Eye-Tracking Video Conference Prompts for Clear Speaker Intent
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Solution Overview
Problem
Non-verbal communication is limited in virtual video conference settings, leading to confusion and inefficiency, particularly in identifying the intended speaker or locating participants in large groups.
Innovation Solution
Implement gaze-based video conference prompts using eye-tracking technology to determine a speaker's gaze direction and generate visual cues indicating the intended participant, and detect scanning behaviors to optimize participant display for efficient location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional video conference interfaces are used, then device complexity is low, but non-verbal communication is limited and user experience is poor
Solution Approach 1:
The patent replaces traditional mechanical/video-based communication with eye-tracking technology to detect gaze direction. The system uses eye movement data to automatically determine speaker intent and generate visual prompts, eliminating the need for complex manual interaction mechanisms while enhancing non-verbal communication capabilities.
Solution Approach 2:
The patent introduces an intermediary system that processes eye-tracking data and translates it into visual prompts displayed on screens. This intermediary layer converts raw eye movement information into meaningful communication cues, bridging the gap between speaker intent and listener understanding without requiring direct complex interaction.
2Loss of time
If participants are displayed in a compact format to reduce interface complexity, then ease of operation improves, but time to locate participants increases
Solution Approach 1:
The system performs preliminary processing of eye-tracking data to predict which participants the speaker is likely to refer to. By analyzing gaze patterns and eye movement trajectories, the system pre-identifies target participants before the speaker finishes speaking, enabling faster location and reducing search time.
Solution Approach 2:
The patent implements a feedback loop where eye-tracking data continuously monitors speaker gaze direction and dynamically updates the displayed participant list. This real-time feedback allows the interface to adapt to speaker intent, highlighting relevant participants and reducing the time needed to locate the intended target.
3Loss of information
If eye tracking technology is implemented to improve communication clarity, then information accuracy improves, but device complexity increases
Solution Approach 1:
The patent extracts only the essential information from eye-tracking data - specifically the gaze direction and fixation points - while filtering out unnecessary processing complexity. By focusing solely on extracting spatial information about where the speaker is looking, the system achieves clear intent identification without implementing the full complexity of comprehensive eye-tracking analysis.
Solution Approach 2:
The system creates a simplified representation or copy of the speaker's gaze behavior as visual prompts displayed on the interface. Instead of directly processing complex eye movement data, the system generates simplified visual copies of the gaze trajectory and fixation points, making the information accessible and actionable while reducing processing complexity.
Data Source
AI summary
Techniques for gaze-based video conference prompts are described that leverage eye-tracking algorithms within a video conference setting to perform a variety of functionality. For instance, a computing device displays a user interface of a video conference session that includes representations of video conference attendees. The computing device receives video content that depicts a user of the computing device and uses eye tracking techniques to determine a gaze location of the user based on the video content. The gaze location corresponds to one of the representations of an attendee. The computing device then communicates a prompt to one or more of the attendees that indicates the gaze location. In another example, the computing device detects a behavior of the user, e.g., a scanning behavior. Responsive to detection of the behavior, the computing device performs an action within the user interface, such as to display a roster view of the attendees.