In-Store Shopping Navigation Using Camera-Based AI and Radio Detection
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Solution Overview
Problem
Existing shopping systems are not updated in real-time and lack accurate information on product availability and location within specific stores, leading to inconvenience for users, and existing detection methods are unable to accurately determine the presence of objects behind other physical objects or provide biophysical information.
Innovation Solution
A system utilizing a global positioning system, SKU database, and machine learning to provide real-time product availability and location information, combined with drone assistance and image recognition for efficient routing and checkout processes, and an object detection system using radio frequencies to detect items behind obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional shopping systems are used to check product availability online, then users can confirm product information before visiting the store, but the information is not updated in real-time and users may find products unavailable when they arrive at the store
Solution Approach 1:
The system performs preliminary actions by continuously updating product availability information in real-time before the user arrives at the store. The mobile application checks the store's inventory system ahead of time and notifies the user of current product availability, ensuring information is current before the user commits to traveling to the store.
Solution Approach 2:
The system implements feedback by establishing a real-time communication channel between the store's inventory system and the user's mobile device. The inventory system continuously feeds updated availability information to the user, and the user's location and shopping list data are fed back to the system for dynamic route optimization and availability confirmation.
2Measurement precision
If existing detection methods such as infrared or sonar are used to detect physical presence, then users can detect objects in the environment, but the methods are incapable of determining biophysical information or detecting objects behind other physical objects
Solution Approach 1:
The system merges multiple detection technologies including cameras, radio frequency identification (RFID) readers, and weight sensors into a unified detection network. Cameras detect visual information and product locations, RFID readers detect products through shelving and packaging materials, and weight sensors detect items on shelves, creating a comprehensive detection system that overcomes the limitations of individual methods.
Solution Approach 2:
The system introduces RFID tags as intermediaries attached to products, which enable detection through physical obstacles. These passive tags reflect radio frequency signals back to readers, allowing the system to detect product presence, location, and movement even when blocked by shelves, packaging, or other objects that would prevent direct line-of-sight detection.
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 users to navigate stores efficiently, ensures accurate product detection and availability, and streamlines the checkout process by providing real-time data and precise location information, while also detecting hidden objects with high accuracy.
Implementation Method 1
A radio transmitter is configured to send signals that project onto desired objects or areas. The signals travel through solid surfaces and creating a return signal when they interact with surfaces or objects that are received by a radio receiver
Implementation Method 2
a camera, the data from the camera and the signals received from the radio receiver combined so as to provide a comprehensive image or accounting for all items within an area
Data Source
AI summary
A system for enabling in store routing of a user generated shopping list using existing store cameras and artificial intelligence and machine learning is provided. The system uses a pixelbuffer comparison of items imaged in real time to compared to a database of machine learned images. The system further provides item recognition and detection through machine learning so as to improve a shoppers experiences. The system and method further includes drone assistance means and radio signal item and biological detection so as to improve accuracy. Other features to improve guidance and accuracy include landmark navigation and masking to improve accuracy of item recognition and detection. The system may be a standalone kiosk.