Capsule Net Product Orientation Detection for Retail Safety
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Solution Overview
Problem
In retail shopping environments, existing solutions fail to effectively address the dynamic challenges of product handling, particularly for hazardous or improperly oriented items that can cause injury, such as sharp objects or those out of reach, which evolve over time and are not adequately addressed by current technologies.
Innovation Solution
A method and system utilizing a Capsule Neural Network (Capsule Net) and bidirectional Long Short-Term Memory (LSTM) network to identify product types, orientations, and safety handling instructions, generating real-time guidance through a product assistance system that includes IoT sensors and a handheld device, ensuring safe handling of products by users, including those with disabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing product detection solutions are used, then product selection and rack guidance are provided, but they fail to detect improperly oriented products that can cause injury
Solution Approach 1:
The system performs preliminary detection of product orientations and identifies improperly oriented products before users attempt to handle them. The capsule network analyzes product orientations in advance and generates safety instructions proactively, preventing potential injuries rather than reacting after incidents occur.
Solution Approach 2:
The patent replaces traditional mechanical or manual product monitoring with an AI-based capsule network that uses image processing and neural networks to detect product orientations. This substitution enables automated, accurate detection of improper orientations without requiring physical inspection or manual monitoring.
2Productivity
If manual product monitoring is implemented, then product orientation can be checked, but it cannot address dynamic changes in real-time
Solution Approach 1:
The system replaces slow manual monitoring with automated capsule networks and bidirectional LSTM models that process images and detect orientation changes in real-time. These AI systems continuously analyze product orientations and immediately generate safety instructions when improper orientations are detected, achieving high-speed real-time monitoring.
Solution Approach 2:
The patent implements continuous monitoring through LSTM models that track product orientations over time and detect changes dynamically. The system maintains ongoing analysis of product placements rather than periodic checks, ensuring real-time detection of orientation changes and continuous provision of safety guidance.
3Ease of operation
If generic product detection is used, then basic product identification is achieved, but specific safety handling instructions cannot be provided
Solution Approach 1:
The system applies local quality by providing product-specific safety handling instructions tailored to each individually detected improper orientation. Rather than generic alerts, the capsule network generates customized guidance for each product based on its specific type, orientation, and location, ensuring appropriate handling instructions for diverse product categories.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors product orientations, compares them against safe orientation standards, and provides real-time safety instructions to users. The bidirectional LSTM model learns from detected patterns and improves its ability to provide accurate safety guidance based on feedback from ongoing detections.
Data Source
AI summary
The present invention discloses a method and a system for assisting user with product handling in a retail shopping. The method comprising identifying one or more products from a list of products that user intends to purchase, neighbouring products and orientations of the one or more products and the neighbouring products from a plurality of image frames and rack information, comparing the identified orientations with historic orientations of products and neighbouring products associated with the rack information, identifying at least one product from the one or more products and the neighbouring products that is improperly oriented based on comparison, determining product safety handling instructions of the identified at least one product and assisting the user in real-time with handling of the identified at least one product based on the product safety handling instructions.