18158983. METHODS AND SYSTEMS FOR CREATING REFERENCE IMAGE TEMPLATES FOR IDENTIFICATION OF PRODUCTS ON PRODUCT STORAGE STRUCTURES OF A PRODUCT STORAGE FACILITY simplified abstract (Walmart Apollo, LLC)

METHODS AND SYSTEMS FOR CREATING REFERENCE IMAGE TEMPLATES FOR IDENTIFICATION OF PRODUCTS ON PRODUCT STORAGE STRUCTURES OF A PRODUCT STORAGE FACILITY

Organization Name

Walmart Apollo, LLC

Inventor(s)

Ashlin Ghosh of Ernakulam (IN)

Raghava Balusu of Achanta (IN)

Abhinav Pachauri of Kanpur (IN)

Avinash M. Jade of Bangalore (IN)

Lingfeng Zhang of Dallas TX (US)

Amit Jhunjhunwala of Bangalore (IN)

William Craig Robinson, Jr. of Centerton AR (US)

Benjamin R. Ellison of San Francisco CA (US)

Srinivas Muktevi of Bengaluru (IN)

Zhaoliang Duan of Frisco TX (US)

METHODS AND SYSTEMS FOR CREATING REFERENCE IMAGE TEMPLATES FOR IDENTIFICATION OF PRODUCTS ON PRODUCT STORAGE STRUCTURES OF A PRODUCT STORAGE FACILITY - A simplified explanation of the abstract

This abstract first appeared for US patent application 18158983 titled 'METHODS AND SYSTEMS FOR CREATING REFERENCE IMAGE TEMPLATES FOR IDENTIFICATION OF PRODUCTS ON PRODUCT STORAGE STRUCTURES OF A PRODUCT STORAGE FACILITY

The patent application describes systems and methods for creating reference template images to detect and recognize products at a product storage facility.

• Image capture device with a field of view capturing the product storage structure.
• Computing device analyzes images to detect individual products on the storage structure.
• Identified products are cropped from the images to generate cropped images.
• A cluster of cropped images is created, and one is selected as a reference template image for each product.

1. 1. 1. Potential Applications:

- Inventory management in warehouses. - Retail product recognition for automated checkout systems. - Quality control in manufacturing facilities.

1. 1. 1. Problems Solved:

- Efficient product detection and recognition in storage facilities. - Streamlining inventory processes. - Enhancing automation in product identification.

1. 1. 1. Benefits:

- Increased accuracy in product recognition. - Time-saving in inventory management. - Improved efficiency in storage facilities.

1. 1. 1. Commercial Applications:
         1. Title: Automated Product Recognition System

This technology can be utilized in warehouses, retail stores, and manufacturing facilities to streamline inventory management, enhance product recognition, and improve overall operational efficiency. The market implications include cost savings, increased productivity, and improved customer satisfaction.

1. 1. 1. Prior Art:

There may be prior art related to computer vision systems for product recognition and inventory management in storage facilities. Researchers can explore databases, academic journals, and patent repositories for relevant information.

1. 1. 1. Frequently Updated Research:

Researchers are constantly developing new algorithms and technologies to improve product recognition and inventory management systems. Stay updated on advancements in computer vision, artificial intelligence, and automation in the logistics industry.

1. 1. 1. 1. Questions about Product Recognition Technology:

1. How does this technology improve efficiency in inventory management processes?

  - This technology automates the detection and recognition of products, saving time and reducing human error in inventory processes.
  

2. What are the potential applications of this technology beyond product storage facilities?

  - This technology can be applied in retail settings for automated checkout systems and in manufacturing facilities for quality control processes.

Original Abstract Submitted

Systems and methods of creating reference template images for detecting and recognizing products at a product storage facility include an image capture device having a field of view that includes a product storage structure of the product storage facility, and a computing device including a control circuit and being communicatively coupled to the image capture device. The computing device obtains images of the product storage structure captured by the image capture device, analyzes the obtained images to detect individual ones of the products located on the product storage structure. Then, the computing device identifies the individual ones of the products detected in the images and crops each of the individual ones of the identified products from the images to generate cropped images. The computing device then creates a cluster of the cropped images, and selects one of the cropped images as a reference template image of an identified individual product.