Heuristic method of classification

Data processing: artificial intelligence – Machine learning

Reexamination Certificate

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C706S014000, C706S900000

Reexamination Certificate

active

11273432

ABSTRACT:
The invention concerns heuristic algorithms for the classification of Objects. A first learning algorithm comprises a genetic algorithm that is used to abstract a data stream associated with each Object and a pattern recognition algorithm that is used to classify the Objects and measure the fitness of the chromosomes of the genetic algorithm. The learning algorithm is applied to a training data set. The learning algorithm generates a classifying algorithm, which is used to classify or categorize unknown Objects. The invention is useful in the areas of classifying texts and medical samples, predicting the behavior of one financial market based on price changes in others and in monitoring the state of complex process facilities to detect impending failures.

REFERENCES:
patent: 4122343 (1978-10-01), Risby et al.
patent: 4122518 (1978-10-01), Castleman et al.
patent: 4697242 (1987-09-01), Holland et al.
patent: 4881178 (1989-11-01), Holland et al.
patent: 5136686 (1992-08-01), Koza
patent: 5352613 (1994-10-01), Tafas et al.
patent: 5553616 (1996-09-01), Ham et al.
patent: 5649030 (1997-07-01), Normile et al.
patent: 5687716 (1997-11-01), Kaufmann et al.
patent: 5697369 (1997-12-01), Long, Jr. et al.
patent: 5716825 (1998-02-01), Hancock et al.
patent: 5719060 (1998-02-01), Hutchens et al.
patent: 5790761 (1998-08-01), Heseltine et al.
patent: 5839438 (1998-11-01), Graettinger et al.
patent: 5905258 (1999-05-01), Clemmer et al.
patent: 5946640 (1999-08-01), Goodacre et al.
patent: 5974412 (1999-10-01), Hazlehurst et al.
patent: 6025128 (2000-02-01), Veltri et al.
patent: 6081797 (2000-06-01), Hitt
patent: 6114114 (2000-09-01), Seilhamer et al.
patent: 6128608 (2000-10-01), Barnhill
patent: 6157921 (2000-12-01), Barnhill
patent: 6225047 (2001-05-01), Hutchens et al.
patent: 6295514 (2001-09-01), Agrafiotis et al.
patent: 6329652 (2001-12-01), Windig et al.
patent: 6427141 (2002-07-01), Barnhill
patent: 6493637 (2002-12-01), Steeg
patent: 6558902 (2003-05-01), Hillenkamp
patent: 6571227 (2003-05-01), Agrafiotis et al.
patent: 6579719 (2003-06-01), Hutchens et al.
patent: 6615199 (2003-09-01), Bowman-Amuah
patent: 6631333 (2003-10-01), Lewis et al.
patent: 6675104 (2004-01-01), Paulse et al.
patent: 6680203 (2004-01-01), Dasseux et al.
patent: 6844165 (2005-01-01), Hutchens et al.
patent: 6925389 (2005-08-01), Hitt et al.
patent: 2002/0046198 (2002-04-01), Hitt
patent: 2002/0193950 (2002-12-01), Gavin et al.
patent: 2003/0054367 (2003-03-01), Rich et al.
patent: 2003/0077616 (2003-04-01), Lomas
patent: 2003/0129589 (2003-07-01), Koster et al.
patent: 2003/0134304 (2003-07-01), van der Greef et al.
patent: 2005/0260671 (2005-11-01), Hitt et al.
patent: WO 93/05478 (1993-03-01), None
patent: WO 99/41612 (1999-08-01), None
patent: WO 99/47925 (1999-09-01), None
patent: WO 99/58972 (1999-11-01), None
patent: WO 00/49410 (2000-08-01), None
patent: WO 00/55628 (2000-09-01), None
patent: WO 01/20043 (2001-03-01), None
patent: WO 01/31579 (2001-05-01), None
patent: WO 01/31580 (2001-05-01), None
patent: WO 01/84140 (2001-11-01), None
patent: WO 02/06829 (2002-01-01), None
patent: WO 02/059822 (2002-08-01), None
patent: WO 02/088744 (2002-11-01), None
patent: WO 03/031031 (2003-04-01), None
Adam, B. et al., “Serum Protein Fingerprinting Coupled with a Pattern-matching Algorithm Distinguishes Prostate Cancer from Benign Prostate Hyperplasia and Healthy Men,” Cancer Research, Jul. 1, 2002, pp. 3609-3614, vol. 62.
Alaiya, A. A. et al., “Classification of Human Ovarian Tumors Using Multivariate Data Analysis of Polypeptide Expression Patterns,” Int. J. Cancer, 2000, pp. 731-736, vol. 86.
Ashfaq, R. et al., “Evaluation of PAPNET™ System for Rescreening of Negative Cervical Smears,” Diagnostic Cytopathology, 1995, pp. 31-36, vol. 13, No. 1.
