Topic-Based Message Search Using Natural Language Analysis
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Solution Overview
Problem
Current conversation-based collaboration tools face challenges in effectively searching and sharing messages due to undifferentiated message flows, ineffective keyword searching, and difficulties in sharing context-related messages, especially when messages are private.
Innovation Solution
Implementing a method that uses natural language analysis to extract topics from messages, storing them in a repository, and allowing authorized users to search and share messages based on these topics, while managing message accessibility and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If keyword searching is used in conversation-based collaboration tools, then search capability is provided, but search effectiveness deteriorates due to undifferentiated message flows
Solution Approach 1:
The patent segments the undifferentiated message flow by extracting and categorizing topics from messages using natural language analysis. Messages are divided into topic-based groups, allowing users to search within specific topic contexts rather than through undifferentiated keyword matching, thereby improving search effectiveness.
Solution Approach 2:
The patent introduces topic extraction and classification as an intermediary layer between raw messages and search queries. This intermediary process analyzes message content, identifies topics, and organizes messages by topic, enabling more effective search results without requiring changes to the basic keyword search interface.
2Ease of operation
If all messages are made accessible for sharing, then sharing capability is improved, but privacy protection deteriorates
Solution Approach 1:
The patent applies different accessibility qualities to different messages based on their topic and sensitivity. Rather than making all messages uniformly accessible or inaccessible, the system selectively controls sharing based on local characteristics of each message, balancing sharing capability with privacy protection.
Solution Approach 2:
The patent implements feedback mechanisms where the system analyzes message content, determines appropriate privacy levels, and automatically applies accessibility controls. User feedback on sharing decisions further refines the system's understanding of privacy requirements, improving both sharing capability and privacy protection over time.
3Productivity
If topic-based categorization is implemented, then message organization is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service topic extraction using natural language analysis that automatically categorizes messages without requiring manual intervention. The system serves itself by analyzing message content, identifying topics, and organizing messages, reducing the complexity burden on users while maintaining high message organization productivity.
Solution Approach 2:
The patent performs preliminary topic extraction and classification actions as messages are received and stored. By organizing messages by topic in advance, the system reduces the complexity of future search and retrieval operations, as messages are pre-categorized rather than requiring complex analysis at query time.
4Measurement precision
If natural language analysis is applied to extract topics, then search relevance is improved, but processing time increases
Solution Approach 1:
The patent performs topic extraction as a preliminary action when messages are first received and stored in the repository. By completing the natural language analysis and topic classification upfront, the system avoids repeating this computationally intensive process during search operations, thereby improving search relevance while minimizing additional processing time during queries.
Data Source
AI summary
A topic of a message in narrative text form is extracted from the message. The message and the topic are stored in a message repository. The message is produced from the message repository responsive to a search by an authorized user for messages relating to the topic.