The Global Artificial Intelligence Revolution Challenges Patent Eligibility Laws

This Article examines patent eligibility jurisprudence of artificial intelligence in the United States, Europe, France, Japan, and Singapore. It identifies de facto requirements of patent-eligible artificial intelligence. It also examines the adaptability of patent eligibility jurisprudence to adapt with the growth of artificial intelligence

Generating Rembrandt: Artificial Intelligence, Copyright, and Accountability in the 3A Era--The Human-like Authors are Already Here- A New Model

Artificial intelligence (AI) systems are creative, unpredictable, independent, autonomous, rational, evolving, capable of data collection, communicative, efficient, accurate, and have free choice among alternatives. Similar to humans, AI systems can autonomously create and generate creative works. The use of AI systems in the production of works, either for personal or manufacturing purposes, has become common in the 3A era of automated, autonomous, and advanced technology. Despite this progress, there is a deep and common concern in modern society that AI technology will become uncontrollable. There is therefore a call for social and legal tools for controlling AI systems’ functions and outcomes. This Article addresses the questions of the copyrightability of artworks generated by AI systems: ownership and accountability. The Article debates who should enjoy the benefits of copyright protection and who should be responsible for the infringement of rights and damages caused by AI systems that independently produce creative works. Subsequently, this Article presents the AI Multi- Player paradigm, arguing against the imposition of these rights and responsibilities on the AI systems themselves or on the different stakeholders, mainly the programmers who develop such systems. Most importantly, this Article proposes the adoption of a new model of accountability for works generated by AI systems: the AI Work Made for Hire (WMFH) model, which views the AI system as a creative employee or independent contractor of the user. Under this proposed model, ownership, control, and responsibility would be imposed on the humans or legal entities that use AI systems and enjoy its benefits. This model accurately reflects the human-like features of AI systems; it is justified by the theories behind copyright protection; and it serves as a practical solution to assuage the fears behind AI systems. In addition, this model unveils the powers behind the operation of AI systems; hence, it efficiently imposes accountability on clearly identifiable persons or legal entities. Since AI systems are copyrightable algorithms, this Article reflects on the accountability for AI systems in other legal regimes, such as tort or criminal law and in various industries using these systems

Artificial Intelligence Inventions & Patent Disclosure

Artificial intelligence (“AI”) has attracted significant attention and has imposed challenges for society. Yet surprisingly, scholars have paid little attention to the impediments AI imposes on patent law’s disclosure function from the lenses of theory and policy. Patents are conditioned on inventors describing their inventions, but the inner workings and the use of AI in the inventive process are not properly understood or are largely unknown. The lack of transparency of the parameters of the AI inventive process or the use of AI makes it difficult to enable a future use of AI to achieve the same end state. While patent law’s enablement doctrine focuses on the particular result of the invention process, in contrast, this Article suggests that AI presents a lack of transparency and difficulty in replication that profoundly and fundamentally challenge disclosure theory in patent law. A reasonable onlooker or a patent examiner may find it difficult to explain the inner workings of AI. But even more pressing is a non-detection problem—an overall lack of disclosure of unidentified AI inventions, or knowing whether the particular end state was produced by the use of AI. The complexities of AI require enhancing the disclosure requirement since the peculiar characteristics of the end state cannot be described by the inventive process that produced it. This Article introduces a taxonomy of AI and argues that an enhanced AI patent disclosure requirement mitigates concerns surrounding the explainability of AI-based tools and the inherent inscrutability of AI-generated output. Such emphasis of patent disclosure for AI may steer some inventors toward trade secrecy and push others to seek patent protection against would-be patent infringers despite added ex ante costs and efforts. Utilitarian and Lockean theories suggest justifications for enhanced AI patent disclosure while recognizing some objections. Turning to the prescriptive, this Article proposes and assesses, as means for achieving enhanced disclosure, a variety of disclosure-specific incentives and data deposits for AI. It concludes by offering insights for innovation and for a future empirical study to verify its theoretical underpinnings

Prospect patents, data markets, and the commons in data-driven medicine : openness and the political economy of intellectual property rights

