Intellectual Property Law Research Paper Topics

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Welcome to the realm of intellectual property law research paper topics , where we aim to guide law students on their academic journey by providing a comprehensive list of 10 captivating and relevant topics in each of the 10 categories. In this section, we will explore the dynamic field of intellectual property law, encompassing copyrights, trademarks, patents, and more, and shed light on its significance, complexities, and the diverse array of research paper topics it offers. With expert tips on topic selection, guidance on crafting an impactful research paper, and access to iResearchNet’s custom writing services, students can empower their pursuit of excellence in the domain of intellectual property law.

100 Intellectual Property Law Research Paper Topics

Intellectual property law is a dynamic and multifaceted field that intersects with various sectors, including technology, arts, business, and innovation. Research papers in this domain allow students to explore the intricate legal framework that governs the creation, protection, and enforcement of intellectual property rights. To aid aspiring legal scholars in their academic pursuits, this section presents a comprehensive list of intellectual property law research paper topics, categorized to encompass a wide range of subjects.

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  • Fair Use Doctrine: Balancing Creativity and Access to Knowledge
  • Copyright Infringement in the Digital Age: Challenges and Solutions
  • The Role of Copyright Law in Protecting Creative Works of Art
  • The Intersection of Copyright and AI: Legal Implications and Challenges
  • Copyright and Digital Education: Analyzing the Impact of Distance Learning
  • Copyright and Social Media: Addressing Infringement and User Rights
  • Copyright Exceptions for Libraries and Educational Institutions
  • Copyright Law and Virtual Reality: Emerging Legal Issues
  • Copyright and Artificial Intelligence in Music Creation
  • Copyright Termination Rights and Authors’ Works Reversion
  • Patentable Subject Matter: Examining the Boundaries of Patent Protection
  • Patent Trolls and Innovation: Evaluating the Impact on Technological Advancement
  • Biotechnology Patents: Ethical Considerations and Policy Implications
  • Patent Wars in the Pharmaceutical Industry: Balancing Access to Medicine and Innovation
  • Standard Essential Patents: Analyzing the Role in Technology Development and Market Competition
  • Patent Thickets and the Challenges for Startups and Small Businesses
  • Patent Pooling and Collaborative Innovation: Advantages and Legal Considerations
  • Patent Litigation and Forum Shopping: Analysis of Jurisdictional Issues
  • Patent Law and Artificial Intelligence: Implications for Inventorship and Ownership
  • Patent Exhaustion and International Trade: Legal Complexities in Global Markets
  • Trademark Dilution: Protecting the Distinctiveness of Brands in a Global Market
  • Trademark Infringement and the Online Environment: Challenges and Legal Remedies
  • The Intersection of Trademark Law and Freedom of Speech: Striking a Balance
  • Non-Traditional Trademarks: Legal Issues Surrounding Sound, Color, and Shape Marks
  • Trademark Licensing: Key Considerations for Brand Owners and Licensees
  • Trademark Protection for Geographical Indications: Preserving Cultural Heritage
  • Trademark Opposition and Cancellation Proceedings: Strategies and Legal Considerations
  • Trademark Law and Counterfeiting: Global Enforcement Challenges
  • Trademark and Domain Name Disputes: UDRP and Legal Strategies
  • Trademark Law and Social Media Influencers: Disclosure and Endorsement Guidelines
  • Trade Secrets vs. Patents: Choosing the Right Intellectual Property Protection
  • Trade Secret Misappropriation: Legal Protections and Remedies for Businesses
  • Protecting Trade Secrets in the Digital Age: Cybersecurity Challenges and Best Practices
  • International Trade Secret Protection: Harmonization and Enforcement Challenges
  • Whistleblowing and Trade Secrets: Balancing Public Interest and Corporate Secrets
  • Trade Secret Licensing and Technology Transfer: Legal and Business Considerations
  • Trade Secret Protection in Employment Contracts: Non-Compete and Non-Disclosure Agreements
  • Trade Secret Misappropriation in Supply Chains: Legal Implications and Risk Mitigation
  • Trade Secret Law and Artificial Intelligence: Ownership and Trade Secret Protection
  • Trade Secret Protection in the Era of Open Innovation and Collaborative Research
  • Artificial Intelligence and Intellectual Property: Ownership and Liability Issues
  • 3D Printing and Intellectual Property: Navigating the Intersection of Innovation and Copyright
  • Blockchain Technology and Intellectual Property: Challenges and Opportunities
  • Digital Rights Management: Addressing Copyright Protection in the Digital Era
  • Open Source Software Licensing: Legal Implications and Considerations
