Abstract: An online concierge system applies a predictive model to predict demand of items, and facilitates preemptive picking of items in advance of receiving orders to enable efficient procurement and delivery. The online concierge system may apply a time-series model and/or machine learning model that predicts demand based on historical data. Depending on the predicted demand, items may be preemptively moved from a storage location to a staging area that enables the items to be more rapidly processed and delivered to customers when orders come in.

Abstract: An inventory interaction model predicts user interactions with items to be included in an item assortment in a warehouse. The item is described with features that include the co-located items and the respective user interactions, so that the item interactions for the evaluated item incorporate item-item effects in its predictions. To train the model effectively in the absence of prior interaction data for an item, training examples are generated from existing item and user interaction data of co-located items by selecting a portion of the items for the examples and including co-located item data, labeling the training example output with item interactions for the item. The trained model is then applied for an item assortment by describing co-located item features of the item assortment in evaluating candidate items.

Abstract: An online concierge system applies a predictive model to predict demand of items, and facilitates preemptive picking of items in advance of receiving orders to enable efficient procurement and delivery. The online concierge system may apply a time-series model and/or machine learning model that predicts demand based on historical data. Depending on the predicted demand, items may be preemptively moved from a storage location to a staging area that enables the items to be more rapidly processed and delivered to customers when orders come in.

Abstract: An online concierge shopping system fulfills orders using workers who pick items at a warehouse to complete an order and workers to deliver the orders to a customer's location. To optimize the staffing of workers for each task, the system uses a trained model to predict the number of workers needed to achieve an optimal outcome based on an input set of contextual information. The system also schedules specific workers to various shifts using the predicted number of workers needed and then searching a feasibility space for an optimal solution. The trained model may be updated based on performance observations.

Abstract: An online concierge shopping system fulfills orders using workers who pick items at a warehouse to complete an order and workers to deliver the orders to a customer's location. To optimize the staffing of workers for each task, the system uses a trained model to predict the number of workers needed to achieve an optimal outcome based on an input set of contextual information. The system also schedules specific workers to various shifts using the predicted number of workers needed and then searching a feasibility space for an optimal solution. The trained model may be updated based on performance observations.

Abstract: An inventory interaction model predicts user interactions with items of a location for a physical warehouse included with other warehouses in a region. The location is described with features that include the nearby locations and the respective user interactions with the respective item assortments, so that the item interactions for the evaluated location incorporate location-location effects in model predictions. To effectively train the model in the absence of prior interaction data for a location, training examples are generated from existing locations and user interaction data of item assortments by selecting a portion of the locations for the training examples and including nearby location interaction data, labeling the training example output with item interactions for the location. The trained model is then applied for an item assortment at a location by describing nearby locations in evaluating candidate locations and item assortments.

Abstract: An online system evaluates different item assortments for a physical warehouse having limited capacity to stock items. Each item assortment is stocked at the physical warehouse in proportion to an assortment split weight. The items at the warehouse are available for users to order, for example to be gathered by a picker and physically delivered to users near the warehouse. Rather than display all items actually stocked at the physical warehouse to all users, the different item assortments are displayed to different users. Users may order items from the assigned item assortment and, because both item assortments are actually stocked at the physical warehouse, orders from either item assortment may be successfully fulfilled for delivery. The different user interfaces thus permit evaluation of the preferred item assortment by users while maintaining expected delivery capability and while using the same storage capacity of the physical warehouse.

Abstract: An inventory interaction model predicts user interactions with items to be included in an item assortment in a warehouse. The item is described with features that include the co-located items and the respective user interactions, so that the item interactions for the evaluated item incorporate item-item effects in its predictions. To train the model effectively in the absence of prior interaction data for an item, training examples are generated from existing item and user interaction data of co-located items by selecting a portion of the items for the examples and including co-located item data, labeling the training example output with item interactions for the item. The trained model is then applied for an item assortment by describing co-located item features of the item assortment in evaluating candidate items.

Abstract: An online concierge system facilitates creation of shopping lists of items for ordering from a physical retail store and at least partial self-service fulfillment of orders by the customer. To support fulfillment by the customer, the online concierge system may intelligently select one or more items of the order to be picked by a third-party picker and prepopulated to a shopping cart reserved for the customer in advance of the customer arriving at the retail location. The items for prepopulating may be selected based on various factors that optimize prepopulation decisions on an item-by-item basis in accordance with various machine learning models. The online concierge system may furthermore facilitate procurement of the remaining items by the customer through a customer client device that may track item procurement and/or provide guidance for locating items.

Abstract: An online concierge system facilitates ordering of items by customers, procurement of the items from physical retailers by pickers assigned to the orders, and delivery of the orders to customers. To enable efficient procurement, the online concierge system may facilitate preemptive picking of items for staging at a rapid fulfillment area of the physical retailer, and pickers may selectively pick items from the rapid fulfillment area instead of their standard storage locations. Decisions on which items to preemptively pick may be based on a predictive optimization model that scores and ranks items for predictive picking in accordance with various optimization criteria. In the course of fulfilling orders, pickers may furthermore be assigned to replenish items from the standard storage locations to the rapid fulfillment area to satisfy future predicted or actual orders in a manner that optimizes a cost metric.

Abstract: An online concierge system facilitates ordering of items by customers, procurement of the items from physical retailers by pickers assigned to the orders, and delivery of the orders to customers. To enable efficient procurement, the online concierge system may facilitate preemptive picking of items for staging at a rapid fulfillment area of the physical retailer, and pickers may selectively pick items from the rapid fulfillment area instead of their standard storage locations. Decisions on which items to preemptively pick may be based on a predictive optimization model that scores and ranks items for predictive picking in accordance with various optimization criteria. In the course of fulfilling orders, pickers may furthermore be assigned to replenish items from the standard storage locations to the rapid fulfillment area to satisfy future predicted or actual orders in a manner that optimizes a cost metric.

Abstract: An online concierge shopping system fulfills orders using workers who pick items at a warehouse to complete an order and workers to deliver the orders to a customer's location. To optimize the staffing of workers for each task, the system uses a trained model to predict the number of workers needed to achieve an optimal outcome based on an input set of contextual information. The system also schedules specific workers to various shifts using the predicted number of workers needed and then searching a feasibility space for an optimal solution. The trained model may be updated based on performance observations.

Abstract: An online concierge system applies a predictive model to predict demand of items, and facilitates preemptive picking of items in advance of receiving orders to enable efficient procurement and delivery. The online concierge system may apply a time-series model and/or machine learning model that predicts demand based on historical data. Depending on the predicted demand, items may be preemptively moved from a storage location to a staging area that enables the items to be more rapidly processed and delivered to customers when orders come in.

Abstract: An apparatus for receiving multimedia content over a multicast enabled network includes a communications module, a stream assembly module, and a display module. The communications module receives and stores a plurality of multicast data packets pertaining to one or more video streams on a client device. The stream assembly module assembles at least a portion of the data packets pertaining to a video stream into a video stream. The display module displays an assembled video stream.

Abstract: An exhaust gas aftertreatment system having a housing and a plurality of separate diesel particulate filter bricks disposed in the housing. A first one of such bricks is disposed upstream of a second one of the bricks. The second one of the bricks has channels with closed downstream ends and open upstream ends and channels with closed upstream ends and open downstream ends and the upstream brick has channels with closed downstream ends and open upstream ends and bypass channels with open upstream ends and open downstream ends.