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公开(公告)号：US10963754B1
公开(公告)日：2021-03-30
申请号：US16144927
申请日：2018-09-27
Applicant: Amazon Technologies, Inc.
Inventor： Avinash Aghoram Ravichandran , Paulo Ricardo dos Santos Mendonca , Rahul Bhotika , Stefano Soatto
Abstract: Techniques for training an embedding using a limited training set are described. In some examples, the embedding is trained by generating a plurality of vectors from a random sample of the limited set of training data classes using a layer of the particular machine learning classification model, randomly selecting samples from the plurality of vectors into a set of samples, computing at least one distance for each sampled class from a center parameter for the class using the set of samples, generating a discrete probability distribution over the classes for a query point based on distances to a center parameter for each of the classes in the embedding space, calculating a loss value for the modified prototypical network, the calculation of the loss value being for a fixed geometry of the embedding space and including a measure of the difference between distributions, and back propagating.
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公开(公告)号：US11423265B1
公开(公告)日：2022-08-23
申请号：US16917721
申请日：2020-06-30
Applicant: Amazon Technologies, Inc.
Inventor： Hao Chen , Hao Wu , Hao Li , Michael Quang Thai Lam , Xinyu Li , Kaustav Kundu , Meng Wang , Joseph P Tighe , Rahul Bhotika
Abstract: Methods, systems, and computer-readable media for content moderation using object detection and image classification are disclosed. A content moderation system performs object detection on an input image using one or more object detectors. The object detection finds one or more elements in the input image. The content moderation system performs classification based at least in part on the input image using one or more image classifiers. The classification determines one or more values indicative of one or more content types in the input image. The content moderation system determines one or more scores for one or more content labels corresponding to the one or more content types. At least one of the scores represents a finding of one or more of the content types in the input image. The content moderation system generates output indicating the finding of the one or more of the content types.
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公开(公告)号：US12147878B2
公开(公告)日：2024-11-19
申请号：US17106026
申请日：2020-11-27
Applicant: Amazon Technologies, Inc.
Inventor： Barath Balasubramanian , Rahul Bhotika , Niels Brouwers , Ranju Das , Prakash Krishnan , Shaun Ryan James McDowell , Anushri Mainthia , Rakesh Madhavan Nambiar , Anant Patel , Avinash Aghoram Ravichandran , Joaquin Zepeda Salvatierra , Gurumurthy Swaminathan
Abstract: Techniques for feedback-based training may include selecting a scoring machine learning model based at least in part on a test metric, and applying the model on an unlabeled dataset to generate, per dataset item of the unlabeled dataset, a prediction and an importance ranking score for the prediction. Techniques for feedback-based training may further include selecting, based on the importance ranking scores, a result of the application of the model on the unlabeled dataset, providing the result and requesting feedback on the result via a graphical user interface, receiving the feedback via the graphical user interface, adding data from the unlabeled dataset into a training dataset when the feedback indicates a verified result, and retraining the model using the training dataset with the data added from the unlabeled dataset to generate a retrained model.
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公开(公告)号：US11257006B1
公开(公告)日：2022-02-22
申请号：US16196662
申请日：2018-11-20
Applicant: Amazon Technologies, Inc.
Inventor： Oron Anschel , Amit Adam , Shahar Tsiper , Hadar Averbuch Elor , Shai Mazor , Rahul Bhotika , Stefano Soatto
IPC: G06N20/00 , G06K9/00 , G06F40/169
Abstract: Techniques for auto-generation of annotated real-world training data are described. An electronic document is analyzed to determine text represented in the document and corresponding locations of the text. A representation of the electronic document is modified to include markers and printed. The printed document is photographed in real-world environments, and the markers within the digital photographs are analyzed to allow for the depiction of the document within the photographs to be rectified. The text and location data are used to annotate the rectified images.
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公开(公告)号：US11983243B2
公开(公告)日：2024-05-14
申请号：US17106023
申请日：2020-11-27
Applicant: Amazon Technologies, Inc.
Inventor： Barath Balasubramanian , Rahul Bhotika , Niels Brouwers , Ranju Das , Prakash Krishnan , Shaun Ryan James Mcdowell , Anushri Mainthia , Rakesh Madhavan Nambiar , Anant Patel , Avinash Aghoram Ravichandran , Joaquin Zepeda Salvatierra , Gurumurthy Swaminathan
IPC: G06N20/00 , G06F9/451 , G06F18/21 , G06F18/214 , G06N3/088 , G06N3/09 , G06V10/70 , G06V10/774 , G06V10/778 , H04L9/40
CPC classification number: G06F18/2148 , G06F9/451 , G06F18/2155 , G06F18/2178 , G06N3/088 , G06N3/09 , G06N20/00 , G06V10/70 , G06V10/7753 , G06V10/7784 , H04L63/1425 , G06T2207/20081
Abstract: Techniques for anomaly detection are described. An exemplary method includes receiving one or more requests to train an anomaly detection machine learning model using feedback-based training, the request to indicate one or more of a type of analysis to perform, a model selection indication, and a configuration for a training dataset; training the anomaly detection machine learning model according to the one or more requests using the training data; performing feedback-based training on the trained anomaly detection machine learning model; and using the retrained anomaly detection machine learning model.