  • Augmented Reality and Virtual Reality: Legal Issues in Content Creation and Distribution
  • Internet of Things (IoT) and Intellectual Property: Legal Challenges and Policy Considerations
  • Big Data and Intellectual Property: Privacy and Data Protection Concerns
  • Artificial Intelligence and Patent Offices: Automation and Efficiency Implications
  • Intellectual Property Implications of 5G Technology: Connectivity and Innovation Challenges
  • Music Copyright and Streaming Services: Analyzing Legal Challenges and Solutions
  • Fair Use in Documentary Films: Balancing Copyright Protection and Freedom of Expression
  • Intellectual Property in Video Games: Legal Issues in the Gaming Industry
  • Digital Piracy and Copyright Enforcement: Approaches to Tackling Online Infringement
  • Personality Rights in Media: Balancing Privacy and Freedom of the Press
  • Streaming Services and Copyright Licensing: Legal Challenges and Royalty Distribution
  • Fair Use in Parody and Satire: Analyzing the Boundaries of Creative Expression
  • Copyright Protection for User-Generated Content: Balancing Authorship and Ownership
  • Media Censorship and Intellectual Property: Implications for Freedom of Information
  • Virtual Influencers and Copyright: Legal Challenges in the Age of AI-Generated Content
  • Intellectual Property Protection in Developing Countries: Promoting Innovation and Access to Knowledge
  • Cross-Border Intellectual Property Litigation: Jurisdictional Challenges and Solutions
  • Trade Agreements and Intellectual Property: Impact on Global Innovation and Access to Medicines
  • Harmonization of Intellectual Property Laws: Prospects and Challenges for International Cooperation
  • Indigenous Knowledge and Intellectual Property: Addressing Cultural Appropriation and Protection
  • Intellectual Property and Global Public Health: Balancing Innovation and Access to Medicines
  • Geographical Indications in International Trade: Legal Framework and Market Exclusivity
  • International Licensing and Technology Transfer: Legal Considerations for Multinational Corporations
  • Intellectual Property Enforcement in the Digital Marketplace: Comparative Analysis of International Laws
  • Digital Copyright and Cross-Border E-Commerce: Legal Implications for Online Businesses
  • Intellectual Property Strategy for Startups: Maximizing Value and Mitigating Risk
  • Licensing and Franchising: Legal Considerations for Expanding Intellectual Property Rights
  • Intellectual Property Due Diligence in Mergers and Acquisitions: Key Legal Considerations
  • Non-Disclosure Agreements: Safeguarding Trade Secrets and Confidential Information
  • Intellectual Property Dispute Resolution: Arbitration and Mediation as Alternative Methods
  • Intellectual Property Valuation: Methods and Challenges for Business and Investment Decisions
  • Technology Licensing and Transfer Pricing: Tax Implications for Multinational Corporations
  • Intellectual Property Audits: Evaluating and Managing IP Assets for Businesses
  • Trade Secret Protection and Non-Compete Clauses: Balancing Employer and Employee Interests
  • Intellectual Property and Startups: Strategies for Funding and Investor Relations
  • Intellectual Property and Access to Medicines: Ethical Dilemmas in Global Health
  • Gene Patenting and Human Dignity: Analyzing the Moral and Legal Implications
  • Intellectual Property and Indigenous Peoples: Recognizing Traditional Knowledge and Culture
  • Bioethics and Biotechnology Patents: Navigating the Intersection of Science and Ethics
  • Copyright, Creativity, and Freedom of Expression: Ethical Considerations in the Digital Age
  • Intellectual Property and Artificial Intelligence: Ethical Implications for AI Development and Use
  • Genetic Engineering and Intellectual Property: Legal and Ethical Implications
  • Intellectual Property and Environmental Sustainability: Legal and Ethical Perspectives
  • Cultural Heritage and Intellectual Property Rights: Preservation and Repatriation Efforts
  • Intellectual Property and Social Justice: Access and Equality in the Innovation Ecosystem
  • Innovation Incentives and Intellectual Property: Examining the Relationship
  • Intellectual Property and Technology Transfer: Promoting Innovation and Knowledge Transfer
  • Intellectual Property Rights in Research Collaborations: Balancing Interests and Collaborative Innovation
  • Innovation Policy and Patent Law: Impact on Technology and Economic Growth
  • Intellectual Property and Open Innovation: Collaborative Models and Legal Implications
  • Intellectual Property and Startups: Fostering Innovation and Entrepreneurship
  • Intellectual Property and University Technology Transfer: Challenges and Opportunities
  • Open Access and Intellectual Property: Balancing Public Goods and Commercial Interests
  • Intellectual Property and Creative Industries: Promoting Cultural and Economic Development
  • Intellectual Property and Sustainable Development Goals: Aligning Innovation with Global Priorities

