Context-Aware Prompts on Electronic Devices to Reduce Alert Fatigue
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Solution Overview
Problem
Users often ignore or fail to adequately consider prompts and alerts on computing devices due to alert fatigue, leading to productivity losses and errors in workflow execution.
Innovation Solution
An alerting system that dynamically adjusts the intrusiveness of prompts and alerts based on user behavior, context, and workflow timing to ensure appropriate engagement and consideration, using machine learning algorithms to recognize patterns and adapt prompt and alert presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If prompts and alerts are displayed frequently to ensure important information is communicated, then information delivery is improved, but user attention and engagement deteriorate due to alert fatigue
Solution Approach 1:
The system dynamically adjusts prompt characteristics (intrusiveness, timing, modality) based on real-time analysis of user behavior patterns, context, and workflow state. This allows the same information delivery function to adapt its approach, being more intrusive when users are attentive and less intrusive when alert fatigue is detected, thereby maintaining both information delivery effectiveness and user engagement.
Solution Approach 2:
The system changes multiple parameters of prompts including intrusiveness level, display timing, duration, and modality based on classification of the prompt's importance and detected user state. By varying these parameters dynamically, the system optimizes the balance between ensuring information is noticed and maintaining user comfort and engagement.
2Loss of information
If prompts are made more intrusive to ensure user attention, then user attention is improved, but workflow efficiency and user comfort deteriorate
Solution Approach 1:
The system applies different levels of intrusiveness to different prompts based on their classification and the specific context in which they appear. Rather than uniformly increasing intrusiveness for all prompts, the system locally adapts each prompt's characteristics to match its importance level and the user's current state, ensuring critical information receives attention while minimizing disruption to workflow for less critical information.
Solution Approach 2:
The intrusiveness of prompts is not fixed but dynamically adjusted based on real-time analysis of user behavior patterns, context, and workflow timing. The system learns from user responses and adapts its approach, increasing intrusiveness only when necessary to ensure attention for important information while maintaining workflow efficiency for routine communications.
3Ease of operation
If the system adapts to user behavior patterns to reduce intrusiveness, then user comfort is improved, but the ability to ensure attention for important information deteriorates
Solution Approach 1:
The system continuously monitors user responses to prompts and uses this feedback to refine its classification and adjustment strategies. By analyzing whether users actually pay attention to or act on prompted information, the system can distinguish between cases where reduced intrusiveness is appropriate and cases where important information may have been missed, thereby maintaining reliability while improving comfort.
Solution Approach 2:
The system performs preliminary classification of prompts based on their importance and potential impact before displaying them. This preliminary assessment allows the system to pre-determine appropriate intrusiveness levels and delivery methods, ensuring that information likely to be important receives appropriate attention while less critical information can be delivered more comfortably to the user.
Data Source
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AI summary
Methods and devices include determining that a prompt is to be displayed on a display of a user device to present information to a user of the user device, to receive an input from the user of the user device, or a combination. The methods and devices further include determining a classification of the prompt based, at least in part, on the information to be presented, the input to be received, context associated with current use of the user device by the user, or a combination thereof. The methods and devices further include causing, at least in part, a presentation of a graphical user interface element on the display of the user device based, at least in part, on the classification of the prompt.