A method for predicting preparation times includes: retrieving set of item-level records from a database for preparation menu items for all subscriber restaurants; training and executing a first neural network to generate embeddings for each of the menu items; for a first subset of the set, calculating actual item-level preparation time vectors; for a second subset of the historical set, generating estimated item-level preparation time vectors; retrieving a set of order-level records for preparation of orders from the database; training a second neural network to predict the order-level preparation times, wherein inputs to the second neural network comprise one or more of the item-level preparation time vectors and metadata taken from the order-level records; and following training, executing the second neural network to generate predicted order-level preparation times for current orders within a restaurant.

Coordinated conveyors in an automated system. In an embodiment, the system comprises transport conveyor(s) and storage conveyors, wherein each storage conveyor comprises a plurality of segments configured to hold at least one item, and a portion that is aligned with a portion of a transport conveyor, such that items are moveable from the storage conveyor to the transport conveyor. Software module(s), executed by a processor, receive an instruction to collect item(s) at a single destination location, and, for each of the item(s), identify a segment on a storage conveyor on which the item is held, control that storage conveyor to align the segment with the transport conveyor, move the item from the segment onto the transport conveyor, control the transport conveyor to align the item with the destination location, and move the item from the transport conveyor to the destination location.

An inventory tracking and management system includes storage devices comprising carts, cabinets, or shelves, sensors and/or monitoring devices associated with the storage devices, a central database connecting the storage devices, sensors, and monitoring devices within a hospital, and a processing server associated with the central database. The processing server including a software system controlling operation of the inventory tracking and management system.

Techniques for automatic recognition of items for point of sale (POS) systems are disclosed. A request to identify a first item for purchase is received. The request includes a first image captured at a POS system. The first image is analyzed using a machine learning model configured for image recognition, and in response a first product code is determined for the first item. This includes identifying the first product code as a primary product code from among a plurality of product codes, identifying a second item relating to the first item, where the second item is visually similar to the first item, and determining a second product code for the second item. The first product code, and the second product code, are transmitted to the POS system. The POS system is configured to present the first item and the second item as options for purchase using a user interface.

A scale-based order management and fulfillment system includes a computer system configured as an order manager, the computer system connected for electronic receipt of customer orders from one or more external devices or systems; and a plurality of scales within a store. The computer system is configured to dynamically process customer orders for fulfillment.

A non-transitory computer-readable recording medium stores therein an information processing program that causes a computer to execute a process including, specifying, from a plurality of images taken by one or more camera devices, a person who visits a store and a first object that contains one or more first commodity products and is used by the person, specifying, from the plurality of images, a location of the person in the store, specifying, based on the location of the person and a location of each of a plurality of terminals in the store, a first terminal in which the person uses from among the plurality of terminals, associating the one or more first commodity products contained in the first object with the first terminal, and performing authentication processing of the one or more first commodity products contained in the first object by using the certificate information.

Methods and systems for managing inventory items within a supply chain are disclosed. One method includes receiving, at a software tool, inputs related to a plurality of inventory items, the inputs including a cost of holding each of the plurality of inventory items at a location type. The method includes determining, individually for each inventory item of a plurality of inventory items, an optimal inventory balance across a plurality of locations, wherein the optimal inventory balance is a predetermined statistical availability level set based on a desired customer availability of the inventory item. The method further includes automatically generating one or more inventory adjustment requests to achieve the optimal inventory balance across each of the plurality of locations for each of the plurality of inventory items.

Sever-based order persistence and/or fulfillment is described herein. In an example, server(s) associated with a payment processing service may receive, from a point-of-sale (POS) device associated with a merchant, an order associated with at least one item available for purchase from a physical location of the merchant. The server(s) may store the order in a storage data structure. In an example, the server(s) may determine an occurrence of a trigger event and may update a status of the order based at least in part on the trigger event. In some examples, the trigger event may correspond to an interruption in a connection with the POS device, fulfillment of the order, etc. The status of the order can indicate whether the order is to be sent to the POS device, removed from the storage data structure, etc.

Methods of detecting scan avoidance events when items are passed through a field of view (FOV) of a scanner are disclosed herein. An example method, during a decode session, receiving, at one or more processors of the symbology reader, an image of an object; during a timeout period, detecting, at the one or more processors, an indicia in the image of the object, the indicia having a decodable payload; during the timeout period, attempting to decode the indicia to identify the decodable payload, at the one or more processors; and after the timeout period expires, when at least one portion but less than all portions of the indicia is decodable, determining a potential scan avoidance attempt and generating a scan avoidance alarm signal.

A receipt capture device can collect transaction information from transactions conducted at a point of sale system by capturing receipt data transmitted from the point of sale system for the purpose of printing receipts at an external receipt printer. The receipt capture device can then send the collected receipt data to an online system for analysis. At the online system, received receipt data can be decoded from the printer-readable format it is transmitted in and used to enhance the online system's understanding of transactions occurring at a retailer associated with the point of sale system. For example, the online system can determine an approximate inventory of items available at purchase at the retailer by aggregating items recently purchased in transactions at the point of sale system.