Amazon Technologies, Inc.

Siddharth Vivek Joshi

Stefano Stefani

Warren Vincent Barkley

Fedor Zhdanov

Sindhu Chejerla

Prateek Sharma

Rahul Sharma

Pietro Perona

Scott Kerns Windsor

Charles Eric Dannaker

Anuj Gupta

Zachary Crisman

Jamie J Sheehan

Kriti Bharti

Jon I Turow

Jonathan Thomas Greenlee

Vishal Bethur Sathyamurthy

Wei Xiao

Jonathan Hedley

Mark Chien

James Robert Blair

James Andrew Trenton Lipscomb

Canku Alp Calargun

Daryn Edward Nakhuda

David Aronchick

Hyma Srinivasa Murthy

Zahid Rahman

Sankalp Srivastava

Alisa V Shinkorenko

Krzysztof Chalupka

Pranav Sachdeva

A request is received associated with a review. Within first content, a first field of interest and a second field of interest are identified and within second content, a third field of interest and a fourth field of interest are identified. A review is generated that includes a first indication of the first field of interest and a second indication of the second field of interest within the first content, as well as a third indication of the third field of interest and a fourth indication of the fourth field of interest within the second content. The review is transmitted to a device of a reviewer for reviewing the content.

Providing content reviews based on AI/ML output

A request is received associated with reviewing content. As part of the request, one or more conditions are received and the content is analyzed to identify a first field of interest and a second field of interest. The first field of interest and the second field of interest represent fields of interest associated with the review of the content. At least one of the first field of interest or the second field of interest may not satisfy the one or more conditions and the content, or a portion thereof, may be sent for review.

Determining content to present for human review

A request associated with reviewing content for a field of interest is received. A confidence is determined associated with the content including the field of interest. A machine learning (ML) model determines a first confidence associated with the content includes the field of interest. The field of interest is transmitted for review in instances where the first confidence is less than a confidence threshold. After review, an indication associated with a reviewer reviewing the content and the first confidence associated with the ML model identifying the field of interest is updated to a second confidence.

Adjusting confidence thresholds based on review and ML outputs

Techniques for active learning-based data labeling are described. An active learning-based data labeling service enables a user to build and manage large, high accuracy datasets for use in various machine learning systems. Machine learning may be used to automate annotation and management of the datasets, increasing efficiency of labeling tasks and reducing the time required to perform labeling. Embodiments utilize active learning techniques to reduce the amount of a dataset that requires manual labeling. As subsets of the dataset are labeled, this label data is used to train a model which can then identify additional objects in the dataset without manual intervention. The label data can be added to an augmented manifest, the augmented manifest can be used to filter the dataset to perform further labeling jobs on the same or different subsets of the dataset.

Active learning-based data labeling service using an augmented manifest

Technology for rewarding affiliates is provided. In one example, a method may include determining a physical location of a computing device. A purchase intent from the computing device while at the physical location may be identified. An affiliate associated with the physical location may be rewarded for the purchase intent.

Rewarding affiliates

Techniques for active learning-based data labeling are described. An active learning-based data labeling service enables a user to build and manage large, high accuracy datasets for use in various machine learning systems. Machine learning may be used to automate annotation and management of the datasets, increasing efficiency of labeling tasks and reducing the time required to perform labeling. Embodiments utilize active learning techniques to reduce the amount of a dataset that requires manual labeling. As subsets of the dataset are labeled, this label data is used to train a model which can then identify additional objects in the dataset without manual intervention. The process may continue iteratively until the model converges. This enables a dataset to be labeled without requiring each item in the dataset to be individually and manually labeled by human labelers.

Active learning loop-based data labeling service

Where a particular item has certain restrictions or requirements which should be followed when the item is stored or transported, a transit plan or storage plan for the item may be developed using the systems and methods of the present disclosure. In particular, where the restrictions or requirements may change over time (i.e., during the period in which the item is stored or transported), the transit plan or storage plan may include accommodations for changing the conditions of the item in order to comply with the restrictions or requirements. In particular, the transit plan or storage plan may call for the reorientation of an item that has orientation-specific requirements as the item is in storage or in transit.

Dynamically generating orientation information for containers

Tracking waiting lists associated with merchants and presenting deals to users based on adding the users to waiting lists is described. A service provider may enable merchants to track waiting lists associated with their own services and services of other merchants. Users may be automatically added to waiting lists of merchants based at least in part on a service provider determining that a user is at a geographic location of a merchant. Based at least in part on adding a user to a waiting list for a particular merchant, the service provider may access and select deals from one or more alternative merchants. The one or more alternative merchants may not have waiting lists or may have waiting lists with fewer groups than the waiting list for the particular merchant. The service provider may offer the deals to the user via a user device.

Computerized waiting list tracking system

Disclosed are various embodiments for processing electronic messages and/or reply electronic messages. A contact entry associated with a user issue is created. A unique token associated with the contact entry is generated. Electronic messages sent to the user are generated with a reply-to address that incorporates the token.

Processing electronic mail replies

Disclosed are various embodiments for processing email and/or reply emails. A contact entry associated with a user issue is created. The contact entry is associated with an issue category based upon a context of the contact entry. A unique email token associated with the contact entry is generated. Emails sent to the user are generated with a reply-to address that incorporates the email token.

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Seattle, WA, United States of America

Siddharth Vivek Joshi