Conversational Data Thread De-threading and Visualization
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Solution Overview
Problem
Existing electronic communication systems face challenges in analyzing and visualizing team conversational data due to the large volume and heterogeneous nature of the data, particularly with interleaved conversational threads, making it difficult to grasp meaningful patterns and understand team collaboration effectively.
Innovation Solution
A method and system for analyzing conversational messages that involves de-threading interleaved messages to identify conversational threads and generating visualizations organized by time intervals, using techniques such as neural networks for similarity calculation and natural language analysis for keyword extraction, integrated with a calendar-based interface for multi-scale exploration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conversational data is stored in interleaved format as generated by messaging platforms, then data volume and heterogeneity are preserved, but difficulty in grasping meaningful patterns increases
Solution Approach 1:
The patent segments the interleaved conversational data into distinct conversational threads by identifying message sequences with related content and temporal proximity. This segmentation transforms the heterogeneous interleaved data into organized thread structures, making patterns detectable while preserving the complete data volume through comprehensive thread extraction.
2Adaptability or versatility
If multiple topics are discussed simultaneously in interleaved conversations, then communication realism is maintained, but difficulty in digesting and understanding increases
Solution Approach 1:
The system segments interleaved conversations into distinct threads based on topic continuity and participant patterns. This allows multiple simultaneous topics to be maintained in the data while presenting them as separate, digestible units in the visualization, improving ease of understanding without sacrificing communication realism.
Solution Approach 2:
The patent introduces a temporal dimension to the visualization by organizing threads along a time axis, allowing users to perceive the chronological unfolding of multiple topics. This dimensional addition transforms the complexity of simultaneous discussions into a structured temporal narrative that is easier to digest while maintaining communication authenticity.
3Loss of information
If de-threading and visualization processing is performed on large volumes of interleaved data, then meaningful patterns are revealed, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the essential features needed for thread identification and visualization from the large volumes of interleaved data, such as message content similarity, temporal metadata, and participant information. This selective extraction reduces processing requirements while maintaining the quality of information insights through focused analysis on critical data elements.
Solution Approach 2:
The system performs preliminary processing to pre-identify conversational threads and organize data structures before final visualization generation. This preliminary action reduces the computational burden during the main processing phase by pre-sorting and pre-grouping data, thereby reducing overall processing time while preserving information quality.
Data Source
AI summary
A method of analyzing conversational messages may be provided. The method including receiving a query defining a timespan of messages, retrieving at least two conversational messages associated with the defined timespan from a plurality of interleaved messages, de-threading the at least two conversational messages to identify at least one conversational thread, and generating a visualization of conversational threads based on the defined timespan, the at least one conversational thread, the visualization organized into time intervals based on the defined timespan.