Amazon Technologies, Inc.

Other

Tyson Christopher Trautmann

Jeremy Boynes

Jeenandra Kumar Uttamchand

Carlos Alejandro Arguelles

Diwakar Chakravarthy

Peter Varnum Commons

Adam Lloyd Days

Gavin Richard Jewell

Brian Jaffery Tajuddin

Erin Harding Kraemer

Manoj Srivastava

Praveen Kambam Sugavanam

Brandon William Porter

Thomas Lowell Keller

Benjamin Warren Mercier

Llewellyn James Mason

Zachary Jared Wiggins

Michael Luis Collado

David John Edwards, Jr

Elton Victor Pinto

Scott James McKee

Tony Jay Lee

Yi-Tao Wang

Soo Young Yang

Andrew Burnell

Disclosed are various embodiments for implementing a blockchain utilizing routed packets to generate blocks. A given packet can be signed and analyzed to determine whether the signed packet satisfies root packet criteria. Packets that satisfy root packet criteria can be analyzed to determine whether they collectively satisfy a predetermined block criteria. A block can be added to the blockchain when the block criteria is met.

Routing based blockchain

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.

Optimization of application parameters

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.

Enhanced customer shopping experience in physical store locations

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.

Modification of program code for execution in a multi-tenant or distributed computing environment

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.

Machine generated service cache

Growing community of inventors

Seattle, WA, United States of America

Tyson Christopher Trautmann