Scholars who point to political influences and the regulatory function of patent courts in the USA have long questioned the courts’ subjective interpretation of what ‘things’ can be claimed as inventions. The present article sheds light on a different but related facet: the role of the courts in regulating knowledge production. I argue that the recent cases decided by the US Supreme Court and the Federal Circuit, which made diagnostics and software very difficult to patent and which attracted criticism for a wealth of different reasons, are fine case studies of the current debate over the proper role of the state in regulating the marketplace and knowledge production in the emerging information economy. The article explains that these patents are prospect patents that may be used by a monopolist to collect data that everybody else needs in order to compete effectively. As such, they raise familiar concerns about failure of coordination emerging as a result of a monopolist controlling a resource such as datasets that others need and cannot replicate. In effect, the courts regulated the market, primarily focusing on ensuring the free flow of data in the emerging marketplace very much in the spirit of the ‘free the data’ language in various policy initiatives, yet at the same time with an eye to boost downstream innovation. In doing so, these decisions essentially endorse practices of personal information processing which constitute a new type of public domain: a source of raw materials which are there for the taking and which have become most important inputs to commercial activity. From this vantage point of view, the legal interpretation of the private and the shared legitimizes a model of data extraction from individuals, the raw material of information capitalism, that will fuel the next generation of data-intensive therapeutics in the field of data-driven medicine

Artificial Intelligence and Intellectual Property Law: From Diagnosis to Action

The use of “artificial intelligence” systems becomes ever more widespread and far-reaching. Technological and economic concepts for an AI-based future are about to be implemented. It is, hence, time for the intellectual property system to develop answers to the challenges brought about by AI. Against this background, Zurich University’s Center for Intellectual Property and Competition Law (CIPCO) has initiated a joint research project on AI/IP with the Swiss Intellectual Property Institute (IPI). A first stage of this project has evaluated the state of the legal and economic discourse. These insights form the basis for policy recommendations on how the intellectual property system ought to be adapted to AI-related developments. The present paper describes – as draft work in progress – the project setup and summarizes its results gained so far. In doing so, it addresses key AI/IP issues, including business models of AI innovation leaders, inventorship/creatorship of AI systems de lege lata and de lege ferenda, the DABUS litigation, the discussion on whether new types of IP rights are necessary to protect AI inventions, the allocation of entitlements and liability regarding such innovations, AI-related revisions in the guidelines of important patent and trademark offices, the use such offices make of AI tools, the need for new protection carve-outs (e.g. to foster text and data mining), as well as AI’s potential raising the bar-effect. Der Einsatz von Systemen der „künstlichen Intelligenz“ wird immer verbreiteter und weitreichender. Viele technische und ökonomische Zukunftsszenarien stehen an der Schwelle zur Realisierung. Damit wird es auch für das Immaterialgüterrecht dringender, dort Antworten zu entwickeln, wo es sich durch KI herausgefordert sieht. Das Center for Intellectual Property and Competition Law (CIPCO) der Universität Zürich hat daher ein KI/IP-Kooperationsprojekt mit dem Schweizerischen Institut für Geistiges Eigentum (IGE) aufgenommen. Eine erste Projektphase hat den Stand des ökonomischen und rechtlichen Diskurses ermittelt und bildet damit die Grundlage für Empfehlungen zur künftigen Ausgestaltung des Immaterialgüterrechts in diesem Bereich. Der vorliegende Beitrag – bei dem es sich noch um einen weiterzuentwickelnden Entwurf handelt – legt hierüber Rechenschaft ab. Er beleuchtet nicht nur die Projektausgestaltung, sondern auch die gegenwärtigen KI/IP-Zentralthemen, etwa die Geschäftsmodelle von KI-Innovationsführern, Erfinder- bzw. Urheberschaft von KI-Systemen de lege lata und de lege ferenda, die Rechtsprechung zu DABUS, die Diskussion um die Notwendigkeit neuer Schutzrechte für KI-Innovationen, die Allokation von Rechtspositionen und Haftungsverantwortung an solchen Innovationen jenseits der Erfinder /Urheberfrage, die KI-bezogenen Neuerungen in den Leitlinien wichtiger Patent- und Markenämter sowie den Einsatz von KI-Instrumenten durch diese Ämter, neue Schutzschranken zur Förderung von KI und KI als Schutzhürden erhöhender Faktor

Artificial Intelligence and Patent Ownership

Invention by artificial intelligence (AI) is the future of innovation. Unfortunately, as discovered through Freedom of Information Act requests, the U.S. patent regime has yet to determine how it will address patents for inventions created solely by AI (AI patents). This Article fills that void by presenting the first comprehensive analysis on the allocation of patent rights arising from invention by AI. To this end, this Article employs Coase Theorem and its corollaries to determine who should be allowed to secure these patents to maximize economic efficiency. The study concludes that letting firms using AI to create new technologies (as opposed to software companies, programmers, or downstream parties) to obtain the resulting patents is the optimal policy

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