Square, Inc.

Block, Inc.

Doordash, Inc.

Abhay Raj Kumar

Mai Leduc

Gian Perrone

Rong Yan

Dan Hipschman

John Berian James

Jesse Lee Reiss

Ajit Kalidindi Varma

Matthew Rhodes

Richard David Din

Mason Ng

Anthony Bishopric

Zachary Brock

James F Butts, Iii

Daniel Hipschman

Matthew O'Connor

Austin Broyles

Alexey Kalinichenko

Matt Mihic

John Coughlin

Berian James

Simo Leone

Transaction modification based on modeled profiles is described. In an example, transaction data can be received from merchant computing devices associated with merchants associated with a payment processing system. A model can be trained to generate profiles using, as training data, one or more of merchant data, buyer data, or the transaction data. Upon receiving an indication of a particular transaction between a buyer and a merchant, it can be determined that a characteristic of the transaction corresponds to a profile of the generated profiles. Based on the determination that the characteristic corresponds to the profile and the transaction data, a recommendation can be generated for a modification of the transaction to add an item or replace an item.

Transaction modification based on modeled profiles

A service provider may receive item price information and/or point of sale (POS) transaction information from a plurality of merchant devices associated with a plurality of merchants. The service provider may categorize the plurality of merchants according to the types of items offered by the respective merchants, and may further categorize the merchants according to the locations at which they conduct business. The service provider may subsequently determine a price recommendation or other pricing information for a particular item offered by a particular merchant. A merchant device of the particular merchant may present the pricing information in a user interface that enables the merchant to view projected sales at various different price points. Further, the merchant may be able to specify one or more factors used for determining the recommend price, such as a desired business model, a desired clientele, a desired profit margin, or the like.

Determining pricing information from merchant data

Transaction modification based on modeled profiles is described. In an example, transaction data can be received by a payment processing system. The payment processing system can generate, using a trained model, profiles. Based at least in part on (i) receiving an indication that a buyer and a merchant are engaged in, or likely to be engaged in, a transaction and (ii) determining that a characteristic of the transaction corresponds to a profile, generating a communication to enable the buyer or the merchant to modify the transaction in association with processing of the transaction.

A method of managing a cardless payment transaction between a customer and a merchant includes receiving, from a device of a customer, an indication of consent to join the customer to a cardless payment transaction, where the cardless payment transaction is between one or more other customers and the merchant; receiving from the customer device, location data indicating that the customer device is in proximity with the merchant; joining the customer with the cardless payment transaction between the one or more other customers and the merchant; receiving transaction data between the merchant and each customer; and submitting, for each customer associated with the cardless payment transaction, a transaction between the merchant and the customer to a financial service for authorization.

Cardless payment transactions with multiple users

Buyer profile management is described herein. In an example, a payment processing service can store, in a database, buyer profiles associated with buyers involved in transactions with one or more merchants of the payment processing service. The payment processing service can receive transaction data associated with a new transaction between a buyer and a merchant and can determine whether the transaction data corresponds to any of the buyer profiles. Based at least in part on determining that the transaction data corresponds to one of the buyer profiles, the payment processing service can update the database storing the buyer profiles, wherein updating the database comprises merging two or more buyer profiles. A recommendation can be made based on the merged buyer profile prior to completion of the new transaction.

Buyer profile management

In some examples, a computing device may receive, from a first user device, a communication indicating a request to create a combined order for items offered by one or more merchants. For example, the combined order may enable a plurality of users to independently purchase individual ones of the items from the one or more merchants. The combined order may be created having an initial condition and may be accessible by the plurality of users for adding items to the combined order. At least one selection of a respective item may be received from each user of the plurality of users. For instance, each selection may change the condition of the combined order. In addition, the computing device may process the items added to the combined order as a single order to perform at least one action.

Adaptive combined order management

A particular item may be categorized based on delivery times of the particular item. In some instances, the particular item may be ranked across multiple merchants that are offering the particular item for acquisition. The ranking may be based on an estimated amount of delivery time to deliver the particular item, for the individual merchants, to a current location of a user. The estimated amount of delivery time may be based on a preparation time for the respective merchant to prepare the particular item, a courier time for a courier to transport the particular item to the current location of the user, and so on. Information regarding the ranking may be displayed or otherwise output to enable the user to identify and order the particular item from a merchant that satisfies the user's needs.

Categorization of items based on item delivery time

Techniques for providing items recommendations based on a state of a merchant device are described. For example, a determination of whether or not a merchant device is in an online mode or an offline mode may be made. When it is determined that the merchant device is in the online mode, a recommendation that is received for the current transaction may be presented. When it is determined that the merchant device is in the offline mode, a recommendation that is previously received may be presented. In other examples, other types of recommendations may be presented.

Determining item recommendations from merchant data

In some examples, a first device of a first buyer may receive a selection of a first item offered by a selected merchant. The first device may determine whether any second devices are within a threshold distance of the first device. Based on a second device being within the threshold distance, the first device may send, to the second device, a communication for creating a combined order. Receipt of the communication may cause the second device to present, to a second buyer, an interface for creating a combined order. The second buyer may use the second device to select a second item to order from the merchant. At least one of the first device or the second device may send combined order information to a service computing device, which may send the combined order to the merchant and schedule a courier to deliver the ordered items to the buyers.

Managing communications between proximate mobile devices

Item-level information of a particular item may be processed to categorize the particular item. In some instances, the particular item may be ranked across multiple merchants that are offering the particular item for acquisition. The ranking may be based on the item-level information that relates to an attribute of the particular item for the individual merchants. For example, the item-level information may include a cost of the particular item at a merchant, a rating of the particular item for a merchant, a number of calories of the particular item for a merchant, and so on. Information regarding the ranking may be displayed or otherwise output to enable the user to identify and order the particular item from a merchant that satisfies the user's needs.

Categorization of items based on attributes

A service provider may receive transaction information from a plurality of merchant devices for a plurality of point of sale transactions conducted between buyers and merchants. The service provider can associate the transaction information with respective buyer profiles of a plurality of buyer profiles. In some examples, the service provider may determine a subset of the buyer profiles based on at least a portion of the buyer profiles in the subset including transaction information that is related to a particular one of the merchants. The service provider may further determine at least one characteristic associated with at least some of the buyer profiles in the subset, and may send a recommendation to the particular merchant related to the at least one characteristic. As an example, the recommendation may be for improving or optimizing the business of the particular merchant.

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San Francisco, CA, United States of America

Abhay Raj Kumar