Amazon Technologies, Inc.

Michael Christopher Wenneman

Samuel Scott Gigliotti

Marco Argenti

Michael Zohar Podwal

Christophe Charpentier

Kunal Bhasin

Bhavesh Anil Doshi

Robert King Myers

Wei Yu

Clayton Matthew Magouyrk

Anand Chakraborty

Ronald Anthony Kornfeld

Paul J Furio

Scott Allen Dixon

Nicholas John Morren

Scott Robert Parish

Aaron D Kujat

David Adam Edelstein

Samir Bhayyaji Khobragade

Nengwu Zhu

Joshua Stephen Ullom

Robin Kim Har

Lorian MacLean Kiesel Taylor

Hyen Vui Chung

Dmytro Ivashchenko

Qihui Li

Joseph S Trelin

Nathan Manning

Yubai Di

Vikram Gul Advani

Richard John Muszynski

A vehicle information service implemented on one or more computers of a service provider network implements a first application programmatic interface (API) that allows a client to define inclusion parameters and a sample size for a fleet of vehicles from which vehicle data is to be collected. The vehicle information service also implements a second API that notifies the client when the requested vehicle data has been collected from the vehicle fleet. Additionally, the vehicle information service provides the client access to the collected vehicle data. The vehicle information service manages the collection of the vehicle data from the client defined vehicle fleet without requiring further client involvement and notifies the client when the collection of the vehicle data is complete.

Vehicle fleet information service

A stream of storage requests can be received for data objects stored by a storage service. A streaming algorithm can be utilized to identify the most frequently accessed objects stored by the storage service. A statistical distribution of the most frequently accessed objects can then be generated and utilized to estimate the number of infrequently accessed objects. Machine learning can also be utilized to identify correlations between attributes of objects stored by the storage service and their associated access rates. For instance, machine learning can be utilized to determine that objects stored in a certain location or having other characteristics typically have low access rates. Information regarding the number of infrequently accessed objects and their learned attributes can be utilized to take action with regard to the infrequently accessed objects, such as moving the infrequently accessed objects to long-term storage.

Network service for identifying infrequently accessed data in a data stream

In large distributed computing environments, application execution may be distributed between a plurality of groups, the plurality of groups containing a set of host computer systems responsible for the execution of one or more operations of the application. Group membership may be determined by generating configuration information based at least in part on the plurality of groups. The configuration information may be provided to a plurality of host computer systems and each host computer system of the plurality of host computer systems may determine membership to a particular group of the plurality of groups based at least in part on the configuration information.

Membership self-discovery in distributed computing environments

Disclosed are various embodiments for facilitating item returns on behalf of customers of an electronic commerce site. Embodiments of the disclosure can, in one example, facilitate item returns on behalf of a buyer where the seller is a seller in a multi-seller marketplace. A return shipping label can be generated on behalf of the seller as well as communications between buyer and seller archived in the event of a subsequent dispute.

Returns in a multi-seller marketplace

Various embodiments can predict a user's intended driving route in order to provide the user with traffic warnings for traffic conditions along the same. A user's driving route, in at least one embodiment, is predicted by collecting travel data, such as information associated with the date, time, location, and direction for trips made within a network of roads over time. Instead of keeping the travel data anonymous, the travel data is associate or linked to the user's account or stored in a user profile in order to build a history of travel patterns for the user over time. The travel patterns can then be used to predict when a user is going to travel or make a trip and, upon identifying a context indicative of a travel pattern, traffic information for a route associated with the pattern is obtained and provided to the user's computing device.

Predictive travel notifications

Theme-differentiated maps are generated from conventional two and three-dimensional mapping data. Dynamic cartography models are applied to the data to deliver maps with stylized topographies. In response to a search request from a client device for a map, points-of-interest within a geographic area resulting from the search are identified. Renderable representations of the points-of-interest are altered to differentiate the search results from other features in the geographic area. The resulting renderable representations are then transmitted to the client device for rendering.

Dynamic cartography mapping system

Various embodiments provide a method for randomly selecting a region on a map for testing and a map of the region can be generated using multiple map rendering engines. A screenshot of each of the generated maps can be obtained and text associated with map labels, such as street, city, and attraction names, can be recognized using an optical character recognition (OCR) engine. At this point, the recognized text from each rendering engine can then be compared to identify at least one error or inconsistency. In at least one embodiment, categories of errors that need most attention in the specific geographic areas can be identified and a human quality assurance tester can isolate these instances and narrow down the same to identify the rendering or data problem.

Identifying rendering differences between label rendering engines

Various embodiments enable a user to navigate within an application where features of the navigation vary depending on a context of the user or particular features of the application. For example, a user could request directions, in a turn-by-turn mode of the computing device, to a destination. Accordingly, mapping information for a region and a route to the destination through the region can be displayed. Along with the mapping information, a navigation element can be displayed that enables the user to navigate through the mapping information. In one example, the navigation element is displayed off-center from the mapping information such that it does not obscure the mapping information while the user provides touch inputs to navigate within the same.

Contextual based navigation element

Various embodiments provide a graphical element displayed through a mapping application that visually represents at least one point of interest (POI) data point on a map for a location. In one example, a single multi-dimensional graphical element can identify a location and identity of multiple POIs associated with the same geocode or physical location on a map, such as a multi-tenant building. In this example, each surface of the graphical element may represent a different POI. In another example, a multi-dimensional graphical element can identify a location and information for a single POI. In this example, each surface can represent different information, such as a surface for customer reviews, a surface for store hours, address, or contact information, a surface for a restaurant's menu, a surface for promotions, and the like.

User interface points of interest approaches for mapping applications

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Seattle, WA, United States of America

Michael Christopher Wenneman