Alertness Services Dynamic Notification Control
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Solution Overview
Problem
Current computing devices lack the ability to adapt notification presentation based on a user's actual alertness level, often delivering unwanted notifications that can disrupt sleep or concentration.
Innovation Solution
A method that involves receiving interaction data from users, such as biometric, inertia, and software-usage data, to determine an alertness inference, which is then used to alter the presentation of messages, including withholding or modifying notifications to suit the user's alertness state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If do-not-disturb settings are used to withhold notifications at certain times, then sleep disruption is reduced, but notifications may be withheld even when the user is alert and able to receive them
Solution Approach 1:
The system continuously monitors user interaction data (typing speed, scroll behavior, app switching frequency) and uses this feedback to dynamically adjust notification delivery. This allows the system to adapt to the user's actual alertness state rather than relying on fixed time-based rules, resolving the contradiction by ensuring notifications are delivered when the user is alert while still protecting sleep time.
Solution Approach 2:
The notification system transitions from static, time-based do-not-disturb settings to a dynamic system that adjusts notification behavior in real-time based on monitored user interactions. The system continuously evaluates interaction metrics and modifies notification delivery accordingly, allowing it to adapt to changing alertness levels throughout the day and night.
2Ease of operation
If notifications are delivered based on fixed time schedules, then implementation is simple, but notification relevance to user alertness state is poor
Solution Approach 1:
The system automatically monitors user interaction patterns and makes intelligent decisions about notification delivery without requiring manual configuration or user input. The device serves itself by analyzing its own usage data and autonomously adjusting notification behavior, maintaining ease of operation while achieving sophisticated adaptability to user alertness states.
Solution Approach 2:
The system changes the parameters used for notification delivery from fixed time-based parameters to dynamic parameters derived from real-time interaction data analysis. By monitoring metrics such as typing speed, scroll rate, and app switching frequency, the system adjusts notification delivery parameters adaptively, achieving both simplicity and sophistication.
3Adaptability or versatility
If interaction data monitoring is implemented to determine alertness, then notification relevance improves, but device complexity increases
Solution Approach 1:
The system uses existing multi-functional components already present in modern devices (camera for eye tracking, microphone for voice activity, motion sensors for device handling) to gather alertness indicators. By leveraging these existing sensors for dual purposes (original function plus alertness detection), the system achieves sophisticated alertness monitoring without adding significant hardware complexity.
Solution Approach 2:
The system replaces complex mechanical or hardware-based alertness detection with software-based analysis of existing interaction data. Instead of adding specialized sensors or hardware, the system uses algorithms to analyze patterns in normal device usage data, substituting computational complexity for hardware complexity while achieving the same goal.
Data Source
AI summary
User alertness can be monitored and leveraged while the user is interacting with a computing device, such as a mobile device (e.g., a smartphone or tablet). By monitoring interaction data, an alertness inference of the user can be generated. The interaction data can include biometric data of the user (e.g., blink rate, eye focus, and breathing rate), inertia data of the device (e.g., swaying and orientation), and software-usage data of the device (e.g., button press speed and accuracy, app or action being used, and response times). The alertness inference can be a score measuring a degree of alertness of the user, from a deep sleep through fully alert. The alertness inference can be leveraged to automatically alert presentation of a message (e.g., notification) on the device, such as withholding presentation of the message or presenting it in different fashion (e.g., silently).