Message Clustering for Spam Filtering and Routing Efficiency
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Solution Overview
Problem
Current messaging transport systems lack the ability to differentiate and effectively handle messages based on content, leading to inefficiencies in routing and management, particularly with the proliferation of unauthorized commercial messages, such as spam, across messaging platforms.
Innovation Solution
A Message Processing System that analyzes and categorizes messages by identifying clusters with similar content or characteristics, allowing for platform-specific actions based on configuration information, utilizing machine learning and policy controls to prioritize, tag, or block messages, and providing granular categorization and monetization strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If messaging transport systems route messages without differentiation, then message transmission speed is maintained, but message handling efficiency deteriorates
Solution Approach 1:
The system performs preliminary analysis of message content and characteristics before routing decisions are made. Message clusters are identified and categorized in advance based on similarity metrics, allowing the transport system to apply pre-determined routing rules and actions rather than making complex decisions in real-time during message transmission.
Solution Approach 2:
A message analysis system acts as an intermediary between the messaging transport system and the destination messaging platform. This intermediary component analyzes message content, identifies clusters, determines categories, and provides routing recommendations, thereby enabling differentiated handling without requiring the core transport system to become significantly more complex.
2Measurement precision
If messages are analyzed and categorized by content, then message routing accuracy is improved, but processing time increases
Solution Approach 1:
The system applies partial analysis by focusing on key message characteristics and content features that are most indicative of message category and routing requirements. Rather than performing exhaustive analysis of every message attribute, the system identifies and analyzes the most relevant features to achieve sufficient categorization accuracy while minimizing processing time.
Solution Approach 2:
The message analysis process is segmented into distinct stages: initial message clustering based on similarity metrics, category determination for identified clusters, and routing decision generation. This segmentation allows the system to process messages in efficient batches rather than analyzing each message individually in full detail, reducing overall processing time while maintaining accuracy.
3Reliability
If message clusters are identified and differentiated actions are taken, then spam filtering effectiveness is improved, but system complexity increases
Solution Approach 1:
The system changes parameters by analyzing message content characteristics and similarity metrics to identify clusters of messages with comparable features. By grouping messages based on these parameter similarities and applying category-based filtering rules to clusters rather than evaluating each message individually, the system achieves effective spam filtering while managing complexity through pattern recognition and batch processing.
Data Source
AI summary
Techniques are described herein for processing intra- and inter-messaging platform communications, including by receiving and analyzing messages originating from one sender for distribution to a recipient, where the sender and recipient may be on a same or separate messaging platform. Clusters of such messages with similar contents or other similar characteristics are identified and categorized, such as in accordance with configuration information regarding one or both of the originating and destination messaging platforms. Based on a determination of one or more categories associated with such an identified message cluster, as well as an analysis of metadata associated with the profile of the sender of the messages, various actions may be taken with respect to such message clusters or with parties associated with such message clusters, including actions based at least in part on the configuration information.