Amazon Technologies, Inc.

Llewellyn James Mason

Francis J Kane, Jr

Peter Varnum Commons

Daniel Bruce Lloyd

Peter Thomas Killalea

Adam Brent Johnson

Olga I Kuznetsova

Min Chong

Brian Chang

John Riley Lynch

Francisco Jose Peredo Noguez

Brandon William Porter

Tom Killalea

Ron Kohavi

Himanshu Khurana

Samuel A Minter

Tyson Christopher Trautmann

David John Edwards, Jr

Elton Victor Pinto

Scott James McKee

Kelly L Duggar

Valentin Radu Munteanu

Pedrito Uriah Maynard-Zhang

Arne C Knudson

In Jae Lee

Kelly L Wiggins

Tony Jay Lee

An interactive user interface is disclosed that enables users to efficiently access and/or submit component-specific reviews of components of a product. The user interface displays an image of the product, and displays region identifiers that identify image regions corresponding to product components that are separately reviewable. When a user selects a particular region, the user interface may display component-specific reviews of the corresponding component, and/or may display an option to submit a component-specific review of the component. The user interface may be included in an interactive catalog page, such as a web page.

System with interactive user interface for efficiently accessing component-level reviews

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

Methods, devices and program products are provided which comprise the under control of one or more computer systems configured with specific executable instructions. The methods, devices and program products provide an image of an item for display, and the item is comprised of a plurality of components. The methods, devices and program products receive a selection of a component of interest from the plurality of components of the item, and obtain a component-level rating associated with the component of interest.

Component-level rating system and method

Content items, such as e-books, audio files, video files, etc., may be recommended to a user based on the user's presence at a geolocation or venue. Geolocation is the geospatial location of the user, while a venue is a designated area for an activity, such as a concert hall, aircraft, waiting room, etc. The recommendations may be of content items relating to the geolocation or venue, or they may be content items being accessed by others who are, or have been, in approximately the same geolocation or venue.

Recommendation of media content items based on geolocation and venue

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

Campaign messages may be prioritized for a given potential recipient by generating one or more learning structures associated with the campaign message. The learning structure(s) is then evaluated relative to the potential recipient to determine potential effectiveness of the campaign messages for the potential recipient. One such learning structure is a decision tree comprising a hierarchy of attributes associated with past recipients of the campaign message. Attributes associated with the potential recipient are then applied to the decision tree to obtain a metric of potential value of the campaign message to the potential recipient.

Personalized campaign planner

The controversiality of an offering in a computer implemented system is computed based on user satisfaction feedback. A controversiality index can be provided to indicate the extent to which the offering is controversial.

Method and system for determining whether an offering is controversial based on user feedback

A decision tree is provided as a machine learning classifier to predict a user attribute, such as a geographical location of a user, based on a network address. More specifically, the decision tree is constructed via machine learning on a set of sample data that reflects a relationship between a network address and a user attribute of a 'known user' whose profile information is recognizable. For a given network address, the decision tree can be used as a machine learning classifier to predict the most likely user attribute of a potential user. With the predicted attribute, a network service can target a group of potential users for various campaigns without recognizing the identities of the potential users.

Predicting geographic location associated with network address

Growing community of inventors

Seattle, WA, United States of America

Llewellyn James Mason