Adaptive OTA Notification System for Vehicle Software Updates
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Solution Overview
Problem
Modern vehicles face challenges in managing software updates, as users' preferences for installing updates vary based on contextual factors like location, time, and historical behavior, leading to inconsistent acceptance rates and potential inconvenience.
Innovation Solution
A system that uses machine learning to compute user preference values based on historical selections and contextual information, prompting users to install updates only when the preference value exceeds a defined threshold, thereby adapting the frequency and intrusiveness of update notifications to improve acceptance rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users are prompted to install software updates frequently, then update acceptance rate may improve, but user inconvenience increases
Solution Approach 1:
The notification system dynamically adjusts its behavior based on learned user preferences and contextual factors. The system transitions from static, fixed notification schedules to adaptive, context-aware prompting, modifying notification timing and frequency based on real-time user behavior patterns and environmental context.
Solution Approach 2:
The system implements a feedback loop where user responses to update notifications are continuously monitored and used to refine future notification strategies. User acceptance or rejection patterns feed back into the machine learning model, which adjusts prediction accuracy over time to better align with actual user preferences.
2Reliability
If update notifications are personalized to user preferences, then user acceptance improves, but system complexity increases
Solution Approach 1:
The system performs self-learning by automatically observing user behaviors and extracting preference patterns without requiring explicit user programming or configuration. The machine learning model autonomously improves its understanding of user preferences through continuous data collection and analysis, reducing the need for manual system configuration.
Solution Approach 2:
The patent replaces traditional rule-based notification systems with machine learning-based predictive modeling. Instead of using fixed thresholds or simple user profiles, the system employs computational algorithms that automatically analyze complex behavioral patterns and contextual data to generate personalized notification strategies.
3Measurement precision
If the system learns from historical user behavior, then notification accuracy improves, but data processing requirements increase
Solution Approach 1:
The system applies partial learning by focusing computational resources on the most influential behavioral factors and contextual features. Rather than processing every possible data point equally, the model identifies and prioritizes key decision-making factors that have the greatest impact on update acceptance, reducing overall computational burden while maintaining prediction accuracy.
Data Source
AI summary
A vehicle includes a controller and a processor. The processor is programmed to prompt the user with a selection to install a software update to the controller responsive to a user preference value, computed from user selections whether or not to install software updates associated with contextual information that matches a software update to be installed, exceeding a threshold defined by a priority of the software update, and update the user preference value per the selection.