Conversation-Based Notification Management for AR Headsets
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Solution Overview
Problem
Augmented reality (AR) head-mounted displays (HMDs) face challenges in managing notifications effectively, as they cannot easily defer displaying notifications like smartphones, due to their immersive nature, leading to potential interruptions during real-world conversations.
Innovation Solution
A system and method for conversation-based notification management, which uses sensor data from cameras and microphones to determine the probability of a user being in a real-world conversation and compares this with the importance of notifications to decide whether to display them, allowing for context-aware notification handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If notifications are displayed through the HMD, then the user receives important information, but the user may be interrupted during real-world conversations
Solution Approach 1:
The notification management system dynamically adjusts its behavior based on real-time contextual factors. The system continuously monitors sensor data including audio levels, camera input, and device usage patterns to determine whether the user is engaged in a conversation or performing tasks that would be interrupted by notifications. This dynamic adaptation allows the system to optimize between notification delivery and conversation continuity based on current conditions.
Solution Approach 2:
The system employs feedback mechanisms by continuously analyzing sensor data from the HMD to assess the user's current state. Audio sensors detect conversation presence, cameras monitor user attention direction, and usage patterns provide feedback on user engagement. This feedback loop enables the notification management system to make informed decisions about when to suppress or display notifications, balancing information delivery with conversation respect.
2Object-affected harmful factors
If notifications are suppressed during conversations, then conversation continuity is maintained, but important notifications may be missed
Solution Approach 1:
The system introduces an intermediary assessment layer between the notification source and the display output. This intermediary evaluates the importance of each notification against the detected conversation context. High-importance notifications (such as urgent alerts, critical messages, or time-sensitive information) can override the conversation detection and still be displayed, while lower-importance notifications are suppressed. This intermediary mechanism ensures that important information is not lost while maintaining conversation continuity for routine matters.
Solution Approach 2:
The notification management system changes parameters such as display timing, notification priority thresholds, and suppression duration based on the detected conversation state. When a conversation is detected, the system adjusts notification parameters to reduce interruptions, but maintains the capability to display high-priority notifications. The system dynamically modifies these parameters based on conversation duration, audio level, and user behavior patterns.
3Object-affected harmful factors
If simple rule-based notification suspension is used, then conversation interruptions are reduced, but the solution is over-exclusive and prevents display of important notifications
Solution Approach 1:
The notification management system segments the notification handling process into multiple independent evaluation stages. Instead of a single binary decision (suppress or display), the system divides the process into: detection stage (identifying conversation state), assessment stage (evaluating notification importance), decision stage (determining display suppression), and override stage (allowing important notifications through). This segmentation enables nuanced handling of different notification types and contexts.
Solution Approach 2:
The system transitions from static rule-based notification suppression to dynamic context-aware management. It continuously monitors multiple contextual parameters including audio levels, conversation duration, user attention, and device usage patterns. Based on this dynamic assessment, the system adjusts its notification behavior in real-time, allowing flexibility to handle different situations appropriately rather than applying a one-size-fits-all suppression rule.
Data Source
AI summary
A method for dynamic notification management at a head mounted display (HMD) includes presenting, at the HMD, an augmented reality display, receiving, at a processor controlling the HMD, a notification from an application for display at the HMD, receiving, at the processor at a first time, first sensor data from one or more of a camera or a microphone, determining, based on the first sensor data, a first value of one or more factors associated with a probability that a user of the HMD is currently in a real-world conversation, determining an importance value of the received notification at the first time, and determining whether to display the notification from the application based on a comparison of the first value of the one or more factors associated relative to the importance value of the received notification.