Abstract: Systems and methods of analyzing on-shelf price tag labels and products at a product storage facility include an image capture device that captures one or more images of one or more product storage structures at a product storage facility. A computing device communicatively coupled to the image capture device analyzes the images of the product storage structures captured by the image capture device and detects individual price tag labels and individual products located on the product storage structure. Based on the detection of the price tag labels and the products, the computing device also defines separate product storage spaces of the product storage structure, determines which price tag labels are allocated to which of the separate product storage spaces, and associates in a database the price tag labels allocated to the product storage spaces with the products stored in those product storage spaces.

Abstract: Systems and methods of monitoring inventory of a product storage facility include an image capture device configured to move about the product storage areas of the product storage facility and capture images of the product storage areas from various angles. A computing device coupled to the image capture device obtains the images of the product storage areas captured by the image capture device and processes the obtained images of the product storage areas to detect individual products captured in the obtained images. Based on detection of the individual products captured in the images, the computing device analyzes each of the obtained images to detect one or more adjacent product storage structures (shelves, pallets, etc.) and identifies and selects a single image that fully shows a product storage structure of interest and fully shows each of the products stored on the product storage structure of interest.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to updating inventory of products. In some embodiments, there is provided a system for updating inventory of products including a database; at least one image capture device; and a control circuit. The control circuit is configured to: process an image by: detecting units of one or more products in the image; grouping each detected unit into one or more clusters based on at least one of textual similarities, visual similarities, geometrical similarities, or relative spatial distance; detecting a product identifier for each cluster; identifying a product corresponding to the detected product identifier of each cluster; and counting the detected units associated with each cluster. The control circuit is configured to cause an update to the inventory of products in the database based on the counted detected units of each cluster.

Abstract: Systems and methods for item price assignment. A line recommendation engine receives unassigned records from a queue and stores one or more line recommendations for the unassigned item record in a recommendation database. The line recommendation engine can determine a new line should be recommended, and/or which existing lines the unassigned item record could be assigned to. A user interface can display the one or more line recommendations for the unassigned item record to a user and receive an input indicating a selected line identifier for the unassigned item record. A machine learning engine can modify a parameter of the line recommendation engine based on the selected line identifier.

Abstract: Systems and methods for item price assignment. A line recommendation engine receives unassigned records from a queue and stores one or more line recommendations for the unassigned item record in a recommendation database. The line recommendation engine can determine a new line should be recommended, and/or which existing lines the unassigned item record could be assigned to. A user interface can display the one or more line recommendations for the unassigned item record to a user and receive an input indicating a selected line identifier for the unassigned item record. A machine learning engine can modify a parameter of the line recommendation engine based on the selected line identifier.

Abstract: The system and method described herein provide a computationally efficient clearance markdown planning system that may quickly automate the calculations of many possible markdown plans to determine the impact of various pricing options on total sales volume and revenue. While considering business constraints and other parameters, the markdown planning system may determine an optimized markdown plan for a particular product, season, and locale. In some aspects, the markdown planning system may use scaling and a variation of dynamic programming (DP) to quickly calculate and compare different potential markdown plans.