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公开(公告)号：US20220172100A1
公开(公告)日：2022-06-02
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 are described. An exemplary method includes receiving a request to perform feedback-based retraining, the request including one or more of an identifier of one or more models to retrain, an identifier of a dataset to use for retraining, an identifier of a dataset to use for testing, an indication of a threshold for an anomaly, an indication of how to display items to verify, and an indication of where to store historical information; applying the selected scoring machine learning model on an unlabeled dataset to generate, per dataset item of the unlabeled dataset, at least one of a score and a confidence for the score; providing a result of the application of the selected scoring machine learning model on an unlabeled dataset to request feedback in the form of a graphical user interface; receiving the requested feedback via the graphical user interface; adding data from the unlabeled dataset into the training dataset when the received requested feedback indicates a verified result; and retraining the selected scoring machine learning model using the training data with the added data from the unlabeled dataset.
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公开(公告)号：US20220171995A1
公开(公告)日：2022-06-02
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
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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公开(公告)号：US20200160050A1
公开(公告)日：2020-05-21
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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