The intellectual property law research paper topics presented here are intended to inspire students and researchers to delve into the complexities of intellectual property law and explore emerging issues in this ever-evolving field. Each topic offers a unique opportunity to engage with legal principles, societal implications, and practical challenges. As the landscape of intellectual property law continues to evolve, there remains an exciting realm of uncharted research areas, waiting to be explored. Through in-depth research and critical analysis, students can contribute to the advancement of intellectual property law and its impact on innovation, creativity, and society at large.

Exploring the Range of Topics in Human Rights Law

Human rights law is a vital field of study that delves into the protection and promotion of fundamental rights and freedoms for all individuals. As a cornerstone of international law, human rights law addresses various issues, ranging from civil and political rights to economic, social, and cultural rights. It aims to safeguard the inherent dignity and worth of every human being, regardless of their race, religion, gender, nationality, or other characteristics. In this section, we will explore the diverse and expansive landscape of intellectual property law research paper topics, shedding light on its significance and the vast array of areas where students can conduct meaningful research.

  • Historical Perspectives on Human Rights : Understanding the historical evolution of human rights is essential to comprehend the principles and norms that underpin modern international human rights law. Research papers in this category may explore the origins of human rights, the impact of significant historical events on the development of human rights norms, and the role of key figures and organizations in shaping the human rights framework.
  • Human Rights and Social Justice : This category delves into the intersection of human rights law and social justice. Intellectual property law research paper topics may encompass the role of human rights in addressing issues of poverty, inequality, discrimination, and marginalization. Researchers can analyze how human rights mechanisms and legal instruments contribute to advancing social justice and promoting inclusivity within societies.
  • Gender Equality and Women’s Rights : Gender equality and women’s rights remain crucial subjects in human rights law. Research papers in this area may explore the legal protections for women’s rights, the challenges in achieving gender equality, and the impact of cultural and societal norms on women’s human rights. Intellectual property law research paper topics may also address specific issues such as violence against women, gender-based discrimination, and the role of women in peacebuilding and conflict resolution.
  • Freedom of Expression and Media Rights : The right to freedom of expression is a fundamental human right that forms the basis of democratic societies. In this category, researchers can examine the legal dimensions of freedom of expression, including its limitations, the role of media in promoting human rights, and the challenges in balancing freedom of expression with other rights and interests.
  • Human Rights in Armed Conflicts and Peacebuilding : Armed conflicts have severe implications for human rights, necessitating robust legal frameworks for protection. Topics in this category may focus on humanitarian law, the rights of civilians during armed conflicts, and the role of international organizations in peacebuilding and post-conflict reconstruction.
  • Refugee and Migration Rights : With the global refugee crisis and migration challenges, this category addresses the legal protections and challenges faced by refugees and migrants. Research papers may delve into the rights of asylum seekers, the principle of non-refoulement, and the legal obligations of states in providing humanitarian assistance and protection to displaced populations.
  • Economic, Social, and Cultural Rights : Economic, social, and cultural rights are integral to human rights law, ensuring the well-being and dignity of individuals. Topics may explore the right to education, health, housing, and adequate standards of living. Researchers may also examine the justiciability and enforcement of these rights at national and international levels.
  • Human Rights and Technology : The digital age presents new challenges and opportunities for human rights. Research in this category can explore the impact of technology on privacy rights, freedom of expression, and the right to access information. Intellectual property law research paper topics may also cover the use of artificial intelligence and algorithms in decision-making processes and their potential implications for human rights.
  • Environmental Justice and Human Rights : Environmental degradation has significant human rights implications. Researchers can investigate the intersection of environmental protection and human rights, examining the right to a healthy environment, the rights of indigenous communities, and the role of human rights law in addressing climate change.
  • Business and Human Rights : The responsibilities of corporations in upholding human rights have gained increasing attention. This category focuses on corporate social responsibility, human rights due diligence, and legal mechanisms to hold businesses accountable for human rights violations.

The realm of human rights law offers an expansive and dynamic platform for research and exploration. As the international community continues to grapple with pressing human rights issues, students have a unique opportunity to contribute to the discourse and advance human rights protections worldwide. Whether examining historical perspectives, social justice, gender equality, freedom of expression, or other critical areas, research in human rights law is a compelling endeavor that can make a positive impact on the lives of people globally.

How to Choose an Intellectual Property Law Topic

Choosing the right intellectual property law research paper topic is a crucial step in the academic journey of law students. Intellectual property law is a multifaceted and rapidly evolving field that covers a wide range of subjects, including patents, copyrights, trademarks, trade secrets, and more. With such diversity, selecting a compelling and relevant research topic can be both challenging and exciting. In this section, we will explore ten practical tips to help students navigate the process of choosing an engaging and impactful intellectual property law research paper topic.

  • Identify Your Interests and Passion : The first step in selecting a research paper topic in intellectual property law is to identify your personal interests and passion within the field. Consider what aspects of intellectual property law resonate with you the most. Are you fascinated by the intricacies of patent law and its role in promoting innovation? Or perhaps you have a keen interest in copyright law and its influence on creative expression? By choosing a topic that aligns with your passions, you are more likely to stay motivated and engaged throughout the research process.
  • Stay Updated on Current Developments : Intellectual property law is a dynamic area with continuous developments and emerging trends. To choose a relevant and timely research topic, it is essential to stay updated on recent court decisions, legislative changes, and emerging issues in the field. Follow reputable legal news sources, academic journals, and intellectual property law blogs to remain informed about the latest developments.
  • Narrow Down the Scope : Given the vastness of intellectual property law, it is essential to narrow down the scope of your research paper topic. Focus on a specific subfield or issue within intellectual property law that interests you the most. For example, you may choose to explore the legal challenges of protecting digital copyrights in the music industry or the ethical implications of gene patenting in biotechnology.
  • Conduct Preliminary Research : Before finalizing your research paper topic, conduct preliminary research to gain a better understanding of the existing literature and debates surrounding the chosen subject. This will help you assess the availability of research material and identify any gaps or areas for further exploration.
  • Review Case Law and Legal Precedents : In intellectual property law, case law plays a crucial role in shaping legal principles and interpretations. Analyzing landmark court decisions and legal precedents in your chosen area can provide valuable insights and serve as a foundation for your research paper.
  • Consult with Professors and Experts : Seek guidance from your professors or intellectual property law experts regarding potential intellectual property law research paper topics. They can offer valuable insights, suggest relevant readings, and provide feedback on the feasibility and relevance of your chosen topic.
  • Consider Practical Applications : Intellectual property law has real-world implications and applications. Consider choosing a research topic that has practical significance and addresses real challenges faced by individuals, businesses, or society at large. For example, you might explore the role of intellectual property in facilitating technology transfer in developing countries or the impact of intellectual property rights on access to medicines.
  • Analyze International Perspectives : Intellectual property law is not confined to national boundaries; it has significant international dimensions. Analyzing the differences and similarities in intellectual property regimes across different countries can offer a comparative perspective and enrich your research paper.
  • Propose Solutions to Existing Problems : A compelling research paper in intellectual property law can propose innovative solutions to existing problems or challenges in the field. Consider focusing on an area where there are unresolved debates or conflicting interests and offer well-reasoned solutions based on legal analysis and policy considerations.
  • Seek Feedback and Refine Your Topic : Once you have narrowed down your research paper topic, seek feedback from peers, professors, or mentors. Be open to refining your topic based on constructive criticism and suggestions. A well-defined and thoughtfully chosen research topic will set the stage for a successful and impactful research paper.

