Doordash, Inc.

Block, Inc.

Wensi Xu

Alexei Savtchenko

Michael Kane

Christopher Hollindale

Ihsan Kabir

Jianye Xu

Juan Hernandez

Techniques for optimizing a food service configured to receive food orders from multiple customers, and consolidate orders based on respective days, times, and locations associated therewith. The food orders may include deliveries, customer pick-ups, and dine-in options for eating at a restaurant. The optimized food service may be configured to assign a restaurant to the consolidated orders and send a preparation instruction to the restaurant to prepare the consolidated orders for a designated pick-up or dining time. The optimized food service may additionally instruct an order supervisor to travel to the restaurant to assist in preparation of the consolidated orders. The order supervisor may additionally serve a food order, such as for a dine-in option or full service, catered delivery. For delivery orders, the optimized food service may assign a courier and, in some examples, a delivery assistant, to deliver the orders to respective customers.

Food delivery optimization

Techniques described herein are directed to an intelligent lending platform in which a payment service may receive respective lending queries from users, analyze the queries using a machine-learned model and, if the queries are denied, output respective signals indicating that the respective queries are denied and the reasons for each respective decline. In some instances, the described techniques analyze data associated with each respective denied query to determine high-level reasons for the decline and may present these reasons in a manner that is both understandable to and actionable by the respective user. That is, the techniques may automatically identify the most significant reasons why a particular lending query was denied and may automatically output these reasons to a user in a manner that the user is able to easily comprehend and act upon to increase the chance of receiving an approval upon a subsequent lending query.

Automatically determining adverse action reason codes

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San Francisco, CA, United States of America

Wensi Xu