AI & IP Innovation & Creativity in an Age of Accelerated Change, 52 Akron L. Rev. 813 (2018)

From a glimmer in the eye of a Victorian woman ahead of her time, AI has become a cornerstone of innovation that “will be the defining technology of our time.” Around 2016, the convergence of computing power, funding, data, and open-source platforms tipped us into an AIdriven 4IR. AI can make a difference in accelerating disruptive innovation by bringing a data-driven approach to invention and creation. To do so, the law must embrace change and innovation as an imperative in a journey towards an ever-shifting horizon. In the creative arts, the work for hire doctrine provides a pragmatic legal vehicle for interests to vest and negotiated by the commercial interests best placed to encourage investment in both the technology and its downstream uses. Like humangenerated work, AI-generated work is an amalgam of mimicry mined from our own learning and experience. The training data it draws upon, both for expressive and non-expressive sues, are merely grist for AI’s mill. Consequently, fair use must be liberally applied to prevent holdup by copyright owners and stifle transformative uses enabled by AI. AI can also be used to decipher complex copyright infringement cases such as those involving musical compositions. In the technological arts, the controversy will revolve around who owns innovative breakthroughs primarily or totally attributed to AI. How should these breakthroughs affect the regard for the notion of PHOSITA? How does AI change the equation when it comes to infringement? And how can AI help save the patent system from obsolescence? In these, AI both enables and challenges how we reward individuals whose ingenuity, industry, and determination overcame the frailty of the human condition to offer us inventions that make our lives more efficient and pleasurable. It will take a clear-eyed view to ensure that copyright and patent laws do not impede the very progress they were designed to promote

The (Unnoticed) Revitalization of the Doctrine of Equivalents

Over the past century, few patent issues have been considered so often by the Supreme Court of the United States as the doctrine of equivalents (“DOE”). This judge-made rule deals with a question that lies at the heart of patent policy—what is the best way to define property rights in an invention? The doctrine gives patentees an opportunity to ensnare an accused device that does not literally infringe a patent claim if the accused device is substantially similar to each claim limitation. Patentees enjoy this advantage, but it comes at a cost to the public, who must face the uncertainty of whether claims actually mean what they say. This tension chafed the Justices and split the Court almost down the middle in two early cases. From those controversial beginnings to the present day, judges, practitioners, and academics continue to debate the doctrine’s proper scope and continued vitality

AI, Equity, and the IP Gap

Artificial intelligence (AI) has helped determine vaccine recipients, prioritize emergency room admissions, and ascertain individual hires, sometimes doing so inequitably. As we emerge from the Pandemic, technological progress and efficiency demands continue to press all areas of the law, including intellectual property (IP) law, toward incorporating more AI into legal practice. This may be good when AI promotes economic and social justice in the IP system. However, AI may amplify inequity as biased developers create biased algorithms with biased inputs or rely on biased proxies. This Article argues that policymakers need to take a thoughtful and concerted approach to graft AI into IP law and practice if social justice principles of access, inclusion, and empowerment flow from their union. It explores what it looks like to obtain AI justice in the IP context and focuses on two areas where IP law impedes equitable AI-related outcomes. The first involves the civil rights concerns that stem from trade secrets blocking access and deflecting accountability in biased algorithms or data. The second concerns the patent and copyright doctrine biases perpetuating historical inequity in AI-augmented processes. The Article also ad- dresses how equity by design should look and provides a roadmap for implementing equity audits to mitigate bias. Finally, it briefly examines how AI would assist with adjudicating equitable IP law doctrines, which also tests the outer limits of what bounded AI processes can do

JUDGING EQUIVALENTS

JUDGING EQUIVALENT