Web Conference Engagement Feedback via Semantic Activity Analysis
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Solution Overview
Problem
Existing web conferencing systems lack effective real-time feedback mechanisms to gauge user engagement during presentations, often misinterpreting user activities and failing to provide comprehensive, context-aware feedback to presenters.
Innovation Solution
A method and system that initiate a web conferencing session, where a semantic engine performs automatic machine learning to generate a presentation concepts list, and an Activity Scanner Agent monitors user activities to determine interest levels by comparing them to the presentation concepts, providing real-time feedback without requiring active user participation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional web conferencing systems are used to deliver presentations, then the presentation can be delivered to the audience, but real-time feedback on user engagement is not obtained
Solution Approach 1:
The system implements automatic feedback collection by monitoring user activities (mouse clicks, keyboard input, application switching) during the presentation and comparing them against the presentation content timeline. This automated feedback mechanism eliminates the need for manual engagement assessment while providing real-time data to the presenter about audience interest levels.
Solution Approach 2:
The system performs self-service by automatically analyzing user behavior patterns and generating engagement metrics without requiring active participation from users. The semantic engine autonomously processes the presentation content and user activity logs to determine engagement levels, freeing users to simply view the presentation without additional interaction requirements.
2Measurement precision
If user activities are monitored to determine engagement, then real-time feedback is obtained, but user privacy and system resources are consumed
Solution Approach 1:
The system introduces an intermediary layer (the activity scanner and semantic engine) that processes user behavior data locally and anonymizes it before analysis. The monitoring focuses on aggregate engagement patterns rather than individual user identification, and the system compares activities against presentation context to determine engagement without exposing raw personal data.
Solution Approach 2:
The system changes the parameter of measurement from detailed individual user tracking to aggregated engagement level assessment. By analyzing patterns such as activity frequency, timing, and type in relation to presentation content, the system derives engagement metrics that reflect collective audience interest while minimizing intrusion into individual user privacy.
3Adaptability or versatility
If semantic analysis is performed on presentation content, then context-aware feedback is generated, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-processing the presentation content during the setup phase to create a structured semantic model and timeline of key concepts. This advance preparation allows the runtime system to quickly compare user activities against the pre-analyzed content structure, enabling real-time engagement assessment without the computational overhead of performing full semantic analysis during the live presentation.
Data Source
AI summary
A method, computer program product, and system includes a processor(s) initiating a web conferencing session between a host and a client, by the client receiving a presentation and transmitting the presentation to a semantic engine, wherein the semantic engine performs an automatic machine learning session to generate a presentation concepts list comprising concepts relevant to the presentation, and progressively displaying the presentation in a thin client application on the client. The processor(s) monitors on the client, during the web conferencing session, activities executed on the client and extracts web concepts related to the activities executed on the client. The processor(s) determines an interest level of a user of the client in the presentation, based on determining a presence or absence of a relationship of each web concept to one or more of the concepts relevant to the presentation and displays the interest level in a graphical user interface.