Choosing the right intellectual property law research paper topic requires careful consideration, passion, and a keen awareness of current developments in the field. By identifying your interests, staying updated on legal developments, narrowing down the scope, conducting preliminary research, and seeking guidance from experts, you can select a compelling and relevant topic that contributes to the academic discourse in intellectual property law. A well-chosen research topic will not only showcase your expertise and analytical skills but also provide valuable insights into the complexities and challenges of intellectual property law in the modern world.

How to Write an Intellectual Property Law Research Paper

Writing an intellectual property law research paper can be an intellectually stimulating and rewarding experience. However, it can also be a daunting task, especially for students who are new to the intricacies of legal research and academic writing. In this section, we will provide a comprehensive guide on how to write an effective and impactful intellectual property law research paper. From understanding the structure and components of the paper to conducting thorough research and crafting compelling arguments, these ten tips will help you navigate the writing process with confidence and proficiency.

  • Understand the Paper Requirements : Before diving into the writing process, carefully review the requirements and guidelines provided by your professor or institution. Pay attention to the paper’s length, formatting style (APA, MLA, Chicago/Turabian, Harvard, etc.), citation guidelines, and any specific instructions regarding the research paper topic or research methods.
  • Conduct In-Depth Research : A strong intellectual property law research paper is built on a foundation of comprehensive and credible research. Utilize academic databases, legal journals, books, and reputable online sources to gather relevant literature and legal precedents related to your chosen topic. Ensure that your research covers a wide range of perspectives and presents a well-rounded analysis of the subject matter.
  • Develop a Clear Thesis Statement : The thesis statement is the central argument of your research paper. It should be concise, specific, and clearly convey the main point you will be arguing throughout the paper. Your thesis statement should reflect the significance of your research topic and its contribution to the field of intellectual property law.
  • Create an Outline : An outline is a roadmap for your research paper, helping you organize your thoughts and ideas in a logical and coherent manner. Divide your paper into sections, each representing a key aspect of your argument. Within each section, outline the main points you will address and the evidence or analysis that supports your claims.
  • Introduction : Engage and Provide Context: The introduction of your research paper should captivate the reader’s attention and provide essential context for your study. Start with a compelling opening sentence or anecdote that highlights the importance of the topic. Clearly state your thesis statement and provide an overview of the main points you will explore in the paper.
  • Literature Review : In the early sections of your research paper, include a literature review that summarizes the existing research and scholarship on your topic. Analyze the key theories, legal doctrines, and debates surrounding the subject matter. Use this section to demonstrate your understanding of the existing literature and to identify gaps or areas where your research will contribute.
  • Legal Analysis and Argumentation : The heart of your intellectual property law research paper lies in your legal analysis and argumentation. Each section of the paper should present a well-structured and coherent argument supported by legal reasoning, case law, and relevant statutes. Clearly explain the legal principles and doctrines you are applying and provide evidence to support your conclusions.
  • Consider Policy Implications : Intellectual property law often involves complex policy considerations. As you present your legal arguments, consider the broader policy implications of your research findings. Discuss how your proposed solutions or interpretations align with societal interests and contribute to the advancement of intellectual property law.
  • Anticipate Counterarguments : To strengthen your research paper, anticipate potential counterarguments to your thesis and address them thoughtfully. Acknowledging and refuting counterarguments demonstrate the depth of your analysis and the validity of your position.
  • Conclusion : Recapitulate and Reflect: In the conclusion of your research paper, recapitulate your main arguments and restate your thesis statement. Reflect on the insights gained from your research and highlight the significance of your findings. Avoid introducing new information in the conclusion and instead, offer recommendations for further research or policy implications.

Writing an intellectual property law research paper requires meticulous research, careful analysis, and persuasive argumentation. By following the tips provided in this section, you can confidently navigate the writing process and create an impactful research paper that contributes to the field of intellectual property law. Remember to adhere to academic integrity and proper citation practices throughout your research, and seek feedback from peers or professors to enhance the quality and rigor of your work. A well-crafted research paper will not only demonstrate your expertise in the field but also provide valuable insights into the complexities and nuances of intellectual property law.

iResearchNet’s Research Paper Writing Services

At iResearchNet, we understand the challenges that students face when tasked with writing complex and comprehensive research papers on intellectual property law topics. We recognize the importance of producing high-quality academic work that meets the rigorous standards of legal research and analysis. To support students in their academic endeavors, we offer custom intellectual property law research paper writing services tailored to meet individual needs and requirements. Our team of expert writers, well-versed in the intricacies of intellectual property law, is committed to delivering top-notch, original, and meticulously researched papers that can elevate your academic performance.

