Amazon Technologies, Inc.

Peter Varnum Commons

Thomas Lowell Keller

Jeffrey M Scherpelz

Muhammad Ali Siddiqui

Kendra Anne Yourtee

Amos Dylan Vance

Llewellyn James Mason

Ivan Eduardo Gonzalez

Olga I Kuznetsova

Tyson Christopher Trautmann

Daniel Peter Cox

Kevin Janhuan Lee

Brandon William Porter

Benjamin Warren Mercier

Samuel Stevens Heyworth

Diwakar Chakravarthy

Erich Lawrence Ringewald

Zachary Jared Wiggins

Michael Luis Collado

David John Edwards, Jr

Elton Victor Pinto

Kelly L Duggar

Scott James McKee

David J Edwards

Amanda M Aten

Tony Jay Lee

Kelly L Wiggins

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

The techniques described herein provide software testing that may concurrently process a user request using a live version of software and a shadow request, which is based on the user request, using a shadow version of software (e.g., trial or test version, etc.). The live version of software, unlike the shadow version, is user-facing and transmits data back to the users while the shadow request does not output to the users. An allocation module may vary allocation of the shadow requests to enable a ramp up of allocations (or possibly ramp down) of the shadow version of software. The allocation module may use allocation rules to dynamically initiate the shadow request based on various factors such as load balancing, user attributes, and/or other rules or logic. Thus, not all user requests may be issued as shadow requests.

Software testing using shadow requests

Techniques described enable creation of a single log containing information from both server-side and client-side logs. A logging service receives page requests and other data from a user operating a computing device. These page requests are then stored in a server-side log. The logging service also receives interactions made by the user on a displayed page. These interactions are then stored in a client-side log separate from the server-side log. The logging service then converts a format of the data in the client-side log and merges the client-side and server-side logs to form a single log. A service provider may then analyze this single log to track how this and other users navigated the pages associated with the page requests.

Merging client-side and server-side logs

This disclosure is directed in part to testing of different versions of software or software components (software versions) and analyzing results of use (e.g., user interaction) of the different software versions. The techniques described herein provide software testing that varies the allocation to enable a ramp up of allocations to/from another software version. The allocation module may use allocation rules to assign requests to each software version based on various factors such as load balancing, user attributes, past user assignment, and/or other rules or logic. An analysis of the different software versions may include an analysis of system performance resulting from operation of each software version. An analysis may determine attributes of each user and then allocate the user to a software version based on at least some of the determined attributes.

Software testing analysis and control

A user interface displays visual representations of user interaction with a series of nodes in a defined pipeline. The pipeline includes several nodes in a defined order with a starting node and an ending node. Each of the nodes is represented by a symbol, such as a circle, with elements, such as arrows, showing the movement of users to and from the respective nodes. User movement to a node outside of the pipeline nodes may also be shown with arrows or other elements. In some implementations such as web analytics, the nodes may be webpages and the users may be viewers who browse from one page to the next. One illustrative pipeline may be a series of webpages that a viewer interacts with in order to complete an online purchase.

Visual representation of user-node interactions

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

Disclosed are various embodiments for using relevance indications provided by a user in performing a search. Search results corresponding to a search query are provided to a user of a client computing device. The user provides a relevance indication corresponding to relevance of at least one of the search results. A subsequent search is performed which takes the relevance indication into account.

Determining search relevance from user feedback

Content displayed in a graphical user interface (GUI) such as a Web browser can be dynamically loaded and unloaded, or otherwise managed, based on factors such as a current view or focus position, allowing a user to view information in a list of objects without having to load all the objects at one time, which could slow down operation of the GUI.

Dynamic content management

Content displayed in a graphical user interface (GUI) such as a Web browser can be dynamically managed based on factors such as a current view or focus position, allowing a user to view information in a list of objects without having to load all the objects at one time, which could slow down operation of the GUI. Where a number of objects to be displayed is known, a page size can be set so that navigational elements of the GUI function as if all the objects are loaded, even though only a portion might be loaded at any given time. A background image can provide information where an object has not yet been loaded, providing a type of temporary content for the entire page without slowing down operation of the GUI.

Dynamic content navigation

An electronic marketplace for used digital objects is disclosed. Digital objects including e-books, audio, video, computer applications, etc., may be placed in a personalized data store upon purchase from a merchant. Placement may be manual or occur automatically. A server prompt or downloader application may be used to set preferences for manual or automatic placement. Placement may be direct or indirect. Direct placement occurs upon placement of the digital object in the personalized data store directly by the merchant. Indirect placement occurs upon transfer of the digital object to an intermediate storage location, such as the user's electronic device, then placing the digital object in the personalized data store. The user, via streaming, download, or other transfer, may then access content placed in the personalized data store.

Placing digital content in a personalized data store

An online data storage arrangement (or a collection of 'digital lockers') allows online users to share data with other online users. Digital lockers may include a number user created content objects and view objects. A content object comprises a link to one or more data objects that a user may wish to share with others. Such a content object may specify a plurality of permissions that manage what a user can or cannot do with the content object. A content object's permissions may dictate use of the content object when it is shared with other users. A view object is a medium that may be used to share content objects. A user may create a view object that references or includes one or more content objects. Users having authorized access to the view object may use the referenced/included content objects.

Online shared data environment

An online data storage arrangement allows online users to share data with other users having online connectivity. User digital lockers may include a number user created content objects and view objects. A content object is a link to one or more data objects that a user may wish to share with others. Such a content object may specify a plurality of permissions that manage what a user can or cannot do with the content object. A content object's permissions may dictate use of the content object when it is shared with other users. Or, a content object's permissions may dictate use of a copied version of the content object. A view object is a medium that may be used to share content objects. A user may create a view object that references or includes one or more content objects. Users having authorized access to the view object may use the referenced/included content objects.

Growing community of inventors

Issaquah, WA, United States of America

Peter Varnum Commons