Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: providing, via an electronic platform, access to one or more order placement user interfaces; collecting order placement information associated with the one or more order placement user interfaces; analyzing, by a conversion determination network of a machine learning architecture, the order placement information; generating actual conversion information for client sessions based on the actual availability of the order placement options during the client sessions; generating predicted conversion information for the client sessions based on a full availability of all of the order placement options during the client sessions; and generating lost demand information based, at least in part, on the actual conversion information and the predicted conversion information. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: providing, via an electronic platform, access to one or more order placement user interfaces; collecting order placement information associated with the one or more order placement user interfaces; analyzing, by a conversion determination network of a machine learning architecture, the order placement information; generating actual conversion information for client sessions based on the actual availability of the order placement options during the client sessions; generating predicted conversion information for the client sessions based on a full availability of all of the order placement options during the client sessions; and generating lost demand information based, at least in part, on the actual conversion information and the predicted conversion information. Other embodiments are disclosed herein.

Abstract: The embodiments described herein enable the efficient storage and searching of large amounts of data to retrieve desired data with high accuracy and fast response time. The embodiments include for example parsing an input document containing input data into multiple sub-documents based on the structure of the input document and generating for each sub-document, a unique ID, pathway, and metadata. The embodiments also include storage of the ID and pathway for each sub-document in an index database and storage of the input document itself in a separate database. When a query for information from the input document is received, the ID and pathway of the sub document containing the queried information are identified and used to locate the queried information from the input document.

Abstract: The embodiments described herein enable the efficient storage and searching of large amounts of data to retrieve desired data with high accuracy and fast response time. The embodiments include for example parsing an input document containing input data into multiple sub-documents based on the structure of the input document and generating for each sub-document, a unique ID, pathway, and metadata. The embodiments also include storage of the ID and pathway for each sub-document in an index database and storage of the input document itself in a separate database. When a query for information from the input document is received, the ID and pathway of the sub document containing the queried information are identified and used to locate the queried information from the input document.

Abstract: A system is provided and generally includes a server, an associate computing device, and a customer computing device. The server may receive data from the customer computing device indicating that a customer is picking up items from a predetermined location. The server may compute an estimated wait time for the customer based on one or more machine learning processes. In some examples, a number of unexpected customers that may arrive is determined. The machine learning process may compute the estimated wait time based on the number of unexpected customers. The machine learning process may be trained with historical data. The estimated wait time is transmitted to the customer computing device, and is displayed to the customer. In some examples, the server sends a list of customers waiting to be serviced to the associate computing device. The list may be prioritized based on estimated wait times for those customers.

Abstract: A container determination system is provided and generally includes a server, a database, and a customer computing device. The server may obtain, from the database, purchase order data identifying order lines of a purchase order, where each order line is associated with one or more items. The server may associate each order line of the purchase order with an initial container. The server may then optimize the assortment of items amongst the initial containers by attempting to re-associate a selected order line with another container based on whether the other container can hold the items associated with the selected order line, and based on a comparison between an acceptance rate and a randomly selected rate. The re-associations are attempted multiple times to determine optimized assortment information, which may be transmitted to an associate device to have a bagger place the purchased items in containers according to the optimized assortment information.

Abstract: A system to perform certain acts to indicate when an order state within an order database has been modified. The acts can include determining that an order lookup comprises an intent to cancel an order line item. The acts can also include running rules to determine whether the order line item is authorized to be cancelled by synchronizing the cancellation and pushing a cancel state asynchronously to the order database cache. When the order line item is not authorized to be cancelled, the acts can detect that the order state has not been modified. The acts can call to a caching interface to asynchronously push a not cancel state to the order state in the order database cache. The acts can publish the not cancel order state from the order database to the order database cache. The acts can display a message that the order state cannot be modified.

Abstract: The present invention is generally directed to methods, systems, and computer program products for routing order lookups from retail systems. Order data can be synchronized from the order database cache to the order database. The order database can modify an order state of one or more customer orders from the order database. An order lookup can be received from an in-store retail system channel of one or more in-store retail system channels and can indicate a database command type. A database command type can impact a state of the order database when the order data is modified. A data source can service the order lookup based on at least a timestamp of the order data associated with a customer order. A data indicator of the order data in the order lookup can be modified when the database command type impacts the state of the order database.

Abstract: A load and route assignment system is provided and generally includes a computing device and a database. The database may store historical inbound load data and historical outbound load data related to previous inbound and outbound loads. The computing device can obtain and aggregate the historical inbound and outbound load data from the database, and determine an optimal path based on the aggregated historical data. The optimal path, along with load attribute data, may be stored in the database as a tour template for future load executions. The computing device may use the tour templates to determine future load assignments to vehicles. The computing device may also obtain real-time load requests, and match them to one or more of a plurality of tour templates. The computing device may assign the matched real-time load requests to a vehicle for execution in accordance with the corresponding load and tour template.