  • Expert Degree-Holding Writers : Our team consists of experienced writers with advanced degrees in law and expertise in intellectual property law. They possess the necessary knowledge and research skills to create well-crafted research papers that showcase a profound understanding of the subject matter.
  • Custom Written Works : We take pride in producing custom-written research papers that are unique to each client. When you place an order with iResearchNet, you can be assured that your paper will be tailored to your specific instructions and requirements.
  • In-Depth Research : Our writers conduct thorough and comprehensive research to ensure that your intellectual property law research paper is well-supported by relevant legal sources and up-to-date literature.
  • Custom Formatting : Our writers are well-versed in various citation styles, including APA, MLA, Chicago/Turabian, and Harvard. We will format your research paper according to your specified citation style, ensuring accuracy and consistency throughout the paper.
  • Top Quality : We are committed to delivering research papers of the highest quality. Our team of editors reviews each paper to ensure that it meets the required academic standards and adheres to your instructions.
  • Customized Solutions : At iResearchNet, we recognize that each research paper is unique and requires a tailored approach. Our writers take the time to understand your specific research objectives and create a paper that aligns with your academic goals.
  • Flexible Pricing : We offer competitive and flexible pricing options to accommodate students with varying budget constraints. Our pricing is transparent, and there are no hidden fees or additional charges.
  • Short Deadlines : We understand that students may face tight deadlines. Our writers are skilled in working efficiently without compromising the quality of the research paper. We offer short turnaround times, including deadlines as tight as 3 hours.
  • Timely Delivery : Punctuality is a priority at iResearchNet. We ensure that your completed research paper is delivered to you on time, allowing you ample time for review and any necessary revisions.
  • 24/7 Support : Our customer support team is available 24/7 to assist you with any queries or concerns you may have. Feel free to contact us at any time, and we will promptly address your needs.
  • Absolute Privacy : We value your privacy and confidentiality. Your personal information and order details are treated with the utmost confidentiality, and we never share your data with third parties.
  • Easy Order Tracking : Our user-friendly platform allows you to easily track the progress of your research paper. You can communicate directly with your assigned writer and stay updated on the status of your order.
  • Money-Back Guarantee : We are committed to customer satisfaction. If, for any reason, you are not satisfied with the quality of the research paper, we offer a money-back guarantee.

When it comes to writing an exceptional intellectual property law research paper, iResearchNet is your reliable partner. With our team of expert writers, commitment to quality, and customer-centric approach, we are dedicated to helping you succeed in your academic pursuits. Whether you need assistance with choosing a research paper topic, conducting in-depth research, or crafting a compelling argument, our custom writing services are designed to provide you with the support and expertise you need. Place your order with iResearchNet today and unlock the full potential of your intellectual property law research.

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Are you ready to take your intellectual property law research to new heights? Look no further than iResearchNet for comprehensive and professional support in crafting your research papers. Our custom writing services are tailored to cater to your unique academic needs, ensuring that you achieve academic excellence and stand out in your studies. Let us be your trusted partner in the journey of intellectual exploration and legal research.

Take the first step toward unleashing the full potential of your intellectual property law research. Place your order with iResearchNet and experience the difference of working with a professional and reliable custom writing service. Our team of dedicated writers and exceptional customer support are here to support you every step of the way. Don’t let the challenges of intellectual property law research hold you back; empower yourself with the assistance of iResearchNet and set yourself up for academic success.

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Intellectual Property Law Dissertation Topics

Published by Ellie Cross at December 29th, 2022 , Revised On August 15, 2023

A dissertation or a thesis in the study area of intellectual property rights can be a tough nut to crack for students. Masters and PhD students of intellectual property rights often struggle to come up with a relevant and fulfilling research topic; this is where they should seek academic assistance from experts.

An individual, a group, an association, an organisation or a company that wants to claim ownership of a particular design, piece of art, piece of technology, piece of literature, or physical or virtual property must adhere to a specific set of rules. Without these regulations, known as intellectual property rights, concerning parties will not be secure, and anyone could easily steal from them. If someone else attempts to take the property, the original owners are guaranteed the right to keep and reclaim it.

So let’s take a look at the below list of unique and focused intellectual property law dissertation topics, so you can select one more suitable to your requirements and get started with your project without further delay. Don’t forget to read our free guide on writing a dissertation step by step after you have finalised the topic. 

A List Of Intellectual Property Law Dissertation Topics Is Provided Below

  • How can virtual companies ensure that copyright rules are followed while creating their logos, websites, goods, and designs?
  • What does it mean legally to own an original work of art or piece of property?
  • Can the most recent technical developments coexist peacefully with the present patent rules and system?
  • Does the UK’s intellectual property legislation protect the owners and users fairly and securely?
  • Is there a connection between European and British intellectual property laws?
  • Comparison of the institutions and regulations governing intellectual property in the US and the UK
  • What do fair pricing and fair dealing with copyright regulations mean?
  • Can a business or individual assert ownership of a colour scheme or hue?
  • The conflict between business law and trade secrets
  • The Difficult Relationship Between Intellectual Property and Contemporary Art
  • Trade-Related Aspects of IP Rights: A Workable Instrument for Enforcing Benefit Sharing
  • A US-UK Comparison of the Harmonization of UK Copyright and Trademark Damages
  • The difficulties brought by digitalisation and the internet are beyond the capacity of the copyright system to appropriately address them. Discuss
  • Which copyright laws can be cited as protecting software?
  • The law on online copyright infringement facilitation
  • The necessity for companies to safeguard their brand value should serve as the primary
  • Justification for trademark protection. The general welfare is only a secondary concern. Discuss
  • Intellectual property rights are being directly used by businesses and investors: IP privateering and contemporary letters of marque and reprisal
  • Decisions and dynamics in understanding the role of intellectual property in digital technology-based startups
  • Investigating conflicts between appropriable and collaborative openness in innovation
  • Assessing the strength and scope of our system for protecting the intellectual property rights of indigenous people
  • Assessing legal protections for intellectual property rights online
  • Does EU copyright legislation adequately balance the requirements of consumers and inventors?
  • A case study of the US is used to evaluate fair dealing in terms of copyright law.
  • Contrasting and comparing the US and UK intellectual property systems
  • Are consumers and owners protected and treated fairly under EU intellectual property law?
  • What effects has EU legislation had on the UK’s intellectual property system?
  • What more should be done to increase the efficacy of the US’s present intellectual property laws?
  • Analyzing how Brexit may affect the UK’s protection of intellectual property rights
  • An in-depth analysis of the UK’s invention and patenting system: Can the existing, rigid system stimulate innovation?

