Amazon Technologies, Inc.

Other

Terrence R Nightingale

Marvin Michael Theimer

Sameer R Rajyaguru

Scott Allen Mongrain

James G Robinson

Brandon William Porter

Logan Luyet Dillard

Olga I Kuznetsova

Jason Roselander

Thomas Lowell Keller

Disclosed are various embodiments for extracting an excerpt from a representative review of an item, such as an item available for purchase in an electronic commerce system. Attributes or categories used in reviews of an item may be identified and ranked according to consumer preference. Upon ranking the categories, an excerpt may be extracted from a review corresponding to a ranked one of the attributes or categories. The excerpt may be identified and extracted if a number of reviews for an item exceeds a threshold quantity as it may be impractical for a user to read every review written about the item.

Automatic review excerpt extraction

Comparative decision systems and methods are disclosed for gathering and mining data representative of purchase decisions. One disclosed comparative decision system detects when a user is comparing items and provides the user with the ability to create a research note storing comparative information for the alternative items. The system displays information about items according to a variety of factors. The user can customize the factors and enter information for each alternative item according to the various factors. Some information may be pre-populated by the system. The research note may be made visible to other users, and may be suggested to another user based on the note's expected helpfulness and relevance to that user. One disclosed method for mining data stored within research notes identifies which factors are given higher relative priorities by users considering a purchase. Another mining method analyzes the effects of price changes on item popularity.

Method and system for exposing research note data based upon user activity data

A computing system for searching for and identifying data repository deficits includes an interactive database of items, a data repository that stores a first type of information related to items, a search engine configured to identify items that do not have an amount of the first type of information that meets a first threshold, a candidate selection interface that provides functionality for the user to select an item from recommended candidate items needing the first type of information, a user interface via which the user can provide the first type of information for an item selected by the user, a game system configured to calculate for a game scores and rankings based at least in part on data stored in the data repository.

Methods and systems for searching for and identifying data repository deficits

Disclosed are various embodiments for extracting a representative review excerpt from a plurality of reviews associated with an item available for purchase in an electronic commerce system. A review extractor identifies a lexicon appearing in the reviews and associates each of the reviews with at least one category where the categories are based on the identified common lexicon. The review extractor then ranks the categories according to a ranking of attributes specified by consumer preferences. Upon ranking the categories, the review extractor then extracts a representative review excerpt based on a correlation between the number of reviews associated with each category and the ranking of that category as indicated by the consumer preferences.

System for mining research data

System for enabling electronic catalog users to create and share research data

Method for exposing research note data based upon user activity data

Techniques described enable an entity, such as a company employing an e-commerce website, to leverage user-provided content, such as customer reviews of an item, to better customers' shopping experiences. To do so, customer reviews pertaining to an item may be examined to determine if the reviews contain links to other items. These links within the customer reviews may then be aggregated and sorted (e.g., ranked) according to certain criteria. The links may be sorted based on a number of times that the links are used in the reviews and/or on ratings of the items associated with the links. One or more of the links may then be displayed on the website. For instance, these links may appear on an item review page adjacent the customer reviews. Customers navigating to the item review page may then peruse the customer reviews as well as the displayed links that customers have used within the reviews.

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Marysville, WA, United States of America

Terrence R Nightingale