Real-Time Communication Session Labels With Sliding-Window Analysis
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Solution Overview
Problem
Existing communication platforms rely on human moderators to assign static labels to communication sessions, which become inaccurate when the discussion topic changes rapidly, leading to inefficiencies in session management and user engagement.
Innovation Solution
A system that dynamically generates labels for communication sessions using a sliding window to extract relevant words in real-time, adjusting the window size based on content characteristics and user-specific interests, and provides personalized session notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a human moderator assigns a predefined label to a communication session, then the label provides initial information about the session topic, but the label becomes inaccurate when the discussion topic changes rapidly
Solution Approach 1:
The patent implements dynamic label generation that automatically updates in real-time based on the evolving content of communication sessions. Instead of static predefined labels, the system continuously analyzes communications and generates labels that reflect the current discussion topic, resolving the contradiction between initial label provision and timely updates when topics change.
Solution Approach 2:
The system enables self-service by automatically generating and updating session labels without requiring human moderator intervention. The automated label generation system monitors communications and updates labels autonomously, eliminating the time loss associated with manual label updates while maintaining accuracy.
2Measurement precision
If a human moderator manually updates the predefined label to match changing topics, then the label remains accurate, but this is impractical for high-frequency real-time communications
Solution Approach 1:
The patent replaces the mechanical system of manual label updating by human moderators with an automated computational system. The system uses natural language processing and machine learning algorithms to automatically analyze communications and generate labels, achieving both high accuracy and high speed that is impossible for human operators in real-time high-frequency communications.
Solution Approach 2:
The automated label generation system performs self-service by autonomously monitoring communications, analyzing content, and updating labels without human intervention. This enables the system to maintain accurate labels at speeds comparable to the high-frequency communications themselves, resolving the productivity limitation of manual updates.
3Adaptability or versatility
If a general predefined label is assigned to encompass possible diverging topics, then the label remains broadly applicable, but it becomes too vague to usefully distinguish one communication session from another
Solution Approach 1:
The patent implements dynamic label generation that adapts to the specific content of each communication session. Instead of using static general labels, the system continuously analyzes the actual discussions and generates specific labels that precisely describe the current topic while remaining applicable to the session context, resolving the contradiction between broad applicability and specific distinction.
Data Source
AI summary
Systems and methods are described for generating indications of real-time communication sessions. An ongoing communication session is monitored to identify a most recent subset of communications, the most recent subset of communications being defined by a sliding window. The most recent subset of communications is analysed to identify one or more relevant words, based on at least a user-specific relevancy criterion, the user-specific relevancy criterion being relevant to a user-specific topic associated with a given user profile. Responsive to identifying the one or more relevant words, an indication of the ongoing communication session is provided to a user device associated with the given user profile.