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When choosing a topic in intellectual property law, make sure your selection is based on your interests.

As an intellectual property rights law student, there are many areas you might base your thesis or dissertation on. For example, a copyright lawyer can defend the rights of creative works; a patent lawyer can provide lawful protection for inventors; and a trademark lawyer can assist with the protection of trademarks. There are also rights related to plant varieties, trade dresses, and industrial designs that you could investigate.

Dissertations take a lot of time and effort to complete. It is essential to seek writing assistance if you are struggling to complete the paper on time to ensure you don’t end up failing the module.

ResearchProspect is an affordable dissertation writing service with a team of expert writers who have years of experience in writing dissertations and are familiar with the ideal format.  P lace your order now !

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How to find intellectual property law dissertation topics.

To find Intellectual Property Law dissertation topics:

  • Study recent IP developments.
  • Examine emerging technologies.
  • Analyze legal debates and cases.
  • Explore global IP issues.
  • Consider economic implications.
  • Select a topic aligning with your passion and career goals.

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  • J Adv Pharm Technol Res
  • v.2(2); Apr-Jun 2011

Intellectual property rights: An overview and implications in pharmaceutical industry

Chandra nath saha.

Quality Assurance Department, Claris Lifesciences Ltd., Ahmedabad, Gujarat, India

Sanjib Bhattacharya

1 Pharmacognosy Division, Bengal School of Technology (A College of Pharmacy), Sugandha, Hooghly, West Bengal, India

Intellectual property rights (IPR) have been defined as ideas, inventions, and creative expressions based on which there is a public willingness to bestow the status of property. IPR provide certain exclusive rights to the inventors or creators of that property, in order to enable them to reap commercial benefits from their creative efforts or reputation. There are several types of intellectual property protection like patent, copyright, trademark, etc. Patent is a recognition for an invention, which satisfies the criteria of global novelty, non-obviousness, and industrial application. IPR is prerequisite for better identification, planning, commercialization, rendering, and thereby protection of invention or creativity. Each industry should evolve its own IPR policies, management style, strategies, and so on depending on its area of specialty. Pharmaceutical industry currently has an evolving IPR strategy requiring a better focus and approach in the coming era.

INTRODUCTION

Intellectual property (IP) pertains to any original creation of the human intellect such as artistic, literary, technical, or scientific creation. Intellectual property rights (IPR) refers to the legal rights given to the inventor or creator to protect his invention or creation for a certain period of time.[ 1 ] These legal rights confer an exclusive right to the inventor/creator or his assignee to fully utilize his invention/creation for a given period of time. It is very well settled that IP play a vital role in the modern economy. It has also been conclusively established that the intellectual labor associated with the innovation should be given due importance so that public good emanates from it. There has been a quantum jump in research and development (R&D) costs with an associated jump in investments required for putting a new technology in the market place.[ 2 ] The stakes of the developers of technology have become very high, and hence, the need to protect the knowledge from unlawful use has become expedient, at least for a period, that would ensure recovery of the R&D and other associated costs and adequate profits for continuous investments in R&D.[ 3 ] IPR is a strong tool, to protect investments, time, money, effort invested by the inventor/creator of an IP, since it grants the inventor/creator an exclusive right for a certain period of time for use of his invention/creation. Thus IPR, in this way aids the economic development of a country by promoting healthy competition and encouraging industrial development and economic growth. Present review furnishes a brief overview of IPR with special emphasis on pharmaceuticals.

BRIEF HISTORY

The laws and administrative procedures relating to IPR have their roots in Europe. The trend of granting patents started in the fourteenth century. In comparison to other European countries, in some matters England was technologically advanced and used to attract artisans from elsewhere, on special terms. The first known copyrights appeared in Italy. Venice can be considered the cradle of IP system as most legal thinking in this area was done here; laws and systems were made here for the first time in the world, and other countries followed in due course.[ 4 ] Patent act in India is more than 150 years old. The inaugural one is the 1856 Act, which is based on the British patent system and it has provided the patent term of 14 years followed by numerous acts and amendments.[ 1 ]

Types of Intellectual Properties and their Description

Originally, only patent, trademarks, and industrial designs were protected as ‘Industrial Property’, but now the term ‘Intellectual Property’ has a much wider meaning. IPR enhances technology advancement in the following ways:[ 1 – 4 ]

  • (a) it provides a mechanism of handling infringement, piracy, and unauthorized use
  • (b) it provides a pool of information to the general public since all forms of IP are published except in case of trade secrets.

IP protection can be sought for a variety of intellectual efforts including

  • (i) Patents
  • (ii) Industrial designs relates to features of any shape, configuration, surface pattern, composition of lines and colors applied to an article whether 2-D, e.g., textile, or 3-D, e.g., toothbrush[ 5 ]
  • (iii) Trademarks relate to any mark, name, or logo under which trade is conducted for any product or service and by which the manufacturer or the service provider is identified. Trademarks can be bought, sold, and licensed. Trademark has no existence apart from the goodwill of the product or service it symbolizes[ 6 ]
  • (iv) Copyright relates to expression of ideas in material form and includes literary, musical, dramatic, artistic, cinematography work, audio tapes, and computer software[ 7 ]
  • (v) Geographical indications are indications, which identify as good as originating in the territory of a country or a region or locality in that territory where a given quality, reputation, or other characteristic of the goods is essentially attributable to its geographical origin[ 8 ]

