Audience Interaction Prediction Using Sensor Data and Machine Learning
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Solution Overview
Problem
Current computer technologies used for online and in-person presentations do not enable presenters to predict which audience members will interact, such as by asking questions or providing comments, or the nature of the interaction, which can hinder the presenter's ability to focus on specific audience members or topics.
Innovation Solution
A method and system that detect physical movements of audience members using dispersed network-configured sensors, convert these signals into vectorized data structures, and use machine learning classification and prediction models to identify likely audience interactions, including the identity and topic of the interaction, generating predictive alerts for the presenter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional presentation systems are used, then the system is simple and easy to operate, but the presenter cannot predict audience interactions or focus on specific audience members
Solution Approach 1:
The system performs preliminary classification of audience members into interaction likelihood categories (high, medium, low) based on their attributes and real-time sensor data before interactions occur. This allows the presenter to proactively focus on likely interactors rather than reactively responding to unexpected questions, thereby reducing information loss about upcoming interactions.
Solution Approach 2:
The patent replaces manual audience monitoring with an automated machine learning-based classification system that processes sensor data and audience attributes algorithmically. This substitution of mechanical/manual observation with computational analysis enables predictive capabilities while managing system complexity through automated processing.
2Loss of information
If no prediction system is used, then the system is simple, but the presenter cannot anticipate which audience members will interact or the nature of interactions
Solution Approach 1:
The system performs preliminary classification of audience members into interaction likelihood categories (high, medium, low) based on their attributes and real-time sensor data before interactions occur. This allows the presenter to proactively focus on likely interactors rather than reactively responding to unexpected questions, thereby reducing information loss about upcoming interactions.
Solution Approach 2:
The system continuously monitors real-time sensor data (camera feeds, microphones, movement detectors) and feeds this information back into the machine learning models, which dynamically update interaction predictions. This feedback loop enables the system to adapt to changing audience states and provide accurate real-time predictions without requiring overly complex manual monitoring systems.
3Productivity
If the presenter manually monitors all audience members, then complete information about interactions is obtained, but the presenter cannot focus on specific individuals and loses time
Solution Approach 1:
The system applies different levels of analysis to different audience members based on their classification. High-likelihood interactors receive detailed real-time monitoring and prediction, while medium and low-likelihood members receive standard monitoring. This localized quality approach enables the presenter to focus attention and system resources on specific individuals who are most likely to interact, improving productivity without losing critical interaction information.
Solution Approach 2:
The machine learning models analyze multiple parameters simultaneously (audience member attributes, real-time sensor data, historical interaction patterns, contextual information) and dynamically adjust prediction accuracy and resource allocation based on the combination of these parameters. This enables efficient processing by adapting the level of analysis to the specific situation rather than uniformly monitoring all parameters for all audience members.
Data Source
AI summary
Cognitive enablement can include detecting sensor-generated signals received from one or more network-configured sensors, the sensor-generated signals corresponding to physical movement of an audience member during a presentation by a presenter. The sensor-generated signals can be converted to vectorized data structures for inputting to a classification model generated with machine learning. The physical movement can be classified as a prelude to a likely audience interaction, the classifying performed by the classification model based on the vectorized data structures. In response to the classifying, an audience member attribute associated with the audience member can be determined. An audience interaction can be predicted based on the audience member attribute using a prediction model generated with machine learning. A predictive alert corresponding to the predicted audience interaction can be generated. During the presentation, a user interface for displaying the predictive alert to the presenter can be generated using an electronic device.