Abstract: The present disclosure extends to maintaining an item inventory status at an inventory cache management system. The inventory cache may be refreshed regularly by a threshold-based triggering mechanism. In embodiments, as item inventory breaches certain thresholds, the inventory cache may be refreshed with increased frequency to mitigate overselling or underselling scenarios and reduce overall network traffic for items having relatively high inventory levels.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one more processors and perform: obtaining historical demand data for pickup time slots at a physical store for a first time period; training a machine learning model to create a trained model based on the historical demand data for the pickup time slots over the first time period; generating, using the trained model, a projected demand for future pickup time slots at the physical store for a second time period; generating a time-slot capacity for each of the future pickup time slots at the physical store for the second time period based at least in part on the projected demand; and sending the time-slot capacities for the future pickup time slots to the physical store. Other embodiments are described.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one more processors and perform training a machine learning model based on historical input data for pickups by users that occurred during a historical time period and output data comprising actual wait times that occurred for the pickups by the users; receiving an order from a user for a pickup from a physical store during a selected time slot of a selected date; adding the order to a queue of assembled checked-in orders; determining an estimated arrival time based on geo-tracking; generating an estimated wait time using the machine learning model, as trained, and can be based on input data; and sending the estimated wait time to at least one of the physical store or the mobile device of the user. Other embodiments are disclosed.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform receiving orders from physical stores for fulfillment from a distribution center, each of the orders comprising a set of items and a requested delivery date; generating a stack building plan for each of the orders using simulated annealing; obtaining routes for delivering the orders in trailers from the distribution center to the physical stores based at least in part on the stack building plan; and generating a load design for each of the routes to deliver in a trailer of the trailers a load for one or more of the orders, such that floor spot assignments for stacks for each of the one or more of the orders in the load carried by the trailer satisfy sequence-of-delivery constraints and center-of-gravity constraints. Other embodiments are disclosed.

Abstract: A method and system of automated person detection and notification is disclosed. Imaging data is received from at least one imaging device configured to provide a field-of-view of a predetermined area associated with a retail location. An image recognition process is implemented and configured to identify at least one person in the image data and an alert indicating at least one person is identified within the image data is generated. The alert is provided to at least one device registered to a predetermined user.

Abstract: A new approach is proposed that contemplates systems and methods for automated curbside fulfillment. Traditionally, a user would check in for item order fulfillment at a kiosk within a distribution center to receive an item order after a time dictated by a merchant. However, automated curbside fulfillment allows a user to receive an item order without exiting the user's vehicle and at a time dictated by the user, not by the merchant. This enhances the user's experience by reducing the amount of time that a user must take for item order fulfillment after availability at the distribution center and by catering to the user's schedule.

Abstract: A system is provided and generally includes a server and an associate computing device. The server may receive location video data comprising at least one image from a camera directed to a location, such as a designated area of a parking lot. The server may detect one or more objects in the image, and determine a bounding box based on the detected objects. The server may also determine a confidence value corresponding to the bounding box. The confidence value may be determined based on an area of the image defined by the bounding box and a predefined class. The server may then generate an arrival message based on the determined confidence value, and transmit the arrival message to the associate computing device.

Abstract: A method and system of handling an alternative pick-up using vehicle information is disclosed. Imaging data is received from at least one imaging device. The at least one imaging device is configured to provide a field-of-view of a predetermined area associated with a retail location. A vehicle identifier for each vehicle within the image data is extracted and compared to each user profile associated with an order in a set of orders. Each user profile includes a primary vehicle identifier and at least one secondary vehicle identifier and the extracted vehicle identifier is compared to the at least one secondary vehicle identifier in each user profile. The extracted vehicle identifier is associated with a first order in the set of orders when the extracted vehicle identifier matches the secondary vehicle identifier of the user profile of the first order.

Abstract: A container determination system is provided and generally includes a server, a database, and a customer computing device. The server may obtain, from the database, purchase order data identifying order lines of a purchase order, where each order line is associated with one or more items. The server may associate each order line of the purchase order with an initial container. The server may then optimize the assortment of items amongst the initial containers by attempting to re-associate a selected order line with another container based on whether the other container can hold the items associated with the selected order line, and based on a comparison between an acceptance rate and a randomly selected rate. The re-associations are attempted multiple times to determine optimized assortment information, which may be transmitted to an associate device to have a bagger place the purchased items in containers according to the optimized assortment information.

Abstract: Systems and methods for optimizing delivery vehicle resources (e.g. a plurality of vehicles) are described herein. Available time slots for the plurality of vehicles are determined and presented to a user. In response to receiving a delivery order indicating a selected time slot, the delivery order is assigned to a vehicle from the plurality of vehicles based on a time slot indicated in the delivery order and a set of delivery parameters. A delivery route is calculated for each vehicle having a delivery order based on the set of delivery parameters. An optimized delivery route is calculated for each vehicle having a delivery order based on the set of delivery parameters.

Abstract: A load and route assignment system is provided and generally includes a computing device and a database. The database may store historical inbound load data and historical outbound load data related to previous inbound and outbound loads. The computing device can obtain and aggregate the historical inbound and outbound load data from the database, and determine an optimal path based on the aggregated historical data. The optimal path, along with load attribute data, may be stored in the database as a tour template for future load executions. The computing device may use the tour templates to determine future load assignments to vehicles. The computing device may also obtain real-time load requests, and match them to one or more of a plurality of tour templates. The computing device may assign the matched real-time load requests to a vehicle for execution in accordance with the corresponding load and tour template.