Real-time topic extraction for messaging platforms
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Solution Overview
Problem
In large or very active social networking platforms, users often miss a substantial number of messages in real-time communication channels, leading to an overwhelming burden to catch up, which can result in decreased user engagement.
Innovation Solution
Implementing a system that uses a language model to generate summaries and topics from chat log data, allowing users to easily catch up on conversations by highlighting or segregating messages associated with specific topics, and using personalized rankings to suggest relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users read all missed messages to catch up on conversations, then they obtain complete information, but the time and effort required becomes overwhelming
Solution Approach 1:
The patent extracts key information from the complete message set by generating topic summaries that capture the essence of conversations. The summarization service processes missed messages and extracts only the most relevant topic-level information, allowing users to obtain critical updates without reading every individual message, thus resolving the contradiction between information completeness and time investment.
Solution Approach 2:
The patent segments the overwhelming volume of missed messages into organized topic groups with summaries. Instead of presenting a flat list of all messages, the system divides content into thematic segments (e.g., project updates, team announcements, technical discussions) with concise summaries for each topic, enabling users to efficiently scan and select relevant information without being overwhelmed by the total message count.
2Loss of information
If the messaging platform provides all conversation details, then users have access to complete context, but the interface becomes overwhelming and complex
Solution Approach 1:
The interface is segmented into multiple levels of detail. The primary view presents organized topic summaries with clear headings and concise descriptions. Users can then drill down into specific topics if needed, creating a hierarchical structure that reduces initial interface complexity while preserving access to complete context through progressive disclosure.
Solution Approach 2:
The patent adds a temporal and organizational dimension to the interface by presenting information in chronological topic groups rather than a flat message stream. Each topic is presented with metadata (time, participants, key points) creating a multi-dimensional view that organizes complexity along multiple axes (time, topic, importance) rather than a single overwhelming dimension.
3Loss of information
If users manually review missed messages, then they can understand conversation content, but user engagement decreases due to the burden
Solution Approach 1:
The system performs self-service by automatically generating topic summaries and organizing missed messages before user interaction. The summarization service runs in the background, processing conversations and creating ready-to-consume topic briefs. This automation eliminates the manual effort of reading and synthesizing messages, allowing users to passively receive organized information and actively engage only with topics of interest.
Solution Approach 2:
The system performs preliminary actions by pre-processing missed messages into organized topic summaries before the user needs to review them. The summarization and categorization work is completed in advance, so when users log in or check the platform, the heavy lifting of information processing has already been done, making the catching-up process much easier and more appealing.
Data Source
AI summary
The present technology provides real-time topic extraction and summarization technology for communications channels and messaging platforms. The topics are extracted using a language model that extracts topics from chat log data. A topic that is selected causes the conversation panel to jump to the start of the conversation associated with the selected topic. The messages that are associated with the selected topic are highlighted or segregated from the rest of the messages. The selection of the topic and other types of engagement with topics are stored to build up a database that is used for generating a personalized ranking of topics for respective users. The quality of the topics is scored using a different language model.