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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：US20220172342A1
公开(公告)日：2022-06-02
申请号：US17106028
申请日：2020-11-27
Applicant: Amazon Technologies, Inc.
Inventor： Joaquin ZEPEDA SALVATIERRA , Anant PATEL , Shaun Ryan James MCDOWELL , Prakash KRISHNAN , Ranju DAS , Niels BROUWERS , Barath BALASUBRAMANIAN
Abstract: Techniques for anomaly detection are described. An exemplary method includes receiving a request to create a training data set from at least one image, the request to include an indication of the at least one image and at least one indication of an operation to perform on the at least one image to generate a plurality of images from the at least one image; creating a training dataset by extracting one or more chunks from a first at least one image according to the request; and receiving one or more requests to train an anomaly detection machine learning model using the created training dataset; and training an anomaly detection machine learning model according to one or more requests using the created training data.
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公开(公告)号：US20190156124A1
公开(公告)日：2019-05-23
申请号：US15926745
申请日：2018-03-20
Applicant: Amazon Technologies, Inc.
Inventor： Nitin SINGHAL , Vivek BHADAURIA , Ranju DAS , Gaurav D. GHARE , Roman GOLDENBERG , Stephen GOULD , Kuang HAN , Jonathan Andrew HEDLEY , Gowtham JEYABALAN , Vasant MANOHAR , Andrea OLGIATI , Stefano STEFANI , Joseph Patrick TIGHE , Praveen Kumar Udayakumar , Renjun ZHANG
IPC: G06K9/00
CPC classification number: G06K9/00744 , G06F16/71 , G06K9/00228 , G06K9/00718 , G06K9/00765 , G06K2009/00738
Abstract: Techniques for analyzing stored video upon a request are described. For example, a method of receiving a first application programming interface (API) request to analyze a stored video, the API request to include a location of the stored video and at least one analysis action to perform on the stored video; accessing the location of the stored video to retrieve the stored video; segmenting the accessed video into chunks; processing each chunk with a chunk processor to perform the at least one analysis action, each chunk processor to utilize at least one machine learning model in performing the at least one analysis action; joining the results of the processing of each chunk to generate a final result; storing the final result; and providing the final result to a requestor in response to a second API request is described.
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