GENERATING AN ITEM SELECTION SEQUENCE USING A MACHINE LEARNING MODEL FOR IDENTIFYING FOUNDATIONAL ITEMS

Abstract

An online system receives orders from users and fulfills the orders by dispatching a picker to a physical source to obtain the items for delivery.  Some items in an order may be considered “foundational,” meaning that a user who ordered the items may wish to cancel one or more other items in the order if the foundational item is unavailable (e.g., the item is a critical ingredient for a recipe).  The online system predicts items in the order that are foundational using a trained machine-learning model.  The online system presents the items to the picker in a sequence so the foundational items are obtained earlier by the picker. This enables the picker to observe whether the determined foundational item is available sooner in the picking process, allowing earlier performance of a remedial action and possibly avoiding replacing previously obtained items affected by the unavailability of the foundational item.

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