the need for manual input and ensuring data accuracy.
Solution Approach 2:
The filter node acts as an intermediary between the message routing system and the spam reporting system. It intercepts messages, extracts metadata, stores it in an archive, and automatically provides this information to the spam reporting service when needed, bridging the gap between message delivery and spam analysis.
2Loss of information
If the SRS server requests additional information from users via follow-up messages, then complete spam source identification is possible, but user convenience deteriorates and reporting completion rates decrease
Solution Approach 1:
The filter node performs preliminary extraction and storage of complete message metadata (calling number, originating network address, message content) when messages first pass through it. This pre-captured information is then automatically provided to the spam reporting service, eliminating the need for follow-up user requests and ensuring complete information is available immediately.
Solution Approach 2:
The system automatically retrieves and associates spam message metadata without requiring user action. The filter node self-services the information gathering function, extracting data from the original message and making it available to the reporting system, so users only need to forward the message without any additional input.
3Reliability
If complex anti-spam filters with real-time updates are implemented, then spam detection capability improves, but system complexity and cost increase
Solution Approach 1:
The filter node serves as an intermediary that captures complete message metadata and stores it in an archive. This centralized storage approach simplifies the overall system architecture by separating message interception, data storage, and analysis functions, allowing complex filtering logic to be implemented without increasing user-side device complexity.
Solution Approach 2:
The filter node performs multiple functions: intercepting messages, extracting metadata, storing records in an archive, and providing information to the spam reporting service. This multi-functional component consolidates what would otherwise require separate systems, managing complexity while enabling comprehensive spam detection capabilities.
Data Source
AI summary
A method for user reporting of spam mobile messages includes causing mobile messages to pass through a filter node prior to delivery to a user and, in the filter node, creating a record corresponding to each mobile message. The record includes metadata extracted from the mobile message and the content of the mobile message. Each record is stored in an archive and each message is then routed to the user. The method also includes receiving a report message from the user indicating that a mobile message received by the user is a spam mobile message, retrieving the record corresponding to the spam mobile message from the archive, and associating the metadata in the retrieved record with the report message.