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Patent Agency Ranking
公开(公告)号：US11481683B1
公开(公告)日：2022-10-25
申请号：US16888589
申请日：2020-05-29
Applicant: Amazon Technologies, Inc.
Inventor： Kunwar Yashraj Singh , Joaquin Zepeda Salvatierra , Erhan Bas , Vijay Mahadevan , Jonathan Wu , Rahul Bhotika
Abstract: Techniques for creating machine learning models for direct homography regression for image rectification are described. In certain embodiments, a training service trains an algorithm on a source view of a training image and a homography matrix of the training image into a machine learning model that generates a normalized homography matrix for an input of the source view. The normalized homography matrix may then be utilized to generate a target view of an image input into the machine learning model. The target view of the image may be used in a document processing pipeline for document images captured using cameras.
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公开(公告)号：US11429813B1
公开(公告)日：2022-08-30
申请号：US16697662
申请日：2019-11-27
Applicant: Amazon Technologies, Inc.
Inventor： Avinash Aghoram Ravichandran , Rahul Bhotika , Stefano Soatto , Pietro Perona , Hao Yang
Abstract: This disclosure describes automatically selecting and training one or more models for image recognition based upon training and testing (validation) data provided by a user. A service provider network includes a recognition service that may use models to process images and videos to recognize objects in the images and videos, features on the objects in the images and videos, and/or locate objects in the images and videos. The service provider network also includes a model selection and training service that may select one or more modeling techniques based on the objectives of the user and/or the amount of data provided by the user. Based on the selected modeling technique, the model selection and training service selects and trains one or more models for use by the recognition service to process images and videos using the training data. The trained model may be tested and validated using the testing data.
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公开(公告)号：US11341605B1
公开(公告)日：2022-05-24
申请号：US16588503
申请日：2019-09-30
Applicant: Amazon Technologies, Inc.
Inventor： Kunwar Yashraj Singh , Amit Adam , Shahar Tsiper , Gal Sabina Star , Roee Litman , Hadar Averbuch Elor , Vijay Mahadevan , Rahul Bhotika , Shai Mazor , Mohammed El Hamalawi
Abstract: Techniques for document rectification via homography recovery using machine learning are described. An image rectification system can intelligently make use of multiple pipelines for rectifying document images based on the detected type of device that generated the images. The image rectification system can provide high-quality rectifications without requiring human cooperation, multiple views of the document in multiple images, and/or without being constrained to only be able to process images from one source context.
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9.发明授权公开(公告)号：US10970530B1
公开(公告)日：2021-04-06
申请号：US16189633
申请日：2018-11-13
Applicant: Amazon Technologies, Inc.
Inventor： Amit Adam , Oron Anschel , Or Perel , Gal Sabina Star , Omri Ben-Eliezer , Hadar Averbuch Elor , Shai Mazor , Wendy Tse , Andrea Olgiati , Rahul Bhotika , Stefano Soatto
IPC: G06K9/62 , G06F40/137 , G06F40/169 , G06F40/174 , G06K9/00 , G06N20/00
Abstract: Techniques for grammar-based automated generation of annotated synthetic form training data for machine learning are described. A training data generation engine utilizes a defined grammar to construct a layout for a form, select key-value units to place within the layout, and select attribute variants for the key-value units. The form is rendered and stored at a storage location, where it can be provided along with other similarly-generated forms to be used as training data for a machine learning model.
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公开(公告)号：US10949661B2
公开(公告)日：2021-03-16
申请号：US16198040
申请日：2018-11-21
Applicant: Amazon Technologies, Inc.
Inventor： Rahul Bhotika , Shai Mazor , Amit Adam , Wendy Tse , Andrea Olgiati , Bhavesh Doshi , Gururaj Kosuru , Patrick Ian Wilson , Umar Farooq , Anand Dhandhania
IPC: G06K9/00
Abstract: Techniques for layout-agnostic complex document processing are described. A document processing service can analyze documents that do not adhere to defined layout rules in an automated manner to determine the content and meaning of a variety of types of segments within the documents. The service may chunk a document into multiple chunks, and operate upon the chunks in parallel by identifying segments within each chunk, classifying the segments into segment types, and processing the segments using special-purpose analysis engines adapted for the analysis of particular segment types to generate results that can be aggregated into an overall output for the entire document that captures the meaning and context of the document text.
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