Reusable Bag Image Classification for New vs Used Recognition
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Solution Overview
Problem
Existing systems struggle to accurately distinguish between new and used reusable bags during transactions, leading to misclassification and loss of inventory.
Innovation Solution
A system utilizing artificial intelligence and a machine learning model, such as a neural network, analyzes images of reusable bags to classify them as new or used based on characteristics like frayed edges, crumpled shape, and discoloration, thereby reducing misclassification and inventory loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to determine whether a reusable bag is new or used, then the system is simple to operate, but the classification accuracy is low leading to misclassification and inventory loss
Solution Approach 1:
The patent replaces manual visual inspection with an automated image recognition system using machine learning models. The system captures images of reusable bags and uses trained neural networks to automatically classify them as new or used, eliminating human subjectivity and improving classification accuracy while maintaining operational simplicity through automation.
Solution Approach 2:
The system enables the reusable bag classification process to serve itself by using the bag's own visual characteristics (frayed edges, crumpled shape, discoloration) as input features for the machine learning model. The bag effectively classifies itself through its visual properties without requiring external manual assessment.
2Reliability
If automated image recognition is used to classify reusable bags, then the classification accuracy is improved, but the device complexity increases
Solution Approach 1:
The machine learning model serves multiple functions: it detects various visual characteristics (frayed edges, crumpled shape, discoloration), performs classification, and can be trained on diverse bag types. This multi-functionality consolidates what would otherwise require multiple separate systems into a single unified platform, managing complexity while improving reliability.
Solution Approach 2:
The system performs preliminary training of the machine learning model using labeled datasets of new and used bags before deployment. This preliminary action prepares the model to reliably classify bags in real-time operations, ensuring high classification reliability from the start while the complexity is managed through one-time setup rather than continuous complex operations.
3Loss of substance
If the system classifies bags accurately, then inventory loss is reduced, but the processing time per bag increases due to image analysis
Solution Approach 1:
The system processes bag images in discrete frames or batches rather than continuous real-time analysis. The image capture and analysis occur in periodic intervals as bags move through the checkout area, allowing efficient processing that minimizes time per bag while maintaining accurate classification to prevent inventory loss.
Solution Approach 2:
The system creates digital copies (images) of the physical bags for analysis instead of physically handling or manipulating the bags. This copying approach allows rapid digital processing of multiple bags simultaneously, reducing processing time while the accurate image analysis prevents inventory loss through reliable classification.
Data Source
AI summary
The present disclosure describes a system and method for identifying an item. The system includes a camera that captures a first image of a first item being purchased by a user, a memory, and a processor. The processor determines, based on the first image, a first probability for a first identity of the first item and a second probability for a second identity of the first item and in response to determining that both the first probability and the second probability are below a threshold, determines, based on a shopping history of the user, that the user previously purchased a second item comprising a characteristic. The processor also applies a first weight to the first probability and a second weight to the second probability based on the characteristic, and assigns the first identity to the first item based on the first weighted probability and the second weighted probability.