Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
2008-06-04
2010-11-02
Holmes, Michael (Department: 2129)
Data processing: artificial intelligence
Neural network
Learning task
Reexamination Certificate
active
07827128
ABSTRACT:
In accordance with the invention, a system, method, and apparatus for analyzing advertisement-related data are presented, which may include receiving data related to an aspect of an advertisement and modeling the aspect of the advertisement with a mathematical model. The mathematical model may include a control-signal-related component, a control-signal-independent component, and an error component. Each component may be updated based on at least one of a control signal, the received data, and a previous state of at least one of the components. An updated model may be created based on the updated components. The system, method, and apparatus may also include predicting the aspect of the advertisement using the updated model. Exemplary aspects of and data related to the advertisement may include one or more of the following: a number of impressions, “clicks,” or “conversions” and/or the impression-to-conversion, impression-to-click, or click-to-conversion ratios.
REFERENCES:
patent: 7039598 (2006-05-01), Tobin et al.
patent: 7085682 (2006-08-01), Heller et al.
patent: 7251589 (2007-07-01), Crowe et al.
patent: 7319973 (2008-01-01), Tobin et al.
patent: 7349827 (2008-03-01), Heller et al.
patent: 7457823 (2008-11-01), Shraim et al.
patent: 7460065 (2008-12-01), Ogawa et al.
patent: 7523085 (2009-04-01), Nigam et al.
patent: 7596552 (2009-09-01), Levy et al.
patent: 7600017 (2009-10-01), Holtzman et al.
patent: 7660783 (2010-02-01), Reed
patent: 7698335 (2010-04-01), Vronay
patent: 7711808 (2010-05-01), Parry
patent: 7725414 (2010-05-01), Nigam et al.
patent: 7730120 (2010-06-01), Singh et al.
patent: 7739335 (2010-06-01), Siegel et al.
patent: 2002/0165756 (2002-11-01), Tobin et al.
The Revenue Model of Online Third-Party Payment Based on Game Theory, Yiqun Li; Xuezheng Zhang; Management of e-Commerce and e-Government, 2008. ICMECG '08. International Conference on Digital Object Identifier: 10.1109/ICMECG.2008.53 Publication Year: 2008 , pp. 349-353.
Using Naà ve Bayes Model and Natural Language Processing for Classifying Messages on Online Forum, Do Phuc; Nguyen Thi Kim Phung; Research, Innovation and Vision for the Future, 2007 IEEE International Conference on Digital Object Identifier: 10.1109/RIVF.2007.369164 Publication Year: 2007, pp. 247-252.
Improving Text Classification Performance by Heuristics-Guided Exploration of Derived Features, Wong, A.K.S.; Lee, J.W.T.; Yeung, D.S.; Machine Learning and Cybernetics, 2006 International Conference on Digital Object Identifier: 10.1109/ICMLC.2006.258468 Publication Year: 2006 , pp. 3323-3328.
Information modeling for single sourcing, Kostur, P.; Professional Communication Conference, 2000. Proceedings of 2000 Joint IEEE International and 18th Annual Conference on Computer Documentation (IPCC/SIGDOC 2000) Digital Object Identifier: 10.1109/IPCC.2000.887291 Publication Year: 2000 , pp. 333-342.
Batiz-Mendoza Jaime
Karlsson Niklas
Zhang Jianlong
AOL Advertising Inc.
Finnegan Henderson Farabow Garrett & Dunner LLP
Holmes Michael
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