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Patent Agency Ranking
公开(公告)号：US10666716B1
公开(公告)日：2020-05-26
申请号：US15629656
申请日：2017-06-21
Applicant: Amazon Technologies, Inc.
Inventor： Brian Jaffery Tajuddin , Carlos Alejandro Arguelles , Jeremy Boynes , Adam Lloyd Days , Gavin R. Jewell , Erin Harding Kraemer , Jeenandra Kumar Uttamchand , Manoj Srivastava , Tyson Christopher Trautmann , Praveen Kambam Sugavanam
Abstract: Optimization preferences are defined for optimizing execution of a distributed application. Candidate sets of application parameter values may be tested in test execution environments. Measures of performance for metrics of interest are determined based upon the execution of the distributed application using the candidate sets of application parameter values. Utility curves may be utilized to compute measures of effectiveness for metrics of interest. A multi-attribute rollup operation may utilize the computed measures of effectiveness and weights to compute a grand measure of merit (MOM) for the candidate sets of application parameter values. An optimized set of application parameter values may then be selected based upon the computed grand MOMs. The optimized set of application parameter values may be deployed to a production execution environment executing the distributed application. Production safe application parameters might also be identified and utilized to optimize execution of the distributed application in a production execution environment.
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2.发明授权公开(公告)号：US09772835B1
公开(公告)日：2017-09-26
申请号：US13800783
申请日：2013-03-13
Applicant: Amazon Technologies, Inc.
Inventor： Tyson Christopher Trautmann , Jeremy Boynes , Diwakar Chakravarthy , Jeenandra Kumar Uttamchand , Yi-Tao Wang , Soo Young Yang
Abstract: Program code, such the program code of an application program, can be modified to permit the program code to execute in a multi-tenant execution environment. For example, program code might be modified at compile time, run time, or at another time, in order to enable the program code to properly operate in an execution environment in which applications might be simultaneously executed in process by multiple tenants. Program code might also be modified at run time to enable the program code to execute in a distributed fashion in a distributed computing environment. For example, portions of the program code might be configured at run time to execute in different instances of an execution environment. The program code might be modified at run time to enable the program code to properly execute in multiple instances of the execution environment.
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公开(公告)号：US09245232B1
公开(公告)日：2016-01-26
申请号：US13774767
申请日：2013-02-22
Applicant: Amazon Technologies, Inc.
Inventor： Tyson Christopher Trautmann , Peter Varnum Commons , Diwakar Chakravarthy , Michael Luis Collado , Thomas Lowell Keller , Benjamin Warren Mercier , Zachary Jared Wiggins
CPC classification number: G06N99/005 , G06F17/30902
Abstract: A machine generated service cache that utilizes one or more machine learning classifiers is trained using service requests directed to a human-generated service and service responses generated by the human-generated service in response to the service requests. Once the machine generated service cache has been trained to a predetermined level of performance, the machine generated service cache can be utilized to process actual service requests directed to the human-generated service. The machine generated service cache might be utilized to process service requests for which it is not essential that the returned service response be identical to a response that would be generated by the human-generated service.
Abstract translation: 利用一个或多个机器学习分类器的机器生成的服务高速缓冲存储器是使用针对人类产生的服务的服务请求和由人类生成的服务响应于服务请求生成的服务响应进行训练的。 一旦机器生成的服务高速缓存已经被训练到预定的性能水平，则机器产生的服务高速缓存可以用于处理针对人造服务的实际服务请求。 机器生成的服务高速缓存可以用于处理服务请求，其中所返回的服务响应与由人为生成的服务生成的响应相同并不是必需的。
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公开(公告)号：US10445807B1
公开(公告)日：2019-10-15
申请号：US13689450
申请日：2012-11-29
Applicant: Amazon Technologies, Inc.
Inventor： Peter Varnum Commons , David John Edwards, Jr. , Tony Jay Lee , Llewellyn James Mason , Scott James McKee , Elton Victor Pinto , Brandon William Porter , Tyson Christopher Trautmann
IPC: G06Q30/06
Abstract: This disclosure is directed to, in part, providing customers with an enhanced shopping experience during a visit to a physical store location. The enhanced shopping experience may include providing the customer with customized delivery of product information. The product information may include demonstrations of product use, samples of products, recommendations of related products or areas of interest to a customer, etc. To provide the customized information, the customer may register to be identified while at the physical store location. The physical store location may include sensors that identify a location of the registered customer. A presentation module may then push relevant content to a device located near the customer, possibly in response to a request from the customer and/or a location of the customer.
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公开(公告)号：US09692811B1
公开(公告)日：2017-06-27
申请号：US14286539
申请日：2014-05-23
Applicant: Amazon Technologies, Inc.
Inventor： Brian Jaffery Tajuddin , Carlos Alejandro Arguelles , Jeremy Boynes , Adam Lloyd Days , Gavin R. Jewell , Erin Harding Kraemer , Jeenandra Kumar Uttamchand , Manoj Srivastava , Tyson Christopher Trautmann , Praveen Kambam Sugavanam
Abstract: Optimization preferences are defined for optimizing execution of a distributed application. Candidate sets of application parameter values may be tested in test execution environments. Measures of performance for metrics of interest are determined based upon the execution of the distributed application using the candidate sets of application parameter values. Utility curves may be utilized to compute measures of effectiveness for metrics of interest. A multi-attribute rollup operation may utilize the computed measures of effectiveness and weights to compute a grand measure of merit (MOM) for the candidate sets of application parameter values. An optimized set of application parameter values may then be selected based upon the computed grand MOMs. The optimized set of application parameter values may be deployed to a production execution environment executing the distributed application. Production safe application parameters might also be identified and utilized to optimize execution of the distributed application in a production execution environment.
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