Versata Development Group, Inc.

Trilogy Development Group, Inc.

Daniel Paul Karipides

Shawn A P Smith

Elizabeth M Schwartz

David R Middleton

Andrew S Price

John W Kamm

Susumu Harada

Stephen W Young

James H Ogden

Andrew C Schleit

A multi-dimensional recommended order system generates recommended orders for configurable products. The multi-dimensional recommended order system generates the recommended orders based on a multi-dimensional demand and sales metrics analysis. The multi-dimensional recommended order system determines the recommended order for each product configuration with a goal of moving a future supply mix of complex products to an optimized target supply mix. The recommended order can be generated and evaluated based on demand analysis of not only configured products but also based on analysis for demand of particular product dimensions. Thus, the system determines a recommended order for each configurable product by minimizing imbalances between future supplies and target supplies of product configurations and future supplies and target supplies of dimensions of the product configurations.

Determining recommended orders for configurable products on a multi-dimensional basis

A log file processing system sorts records from large log files and groups them by session without making a complete copy of the log files by capturing a subset of the log files in a sliding memory window and identifying all records in the window that form a complete user session. Records belonging to a complete session are output for analyzing, and the remaining records are output as raw log data for additional processing. Using a ring buffer to implement the sliding memory window, data structures are used to group records by session, to identify completed sessions, and to index into the ring buffer to retrieve records for completed sessions that are to be directly analyzed. Any records remaining in the ring buffer at the end of slide window processing may be output as raw log file data and are processed as incomplete or malformed session records. An embodiment of the log file processing system provides a significant improvement on the speed of data extraction from log files into analyzable session data.

Session-based processing method and system

Embodiments of the components and processes for recording selected Web site data described herein adhere to long-term stability guidelines so as to improve maintainability and viability over time. Additionally, the data recording components can be placed within a software architecture to minimize the number of redundant data recording components. Furthermore, the data recording components can be tailored to specific data recording purposes. Additionally, the data recording components can be designed to take into account system performance issues and minimally impact system performance. Furthermore, the data recording components can be tailored to gather specific data useful for various analytical processes. Additionally, embodiments of the data recording components are relatively easy to implement and able to handle idiosyncrasies and changes of various Web sites in which they are placed.

Data recording components and processes for acquiring selected web site data

A session quality system collects and evaluates data from multiple user world wide web ('web') product selection sessions and correlates the collected data with actual product purchases. The system assembles the data into a set of session profiles and assigns a score to each profile that represents a relative likelihood of purchasing the product. The system matches session data collected from future product selection sessions with a master profile. A weighted average of scores based on matched master profiles provides useful product demand information. Furthermore, the system can determine product demand with varying ranges of resolution. For example, the system can capture specific features of each product, such as color, audio system selections, and wheel types. Since these features can be common across multiple products, manufactures can expand their use of the high resolution product demand information.

Identifying quality user sessions and determining product demand with high resolution capabilities

A validation engine validates product demand data using novel supply-demand imbalance data. Validating the product demand data provides confidence that the demand data is accurate. Confidence in the demand data allows manufacturers, distributors, and others involved in commerce to rely upon the demand data for product manufacture and ordering. The validation engine correlates determined product demand data with product supply data to generate 'imbalance data'. The product supply data represents data for the same time period as the time period of the product demand data. The imbalance data is, in one embodiment, a difference between the product supply data and the product demand data. In one embodiment, the validation engine generates the imbalance data by subtracting product demand data from the supply data associated with the same product. Negative imbalance data indicates undersupplied products, and positive imbalance data indicates oversupplied products.

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Round Rock, TX, United States of America

Daniel Paul Karipides