Abstract: A system and method for a machine learning-based automated electronic time recording for personnel includes identifying, via a scene capturing device, a representation of a time recording space; identifying a body having a time recording pose within the time recording space based on an assessment of the representation of the time recording space; extracting a plurality of distinct features from the representation of the time recording space based on identifying the body having the time recording pose; executing automated user-recognition based on the extracting of the plurality of distinct features; executing automated time recording recognition based on the extracting of the plurality of distinct features; and executing automated electronic time recording, via a time recording application based on the automated user-recognition and the automated time recording recognition.

Abstract: This disclosure is directed to item-identifying carts that may be utilized by users to automatically identify items that the users place in their carts. In order to identify the items placed into or removed from the cart, the cart may analyze image data, as well as weight data indicating a current weight estimation of a basket of the cart. The cart may include a weight sensor that generates a signal comprising a series of weight measurements of the basket of the cart. The cart may use an algorithm that calculates different mean values of different window sizes of the weight measurements of this signal and, thereafter, may calculate an average of these mean values. This average may be stored as the current weight estimation of the basket.

Abstract: 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.

Abstract: A supply chain model generation system independently uses a business entity code and an item code representing an item related to a transaction in result information on a supply chain operation of a plurality of business entities; transaction information is extracted from the result information and a transaction time series information table is generated; a relationship in the transaction time series information table in each business entity is analyzed and a virtual business entity code on a supply side and a virtual business entity code on a demand side are assigned for each business entity code; and further a logical model information table where a virtual item code is assigned with respect to an item code related to the transaction of a business entity of the virtual business entity code on the supply side and a business entity of the virtual business entity code on the demand side is generated.

Abstract: Techniques for determining and presenting invoice criticality information for an invoice on a graphical user interface (GUI) are disclosed. Invoice criticality represents an urgency and/or significance associated with an invoice. An urgency is determined based on a difference between (a) a predicted amount of time for completing processing of the invoice and (b) an amount of time currently remaining until the target completion date. A significance is determined based on a predicted impact level resulting from missing a target completion date for the invoice. A GUI displays invoice criticality information associated with one or more invoices. The GUI presents a suggested action for resolving a predicted hold for a particular invoice. The GUI presents a suggested action based on a current and/or impending processing stage for a particular invoice. The GUI presents correlation information indicating relationships between invoice characteristics, processing times, and/or impact levels.

Abstract: A computer-implemented method includes identifying a first menu item on a standard menu of an establishment. A first plurality of ingredients of the first menu item are determined. A first subset of the first plurality of ingredients of the first menu item are categorized into a substitutable category. A second subset of the first plurality of ingredients of the first menu item are categorized into a non-substitutable category. Each ingredient of the first menu item that is in the substitutable category is compared to user preferences. A substitutable ingredient of the substitutable category is excluded, based on the comparisons, where the substitutable ingredient is in the first menu item. An available substitute is automatically identified for the substitutable ingredient of the substitutable category. A personalized menu of menu items selected from the standard menu is generated, where the personalized menu includes the first menu item modified with the available substitute.

Abstract: One example of computerized inventory redistribution control includes, for each location inventory record in a set of location inventory records, calculating a quantity change that will bring a current item quantity to a different item quantity for the location inventory record. Determining a cost of a minimum-cost redistribution among the physical locations to effect the quantity changes. Determining a scaling factor that maximizes total revenue when the quantity changes are scaled by the scaling factor after deducting the cost scaled by the scaling factor. Generating transfer instructions for a redistribution of the item by scaling the transfer quantities of the minimum-cost redistribution by the scaling factor. Transmitting each transfer instruction to a computing device associated with a physical location indicated in the transfer instruction.

Abstract: Methods and systems for optimizing item assortments are disclosed. One method includes receiving a request for an optimized assortment. Data relating to items within a selected initial item assortment and item universe is accessed. Constraints are applied to the items in the assortment to arrive at an optimized item assortment. Constraints include goals for optimization, lock in and lock out rules, and item attribute rules. The method may be performed by a system including one or more computing devices communicating via a network with one or more data storage devices.

Abstract: The disclosed techniques relate to improving sensor data collection/reporting at an automatic replenishment device (ARD). For example, sensor data associated with the item stored at an ARD may be collected. The ARD can calculate a change in the amount of the item stored by the ARD from the sensor data. If the change is over a reporting threshold, the sensor data (or the amount of change) may be transmitted to a back-end system. Reporting may not occur if additional sensor data (e.g., accelerometer data and/or images captured at the ARD) is not commensurate with a user interaction. If the difference is under the reporting threshold or unlikely due to user interaction, the ARD may refrain from transmitting the sensor data. These techniques conserves power at the ARD, relieves the back-end system from needless processing, and enables more accurate indications of the amount of the item stored by the ARD.

Abstract: A vending device has at least one display area and an evaluator. The display area is formed by a rigid body, and has at least two, spatially-separated product areas, the rigid body of the display area being held by force transmission areas of at least two weighing cells. The evaluator is configured to, at periodic intervals or when a total weight detected by the at least two weighing cells changes: determine new coordinates of a center of gravity from data of the weighing cells, and transmit the new coordinates to a controller. The controller is configured to: determine a product area within the display area based upon changes in the coordinates of the center of gravity, determine, from the change in a total weight, the weight of goods removed from or added to the determined product area, and update an inventory, stored in a memory, for the product.

Abstract: In some examples, a computing device may receive, from a merchant application on a merchant device, an indication that a buyer has initiated a transaction to purchase an item from a merchant associated with the merchant device. A first fulfilment selection may initially be associated with the transaction. The computing device may determine an association of the buyer with a buyer account. A buyer device having a buyer application executable thereon may be associated with the buyer account. The computing device may send, to the buyer application, transaction information related to the transaction to enable the buyer to pay for the transaction via the buyer application and/or change the fulfilment selection for the transaction. The computing device may receive, from the buyer application, at least one of an indication of authorization of payment for the transaction or an indication of a change to the fulfilment selection.