Learning Section Adjusts Consumable Alert Timing
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Solution Overview
Problem
Existing electronic apparatuses, such as ink jet printers, face challenges in displaying consumable alerts at a timing that is user-specific and environment-specific, as the current methods rely on predetermined settings that may not align with the user's desired timing for replacement or refill, leading to inefficient consumable management.
Innovation Solution
The implementation of a machine learning-based system within the electronic apparatus that adjusts the timing and threshold for consumable alerts based on user interaction and consumable levels, using a learning model to determine when to notify the user about remaining consumable amounts, delaying or hastening notifications and adjusting threshold values accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If alert information is displayed based on predetermined timing and settings, then the notification system is simple and reliable, but the notification timing does not match user preferences and usage patterns
Solution Approach 1:
The notification system performs self-learning by automatically observing user responses to alert notifications and adjusting notification timing and thresholds without requiring manual user configuration. The system learns from patterns in user behavior, such as when users acknowledge or ignore notifications, and autonomously optimizes future notification strategies to match user preferences.
Solution Approach 2:
The system implements a feedback loop where user responses to notifications are captured and used to adjust future notification behavior. By monitoring whether users interact with, dismiss, or ignore alert information, the system continuously refines its notification timing and threshold settings to better align with actual user needs and preferences.
2Reliability
If notifications are displayed earlier to ensure users are alerted in time, then users have more time to respond, but users may experience unnecessary notifications before actual replacement is needed
Solution Approach 1:
The notification threshold is made dynamic rather than fixed, allowing it to adjust based on learned user behavior patterns and actual consumable consumption rates. The system adapts the threshold timing to balance early warning needs with avoiding premature notifications, optimizing when alerts are displayed based on real-time observations of user responses and consumable usage patterns.
3Loss of time
If notifications are delayed to avoid premature alerts, then users can use consumables longer, but users may miss important replacement warnings
Solution Approach 1:
The system autonomously determines optimal notification timing by learning from user behavior patterns without requiring manual intervention. It automatically adjusts when to send notifications based on observed user responses, balancing the need to alert users in time for consumable replacement with allowing maximum productive usage of consumables.
Data Source
AI summary
An electronic apparatus includes consumables configured to be restored by replacement or refill, a notification section configured to make a notification of alert information based on remaining amount information of the consumables, a remaining amount information obtaining section configured to obtain the remaining amount information of the consumables, a determination information obtaining section configured to obtain determination information for determining a state of replacement or refill of the consumables, and a learning section configured to perform machine learning of the notification of the alert information based on a learning model obtained by associating the remaining amount information with the determination information.