Electronic Message Classification via Salient Object Extraction
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Solution Overview
Problem
Current methods for analyzing and categorizing electronic message streams fail to effectively capture the context and content, leading to inefficient management of digital information and issues such as non-business emails and inappropriate content proliferation in organizational networks.
Innovation Solution
An algorithmic method that breaks down electronic message information into components, extracts salient objects and flow patterns, and combines them to classify messages in real-time, using signature values and databases for prediction and categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basic metadata and data contents are used for categorization, then the categorization process is simple, but the context and content of electronic message streams are missed, leading to insufficient analysis accuracy
Solution Approach 1:
The patent segments electronic message streams into multiple components including metadata, data contents, context information, and flow patterns. Each component is analyzed separately by dedicated analysis modules, allowing comprehensive categorization without overwhelming system complexity. The segmentation enables parallel processing of different message aspects.
Solution Approach 2:
The patent introduces an intermediary algorithmic method that bridges basic metadata analysis and comprehensive context understanding. This intermediary layer extracts salient objects and flow patterns from message streams, transforming raw data into structured information that improves categorization accuracy without requiring direct complex analysis of all message elements.
2Reliability
If comprehensive context and content analysis is performed, then categorization accuracy improves, but the analysis process becomes complex and resource-intensive
Solution Approach 1:
The system divides comprehensive message analysis into separate functional modules: metadata analysis module, content analysis module, context analysis module, and flow pattern analysis module. Each module handles specific aspects independently, improving reliability through specialized processing while managing complexity through modular architecture.
Solution Approach 2:
The patent performs preliminary extraction of salient objects and flow patterns from electronic message streams before detailed categorization. This preliminary action prepares data in advance, making subsequent comprehensive analysis more reliable while reducing the computational complexity during the main categorization process.
3Productivity
If traditional categorization methods are used, then the system is simple to implement, but non-business emails and inappropriate content cannot be effectively identified and managed
Solution Approach 1:
The patent introduces an intermediary analysis layer that processes message streams to identify salient objects and contextual patterns indicative of non-business or inappropriate content. This intermediary layer acts as a filter between traditional categorization and comprehensive content, improving message management efficiency by pre-identifying problematic messages before detailed analysis.
Solution Approach 2:
The patent replaces traditional mechanical keyword-based filtering with an algorithmic method that analyzes flow patterns, salient objects, and contextual relationships. This substitution enables automatic identification of inappropriate content types such as pornography, cyber-bullying, and sensitive materials through pattern recognition rather than simple keyword matching.
4Measurement precision
If detailed analysis of all message components is performed, then categorization accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary extraction and identification of salient objects and flow patterns from message streams before detailed categorization analysis. This preliminary action prepares critical information in advance, allowing faster subsequent processing while maintaining high analysis precision through pre-identified key elements.
Solution Approach 2:
The patent applies partial analysis to message components by focusing on extracting and analyzing only the most salient objects and critical flow patterns rather than every single message element. This selective partial analysis maintains categorization precision while significantly reducing overall processing time by avoiding unnecessary detailed examination of all message components.
Data Source
AI summary
Methods and systems for analysis and/or classification of electronic message information so as to capture and identify salient objects exchanged during electronic message passing in order to impute certain information about the object, groups of objects, the message, groups of messages, the parties, communities involved in the message exchange or combinations, thereof.