Image analysis – Pattern recognition – Template matching
Reexamination Certificate
2006-05-02
2006-05-02
Miriam, Daniel (Department: 2625)
Image analysis
Pattern recognition
Template matching
Reexamination Certificate
active
07039238
ABSTRACT:
A model is used to represent a set of structured data objects that include elements at defined positions. The model includes distributions of vectors, each distribution corresponding to particular positions in the respective structured data objects, each of the vectors comprising values for the particular positions; and comparing a given set of structured data objects to the model to determine a likelihood that the given set is represented by the model. At least some of the distributions of the model differ such that different states of matching are indicated. Distributions of the model can indicate: dissimilarity between the structured data objects at defined positions; similarity between the structured data objects at defined positions; or similarity to a reference structure data object at defined positions.
REFERENCES:
patent: 5568563 (1996-10-01), Tanaka et al.
patent: 5600826 (1997-02-01), Ando
patent: 5701256 (1997-12-01), Marr et al.
patent: 5787414 (1998-07-01), Miike et al.
patent: 5873052 (1999-02-01), Sharaf
patent: 6128587 (2000-10-01), Sjolander
patent: 6314434 (2001-11-01), Shigemi et al.
patent: 6438496 (2002-08-01), Yoshida et al.
patent: 6606620 (2003-08-01), Sundaresan et al.
patent: 6618725 (2003-09-01), Fukuda et al.
Grate, L, et al., “Tutorial: Stochastic Modeling Techniques: Understanding and Using Hidden Markov Models” University of California, Santa Cruz, CA, pp. 1-34, Jun. 1996.
Grice, JA. Et al., “Reduced Space Sequence Alignment”,CABIOS, vol. 13, pp. 45-53, 1997.
Grundy, WN., et al. “Meta-MEME: Motif-Based Hidden Markov Models of Protein Families”, to appear inComputer Applications in the Biosciences, 1997.
Hughey, R. et al., “Hidden Markov Models for Sequence Analysis: Extension and Analysis of the Basic Method”, ReprintCABIOSvol. 12, pp. 95-107, 1996.
Hughey, R. et al., “SAM : Sequence Alignment and Modeling Software System”,Technical Report UCSC-CRL-96-22, University of California, Santa Cruz, CA, Jul. 1998.
Hughey, R., “Massively Parallel Biosequence Analysis”,Technical Report UCSC-CRL-93-14, University of California, Santa Cruz, CA, Apr. 1993.
Jagla, B. et al., “Adaptive Encoding Neural Networks for the Recognition of Human Signal Peptide Cleavage Sites”BIO, vol. 16, No. 3, Mar. 2000.
Karchin, R. et al., “Weighting Hidden Markov Models for Maximum Discrimination”,Bioinformatics, vol. 14, pp. 772-782, 1998.
Karchin, R., “Hidden Markov Models and Protein Sequence Analysis” from http://www.cse.ucsc.edu/research/compbio/ismb99.handouts//KK185FP.html printed from website Mar. 14, 2002.
Karplus, K. et al., “Hidden Markov Models for Detecting Remote Protein Homologies”,BIO Informatics, vol. 14, No. 10, pp. 846-856; Oct. 1998.
Karplus, K. et al., “Predicting Protein Structure Using Hidden Markov Models”,Proteins:Structure, Function, and Genetic, Suppl., pp. 134-139; Sep. 1997.
Krogh, A. et al., “Hidden Markov Models in Computational Biology. Applications to Protein Modeling”,J. Mol. Biol.vol. 235, pp. 1501-1531; Feb. 1994.
Krogh, A. et al., Predicting Transmembrane Protein Topology with a Hidden Markov Model: Application to Complete GenomesJournal of Molecular Biologyvol. 305, No. 3, pp. 567-580; 2001.
Ladunga, I., “Large-Scale Predictions of Secretory Proteins from Mammalian genomic and EST sequences”Analytical Biotechnology, pp. 13-18; 2000.
Lockless, SW. et al. “Evolutionarily Conserved Pathways of Energetic Connectivity in Protein Families”,Sciencevol. 286, pp. 295-299; Oct. 1999.
McClure, MA.et al., “Parameterization studes for the SAM and HMMER methods of hidden Markov model generation”,Proc. Fourth Int. Conf. Intelligent Systems for Molecular Biology, pp. 155-164, UNLV, Las Vegas.
