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September 03, 2008
Semantic Supercomputing Reaps Competitive Advantage from Patent Data
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Mark Twain wrote, "A country without a patent office and good patent laws is just a crab, and couldn't travel any way except sideways and backways." But a good patent infrastructure without good ways to search it does not move a country's industry forward.
Twain should know. He fought a protracted patent dispute with another man in 1871 over the invention of an elastic vest strap. Twain ultimately prevailed, but could have saved himself a great deal of money, time and frustration had he known about the competing patent beforehand.
Every year billions of dollars are wasted on research and development of inventions that are already protected by patent law -- an estimated $20 billion in the U.S. and €60 billion in Europe, which equals roughly the combined annual revenues of Microsoft and Apple. In fact, these computing titans themselves have fought costly intellectual property wars due to poor patent intelligence, such as the 2004 patent dispute over the iPod user interface, which Apple ultimately lost to Microsoft.
It's no wonder patent information is so costly and difficult to divine. The volume of patent data is overwhelming. The world's collection of patents comprises the largest information repository of the most important achievements of humanity. Since the first patent was issued for a Venetian statue in 1471, 60 million patents have been awarded around the world, with four million patents actively in force today worldwide. And 800,000 new inventions are registered every year. While the data is public, current search tools are inconvenient and inadequate to the needs of professionals. And even if you solve the patent retrieval problem, it's not enough: researchers today need integrated views of correlated patent information, such as corporate affiliations, scientific information, prior art documents, and breaking news on intellectual property.
To address this challenge, researchers are developing computationally-intensive natural language processing (NLP) algorithms in the new field of semantic supercomputing. One company that is tapping the new technology is Vienna, Austria-based Matrixware Information Services (www.matrixware.com). The firm is combining HPC systems with Interactive Supercomputing, Inc.'s (ISC's) Star-P software to tackle the ever-growing challenge of finding patent information hidden in the world's vast patent databases and libraries.
Patents and intellectual property play an increasingly important role as intangible assets of industrial corporations. Over 250,000 companies worldwide depend on patent data. Consequently, professional management of patents and precise retrieval of patent information are essential business processes for industries around the globe.
Companies pioneering semantic computing typically employ teams of computer engineers, mathematicians, linguists and patent specialists to help companies mine patent repositories for intellectual property information. The semantic supercomputing techniques and HPC technology they utilize enable the users to retrieve relevant patent information faster, more easily and at less cost.
Matrixware, for example, employs multicore SGI Altix 4700 blade servers and Linux clusters running Star-P to develop and run its NLP algorithms on terabyte-scale patent data sets. Star-P enables Matrixware's team to continuously code and refine NLP algorithms on their desktops using Python or MATLAB, and then run them interactively on HPC systems with little to no modification. The semantic supercomputing model eliminates the need to re-program applications in C, Fortran or MPI in order to run on parallel systems, resulting in huge productivity gains.
Patent retrieval presents two levels of computational challenges. The first challenge is data centric. The patent information is dispersed among several hundred repositories, dating back as far as the 1700s. These diverse patent collections have evolved through 200 generations of methods of storing documents between then and today. Some of the information is digital data; other is derived from documents that have been scanned and converted with OCR systems, and others are just plain document images. Researchers must wrestle with enormous gaps and inconsistencies in the format of 100 million documents.
Another challenge is database centric. Today, most patent data is stored in relational databases. But the art of managing patent information is based on 4,000 years of library science methods, which conflict with the restrictions imposed by relational databases. This severely limits the accessibility to the data.
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