Data processing: speech signal processing – linguistics – language – Speech signal processing – Recognition
Reexamination Certificate
2006-03-17
2011-10-04
Rider, Justin (Department: 2626)
Data processing: speech signal processing, linguistics, language
Speech signal processing
Recognition
C704S231000, C704S251000, C704S270000, C704S275000, C706S025000
Reexamination Certificate
active
08032375
ABSTRACT:
A generic predictive argument model that can be applied to a set of shot values to predict a target slot value is provided. The generic predictive argument model can predict whether or not a particular value or item is the intended target of the user command given various features. A prediction for each of the slot values can then be normalized to infer a distribution over all values or items. For any set of slot values (e.g., contacts), a number of binary variable s are created that indicate whether or not each specific slot value was the intended target. For each slot value, a set of input features can be employed to predict the corresponding binary variable. These input features are generic properties of the contact that are “instantiated” based o n properties of the contact (e.g., contact-specific features). These contact-specific features can be stored in a user data store.
REFERENCES:
patent: 4713778 (1987-12-01), Baker
patent: 4748670 (1988-05-01), Bahl et al.
patent: 5005203 (1991-04-01), Ney
patent: 5479576 (1995-12-01), Watanabe et al.
patent: 6044347 (2000-03-01), Abella et al.
patent: 6233570 (2001-05-01), Horvitz et al.
patent: 6262730 (2001-07-01), Horvitz et al.
patent: 6301560 (2001-10-01), Masters
patent: 6388592 (2002-05-01), Natarajan
patent: 6434523 (2002-08-01), Monaco
patent: 6694296 (2004-02-01), Alleva et al.
patent: 6836760 (2004-12-01), Bellegarda et al.
patent: 6839680 (2005-01-01), Liu et al.
patent: 6957184 (2005-10-01), Schmid et al.
patent: 6993586 (2006-01-01), Chen et al.
patent: 7031908 (2006-04-01), Huang et al.
patent: 7200559 (2007-04-01), Wang
patent: 7203909 (2007-04-01), Horvitz et al.
patent: 7389234 (2008-06-01), Schmid et al.
patent: 7415414 (2008-08-01), Azara et al.
patent: 7451079 (2008-11-01), Oudeyer
patent: 7454717 (2008-11-01), Hinckley et al.
patent: 7461059 (2008-12-01), Richardson et al.
patent: 7467353 (2008-12-01), Kurlander et al.
patent: 7472118 (2008-12-01), Hirst
patent: 7509321 (2009-03-01), Wong et al.
patent: 7512906 (2009-03-01), Baier et al.
patent: 7526794 (2009-04-01), Chand et al.
patent: 7539724 (2009-05-01), Callaghan
patent: 7565663 (2009-07-01), Knight et al.
patent: 2001/0016814 (2001-08-01), Hauenstein
patent: 2001/0047265 (2001-11-01), Sepe, Jr.
patent: 2002/0013706 (2002-01-01), Profio
patent: 2002/0123876 (2002-09-01), Pokhariyal et al.
patent: 2002/0152071 (2002-10-01), Chaiken et al.
patent: 2003/0009335 (2003-01-01), Schalkwyk et al.
patent: 2003/0195739 (2003-10-01), Washio
patent: 2004/0023656 (2004-02-01), Purdy et al.
patent: 2004/0254957 (2004-12-01), Hyotyniemi et al.
patent: 2004/0267518 (2004-12-01), Kashima et al.
patent: 2005/0038650 (2005-02-01), Bellegarda et al.
patent: 2005/0154580 (2005-07-01), Horowitz et al.
patent: 2006/0009973 (2006-01-01), Nguyen et al.
patent: 2006/0025997 (2006-02-01), Law et al.
patent: 2006/0129396 (2006-06-01), Ju et al.
patent: 2006/0129397 (2006-06-01), Li et al.
patent: 2006/0173686 (2006-08-01), Hwang
patent: 2006/0190253 (2006-08-01), Hakkani-Tur et al.
patent: 2006/0277031 (2006-12-01), Ramsey
patent: 2007/0233497 (2007-10-01), Paek et al.
patent: 2007/0239453 (2007-10-01), Paek et al.
patent: 0899737 (1999-03-01), None
patent: 2390930 (2004-01-01), None
U.S. Appl. No. 11/170,998, filed Jun. 29, 2005, Paek et al.
U.S. Appl. No. 10/718,138, filed Nov. 20, 2003, Wang.
U.S. Appl. No. 09/585,834, filed Jun. 1, 2000, Huang.
U.S. Appl. No. 10/447,399, filed May 29, 2003, Wang.
Chickering. “A Bayesian approach to learning Bayesian networks with local structure.” (1997) Proceedings of the 13th Conference on Uncertainty in Artifical intelligence, pp. 80-89.
