Wearable Display Content Prioritization Using a Conversation Graph
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Solution Overview
Problem
Notifications on wearable devices such as smartglasses can be disruptive and irrelevant due to the lack of appropriate management, leading to a waste of user attention and increased unwanted notifications as the number of applications increases.
Innovation Solution
A wearable device uses a conversation graph to manage display content by detecting real-life interactions, updating the graph based on facial recognition and interactive communications, and prioritizing notifications and messages based on familiarity metrics and cluster groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of applications on the wearable device increases, then the functionality and versatility of the device improve, but the number of unwanted or less useful notifications increases
Solution Approach 1:
The system continuously monitors user interactions with notifications and uses this feedback to dynamically adjust notification priorities. By tracking which notifications the user engages with and which are ignored, the system learns to prioritize relevant notifications and suppress unwanted ones, resolving the contradiction between having multiple applications and receiving fewer unwanted notifications.
Solution Approach 2:
The notification management system automatically prioritizes and filters notifications based on learned user behavior patterns without requiring manual intervention. The system serves itself by continuously improving its notification prioritization algorithm through observed user interactions, thereby managing the increasing number of notifications from multiple applications autonomously.
2Loss of information
If notifications are displayed overlaying the user's view of the world, then the user receives information from the device, but the user's attention is disrupted and the notification may feel interruptive
Solution Approach 1:
The system applies different display strategies to different notifications based on their priority and relevance. High-priority notifications that are deemed relevant to the user's current context are displayed with full visibility, while lower-priority notifications are suppressed or displayed with reduced prominence, thereby delivering necessary information while minimizing attention disruption.
Solution Approach 2:
The notification display system dynamically adjusts its behavior based on the user's current activity and context. Notifications are prioritized and displayed adaptively according to real-time user behavior patterns, ensuring that important information is delivered while minimizing interruptions to the user's natural flow of attention.
3Measurement precision
If facial recognition and conversation graph updating are continuously performed, then the accuracy of notification prioritization improves, but the energy consumption and processing load increase
Solution Approach 1:
The system performs facial recognition and conversation graph updating periodically or event-driven rather than continuously. It triggers these processes based on specific events such as detecting a new notification, recognizing a familiar face, or observing significant user interactions, thereby maintaining prioritization accuracy while reducing overall energy consumption and processing load.
Solution Approach 2:
The system pre-processes and stores facial recognition data and interaction patterns during periods when the device is actively being used, preparing the conversation graph in advance. This preliminary action allows for faster, more accurate notification prioritization when needed without requiring intensive real-time processing, thus balancing accuracy with energy efficiency.
Data Source
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AI summary
According to an aspect, a method includes detecting, by at least one imaging sensor of a wearable device, facial features of an entity, detecting an interactive communication between a user of the wearable device and the entity based on at least image data from the at least one imaging sensor, updating a conversation graph in response to the interactive communication being detected between the user and the entity, and managing content for display on the wearable device based on the conversation graph.