Presence Modeling for Perishable Delivery Time Window Selection
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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 unnecessary resource expenditure on packaging and transportation to avoid spoilage.
Innovation Solution
A location-based presence model is generated using activity data from smart home systems to identify time windows with a high likelihood of consumer presence, allowing for optimized delivery parameters and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional delivery methods are used for perishable items, then item integrity is maintained through excessive packaging and temperature-controlled transportation, but costs and resource consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by generating a presence model before delivery to predict consumer availability. This advance information allows optimization of delivery parameters (timing, packaging, transportation mode) to match actual consumer presence patterns, reducing unnecessary resource expenditure while ensuring item integrity is maintained only when needed.
2Reliability
If conservative delivery parameters are used to ensure item integrity, then delivery reliability improves, but delivery speed and efficiency decrease
Solution Approach 1:
The system dynamically adjusts delivery parameters based on the generated presence model. Instead of using fixed conservative parameters for all deliveries, the system adapts packaging requirements, transportation mode, and timing to match predicted consumer presence patterns. This dynamic approach maintains delivery reliability while improving efficiency by avoiding excessive protective measures when consumer presence is highly likely.
3Measurement precision
If activity data from smart home systems is collected and processed, then delivery timing accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system introduces a presence model as an intermediary that processes activity data from smart home systems. This model acts as a mediator between raw data collection and delivery decision-making, transforming complex activity patterns into simplified presence predictions. This intermediary layer improves delivery timing accuracy while managing system complexity by providing a structured framework for data interpretation.
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.