Smart Shopping Cart Capacity Sensing for Packing Recommendations
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Solution Overview
Problem
Smart shopping carts lack the ability to understand the layout and capacity of their contents, leading to inefficient item placement and non-optimal packing configurations, which can increase order fulfillment latency and reduce fulfillment accuracy.
Innovation Solution
Implementing sensors, such as cameras and load sensors, in smart shopping carts to detect items and measure load, combined with capacity-informed prediction models to recommend items and optimize packing configurations based on real-time cart capacity and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If smart shopping carts are equipped with sensors and cameras to detect items and measure load, then the cart can determine capacity and optimize packing configurations, but the device complexity increases
Solution Approach 1:
The system divides the cart into multiple sensing zones with dedicated cameras and load sensors positioned at specific locations (e.g., front basket, rear basket, handle area) to independently detect items and measure load in different regions. This segmentation allows the complex monitoring function to be distributed across multiple simpler sensing units, making the overall system more manageable while maintaining comprehensive coverage.
Solution Approach 2:
The smart shopping cart integrates multiple functions into a single system: item detection through cameras, load measurement through sensors, capacity calculation, packing optimization, and user guidance. By making the cart multi-functional, the patent reduces the need for separate systems and justifies the added complexity through consolidated functionality that serves multiple purposes simultaneously.
2Productivity
If the cart determines remaining capacity and provides capacity-informed recommendations, then item placement is optimized, but the ease of operation may be reduced due to additional interface requirements
Solution Approach 1:
The cart system automatically monitors its own capacity using integrated sensors and cameras, calculates optimal packing configurations, and provides recommendations without requiring manual input from the user. The system serves itself by autonomously detecting items, measuring load, and generating guidance, thereby maintaining high fulfillment accuracy while minimizing the operational burden on users.
Solution Approach 2:
The system continuously monitors the actual state of items in the cart and provides real-time feedback to both the user and the optimization algorithm. This feedback loop allows the cart to update its capacity assessment dynamically and adjust recommendations accordingly, ensuring high accuracy while presenting information in an intuitive manner that does not complicate user interaction.
3Loss of time
If the cart provides fulfillment instructions and optimal packing configurations, then order fulfillment latency is reduced, but the device complexity increases due to computational requirements
Solution Approach 1:
The system performs preliminary calculations of optimal packing configurations and fulfillment instructions in advance, based on predicted item placements and capacity constraints. By pre-computing these optimizations rather than calculating them in real-time during fulfillment, the system reduces the computational burden during critical operations while still achieving low fulfillment latency.
Solution Approach 2:
The system applies optimization algorithms to specific critical aspects of packing (such as item positioning and cart configuration) while using simplified methods for less critical aspects. This partial optimization approach achieves sufficient improvement in fulfillment speed without the computational complexity of fully optimizing every possible parameter, balancing performance gains with system resource requirements.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances order fulfillment efficiency by optimizing item placement and reducing unnecessary delays through intelligent recommendations and packing strategies.
Implementation Method 1
The cameras can capture image data of the items in the cart
Implementation Method 2
The load sensors can measure load data indicating a total load of items in the cart
Data Source
AI summary
A smart shopping cart may utilize cameras and/or load sensors to provide capacity-informed recommendations. The cameras are positioned facing at least a first basket of the smart shopping cart and configured to capture image data during a visit at a retailer location. The load sensors are configured to measure load data during a visit at the retailer location. The cart detects obtained items entering the first basket based on the image data and the load data. The cart identified remaining capacity in the first basket based on the image data and the load data. The cart applies a capacity-informed model to the one or more obtained items and the remaining capacity in the first basket to identify one or more recommended items. The cart displays, via an electronic display, the one or more recommended items.