公开(公告)号：US10761893B1

公开(公告)日：2020-09-01

申请号：US16199014

申请日：2018-11-23

Applicant: Amazon Technologies, Inc.

Inventor： Vivek Bhadauria ,  Praveenkumar Udayakumar ,  Jonathan Andrew Hedley ,  Vasant Manohar ,  Andrea Olgiati ,  Rakesh Madhavan Nambiar ,  Gowtham Jeyabalan ,  Shubham Chandra Gupta ,  Palak Mehta

IPC: G06F9/46 ,  G06F9/50

Abstract: Techniques are described for automatically scaling (or “auto scaling”) compute resources—for example, virtual machine (VM) instances, containers, or standalone servers—used to support execution of service-oriented software applications and other types of applications that may process heterogeneous workloads. The resource requirements for a software application can be approximated by measuring “worker pool” utilization of instances of each service, where a worker pool represents a number of requests that the service can process concurrently. A scaling service can thus be configured to scale the compute instances provisioned for a service in proportion to worker pool utilization, that is, compute instances can be added as the fleet's worker pools become more “busy,” while compute instances can be removed when worker pools become inactive.

公开(公告)号：US11087081B1

公开(公告)日：2021-08-10

申请号：US16359930

申请日：2019-03-20

Applicant: Amazon Technologies, Inc.

Inventor： Amulya Srivastava ,  Vivek Bhadauria ,  Gowtham Jeyabalan ,  Paul H. Kang ,  Mohammed El Hamalawi

IPC: G06F40/00 ,  G06F40/186 ,  G06N3/04 ,  G06N20/00 ,  G06F40/117 ,  G06F40/169

Abstract: A synthetic document generator that obtains a configuration for a synthetic document derived from real-world documents. The configuration specifies element templates to be included in the synthetic document and weights for the specified element templates. The system generates synthetic documents based on the configuration; the synthetic documents include diversified versions of the element templates specified in the configuration. Annotation documents are generated for the synthetic documents that include information describing the respective synthetic documents. A machine learning model for analyzing real-world documents can then be trained using the synthetic and annotation documents. Feedback from the analysis of real-world documents by the machine learning model can be used to generate a new configuration for generating additional synthetic and annotation documents which are used to further train the model.

公开(公告)号：US11475921B1

公开(公告)日：2022-10-18

申请号：US16996607

申请日：2020-08-18

Applicant: Amazon Technologies, Inc.

Inventor： Gowtham Jeyabalan ,  Shubham Chandra Gupta ,  Jonathan Hedley ,  Nitin Singhal ,  Mark Hawley Yang ,  Jiazhi Ou

IPC: G11B27/06 ,  G11B27/10 ,  G06F9/48 ,  G06N20/00 ,  G06N5/04 ,  G06F9/54 ,  G06V20/40

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; scheduling a job for the first API request using a global scheduler, the global scheduler to schedule, based at least in part on available bandwidth of processing components including a segmenter, a chunk processor, and a reducer, at least one job queue associated at least one of the processing components; 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.

公开(公告)号：US10534965B2

公开(公告)日：2020-01-14

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 Zheng

IPC: G06K9/00 ,  G06T7/00

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.