Astion, M. L. et al., “The Application of Backpropagation Neural Networks to Problems in Pathology and Laboratory Medicine,” Arch Pathol Lab Med, Oct. 1992, pp. 995-1001, vol. 116.
Atkinson, E. N. et al., “Statistical Techniques for Diagnosing CIN Using Fluorescence Spectroscopy: SVD and CART,” Journal of Cellular Biochemistry, 1995, Supplement 23, pp. 125-130.
Babaian, R. J. et al., “Performance of a Neural Network in Detecting Prostate Cancer in the Prostate-Specific Antigen Reflex Range of 2.5 to 4.0 ng/ml,” Urology, 2000, pp. 1000-1006, vol. 56, No. 6.
Bailey-Kellogg, C. et al., “Reducing Mass Degeneracy in SAR by MS by Stable Isotopic Labeling,” Journal of Computational Biology, 2001, pp. 19-36, vol. 8, No. 1.
Belic, I. et al., “Neural Network Methodologies for Mass Spectra Recognition,” Vacuum, 1997, pp. 633-637, vol. 48, No. 7-9.
Belic, I., “Neural Networks Methodologies for Mass Spectra Recognition,” pp. 375-380., additional details unknown.
Berikov, V. B. et al., “Regression Trees for Analysis of Mutational Spectra in Nucleotide Sequences,” Bioinformatics, 1999, pp. 553-562, vol. 15, Nos. 7/8.
Bittl, J. A., “From Confusion to Clarity: Direct Thrombin Inhibitors for Patients with Heparin-Induced Thrombocytopenia,” Catheterization and Cardiovascular Inventions, 2001, 473-475, vol. 52.
Breiman, L. et al., Classification and Regression Trees, Boca Raton, Chapman & Hall/CRC, 1984, pp. 174-265 (Ch. 6, Medical Diagnosis and Prognosis).
Brown, M. P. S. et al. “Knowledge-Based Analysis of Microarray Gene Expression Data by Using Support Vector Machines,” Procedures of the National Academy of Sciences, Jan. 4, 2000, 262-267, vol. 97, No. 1.
Cairns, A. Y. et al., “Towards the Automated Prescreening of Breast X-Rays,” Alistair Caims, Department of Mathematics & Computer Science, University of Dundee, pp. 1-5.
Caprioli, R. M. et al., “Molecular Imaging of Biological Samples: Localization of Peptides and Proteins Using MALDI-TOF MS,” Analytical Chemistry, 1997, pp. 4751-4760, vol. 69, No. 23.
Chace, D. H. et al., “Laboratory Integration and Utilization of Tandem Mass Spectrometry in Neonatal Screening: A Model for Clinical Mass Spectrometry in the Next Millennium,” Acta Paediatr. Suppl. 432, 1999, pp. 45-47.
Chang, E. I. et al., “Using Genetic Algorithms to Select and Create Features for Pattern Classification,” IJCNN International Joint Conference on Neural Networks, Jun. 17-21, 1990, pp. III-747 to III-752.
Christiaens, B. et al., “Fully Automated Method for the Liquid Chromatographic-Tandem Mass Spectrometric Determination of Cyproterone Acetate in Human Plasma using Restricted Access Material for On-Line Sample Clean-Up”, Journal of Chromatography A, 2004, pp. 105-110, vol. 1056.
Chun, J. et al., “Long-term Identification of Streptomycetes Using Pyrolysis Mass Spectrometry and Artificial Neural Networks,” Zbl. Bakt., 1997, pp. 258-266, vol. 285, No. 2.
Cicchetti, D. V., “Neural Networks and Diagnosis in the Clinical Laboratory: State of the Art,” Clinical Chemistry, 1992, pp. 9-10, vol. 38, No. 1.
Ciphergen European Update, 2001, pp. 1-4, vol. 1.
Claydon, M. A. et al., “The Rapid Identification of Intact Microorganisms Using Mass Spectrometry,” Nature Biotechnology, Nov. 1996, pp. 1584-1586, vol. 14.
Claydon, M. A., et al., “The Rapid Identification of Intact Microorganisms Using Mass Spectrometry,” Abstract, 1 page, [online], [retrieved on Feb. 6, 2003]. Retrieved from the internet <URL: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&dh=PubMed&list—uids+963...>.
Crawford, L. R. et al., “Computer Methods in Analytical Mass Spectrometry; Empirical Identification of Molecular Class,” Analytical Chemistry, Aug. 1968, pp. 1469-1474, vol. 40, No. 10.
Curry, B. et al., “MSnet: A Neural Network That Classifies Mass Spectra,” Stanford University, Oct. 1990, To be published in Tetrahedron Computer Methodology, pp. 1-31.
De Brabandere, V. I. et al., Isotope Dilution-Liquid Chromatography/Electrospray I

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