A patent is awarded for an invention, which satisfies the criteria of global novelty, non-obviousness, and industrial or commercial application. Patents can be granted for products and processes. As per the Indian Patent Act 1970, the term of a patent was 14 years from the date of filing except for processes for preparing drugs and food items for which the term was 7 years from the date of the filing or 5 years from the date of the patent, whichever is earlier. No product patents were granted for drugs and food items.[ 9 ] A copyright generated in a member country of the Berne Convention is automatically protected in all the member countries, without any need for registration. India is a signatory to the Berne Convention and has a very good copyright legislation comparable to that of any country. However, the copyright will not be automatically available in countries that are not the members of the Berne Convention. Therefore, copyright may not be considered a territorial right in the strict sense. Like any other property IPR can be transferred, sold, or gifted.[ 7 ]

Role of Undisclosed Information in Intellectual Property

Protection of undisclosed information is least known to players of IPR and also least talked about, although it is perhaps the most important form of protection for industries, R&D institutions and other agencies dealing with IPR. Undisclosed information, generally known as trade secret or confidential information, includes formula, pattern, compilation, programme, device, method, technique, or process. Protection of undisclosed information or trade secret is not really new to humanity; at every stage of development people have evolved methods to keep important information secret, commonly by restricting the knowledge to their family members. Laws relating to all forms of IPR are at different stages of implementation in India, but there is no separate and exclusive law for protecting undisclosed information/trade secret or confidential information.[ 10 ]

Pressures of globalisation or internationalisation were not intense during 1950s to 1980s, and many countries, including India, were able to manage without practising a strong system of IPR. Globalization driven by chemical, pharmaceutical, electronic, and IT industries has resulted into large investment in R&D. This process is characterized by shortening of product cycle, time and high risk of reverse engineering by competitors. Industries came to realize that trade secrets were not adequate to guard a technology. It was difficult to reap the benefits of innovations unless uniform laws and rules of patents, trademarks, copyright, etc. existed. That is how IPR became an important constituent of the World Trade Organization (WTO).[ 11 ]

Rationale of Patent

Patent is recognition to the form of IP manifested in invention. Patents are granted for patentable inventions, which satisfy the requirements of novelty and utility under the stringent examination and opposition procedures prescribed in the Indian Patents Act, 1970, but there is not even a prima-facie presumption as to the validity of the patent granted.[ 9 ]

Most countries have established national regimes to provide protection to the IPR within its jurisdiction. Except in the case of copyrights, the protection granted to the inventor/creator in a country (such as India) or a region (such as European Union) is restricted to that territory where protection is sought and is not valid in other countries or regions.[ 1 ] For example, a patent granted in India is valid only for India and not in the USA. The basic reason for patenting an invention is to make money through exclusivity, i.e., the inventor or his assignee would have a monopoly if,

  • (a) the inventor has made an important invention after taking into account the modifications that the customer, and
  • (b) if the patent agent has described and claimed the invention correctly in the patent specification drafted, then the resultant patent would give the patent owner an exclusive market.

The patentee can exercise his exclusivity either by marketing the patented invention himself or by licensing it to a third party.

The following would not qualify as patents:

  • (i) An invention, which is frivolous or which claims anything obvious or contrary to the well established natural law. An invention, the primary or intended use of which would be contrary to law or morality or injurious to public health
  • (ii) A discovery, scientific theory, or mathematical method
  • (iii) A mere discovery of any new property or new use for a known substance or of the mere use of a known process, machine, or apparatus unless such known process results in a new product or employs at least one new reactant
  • (iv) A substance obtained by a mere admixture resulting only in the aggregation of the properties of the components thereof or a process for producing such substance
  • (v) A mere arrangement or re-arrangement or duplication of a known device each functioning independently of one another in its own way
  • (vi) A method of agriculture or horticulture
  • (vii) Any process for the medicinal, surgical, curative, prophylactic diagnostic, therapeutic or other treatment of human beings or any process for a similar treatment of animals to render them free of disease or to increase their economic value or that of their products
  • (viii) An invention relating to atomic energy
  • (ix) An invention, which is in effect, is traditional knowledge

Rationale of License

A license is a contract by which the licensor authorizes the licensee to perform certain activities, which would otherwise have been unlawful. For example, in a patent license, the patentee (licensor) authorizes the licensee to exercise defined rights over the patent. The effect is to give to the licensee a right to do what he/she would otherwise be prohibited from doing, i.e., a license makes lawful what otherwise would be unlawful.[ 12 ]

The licensor may also license ‘know-how’ pertaining to the execution of the licensed patent right such as information, process, or device occurring or utilized in a business activity can also be included along with the patent right in a license agreement. Some examples of know-how are:

  • (i) technical information such as formulae, techniques, and operating procedures and
  • (ii) commercial information such as customer lists and sales data, marketing, professional and management procedures.

Indeed, any technical, trade, commercial, or other information, may be capable of being the subject of protection.[ 13 ]

Benefits to the licensor:

  • (i) Opens new markets
  • (ii) Creates new areas for revenue generation
  • (iii) Helps overcome the challenge of establishing the technology in different markets especially in foreign countries – lower costs and risk and savings on distribution and marketing expenses

Benefits to the licensee are:

  • (i) Savings on R&D and elimination of risks associated with R&D
  • (ii) Quick exploitation of market requirements before the market interest wanes
  • (iii) Ensures that products are the latest

The Role of Patent Cooperation Treaty

The patent cooperation treaty (PCT) is a multilateral treaty entered into force in 1978. Through PCT, an inventor of a member country contracting state of PCT can simultaneously obtain priority for his/her invention in all or any of the member countries, without having to file a separate application in the countries of interest, by designating them in the PCT application. All activities related to PCT are coordinated by the world intellectual property organization (WIPO) situated in Geneva.[ 14 ]

