Predictive Item Aggregation for Single-Courier Delivery
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Solution Overview
Problem
Existing delivery systems are inefficient when end users need to obtain multiple items from different service providers, often requiring multiple couriers and leading to delayed delivery of priority items due to the proximity and delivery speed of individual service providers.
Innovation Solution
A central server computer uses a machine learning model to predict item aggregates likely to be ordered by end users, strategically placing items at optimal locations for rapid collection and delivery by a single courier, reducing the need for multiple pickups and optimizing delivery routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple couriers are used to retrieve items from different service providers, then each courier can independently obtain items, but the delivery time increases and costs increase due to multiple separate deliveries
Solution Approach 1:
The patent combines multiple separate delivery tasks into a single consolidated delivery route. The system aggregates items from multiple service providers and assigns them to one courier, merging what would otherwise be separate delivery operations. This reduces the number of couriers needed while maintaining delivery efficiency.
Solution Approach 2:
The system performs preliminary aggregation of items from different service providers before the actual delivery occurs. By pre-consolidating items at a central location or in a planned route sequence, the system prepares the delivery in advance, allowing a single courier to efficiently collect and deliver multiple items without making separate trips.
2Device complexity
If a single courier retrieves items from different service providers independently, then the number of couriers is reduced, but delivery time increases due to delays at non-proximate service providers
Solution Approach 1:
The system performs preliminary aggregation of items from different service providers at a central location or in a planned sequence before the courier begins delivery. This advance consolidation ensures that all items are ready for collection, eliminating delays caused by the courier waiting at each service provider location.
Solution Approach 2:
The patent introduces an intermediary system (central server/computer) that coordinates between service providers and the courier. This intermediary manages the aggregation process, optimizes collection routes, and synchronizes item availability, allowing the single courier to operate efficiently without direct delays from service provider locations.
3Device complexity
If items are delivered as a bundle from non-proximate service providers, then a single courier can deliver all items, but the delivery of priority items is delayed due to the slowest service provider
Solution Approach 1:
The patent segments the delivery process into different priority levels. Priority items are identified and separated from standard items, allowing them to be collected and delivered on expedited timelines. The system divides the single courier's route into priority and standard segments, ensuring priority items receive faster service.
Solution Approach 2:
The system performs preliminary identification and prioritization of items before the courier collection begins. Priority items are flagged and prepared for expedited collection, allowing the courier to collect and deliver these items first in the route, rather than waiting to complete collection from all service providers.
4Productivity
If multiple separate orders are placed for items from different service providers, then each item can be delivered independently, but the overall process becomes inefficient and time-consuming
Solution Approach 1:
The patent creates a universal ordering system that handles multiple service providers through a single interface. The central server aggregates items from multiple providers into one consolidated order, allowing the customer to place a single multi-item order rather than separate orders for each service provider. This maintains ease of operation while improving delivery efficiency.
Data Source
AI summary
A method is described. The method includes determining, by a central server computer using a machine learning model, aggregates of items that are likely to be ordered by end users. The method also includes initiating placement of the items in the aggregates of items in one or more locations based on locations of the end users, receiving a request for an aggregate of items, and initiate the fulfillment of the request for the aggregate of items, where the items in the aggregates of items are at the one or more locations.