Versata Development Group, Inc.

Trilogy Development Group, Inc.

James Singh

Neil C Thier

Chris Hyams

Thomas Wyatt Wilbur

Joshua P Walsky

Andrew Leamon

Jeffrey R VanDyke

Christopher M Connors

Andrew F Miller

Thomas H Dillon

Sherrill J Packebush

Timothy Paul Darr

Michael K Grant

Jeremy S Wacksman

Alex Holm Devine

Nirad Sharma

Jacob D Leffler

A recommendation appliance, system and method are provided for generating and deploying additional web page content or functionality (e.g., retail recommendations) to an existing web page server system. For example, the present invention may be embodied as a reverse proxy server that is inserted as an intermediate network node between a web server and the end users accessing the web server. In this position, the recommendation appliance can introduce recommendation messages to web pages generated by the web server without requiring any modification to the code or architecture of the web server. In addition, the appliance may separately track the transaction activities of end users who receive recommendation messages and the transaction activities of end users who do not receive recommendation messages, so that a comparison of the effectiveness of the recommendations may efficiently be demonstrated without requiring any modification to the code or architecture of the web server.

System and method for integrated recommendations

A method of comparing products is disclosed. The method includes selecting a first configuration (which represents a first product with a first attribute), selecting a second configuration (which represents a second product with a second attribute), and displaying the first attribute and the second attribute. The first attribute is defined in the first configuration and the second attribute is defined in the second configuration. The first and second products may be, for example, vehicles of some sort.

Method and apparatus for product comparison

Method for integrated recommendations

A data processing system generates recommendations for on-line shopping by scoring recommendations matching the customer's cart contents product page, configuration page, email and/or history using predetermined scoring criteria, such as item availability, item price, relevance of the item to the customer, item clarity, item exposure, total purchase amount in the cart, margin, recommendation confidence and lift, shopper profile matches, seasonality, customer history, time dependency, product marketing emphasis, substitutability, item popularity and/or product attributes associated with the recommended item. The scored recommendations may be ranked and/or filtered to prioritize the recommendations so that the best recommendation is selected for presentation to the customer. In addition, one or more scoring criteria or product attributes for the recommended item may be used to select a specific selling point/message text for inclusion with the selected recommendation.

System and method for generating effective recommendations

A data processing system normalizes data sets (such as low-resolution transaction data) into high-resolution data sets by mapping generic information into attribute-based specific information that is stored in a database. By establishing a shared domain model for representing items in the recommendation context, catalog and quote history with common terms and concepts, a recommendation engine operating in the shared domain may process the attribute-based representations to make specific and relevant recommendations to the customer. In addition, when certain attribute values are normalized over time, recommendations derived from past order history can be intelligently applied to current orders. The normalized representation of elements in the shared domain may also be used to generate compelling selling point text for each recommendation that is specific to the marketing objectives of the seller and identifies the objectives of the buyer.

Retail recommendation domain model

A computer system provides product selections to a user according to a needs analysis. A database stores pre-generated product configurations and product configuration information. The product configuration information includes product features and product rules governing allowable combinations of the product features. Received product related data can include different types of information such as attribute information and product identifier information. The received product related data is processed to determine the type of received information. If the type is attribute information, the received product related data is filtered in accordance with the product configuration information and one or more product configurations that meet requirements of the received attribute information is identified. If the type is product identifier information, one or more of the pre-generated product configurations that corresponds to the received product related data is identified. Each identified product configuration and each identified pre-generated product configuration is provided to the user.

Method and apparatus for sorting products by features

A method of comparing products is disclosed. The method includes selecting a first configuration representing a first product with a first attribute, selecting a second configuration representing a second product with a second attribute, and displaying the first attribute and the second attribute. As will be noted, the first attribute is defined in the first configuration, and the second attribute is defined in the second configuration.

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Austin, TX, United States of America

James Singh