Dialogue Replay Visualization for Participant Thought Tracking
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Solution Overview
Problem
Existing technologies struggle to accurately ascertain the train of thought of participants in a dialogue, making it difficult to effectively look back on dialogue content.
Innovation Solution
A computer executes procedures to extract time series data of features for each participant, generate synchronized display data with dialogue video, and display this data to visualize the train of thought.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional dialogue recording methods are used, then dialogue content can be captured, but the train of thought of participants cannot be ascertained
Solution Approach 1:
The patent segments the dialogue analysis into multiple feature dimensions (emotional state, attention level, speaking pace, pause frequency) that can be extracted and visualized separately. Each feature is processed independently through specific extraction procedures, allowing comprehensive capture of train of thought without requiring a single complex monolithic system.
Solution Approach 2:
The patent introduces an intermediary visualization layer that translates raw dialogue data and extracted features into comprehensible graphical representations. This intermediary system includes display control procedures that map feature values to visual elements, bridging the gap between complex data extraction processes and human understanding of participant train of thought.
2Measurement precision
If detailed feature extraction is performed for each participant, then train of thought can be visualized, but the complexity of data processing increases
Solution Approach 1:
The patent applies local quality by extracting and visualizing different feature types for different aspects of train of thought. Each participant's dialogue data is processed with specific extraction procedures tailored to capture particular dimensions (emotional, cognitive, behavioral), allowing precise measurement of train of thought while organizing complexity through specialized localized processing routines.
Solution Approach 2:
The patent transforms complex temporal dialogue data into spatial visual representations through time-series graphs and synchronized video displays. By adding visual dimensionality to the data presentation, the system enables precise train of thought measurement while reducing processing complexity through dimensional transformation of the information.
3Loss of information
If time series data of multiple features is extracted and visualized, then comprehensive dialogue analysis is achieved, but synchronization with video becomes complex
Solution Approach 1:
The patent merges multiple feature extraction procedures and video processing into a unified synchronization framework. The display control procedure integrates time-series feature data with video timestamps, combining multiple information streams into a single synchronized visualization that preserves complete dialogue context while managing complexity through unified control logic.
Data Source
AI summary
A computer executes: an extraction procedure of extracting time series data of a feature for each participant in a dialogue from time series data regarding the dialogue, a value of which is changeable according to the train of thought of the participant in the dialogue; a generation procedure of generating display data visualizing the time series data of the feature together with a video of the dialogue in synchronization with a time of the video; and a display procedure of displaying the display data. Accordingly, it is possible to ascertain a train of thought of the participant in the dialogue.