Art Technology Group, Inc.

Oracle Otc Subsidiary LLC

Digitalscirocco, Inc.

Cleverset, Inc.

Robust Decisions

Bruce Douglass D'Ambrosio

Michael Wellman

David G Ullman

Stephen P Jensen

Embodiments include methods, apparatus, and systems associated with providing request-time auctioning of web content and web services. The auction may be performed by a request-time web content auctioning service, which may accept bids of content and/or services from content and/or service providers, and may auction these bids against offers of space on webpages in websites. The auction may, in various embodiments, be performed in real time, such as, for example, during generation of a webpage in response to a request to view a webpage from a visitor to the webpage. The auction service may, in various embodiments, also provide for the auctioning of web services for webpages, such as, for example, content filtering. Other embodiments may be described and claimed.

Dynamic webpage generation including request-time auctioned web content

The present invention includes relational Bayesian network-based tracking methods. Various, distinct embodiments of the present invention include tracking methods for: real-time relational tracking of e-commerce segmentation and personalization, in-residence tracking of elderly and disabled people, real-time relational tracking for ground target tracking, and real-time relational tracking of satellites and satellite constellations.

Tracking methods and systems that employ bayesian networks

A method and apparatus for calculating a score for word selection, which may be used to preprocess sets of words prior to a dimensionality reduction process, employs information about relationships between words themselves (such as synonym relationships) or relationships between items with which the words are associated (such as products in a catalog). In some embodiments, the relationships are also community based; i.e., the relationships are established by a community of users. The relationships may be references to two or more word sets in which the word of interest is common. In one embodiment, the word sets are descriptions of products in an online catalog, the community is the group of people who view the catalog, and the relationships used for calculating the score for a particular word of interest are coreferences (e.g., viewing or purchasing) of pairs of products for which the catalog descriptions both include the particular word.

Community-driven relational filtering of unstructured text

Embodiments may provide for multi-attribute auctioning. Bids and offers which comprise non-price attributes may be received by an auction service, such as web content bids and offers for space on webpages. The service may evaluate attributes of bids and offers in a quantitative manner, developing a value for the bid and offer that includes both price information and other non-price attributes. Valuation may be performed through the use of willingness-to-pay functions which, based on a knowledge of an agent, may determine a numerical value for that agent's willingness to pay according to the non-price attributes. The result from evaluating this function may then be used to determine which auction bids and offers may be matched. The bid or offer can include one or more probability distributions for future performance, based on the published statistics about past performance of agents. Other embodiments may be described and claimed.

Multi-attribute web content auctions

The present invention provides a language, method and system to formulate and evaluate relational Bayesian networks in an e-commerce environment. The present invention employs a specific language for constructing synthetic variables used to predict events in the Bayesian networks. The present system and language allow for efficient and accurate representation, inference, and discovery of the synthetic variables used to model web visitor behavior.

Relational Bayesian modeling for electronic commerce

The present invention provides a language, method and system to formulate and evaluate relational Bayesian networks in an c-commerce environment. The present invention employs a specific language for constructing synthetic variables used to predict events in the Bayesian networks. The present system and language allow for efficient and accurate representation, inference, and discovery of the synthetic variables used to model web visitor behavior.

The present invention includes relation-Bayesian-network-based tracking methods. Various, distinct embodiments of the present invention include tracking methods for: real-time relational tracking of e-commerce segmentation and personalization, in-residence tracking of elderly and disabled people, real-time relational tracking for ground target tracking and real-time relational tracking of satellites and satellite constellations.

Modeling and automatic filtering/tracking real-time events using dynamic relational bayesian networks

System and method of using synthetic variables to generate relational Bayesian network models of internet user behaviors

To identify the best choice from a set of alternatives, team members select the criteria to be used in identifying the best choice. Each criterion describes at least one feature of the alternatives. Each team member then, for each alternative, defines a target value for the feature of the criteria and sets a level of knowledge about the feature of the criteria. Using the target values and the team member's knowledge of each feature, each team member estimates how well the alternatives meet the target values for the features. The system then calculates an expected utility for each alternative based on the team member's estimations and a probability that each alternative is the best choice. Finally, based on the expected utility and probability that each alternative is best, a best choice is identified.

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Seattle, WA, United States of America

Bruce Douglass D'Ambrosio