“Speech Recognition grammar specification version 1.0.” http://www.w3.org/TR/speech-grammar/ last viewed Mar. 17, 2006, 88 pages.
Horvitz, et al. “Harnessing Models of Users' Goals to Mediate Clarification Dialog in Spoken Language Systems.” (2001) 11 pages.
Horvitz, et al. “In pursuit of effective hansdfree decision support: coupling Bayesian interface, speech understanding, and user models” (1995) pp. 3-13.
Johansson. “User Modeling in dialog systems” (2002) Technical Report SAR 02-2, Santa Anna IT Research, 24 pages.
Oviatt. “Predicting hyperarticulate speech during human-computer error resolution” (1998) Speech Communication,. pp. 87-110.
Paek, et al. “Conversation as action uncertainty” (2000) Proceedings of the 16th Conference on Uncertainty in Artifical Intelligence, pp. 455-464.
Roseenfeld. “Two decades of statistical language modeling: where do we go from here?” (2000) Proceedings of the IEEE, 11 pages.
Rosenfeld. “Universal speech interfaces” (2001) Interactions, pp. 34-44.
Webb, et al. “Machine Laerning for User Modeling” (2001) User Modeling and User-Adapted Interaction, pp. 19-29.
Widmer, et al. “Learning in the presence of concept drift and hidden contexts” (1996) Machine Learning, pp. 69-101.
Chickering. Technical Report MSR-TR-2002-103. (2002) 3 pages.
Wang, et al. “Improved Name Recognition with User Modeling” Proceedings of the Eurospeech Conference (2003) 4 pages.
Fonix-Freedom of Speech. “About Fonix VoiceDial” http://www.fonixspeech.com/pages/voicedial.php last viewed Mar. 17, 2006,1 page.
Fonix-Freedom of Speech. “About Fonix VoiceDial 2.1” http://www.fonixspecch.com/pages/voicedial2—1.php last viewed Mar. 17, 2006, 1 page.
Fonix-Freedom of Speech. “About Fonix VoiceCentral” http://www.fonixspeech.com/pages/voicecentral.php last viewed Mar. 17, 2006, 1 page.
Jelinek. “Statistical Methods for Speech Recognition” MIT Press Cambridge, MA (1997).
Woods. “Language Processing for Speech Understanding” Computer Speech Processing. Prentice-Hall International (1983) pp. 304-334.
Akiba et al., “Selective Back-Off Smoothing for Incorporating Grammatical Constraints into the N-Gram Language Model”, Proceedings of the International Conference on Spoken Language Processing, Sep. 2002, vol. 2, pp. 881-884.
Balakrishna et al., “Automatic Creation and Tuning of Context Free Grammars for Interactive Voice Response Systems”, Proceeding of NLP-KE'05, IEEE, Sep. 2005, 158-163.
Bell et al., “Real-time Handling of Fragmented Utterances”, In the Proceedings of NAACL, Jun. 2001, 7 pgs.
Billsus, et al., “A Personal News Agent that Talks, Learns and Explains”, Proceedings of the 3rd Annual Conference on Autonomous Agents, May 1999, pp. 268-275.
Dusan et al., “Adaptive Dialog Based Upon Mulitmodal Language Acquistition”, Proceedings of the Fourth IEEE International Conference on Multimodal Interfaces, Oct. 2002, 6 pgs.
Gorrell, “Using Statistical Language Modelling to Identify New Vocabulary in a Grammar-Based Speech Recognition System”, Proceedings of the 8th European Conference on Speech Communication and Technology (Eurospeech), Sep. 2003, pp. 2729-2732.
Han, et al., “Synthetic Character with Bayesian Network and Behavior Network for Intelligent Smartphone”, Department of Computer Science, Yonsei University, Korea, KES, Sep. 2005, LNAI 3681, pp. 737-743.
Hockey et al., “Targeted Help for Spoken Dialogue Systems: Interlligent feedback improves naive users' performance”, Proceedings of the EACL, Apr. 2003, pp. 147-154.
Kim, et al., “A Keyword Spotting Approach Based on Pseudo N-gram Language Model”, SPECOM, 9th Conference Speech and Computer, Sep. 2004, 4 pgs.
Knight et al., “Comparing grammar-based and robust approaches to speech understanding: a case study”, Eurospeech 2001, Scandinavia, Sep. 2001, 4 pgs.
Office Action dated Dec. 10, 2008 for U.S. Appl. No. 11/278,899, 18 pages.
Office Action dated Nov. 28, 2008 for U.S. Appl. No. 11/278,893, 21 pgs.
Paek et al, “Optimizing Automated Call Routing by Integrating Spoken Dialog Models with Queuing Models,” Proc. of HLT-NAACL, May 2004, pp. 41-48.
Souvignier,
Chickering David M.
Paek Timothy S.
Lee & Hayes PLLC
Microsoft Corporation
Rider Justin
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