Presence-Based Delivery Windows for Temperature-Sensitive Prescriptions
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Solution Overview
Problem
Current methods for transporting perishable items, such as temperature-sensitive drugs, are costly and inefficient due to the need for bulky and expensive packaging to ensure the items are not left unattended for extended periods, leading to resource wastage and potential spoilage.
Innovation Solution
A location-based presence model is generated using activity data from smart home devices to identify optimal delivery windows where a person is likely to be present, allowing for reduced packaging and transportation costs while ensuring the items are safely stored.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If bulky and expensive temperature-controlled packaging is used to ensure items are not left unattended, then the reliability of temperature-sensitive drugs is improved, but the cost and device complexity increase significantly
Solution Approach 1:
The system performs preliminary actions by generating a presence model before delivery occurs, predicting when the recipient will be home based on historical data from smart home devices. This allows the delivery service to schedule delivery during high-probability presence windows, eliminating the need for excessive protective packaging while ensuring reliable temperature maintenance through timely delivery.
Solution Approach 2:
The system uses feedback from smart home devices (door locks, thermostats, cameras) to continuously update and refine the presence model. This feedback loop enables increasingly accurate predictions of recipient availability, allowing the system to optimize delivery timing and reduce packaging requirements based on real-world validation of presence predictions.
2Loss of time
If extended unattended delivery time is allowed, then the loss of time for delivery is reduced, but the risk of spoilage and harmful factors increases
Solution Approach 1:
The system performs preliminary analysis of recipient patterns before delivery to identify optimal time windows when the recipient is most likely to be present. By scheduling delivery during these pre-identified high-probability windows, the system minimizes both delivery time and the risk of temperature exposure, achieving a dual optimization of speed and safety.
Solution Approach 2:
The presence model is dynamic and adapts to changing recipient patterns over time. As the system collects more data from smart home devices, it refines its predictions of when the recipient will be home, allowing it to dynamically adjust delivery timing to minimize unattended exposure while maintaining efficient delivery schedules.
3Reliability
If conservative delivery scheduling is used to ensure someone is present, then the reliability of receipt is improved, but the productivity of the delivery system decreases
Solution Approach 1:
Instead of requiring guaranteed presence (excessive action), the system uses probabilistic presence modeling to identify time windows with high probability of recipient availability. This partial action approach achieves sufficient reliability for most deliveries without the operational constraints of guaranteed presence, maintaining delivery efficiency while improving receipt reliability compared to traditional methods.
Solution Approach 2:
The system changes the parameter of delivery scheduling from fixed or conservative time windows to dynamic, probability-based windows derived from the presence model. This allows the delivery system to optimize for both reliability and productivity by scheduling deliveries during statistically optimal times rather than using overly conservative schedules that reduce throughput.
Data Source
AI summary
Methods and systems for prescription drug shipping selection are provided. The methods and systems include operations comprising: obtaining, by a server, activity data from a plurality of devices associated with a location, the activity data representing different types of activities that take place at the location over a threshold period of time; aggregating, by the server, the activity data to generate a location-based presence model for the location, the location-based presence model indicating likelihoods that a person is present at the location at a plurality of different time windows; and identifying, by the server, based on the location-based presence model, a time window for delivery of a perishable item to the location.