Block, Inc.

Paul Silvis

Rohini Pandhi

Sudheer Anne

Intelligent tender options for payment are described. In an example, buyer and seller profiles can be stored in a datastore associated with a service provider. The profiles can include interaction data including historical transaction data. A machine learning model can be trained using at least a portion of the historical transaction data. Responsive to receiving transaction data for a current transaction, a personalized plurality of tender options is determined based at least in part on (i) the machine learning model, (ii) one or more of a buyer profile, a seller profile, or the transaction data, and (iii) one or more services of the service provider available to the buyer based at least in part on the buyer profile. A GUI is presented on a seller or buyer device including a plurality of actuation mechanisms associated with respective tender options.

Intelligent tender options

Intelligent incentives for invoice payment are described. In an example, transaction data can be received from a device operable by a seller associated with a service provider and via a network transmission between the device and server computing device(s) associated with the service provider. The transaction data can be associated with a transaction between a buyer and the seller. The server computing device(s) can access, from a datastore associated with the server computing device(s) and based at least in part on receiving the transaction data, seller information associated with the seller and, based at least in part on the transaction data and the seller information, the server computing device(s) can generate an invoice for the transaction, wherein at least one term of the invoice is adaptable based at least in part on an operating condition of the seller.

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San Francisco, CA, United States of America

Paul Silvis