Predictive Contactless Delivery System Using Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The COVID-19 pandemic highlights the need for contactless transactions to minimize the risk of virus transmission during recurring physical interactions, such as cash withdrawals or product deliveries, where physical contact with unsanitized devices or items poses a transmission risk.
Innovation Solution
A predictive automated contactless delivery system using machine learning algorithms analyzes historical transaction data to identify recurring transactions and securely facilitates these transactions through contactless means, employing drones or autonomous vehicles for delivery, with secure carriers and advanced navigation and security features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If physical contact transactions are used for recurring deliveries or cash withdrawals, then convenience and efficiency are improved, but the risk of virus transmission increases
Solution Approach 1:
The patent introduces an intermediary delivery system consisting of a delivery device (such as a drone or autonomous vehicle) that acts as a mediator between the item source and the customer. The delivery device transports items without requiring direct human contact, thereby maintaining transaction efficiency while eliminating virus transmission risk through physical contact with unsanitized surfaces or persons.
Solution Approach 2:
The patent replaces traditional mechanical contact-based delivery systems with automated delivery devices that use electronic control and autonomous navigation. This substitution eliminates the need for human physical contact during item handover, resolving the contradiction between maintaining convenient transactions and preventing virus transmission.
2Reliability
If contactless delivery systems are implemented, then virus transmission risk is reduced, but system complexity increases
Solution Approach 1:
The patent designs a universal delivery device that can handle multiple types of items and deliver to various locations using a single platform. The delivery device is configured with programmable instructions that enable it to perform different delivery tasks, reducing the need for multiple specialized systems and thereby managing complexity while maintaining contactless safety.
Solution Approach 2:
The patent utilizes programmable parameters and configurable settings in the delivery device to adapt to different delivery scenarios without requiring hardware changes. By changing software parameters and control instructions, the system can accommodate various item types and delivery conditions, maintaining reliability while controlling system complexity through software flexibility.
Data Source
AI summary
Aspects of the disclosure relate to use of supervised, unsupervised, semi-supervised, or reinforcement-based machine learning algorithm(s) to perform pattern recognition and/or cluster detection on historical transactional data in order to predict events that may occur in the future and are candidates for automated delivery of items from a source to a customer in a contactless manner, via a secure delivery device, to minimize health risk(s) to the customer. The items may be securely delivered in an automated manner such as, for example, by use of a drone or autonomous vehicle, which may have advanced sensors to facilitate various aspects of the delivery. The items may be contained in a secure carrier coupled to the delivery device and may be unlocked locally or remotely using mechanical or digital means. Novel logical systems, architectures, machines, platforms, delivery devices and components thereof, and methods are disclosed.