Centralized AI Product Detection for Fast Retail Model Updates
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Solution Overview
Problem
Retailers face challenges in developing and constantly retraining AI models to detect a large number of products in their stores due to frequent changes in product offerings and packaging, requiring significant time and resources.
Innovation Solution
A centralized product detection system that receives annotation packages from multiple product sources to train AI models, allowing retailers to subscribe to categories of products and receive real-time updates for accurate product detection without maintaining their own models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If retailers develop their own AI models to detect every product in their store, then product detection accuracy is improved, but time and resources required for model development and retraining increase significantly
Solution Approach 1:
The patent introduces a centralized product detection system that acts as an intermediary between product sources and retailers. This system pre-trains AI models on extensive product data from multiple sources and provides them to retailers, eliminating the need for retailers to develop models independently while maintaining high detection accuracy
Solution Approach 2:
The centralized system performs preliminary AI model training in advance on a wide variety of products and packaging variations. This pre-trained knowledge is then made available to retailers, so when new products are introduced, the models are already prepared or can be quickly adapted, significantly reducing retraining time
2Reliability
If retailers constantly retrain AI models to detect new products and packaging changes, then detection reliability is improved, but resource consumption increases
Solution Approach 1:
The patent merges the AI model training function into a centralized system that serves multiple retailers. Instead of each retailer independently retraining models, the centralized system consolidates training resources and shares updated models across all subscribers, reducing overall resource consumption while maintaining detection reliability
Solution Approach 2:
The centralized AI model serves multiple functions: it detects products from various sources, adapts to different packaging variations, and serves multiple retailers simultaneously. This universal model reduces the need for separate training processes for each retailer, lowering resource requirements while maintaining reliable detection across diverse product catalogs
3Adaptability or versatility
If retailers maintain their own AI models for product detection, then detection capability is improved, but device complexity and maintenance burden increase
Solution Approach 1:
The patent extracts the complex AI model development and maintenance functions from individual retailers and places them in a centralized system. Retailers simply subscribe to and receive pre-trained models, eliminating the complexity of model development, training, and maintenance from their operations while retaining full detection capability
Data Source
AI summary
A method of product detection includes receiving, at a product detector from a product source, a first annotation package for a first product and a second annotation package for a second product. An artificial intelligence model is trained to detect the first product based on the first annotation package and the second product based on the second annotation package. The artificial intelligence model is implemented on the product detector. At the product detector, the first product is categorized into a first category of products and the second product is categorized into a second category of products. A subscription is received from a retailer to one of: the first category of products; the second category of products; and the first category and the second category of products. An image is received at the product detector from the retailer. The first product is detected in the image by the artificial intelligence model.