Retail Product Template Updates for Faster Image Recognition
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Solution Overview
Problem
Manual inspection of price tag labels and products at product storage facilities is time-consuming and costly, while optical character-based recognition requires significant system resources and processing costs.
Innovation Solution
An image capture device captures images of product storage structures, a computing device processes these images to detect and crop products, generates embeddings, and updates keyword and feature model templates to facilitate efficient product recognition using machine learning and neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of price tag labels and products is performed, then product recognition accuracy is maintained, but labor time and operational costs increase significantly
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated image recognition system using machine learning models. Image capture devices photograph products on shelves, and trained neural networks automatically identify and verify product-label matching, eliminating the need for manual visual inspection while maintaining high accuracy.
Solution Approach 2:
The system creates digital copies (images) of physical products and processes these copies through machine learning models. By working with image data rather than physical products, the system achieves rapid automated recognition without manual handling, resolving the time-loss contradiction.
2Extent of automation
If optical character-based recognition is used for product labels, then automation level increases, but system resource requirements and processing costs increase significantly
Solution Approach 1:
The patent segments the image processing task into distinct stages: image capture, preprocessing, feature extraction, and classification. By dividing the workload and processing only relevant portions of images through machine learning models, the system achieves automation while reducing overall computational resource consumption compared to full optical character recognition.
Solution Approach 2:
The system changes the approach from traditional optical character recognition parameters to machine learning feature parameters. By training models to recognize product characteristics directly from image features rather than converting text to meaning, the system reduces processing complexity and resource requirements while maintaining automation.
3Measurement precision
If comprehensive image processing of all products is performed, then recognition precision is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial processing by focusing machine learning analysis only on relevant image regions containing products, rather than processing entire images. The system identifies and processes only the necessary portions of visual data, maintaining high recognition precision while improving processing throughput through selective analysis.
Data Source
AI summary
Systems and methods of updating templates for use in recognizing individual products in images captured at a product storage facility include an image capture device that captures one or more images of product storage structure at a product storage facility, a computing device in communication with the image capture device, and an electronic database that stores keyword model templates and feature model templates associated with images of previously recognized individual products detected at the product storage facility. The computing device obtains the keyword and feature model templates associated with a recognized product from the electronic database, extracts the keywords from the products associated with the obtained keyword model templates, identifies products that are similar to the recognized product, and updates the keyword model template for each of the products to include must keywords and negative keywords, facilitating recognition of products in subsequent images captured by the image capture device.