Amazon Technologies, Inc.

Anand Victor

Jeremy Boynes

Kyle Bradley Peterson

Andrew Stuart Huntwork

Zachary Mohamed Shalla

Zhengqiang Xu

Ramnath R Iyer

John Ingram Baker Dugaw

Ankur Goenka

David Edouard Louis Robert

Keshav Aswathnarayana Narsipur

Gagan Kumar Arora

Rahul Gupta

Abhinav Aggarwal

Li Erran Li

Alejandro Ricardo Mottini d'Oliveira

Jarrett Alegre Bato

Zheng Du

Aidan Thomas Cardella

Peng Yang

Vipin Gupta

John Rajiv Malik

Han Zeng

Yi Xu

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

Techniques for determining attributable data in a natural language user input that can be used to identify a specific user are described. A system may use various data signals determined using different components. The system may process the various signals to make a final determination on whether the input includes attributable data. The system may use a first component to detect user-identifiable data in the input. The system may use a second component to determine whether the input is potentially attributable to a particular user.

User data processing

A customer in a computing resource service provider environment provisions a virtual machine instance configured with access to sensitive data in a secure storage facility. The virtual machine instance is provisioned specifically to generate reports using a subset of the sensitive data to satisfy different reporting requirements associated with governing entities and agencies. After generating the reports, the virtual machine instance is then removed from the computing resource service environment such that the sensitive data is inaccessible.

System and methods using ephemeral accounts to limit risk of exposing sensitive data

A plurality of virtual machines instances are instantiated and configured to use a subset of sensitive data to generate reports such that the reports can be visually inspected by authorized personnel or entities for potential abnormalities. After completion of the inspection, the virtual machine instances are then deleted such that the sensitive data that were used to generate the reports become inaccessible.

System and methods using ephemeral accounts to protect user accounts with sensitive data

Disclosed are various embodiments for the mapping of behavior to data. Multiple behavior elements from a code library that are compatible with one or more data elements of a document are determined according to a data contract associated with each one of the behavior elements. Each of the behavior elements is configured to consume one or more data elements and produce a user interface feature. One or more of the behavior elements are selected according to one or more rules. A mapping of the selected behavior elements to the data elements is generated.

Mapping behavior to data

Disclosed are various embodiments for determining categories of items to include in a search results listing. Items to include in a search result listing are identified in response to a search request. The items are classified in multiple categories. A particular seller of multiple sellers is identified based at least in part on the search request. A subset of the categories is determined for which corresponding items are to be included in the search results listing. The subset is determined by applying one or more rules specified by the particular seller.

Display of items from search

Disclosed are various embodiments for the mapping of behavior to data. Multiple behavior elements from a code library that are compatible with one or more data elements of a document are automatically determined according to a data contract associated with each one of the behavior elements. Each of the behavior elements is configured to consume one or more data elements and produce a network page feature according to a behavior. One or more of the behavior elements are selected automatically according to a plurality of rules, thereby producing one or more selected behavior elements. A mapping of the one or more selected behavior elements to the one or more data elements is automatically generated.

Disclosed are various embodiments for determining categories of items to include in a search results page. Representative is a system comprising at least one computing device with a search engine and a network page build process that are executable therein. The search engine identifies a plurality of items to display in a network page in response to a request for search, wherein the items are classified in a plurality of categories. The network page build process applies one or more rules to determine one or more of the categories for which corresponding ones of the items are to be displayed in a network page.

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Bellevue, WA, United States of America

Anand Victor