'Societal Aversion to Math' Challenged in Patent Case Amicus Brief

'Societal Aversion to Math' Challenged in Patent Case Amicus Brief Photo: Jason Doiy University of California at Hastings law professor Robin Feldman

Lawyers often joke that they went to law school because they hate math.

An amicus brief by University of California at Hastings law professor Robin Feldman in Alice Corporation v. CLS Bank International argues that “our societal aversion to math” has muddled the law for software patents and led to the legal battles that have dominated the smart phone industry.

Feldman criticizes software patents that use vague prose—rather than number-heavy computer codes—to describe inventions. This non-specific language is an easy target for “patent trolls,” who buy patents for the purpose of suing companies for infringement, she says.

The Supreme Court has the chance to toughen the standards for software patents in Alice Corporation, which is set for oral argument on March 31. Feldman is calling on the justices to curb these overly general patents that threaten to preempt large swaths of innovation.

The patent at issue in the case is for a program that Alice founder Ian Shepherd devised to decrease “settlement risk,” or the possibility that one party backs out of an online financial transaction.

In 2007, CLS Bank International, a company that also designs settlement-risk programs, sued Alice, claiming that Shepherd’s patents were unenforceable. Alice, meanwhile, counterclaimed. The case reached the en banc U.S. Court of Appeals for the Federal Circuit, which handles patent appeals. In a deeply divided opinion in May 2013, the Federal Circuit sided with CLS Bank, but the court could not agree on a standard for when software is eligible for a patent.

Feldman, whose brief supports neither party, has jumped in to help make sense of this complex area of law.

“Academics have an opportunity and obligation to clarify how problems have developed and suggest solutions,” said Feldman, who relied on her 2012 book “Rethinking Patent Law” in her brief.

At Hastings, Feldman runs the Institute for Innovation Law, which promotes “data-driven law-making” by providing courts and legislators with well-researched guidance on complicated topics. It also hosts the Startup Legal Garage, a clinic where students counsel emerging Silicon Valley companies.

Feldman points to 1994’s In re Alappat as sparking the current confusion in software patent law. The Federal Circuit there endorsed patents that focused on the result of an innovation, rather than on the steps that inventors took to get there. Most other areas of patent law take the latter approach, Feldman said.

“You don’t get a patent for telling us what you wanted to do,” Feldman said. “You get a patent for telling us how you did it.”

Feldman argues that the Alappat court used this incorrect approach as a way to avoid math. A tenet of patent law is that laws of nature—which are often expressed in formulaic language, such as E=MC2—are not patentable. Computer programs, Feldman says, also are often expressed in formulaic language, but they are not necessarily laws of nature.

Alappat’s approach is that patent applicants should avoid formulaic language altogether, and instead rely on abstract descriptions. This ruling, though, created a new problem: overly broad patents.

“That was the opportunity and inspiration for modern patent trolling and smartphone wars.” Feldman said.

Because of their generality, the Alappat patents also threaten to preempt natural phenomena, Feldman warned. She proposes a test that discourages preemption by ensuring that an invention has a “specific commercial application.” She wants inventors to identify in detail the programming innovations that led to the resulting software, so that the patent will cover an application of a law of nature, rather than the law itself.

Feldman explains this concept with a hypothetical invention: a software program used to assess how risky a driver is for car-insurance underwriting. The program calculates how likely a person is to have an accident by looking at characteristics including how often he texts while driving.

A proper patent would not merely describe the invention in those general terms, Feldman wrote. Instead, it would include the exact formula used to produce a person’s risk score and the software language written to create that test.

“Requiring such specificity helps to ensure that the patent does not preempt the natural phenomenon that one who texts and drives is risky, nor blocks all routes for assessing that characteristic,” she wrote. “Rather, the patent holder receives territory commensurate with the inventive concept contributed.”

This approach would help create uniformity throughout patent law, Feldman argues.

“[S]oftware patents are no different than other types of patents, and should be tested in the same rigorous manner required throughout the patent system,” she wrote.

Jamie Schuman is a freelance writer and graduate of The George Washington University Law School.

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