Nielsen, H.et al., “Identification of Prokaryotic and Eukaryotic Signal Peptides and Prediction of Cleavage Sites”,Protein Engineeringvol. 10, No. 1, pp. 1-6; Jan. 1997.
Nielsen, H. et al. “Prediction of Signal Peptides and Signal Anchors by a Hidden Markov Model”,American Association for Artificial Intelligence ISMB, pp. 122-130; 1998.
Nielsen, H. et al. “Machine Learning Approaches for the Prediction of Signal Peptides and Other Protein Sorting Signals”,Protein Engineeringvol. 12, No. 1, pp. 3-9; Jan. 1999.
Paracel, “Hidden Markov Model”, from http://paracel.com/publications/hmm—white—paper.html printed from website Mar. 14, 2002.
Rabiner, LR., “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition”,Proceedings of the IEEE, vol. 77, No. 2, pp. 257-186; Feb. 1989.
Rholam, M. et al., “Role of Amino Acid Sequences Flanking Dibasic Cleavage Sites in Precursor Proteolytic Processing. The Importance of the First Residue C-terminal of the cleavage site”,Eur. J. Biochem.vol. 277, pp. 707-714; Feb. 1995.
Tarnas, C. et al., “Reduced space hidden Markov model training”,Bioinformatics, vol. 14. pp. 401-406, 1998.
UCSC Comp. Biol. Group, “Sequence Alignment and Modeling System” from http://www.cse.ucsc.edu/research/compbio/sam.html printed from website Mar. 14, 2002.
Baldi, P. et al., “Hidden Markov Models of Biological Primary Sequence Information”,Proc. Natl. Acad. Sci. USA,vol. 91, pp. 1059-1063; Feb. 1994.
Barrett, C. et al. “Scoring Hidden Markov Models”,CABIOS, vol. 13, No. 2, pp. 191-199; 1997.
Brakch, N. et al. “Favourable Side-Chain Orientation of Cleavage Site Dibasic Residues of Prohormone in Proteolytic Processing by Prohormone Convertase 1/3”,Eur. J. biochem.vol. 267, pp. 1626-1632; 2000.
Brown, M. et al., “Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families”,Proc. of First Int. Conf. on Intelligent Systems for Molecular Biology, pp. 47-55, Menlo Park, CA, Jul. 1993. AAAI/MIT Press.
Bucher, P. et al., “A Flexible Motif Search Technique based on Generalized Profiles”,Computers and Chemistry, vol. 20 pp. 3-24, Jan. 1996.
Chesneau, V. et al., “N-Arginine Dibasic Convertase (NRD Convertase): A Newcomer to the Family of Processing Endopeptidases”,Biochimicvol. 76, pp. 234-240; Paris, Mar. 1994.
Chou, K-C. et al., “Studies on the Specificity of HIV Protease: An Application of Markov Chain Theory”,Journal of Protein Chemistry, vol. 12, No. 6, pp. 709-724; 1993.
Chou, K-C., “Prediction of Human Immunodeficiency Virus Protease Cleavage Sites in Protein”,Analytical Biochemistryvol. 233, pp. 1-14; 1996.
Chou, K-C. et al., “Predicting Human Immunodeficiency Virus Protease Cleavage Sites in Proteins by a Discriminant Function Method”,Proteins:Structure, Function, and Geneticsvol. 24, pp. 51-72; 1996.
Eddy, SR., “Hidden Markov Models”,Current Opinion in Structural Biology, vol. 6, pp. 361-365, 1996.
Eddy, SR., “Profile Hidden Markov Models”,Bioinformatics, vol. 14,review of HMMs1998.
Eddy, SR. et al., “Maximum Discrimination Hidden Markov Models of Sequence Consensus”,J. Computationsl Biologyvol. 2 pp. 9-23, 1994.
Eddy, SR., “Multiple Alignment Using Hidden Markov Models”,Proc. Third Int. Conf. Intelligent Systems for Molecular Biology, AAAI Press, Menlo Park, pp. 114-120. PostScript; 1995.
Hunt, M. “Automatic Identification of Spoken Names and Addresses-and why we should abolish account number”, Novauris, A James Baker Company Presentation, www.novauris.com, Date Unknown.
Karp Peter D.
Lincoln Patrick Denis
Sonmez Kemal
Toll Lawrence R.
Miriam Daniel
Patterson & Sheridan, LLP.
Sri International
Tong, Esq. Kin-Wah
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