Health Discovery receives notice of allowance of U.S. patent application covering kernel selection methods

Health Discovery Corporation has announced that the U.S. Patent and Trademark Office has issued a notice of allowance of HDC's patent application covering methods for selection of kernels, i.e., functions or algorithms that are used to transform input data into a different configuration, known as "feature space", which allows for easier recognition of patterns within the data.

The kernels covered by the application ' s claims are useful for analysis of data that may possess characteristics such as structure, for example, DNA or protein sequences, spectrographic data, images, documents, graphs, ECG signals, and many others. These kernels are particularly useful where the input data may possess invariances or noise components that can interfere with the ability to accurately extract the desired information. A location-dependent kernel looks for similarity between structures that is relevant to particular locations in the structures, such as a string of text in two different documents. Using the locational kernel, a pair of structures can be viewed according to the similarities of their components. The locational kernel method is not limited to support vector machines (SVMs), but may be used in other kernel-based learning machines. This application also discloses novel feature selection techniques that consider the possibility of structure within and around the "noise vector" in a problem. The inclusion of these features allows the SVM or other kernel-based classifiers to gain the desirable property of noise invariance.

The inventors named in the newly allowed application represent some of the world ' s leading authorities on SVMs and learning machines. Bernhard Schoelkopf is currently a department director at the Max Planck Institute for Biological Cybernetics in T ü bingen, Germany and co-author or co-editor of numerous books and published articles on pattern recognition and learning machines. Peter Bartlett and Andr é Elisseeff are widely published researchers in the fields of pattern recognition, learning machines and bioinformatics.

Once the US Patent and Trademark Office issues the new kernel selection patent, Health Discovery Corporation will hold the exclusive rights to 30 issued U.S. and foreign patents covering uses of SVM and FGM (fractal genomics modeling) technology for discovery of knowledge from large data sets. Other issued patents cover methods and systems for pre-processing of data to enhance knowledge discovery using SVMs, analysis of data using multiple support vector machines and for multiple data sets, and providing SVM analysis services over the Internet. HDC ' s pending U.S. and foreign patent applications cover numerous improvements to and applications of SVMs including computer-aided image analysis using SVMs, with particular application to diagnosis using medical images, methods of feature selection for enhanced SVM efficiency and biomarkers for colon cancer, prostate cancer, BPH and renal cancer discovered with these methods, and use of SVMs for analysis of spectral data, such as mass spectrometry data used for protein analysis.

"We are thrilled that HDC's patent issuances and allowances continue to expand adding significant value to our Intellectual Property Portfolio," said Stephen D. Barnhill, M.D., HDC's Chairman and CEO. "The value of HDC ' s patents is not only strengthened by the increasing number of patents being issued and allowed but also on the expertise and world renowned reputation of the inventors. ”

Savannah-based Health Discovery Corporation (OTCBB:HDVY) is uniquely positioned in the field of pattern recognition technology. Through the application of its patent protected technology, HDC is a biology-oriented biomarker discovery company providing all aspects of First-Phase Biomarker Discovery(sm). The Company's SVM and FGM pattern recognition tools have significant application potential in other sizable commercial markets such as radiology, financial markets, Internet search and spam, homeland security, and other areas where analysis of large volumes of complex data is required.

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