Adaptive User Engagement Model for Machine Interaction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing human-machine interaction technologies fail to effectively tailor the method of content delivery to individual user characteristics, leading to suboptimal user engagement and interaction efficiency.
Innovation Solution
A device that assesses user engagement by monitoring parameters, exposes users to motivators, and adjusts the communication mode based on individual responses to optimize engagement, using a customizable model to select the most effective motivators and support sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional devices deliver the same content in the same way for all users, then the device complexity is reduced, but user engagement deteriorates because individual users respond differently to interaction modes
Solution Approach 1:
The system dynamically adapts the content delivery method based on real-time assessment of user engagement parameters. The device transitions from static, one-size-fits-all content delivery to dynamic, personalized delivery by continuously monitoring engagement levels and adjusting the communication mode accordingly, thereby improving user engagement without requiring complete system redesign
Solution Approach 2:
The system changes operational parameters by selecting different content delivery methods (visual, auditory, textual) based on assessed user characteristics and engagement responses. This parameter adaptation allows the same device to serve different user needs effectively, improving engagement while maintaining manageable complexity through algorithmic decision-making
2Adaptability or versatility
If the device monitors multiple parameters and uses a customized model to assess user engagement, then user engagement is improved through personalization, but device complexity increases
Solution Approach 1:
The system performs self-assessment of user engagement by automatically monitoring parameters and using embedded algorithms to evaluate user responses. This self-service capability eliminates the need for external manual assessment, reducing operational complexity while enabling personalized adaptation through automated parameter analysis and model-based decision-making
Solution Approach 2:
The system implements feedback loops where monitored user engagement parameters feed into customized models that adjust content delivery methods. This feedback mechanism enables continuous optimization of user interaction by using actual user responses to refine future content delivery, improving personalization while managing complexity through systematic data processing
3Adaptability or versatility
If the device selects and exposes users to motivators based on assessed engagement, then user engagement is improved, but the difficulty of detecting and measuring engagement increases
Solution Approach 1:
The system replaces subjective engagement assessment with objective parameter measurement, substituting mechanical/physical monitoring methods with digital sensor-based detection. By using measurable parameters (response time, interaction frequency, engagement level) and automated analysis, the system makes engagement detection more precise and easier to measure while enabling data-driven motivator selection
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The present invention relates to a device that based on a model automatically tailors the communication mode with a user to the individual characteristics of the user without the user having to provide active input regarding their preferences. Instead, the model selects the most suitable motivator to ensure the user is engaged in performing an instructed activity, e.g. a step of controlling the machine in a complex manufacturing process. Thus, conscious biases of the user, such as the user stating to prefer written text but in fact being more responsive to videos, do not hamper the interaction of the device with the user and the efficacy of machine-user interaction is improved.