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公开(公告)号：US11501210B1
公开(公告)日：2022-11-15
申请号：US16698705
申请日：2019-11-27
Applicant: Amazon Technologies, Inc.
Inventor： Fedor Zhdanov , Siddharth Vivek Joshi , Prateek Sharma , Alisa V. Shinkorenko , Warren Barkley , Stefano Stefani , Krzysztof Chalupka , Pietro Perona
Abstract: 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.
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Patent Agency Ranking
2.发明公开公开(公告)号：US20240256636A1
公开(公告)日：2024-08-01
申请号：US18629815
申请日：2024-04-08
Applicant: Amazon Technologies, Inc.
Inventor： Fedor Zhdanov , Emanuele Coviello , Benjamin Alexei London
IPC: G06F18/214 , G06F16/635 , G06F16/68 , G06F18/23 , G06N20/00 , G10L19/00
CPC classification number: G06F18/2155 , G06F16/635 , G06F16/686 , G06F18/23 , G06N20/00 , G10L2019/0001
Abstract: At an artificial intelligence system, training iterations of a first machine learning model are implemented. In a particular iteration, a group of data items are selected from an item collection using active learning, and respective labels selected from a set of tags are obtained for at least some of the items of the group. Using feature processing elements of a different machine learning model, a respective feature set corresponding to individual labeled items is generated in the iteration, and the feature sets are included in a training set used to train the first machine learning model. A trained version of the first machine learning model is stored after a training completion criterion is met.
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公开(公告)号：US11983244B1
公开(公告)日：2024-05-14
申请号：US16017892
申请日：2018-06-25
Applicant: Amazon Technologies, Inc.
Inventor： Fedor Zhdanov , Emanuele Coviello , Benjamin Alexei London
IPC: G06K9/62 , G06F16/635 , G06F16/68 , G06F18/214 , G06F18/23 , G06N20/00 , G10L19/00
CPC classification number: G06F18/2155 , G06F16/635 , G06F16/686 , G06F18/23 , G06N20/00 , G10L2019/0001
Abstract: At an artificial intelligence system, training iterations of a first machine learning model are implemented. In a particular iteration, a group of data items are selected from an item collection using active learning, and respective labels selected from a set of tags are obtained for at least some of the items of the group. Using feature processing elements of a different machine learning model, a respective feature set corresponding to individual labeled items is generated in the iteration, and the feature sets are included in a training set used to train the first machine learning model. A trained version of the first machine learning model is stored after a training completion criterion is met.
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公开(公告)号：US11048979B1
公开(公告)日：2021-06-29
申请号：US16370706
申请日：2019-03-29
Applicant: Amazon Technologies, Inc.
Inventor： Fedor Zhdanov , Siddharth Joshi , Sankalp Srivastava , Rahul Sharma , Pietro Perona , Sindhu Chejerla
Abstract: 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.
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公开(公告)号：US12277192B1
公开(公告)日：2025-04-15
申请号：US16560814
申请日：2019-09-04
Applicant: Amazon Technologies, Inc.
Inventor： Ragav Venkatesan , Xiong Zhou , Gurumurthy Swaminathan , Fedor Zhdanov
IPC: G06F18/214 , G06N3/08 , G06N5/04 , G06N20/00
Abstract: Techniques for zero-shot and few-shot transfer of domain-adapted base networks are described. Multiple machine learning task layers are trained using a shared base feature extractor network. At least one task layer is trained with samples and corresponding labels from a first domain as well as a second domain. At least one other task layer is trained with samples and corresponding labels from only the first domain. Ultimately, the other task layer(s) are adapted to generate labels for the first domain via the base network being weighted based on all trainings.
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公开(公告)号：US11861512B1
公开(公告)日：2024-01-02
申请号：US16698735
申请日：2019-11-27
Applicant: Amazon Technologies, Inc.
Inventor： Siddharth Vivek Joshi , Stefano Stefani , Warren Barkley , James Andrew Trenton Lipscomb , Fedor Zhdanov , Anuj Gupta , Prateek Sharma , Pranav Sachdeva , Sindhu Chejerla , Jonathan Thomas Greenlee , Jonathan Hedley , Jon I. Turow , Kriti Bharti
IPC: G06F7/00 , G06N5/04 , G06N20/00 , G06F16/9535
CPC classification number: G06N5/04 , G06F16/9535 , G06N20/00
Abstract: 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.
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