Message Analysis System for Filtering Repetitive Content
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Solution Overview
Problem
Users receive numerous repetitive and formulaic messages daily, which can lead to important information being missed as these messages are often dismissed or overlooked, necessitating a method to identify and manage repetitive communications effectively.
Innovation Solution
A system and method that analyzes received messages using natural language processing to determine repetitiveness, rates their importance, and alters their presentation to emphasize non-repetitive information, involving techniques such as N-gram extraction, grammar tree analysis, and data storage in knowledge graphs, allowing for customization or revision of repetitive messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If users receive numerous messages rapidly through online social networks and collaboration tools, then information sharing speed is improved, but users may miss important information due to the overwhelming volume of repetitive messages
Solution Approach 1:
The patent introduces an intermediary system (message analysis system) that sits between the message source and the user. This system automatically analyzes incoming messages, identifies repetitive content using N-gram extraction and comparison, and filters out redundant information before presenting it to the user. The intermediary performs natural language processing and message comparison operations to distinguish important new information from repetitive content, thereby preventing information loss while maintaining rapid communication speed
Solution Approach 2:
The system performs preliminary analysis of messages before they reach the user's attention. By pre-processing incoming messages to identify repetitive patterns and extract key information, the system prepares filtered and prioritized content in advance. This preliminary action includes storing message histories, comparing new messages against existing ones, and preparing summaries or alerts for important non-repetitive content, allowing users to quickly grasp essential information without wading through repetitive messages
2Measurement precision
If users manually review each message to identify important information, then information accuracy is improved, but time consumption increases significantly
Solution Approach 1:
The system enables self-service by automatically performing message analysis, repetition detection, and importance ranking without requiring user intervention. The message analysis system autonomously extracts N-grams, compares messages against stored histories, identifies repetitive patterns, and prioritizes content based on importance criteria. This self-service automation maintains high information identification accuracy while eliminating the time users would otherwise spend manually reviewing each message
Solution Approach 2:
The patent replaces the mechanical process of manual message review with automated computational processes. Instead of users physically reading and analyzing each message, the system uses natural language processing algorithms, pattern recognition, and automated comparison mechanisms to identify important information. This substitution of mechanical human review with automated computational analysis maintains accuracy while dramatically reducing time consumption
3Measurement precision
If the system analyzes and processes each message in detail, then message importance detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the message analysis process into distinct modular components: N-gram extraction module, message comparison module, repetition identification module, and importance ranking module. Each component performs a specific function and processes messages in a structured sequence. This segmentation allows the system to achieve detailed analysis accuracy while managing complexity through modular design, where each segment can be independently optimized and maintained
Solution Approach 2:
The system manages complexity by dynamically adjusting analysis parameters based on message characteristics and user preferences. It varies the depth of N-gram extraction, the number of historical messages to compare against, and the importance criteria applied based on message type, sender, and context. This parameter flexibility allows accurate importance detection for critical messages while reducing processing intensity for obviously repetitive content, balancing accuracy with system complexity
4Productivity
If the system filters out repetitive messages, then user productivity is improved, but some useful information may be inadvertently removed
Solution Approach 1:
The system dynamically adjusts its filtering behavior based on real-time analysis of message content, sender credibility, and user interaction patterns. Rather than applying static filtering rules, the system adapts its repetition detection sensitivity and importance ranking criteria dynamically. This allows the system to maintain high productivity by filtering obvious repetitive messages while preserving useful information that may appear repetitive but contains new or updated content based on contextual analysis
Data Source
AI summary
A method, computer system, and a computer program product for recasting repetitive messages is provided. Embodiments may include receiving, by a processor, a message. Embodiments may include determining, by the processor, whether the received message is repetitive. Embodiments may include rating, by the processor, an importance level of the received message. Embodiments may include determining a preference for recasting the received message.