Smart Cart Dispatch Using Computer Vision Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Shoppers often decline shopping carts when they realize they need more items than they can carry, leading to inconvenience and frustration, and existing systems waste resources by automatically deploying carts without prediction.
Innovation Solution
Implementing a computer vision-based ML model to predict shopper needs for a cart and dispatch smart carts autonomously or with human intervention, using local controllers to reduce latency and resource waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If smart carts are automatically deployed to all shoppers, then cart availability is improved, but resource waste and computational overhead increase
Solution Approach 1:
The system performs preliminary analysis by capturing images of shoppers and predicting their cart usage needs before deployment decisions are made. This preliminary action allows the system to pre-identify shoppers who will need carts, avoiding unnecessary deployment to shoppers who won't use them, thus reducing computational overhead while maintaining cart availability for those who need it
Solution Approach 2:
The system uses automated image capture and ML-based prediction to self-determine which shoppers need carts, eliminating the need for manual assessment or universal deployment. The shopper's needs are assessed through their own visual characteristics and shopping behavior patterns, allowing the system to serve only those who require assistance
2Speed
If smart carts are dispatched without prediction, then response time is improved, but resource efficiency deteriorates
Solution Approach 1:
The system captures images and predicts cart usage needs in advance before actual deployment decisions are made. This preliminary prediction step identifies target shoppers who will actually use carts, allowing rapid deployment only to those individuals. The response time remains fast because the prediction happens in real-time before deployment, while resource efficiency improves by avoiding deployment to non-needing shoppers
3Measurement precision
If computer vision ML model is used to predict cart usage, then deployment accuracy is improved, but system complexity increases
Solution Approach 1:
The system replaces complex manual assessment mechanisms with computer vision-based image capture and ML prediction. Instead of requiring human workers to observe and assess shopper needs, the system uses automated image processing and machine learning models to predict cart usage from visual data, improving deployment accuracy while the automated nature reduces operational complexity
Solution Approach 2:
The system uses image captures as a simplified copy or representation of the shopper's physical appearance and shopping context. Rather than requiring direct interaction or complex sensing, the ML model analyzes image copies to predict cart needs, achieving high deployment accuracy through pattern recognition in visual data without requiring complex physical systems
Data Source
AI summary
Techniques relating to machine learning (ML) in a shopping environment. The techniques include identifying one or more images captured in a shopping environment, and determining to automatically dispatch a cart to a shopper in the shopping environment. This includes predicting a use of the cart by the shopper based on providing the one or more images to one or more trained ML models. The techniques further include automatically dispatching the cart to the shopper. The cart automatically navigates in the shopping environment to the shopper.