Amazon Technologies, Inc.

Jarrett Alegre Bato

Anand Victor

Li Erran Li

Alejandro Ricardo Mottini d'Oliveira

Zheng Du

Jagan Cheboluraghava

Artem Pliasunov

Steven H Reis

David Xiao Song

Aidan Thomas Cardella

Nicolas Gaivironsky

Peng Yang

Vipin Gupta

John Rajiv Malik

Devices and techniques are generally described for confidence score generation for label generation. In some examples, first data may be received from a first computing device. In various further examples, first label data classifying at least one aspect of the first data may be received. First metadata associated with how the first label data was generated may be received. In some cases, the first label data may be generated by a first user. In various examples, a first machine learning model may generate a first confidence score associated with the first label data based at least in part on the first data and second data related to label generation by the first person. In various examples, output data comprising the first confidence score may be sent to the first computing device.

Label confidence scoring

Disclosed are various embodiments for user interfaces that differentiate payment instruments having a trusted beneficiary. It is determined that a payee entity has been designated as a trusted beneficiary for a particular payment instrument issued by a particular payment issuer. A user interface is generated that facilitates a user selection of one of a plurality of payment instruments associated with a user account. The plurality of payment instruments include the particular payment instrument. The user interface differentiates the particular payment instrument based at least in part the payee entity being designated as the trusted beneficiary for the particular payment instrument.

User interfaces that differentiate payment instruments having a trusted beneficiary

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Seattle, WA, United States of America

Jarrett Alegre Bato