Multi-sensor Shopping Cart Product Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Brick-and-mortar retailers lack the ability to track customers' shopping cart contents in real-time, preventing timely and effective personalized recommendations, which are a key advantage for e-retailers.
Innovation Solution
Implementing a multi-sensor system that includes cameras, RF transmitters, location sensors, and weight sensors to accurately determine and track products in shopping carts, combining signals for real-time product recognition and personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-detection methods are used for product identification, then device complexity is reduced, but measurement precision and reliability of product recognition deteriorate
Solution Approach 1:
The patent combines multiple detection methods (computer vision, RF signature detection, weight detection) into a unified multi-sensor system. Each detection method targets specific product attributes, and their results are merged to achieve accurate product identification. This resolving the contradiction by improving measurement precision through method diversity while managing device complexity through integrated architecture.
Solution Approach 2:
The patent segments the product identification process into multiple independent detection stages: initial product set identification using computer vision, followed by specific product determination using RF and weight detection. This segmentation allows each detection method to focus on specific tasks, improving overall measurement precision while organizing system complexity into manageable modules.
2Reliability
If multiple detection methods are combined for accurate product recognition, then measurement precision improves, but device complexity increases
Solution Approach 1:
The shopping cart system is designed with multi-functional capabilities: it can detect product presence, identify product categories, determine specific products, track cart location, and monitor weight changes. By making the system universal and capable of performing multiple functions through integrated sensors, the patent improves reliability without proportionally increasing complexity, as each sensor serves multiple purposes in the overall system.
Solution Approach 2:
The patent introduces a service layer that acts as an intermediary between the multiple sensors and the product identification logic. This service receives data from computer vision, RF transmitters, and weight sensors, processes the information, and coordinates the detection methods. The intermediary manages system complexity by providing a unified interface while enabling reliable multi-method detection.
3Loss of information
If real-time tracking of shopping cart contents is implemented, then loss of information is reduced, but use of energy increases
Solution Approach 1:
The patent implements periodic detection cycles where sensors are activated at intervals rather than continuously. The system periodically updates product identification, cart location, and weight measurements. This periodic action reduces energy consumption compared to continuous monitoring while still maintaining real-time tracking capability and minimizing information loss about shopping cart contents.
Solution Approach 2:
The system uses passive detection methods where products themselves provide identification signals (RF tags, barcodes) without requiring active energy from the sensing system. The computer vision system captures images passively, and RF transmitters on products actively broadcast their identity. This self-service approach minimizes the energy burden on the shopping cart system while maintaining continuous information tracking.
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
Enables accurate identification of products in shopping carts, allowing for real-time personalized recommendations and automated self-checkout, enhancing customer experience and basket size.
Implementation Method 1
weight sensors to accurately determine and track products in shopping carts
Implementation Method 2
RF transmitters, location sensors, and weight sensors to accurately determine and track products
Implementation Method 3
cameras, RF transmitters, location sensors, and weight sensors to accurately determine and track products
Data Source
AI summary
Identifying products in a physical store shopping environment. The method includes, using a first detection method, identifying that a given product likely belongs to a given set of products. The method further includes, using one or more other detection methods, determining that the product is likely a specific product from the given set of products.