Artificial Intelligence And Patents.

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Artificial Intelligence and Patents
Wesley Malherek
Artificial intelligence is a fast growing field of technology. Science fiction authors have long often imagined intelligent self-aware machines taking over the world. Recently IBM famously won on the TV game show Jeopardy raising the concern to the public about the possible consequences of smarter machines. Smarter machines may not be the killer robots of “The Terminator” but may have other consequences. IBM’s Watson may become the next Thomas Edison, PHOSITA or Patent examiner.
There has been work in machine learning and artificial intelligence (AI) for many years now. Many challenges have arose in the development of AI, but much progress has been made. In many
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Patent law was designed to protect the work of inventors and promote the sciences. Inventors are the people who actually conceived the invention. Sewall v. Walters, 21 F.3d 411 (Fed. Cir. 1994). Patents give inventors an exclusionary right to their particular invention. Patent protection provides an incentive to compensate assignee companies/inventors for their investment in researching the invention solution. But the rising capability and prevalence of AI systems may pose a threat to this system in several aspects.
Has AI been used in the Patent System before?
Yes, AI has been used effectively in the patent system. John Koza in 2006 was able to use an array of Pentium computers to improve and design around a lens patent. The computers created a complex lens that outperformed a wide-field lens for telescopes patented in 2000 by optical engineers Noboru Koizumi and Naomi Watanabe. The impressive aspect of the computer generated lens is that it is patentable novel and purposely avoids infringing on the patent that it improved on.
Koza’s computers called up a simulator called KOJAC, which is specifically designed for optics simulations. Using various prescription input numbers, KOJAC can predict how well the lens will function in the real world. The numerous prescription variables make the effect of a single simple change difficult to predict.
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