Reminder System for Forgotten Items in Shared Vehicles
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Solution Overview
Problem
Individuals frequently misplace personal items while commuting, especially in shared vehicle environments, leading to frustration, time consumption, and potential loss, as existing systems lack effective automated reminders for forgotten devices or objects.
Innovation Solution
A computer-implemented method and system that generates and transmits reminder messages by predicting the spatial relationship between users, objects, and mobile devices using unique user profiles, BLE tags, and vehicle control systems, sending alerts when the predicted relationship exceeds a predetermined threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated reminder systems are implemented, then loss of items is reduced, but device complexity increases
Solution Approach 1:
The patent introduces a server as an intermediary component that coordinates between mobile devices, vehicles, and users. The server receives location data from mobile devices and vehicles, predicts spatial relationships, and sends reminder messages, thereby distributing system complexity away from individual devices while maintaining reliability through centralized coordination.
Solution Approach 2:
The system implements feedback loops where the server continuously monitors location data from mobile devices and vehicles, compares actual positions with predicted spatial relationships, and sends reminder messages when deviations are detected. This feedback mechanism automatically detects and alerts users about forgotten items, reducing loss without requiring complex user intervention.
2Measurement precision
If spatial relationship prediction is performed continuously, then accuracy of forgotten item detection is improved, but use of energy increases
Solution Approach 1:
The system performs spatial relationship predictions at periodic intervals rather than continuously. The server receives location data at scheduled intervals, updates predictions accordingly, and triggers reminders only when necessary. This periodic operation maintains detection accuracy while significantly reducing energy consumption compared to continuous monitoring.
Solution Approach 2:
The server pre-calculates expected spatial relationships between users, mobile devices, and vehicles based on historical data and typical usage patterns. By having predictions ready in advance, the system can quickly compare actual locations against pre-computed expectations without requiring intensive real-time calculations, thereby reducing energy usage while maintaining precision.
3Reliability
If multiple data sources are integrated, then reliability of reminder accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the data integration task across multiple independent components: mobile devices collect user location data, vehicles provide vehicle location and status data, and the server integrates these separate data streams. Each component maintains simple, focused functionality while the server performs the complex integration, thereby improving reminder accuracy without overwhelming individual devices with complexity.
Solution Approach 2:
The server is designed as a universal platform that handles multiple functions: receiving location data from various sources, predicting spatial relationships, determining when items are forgotten, and sending reminders through different channels. This multi-functional design consolidates complexity into a single component that can process diverse data types and trigger appropriate reminders regardless of the specific scenario.
Data Source
AI summary
A method for generating and transmitting a reminder message includes obtaining a unique user profile. The user profile includes a user identification (ID) indicative of a unique user, an object ID indicative of a unique object, and a device ID indicative of a unique mobile device. The processor is configured to predict a spatial relationship between the user, the object, and the mobile device. The prediction is based, in part, on the user ID, the object ID, and the device ID, where the prediction includes a geographic location for the user, the object, and/or the mobile device. The processor compares a location of at least two of the user, the object and the mobile device with the prediction of the user's spatial relationship with the object and the mobile device. The processor transmits a reminder message when the prediction of the spatial relationship exceeds a predetermined threshold.