Location-Based Presence Modeling for Perishable Item Delivery
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Solution Overview
Problem
Existing delivery methods for perishable items, particularly temperature-sensitive drugs, are inefficient and costly due to the uncertainty of consumer presence at the delivery location, leading to excessive resource use and potential spoilage.
Innovation Solution
A location-based presence model is generated using activity data from IoT devices to identify time windows with a high likelihood of consumer presence, allowing for optimized delivery scheduling and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If delivery is scheduled without considering consumer presence, then delivery can be made at any time, but resource consumption increases and spoilage risk increases
Solution Approach 1:
The system performs preliminary actions by generating a presence model before delivery scheduling, which predicts consumer presence patterns in advance. This allows the delivery system to schedule deliveries during high-probability presence windows, reducing resource consumption and spoilage risk without requiring real-time presence detection during delivery attempts.
Solution Approach 2:
The system uses feedback from IoT device activity data to continuously refine the presence model. By analyzing patterns from smart devices (lights, thermostats, cameras), the system learns consumer presence patterns and uses this feedback to optimize delivery scheduling, reducing wasted delivery attempts and resource consumption.
2Reliability
If insulated and temperature-controlled shipping containers are used, then drug safety and efficacy are maintained, but delivery costs increase
Solution Approach 1:
The system generates a presence model in advance that predicts when the consumer will be present, allowing the delivery system to schedule deliveries during high-probability windows. This reduces the duration that perishable items must be kept in expensive temperature-controlled containers, thereby maintaining drug safety while reducing shipping costs.
Solution Approach 2:
The system dynamically adjusts delivery scheduling based on predicted presence probability. Rather than using fixed delivery windows or always employing maximum temperature control, the system adapts the level of temperature control and delivery timing to match the predicted presence probability, optimizing the balance between drug safety and shipping cost.
3Reliability
If multiple delivery attempts are made to ensure receipt, then delivery reliability improves, but resource consumption and cost increase
Solution Approach 1:
The system performs preliminary analysis of consumer presence patterns using IoT device data before scheduling delivery. By identifying high-probability presence windows in advance, the system can schedule delivery during times when the consumer is most likely to be present, reducing the need for multiple delivery attempts while maintaining high delivery assurance.
Solution Approach 2:
The system uses feedback from the presence model to optimize delivery timing. By continuously learning from activity data and refining presence predictions, the system improves delivery accuracy over time, reducing wasted delivery attempts and associated costs while maintaining reliable delivery assurance.
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.