17961226. AUTOMATED ARTIFICIAL INTELLIGENCE MODEL TRAINING USING PRODUCT IMAGES simplified abstract (Insight Direct USA, Inc.)
Contents
- 1 AUTOMATED ARTIFICIAL INTELLIGENCE MODEL TRAINING USING PRODUCT IMAGES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 AUTOMATED ARTIFICIAL INTELLIGENCE MODEL TRAINING USING PRODUCT IMAGES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
AUTOMATED ARTIFICIAL INTELLIGENCE MODEL TRAINING USING PRODUCT IMAGES
Organization Name
Inventor(s)
Amol Ajgaonkar of Chandler AZ (US)
AUTOMATED ARTIFICIAL INTELLIGENCE MODEL TRAINING USING PRODUCT IMAGES - A simplified explanation of the abstract
This abstract first appeared for US patent application 17961226 titled 'AUTOMATED ARTIFICIAL INTELLIGENCE MODEL TRAINING USING PRODUCT IMAGES
Simplified Explanation
The abstract describes a method of product detection using artificial intelligence models trained on annotation packages for different products.
- Product detection method using artificial intelligence models
- Trained on annotation packages for different products
- Categorizes products into different categories
- Receives subscription from retailer based on product categories
- Detects products in images provided by the retailer
Potential Applications
This technology could be applied in retail stores for inventory management, in e-commerce platforms for product recommendations, and in security systems for detecting unauthorized products.
Problems Solved
This technology helps in automating the process of product detection, categorization, and subscription management, saving time and reducing human error.
Benefits
The benefits of this technology include improved efficiency in product detection, accurate categorization of products, and personalized subscription services for retailers.
Potential Commercial Applications
The potential commercial applications of this technology include retail stores, e-commerce platforms, security companies, and subscription-based services.
Possible Prior Art
One possible prior art for this technology could be existing product detection systems using machine learning models trained on annotated data sets.
Unanswered Questions
How does the system handle variations in product appearance in different images?
The system likely uses advanced image recognition algorithms to detect products based on key features and patterns, regardless of variations in appearance.
How does the system ensure the accuracy of product categorization?
The system may use validation techniques, such as cross-validation and testing on diverse data sets, to ensure the accuracy of product categorization.
Original Abstract Submitted
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.
