Dynamic Shopping System for Pickup Point Sales
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Solution Overview
Problem
Existing delivery systems miss opportunities for additional sales at pickup points, as customers are not presented with relevant products during the delivery of their original order, limiting the merchant's ability to showcase and sell additional items.
Innovation Solution
A dynamic shopping system that uses a neural network-based product matching engine to identify and display additional products at pickup locations, allowing customers to browse and purchase these products alongside their original order, utilizing autonomous vehicles and drones for delivery and inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional delivery systems are used to deliver products to pickup points, then delivery flexibility is improved, but additional sales opportunities at pickup points are lost
Solution Approach 1:
The pickup point is transformed from a single-function delivery location into a multi-functional space that serves both as a product collection point and a retail showcase. The system displays additional products at the pickup point, allowing customers to browse and purchase supplementary items while collecting their original orders, thereby converting a unidirectional delivery process into a dual-purpose experience that maintains delivery flexibility while creating new sales channels.
Solution Approach 2:
The system pre-selects and prepares supplementary products that are relevant to the customer's original order before delivery. Using historical data and product association algorithms, the system identifies complementary items and makes them available at the pickup point in advance, so that when the customer arrives to collect their order, the additional products are already positioned and ready for immediate purchase, eliminating the need for post-delivery marketing efforts.
2Measurement precision
If a neural network-based product matching engine is implemented to identify additional products, then product recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces a product association algorithm as an intermediary component that bridges the gap between complex neural network processing and simple product display. The algorithm pre-processes historical order data and product relationships to generate association rules, which then guide the selection of supplementary products. This intermediary layer translates complex AI predictions into actionable product recommendations, reducing the computational burden on the real-time system while maintaining high recommendation accuracy.
Solution Approach 2:
The system performs preliminary training of the neural network model offline using historical order data, product categories, and customer behavior patterns. The model learns product associations and customer preferences in advance, storing the learned relationships in a database. During actual delivery operations, the system only needs to query the pre-trained model and retrieve relevant product associations, rather than performing complex real-time analysis, thereby significantly reducing operational system complexity while maintaining high recommendation precision.
3Productivity
If historical data analysis is used to optimize resource allocation at pickup points, then resource utilization is improved, but data processing time increases
Solution Approach 1:
The system performs comprehensive historical data analysis and resource optimization calculations in advance, before the actual delivery process begins. By analyzing past delivery patterns, customer preferences, and product performance at different pickup points, the system pre-determines optimal product assortments, inventory levels, and resource allocation strategies. This pre-computed information is stored and quickly retrieved during delivery operations, eliminating the need for time-consuming real-time data processing while maintaining optimized resource utilization.
Solution Approach 2:
The system creates simplified copies or representations of complex historical data patterns in the form of pre-computed lookup tables, association rules, and product templates. Instead of processing raw historical data during delivery operations, the system uses these pre-generated copies that capture the essential patterns and relationships. This copying approach allows rapid query and decision-making during delivery while the computationally intensive data analysis has already been completed in advance, effectively decoupling data processing time from delivery operations.
Data Source
AI summary
Dynamic shopping that includes receiving, at a system, an order for a selected product for purchasing and a package pickup location from a device of a user. The method can further include determining a type of product the user has ordered for pick up at the package pickup location, and the method can use the system for dynamic shopping to determine other products for potential order at the pickup location. The computer implemented method can also add to the order other products for potential order.