Abstract: This application relates to employing trained machine learning processes to predict sales across various sale channels. For example, a computing device may generate features based on historical sales information, and trains the machine learning processes based on the generated features. In some examples, the computing device determines fixed cost effects from selling items across various sales channels, and adjusts the sales information based on the fixed cost effects. The computing device also generates features based on the adjusted sales. The computing device may apply the trained machine learning processes to sales information for one or more items to predict the sales of one or more items across one or more sales channels during a future temporal period. In some examples, the trained machine learning processes generate a ranking of items for a sales channel based on the output generated from the trained machine learning processes.

Abstract: Systems, methods, and computer-readable storage media for categorizing items based on attributes of the item and a shape of the item, where the shape of the item is determined from an image of the item. An exemplary system configured as disclosed herein can receive a request to categorize an item, the item having a plurality of attributes, and receive an image of the item. The system can identify, via a processor configured to perform image processing, a shape of the item based on the image, and transform the plurality of attributes and the shape of the item, into a plurality of quantifiable values. The system can then categorize the item based on the quantifiable values.

Abstract: Systems, methods, and computer-readable storage media for categorizing items based on attributes of the item and a shape of the item, where the shape of the item is determined from an image of the item. An exemplary system configured as disclosed herein can receive a request to categorize an item, the item having a plurality of attributes, and receive an image of the item. The system can identify, via a processor configured to perform image processing, a shape of the item based on the image, and transform the plurality of attributes and the shape of the item, into a plurality of quantifiable values. The system can then categorize the item based on the quantifiable values.