In order to protect invention in other countries, it is required to file an independent patent application in each country of interest; in some cases, within a stipulated time to obtain priority in these countries. This would entail a large investment, within a short time, to meet costs towards filing fees, translation, attorney charges, etc. In addition, it is assumed that due to the short time available for making the decision on whether to file a patent application in a country or not, may not be well founded.[ 15 ]

Inventors of contracting states of PCT on the other hand can simultaneously obtain priority for their inventions without having to file separate application in the countries of interest; thus, saving the initial investments towards filing fees, translation, etc. In addition, the system provides much longer time for filing patent application in the member countries.[ 15 , 16 ]

The time available under Paris convention for securing priority in other countries is 12 months from the date of initial filing. Under the PCT, the time available could be as much as minimum 20 and maximum 31 months. Further, an inventor is also benefited by the search report prepared under the PCT system to be sure that the claimed invention is novel. The inventor could also opt for preliminary examination before filing in other countries to be doubly sure about the patentability of the invention.[ 16 ]

Management of Intellectual Property in Pharmaceutical Industries

More than any other technological area, drugs and pharmaceuticals match the description of globalization and need to have a strong IP system most closely. Knowing that the cost of introducing a new drug into the market may cost a company anywhere between $ 300 million to $1000 million along with all the associated risks at the developmental stage, no company will like to risk its IP becoming a public property without adequate returns. Creating, obtaining, protecting, and managing IP must become a corporate activity in the same manner as the raising of resources and funds. The knowledge revolution, which we are sure to witness, will demand a special pedestal for IP and treatment in the overall decision-making process.[ 17 ]

Competition in the global pharmaceutical industry is driven by scientific knowledge rather than manufacturing know-how and a company's success will be largely dependent on its R&D efforts. Therefore, investments in R&D in the drug industry are very high as a percentage of total sales; reports suggest that it could be as much as 15% of the sale. One of the key issues in this industry is the management of innovative risks while one strives to gain a competitive advantage over rival organizations. There is high cost attached to the risk of failure in pharmaceutical R&D with the development of potential medicines that are unable to meet the stringent safety standards, being terminated, sometimes after many years of investment. For those medicines that do clear development hurdles, it takes about 8-10 years from the date when the compound was first synthesized. As product patents emerge as the main tools for protecting IP, the drug companies will have to shift their focus of R&D from development of new processes for producing known drugs towards development of a new drug molecule and new chemical entity (NCE). During the 1980s, after a period of successfully treating many diseases of short-term duration, the R&D focus shifted to long duration (chronic) diseases. While looking for the global market, one has to ensure that requirements different regulatory authorities must be satisfied.[ 18 ]

It is understood that the documents to be submitted to regulatory authorities have almost tripled in the last ten years. In addition, regulatory authorities now take much longer to approve a new drug. Consequently, the period of patent protection is reduced, resulting in the need of putting in extra efforts to earn enough profits. The situation may be more severe in the case of drugs developed through the biotechnology route especially those involving utilization of genes. It is likely that the industrialized world would soon start canvassing for longer protection for drugs. It is also possible that many governments would exercise more and more price control to meet public goals. This would on one hand emphasize the need for reduced cost of drug development, production, and marketing, and on the other hand, necessitate planning for lower profit margins so as to recover costs over a longer period. It is thus obvious that the drug industry has to wade through many conflicting requirements. Many different strategies have been evolved during the last 10 to 15 years for cost containment and trade advantage. Some of these are out sourcing of R&D activity, forming R&D partnerships and establishing strategic alliances.[ 19 ]

Nature of Pharmaceutical Industry

The race to unlock the secrets of human genome has produced an explosion of scientific knowledge and spurred the development of new technologies that are altering the economics of drug development. Biopharmaceuticals are likely to enjoy a special place and the ultimate goal will be to have personalized medicines, as everyone will have their own genome mapped and stored in a chip. Doctors will look at the information in the chip(s) and prescribe accordingly. The important IP issue associated would be the protection of such databases of personal information. Biotechnologically developed drugs will find more and more entry into the market. The protection procedure for such drug will be a little different from those conventional drugs, which are not biotechnologically developed. Microbial strains used for developing a drug or vaccine needs to be specified in the patent document. If the strain is already known and reported in the literature usually consulted by scientists, then the situation is simple. However, many new strains are discovered and developed continuously and these are deposited with International depository authorities under the Budapest Treaty. While doing a novelty search, the databases of these depositories should also be consulted. Companies do not usually go for publishing their work, but it is good to make it a practice not to disclose the invention through publications or seminars until a patent application has been filed.[ 20 ]

While dealing with microbiological inventions, it is essential to deposit the strain in one of the recognized depositories who would give a registration number to the strain which should be quoted in the patent specification. This obviates the need of describing a life form on paper. Depositing a strain also costs money, but this is not much if one is not dealing with, for example cell lines. Further, for inventions involving genes, gene expression, DNA, and RNA, the sequences also have to be described in the patent specification as has been seen in the past. The alliances could be for many different objectives such as for sharing R&D expertise and facilities, utilizing marketing networks and sharing production facilities. While entering into an R&D alliance, it is always advisable to enter into a formal agreement covering issues like ownership of IP in different countries, sharing of costs of obtaining and maintaining IP and revenue accruing from it, methods of keeping trade secrets, accounting for IP of each company before the alliance and IP created during the project but not addressed in the plan, dispute settlements. It must be remembered that an alliance would be favorable if the IP portfolio is stronger than that of concerned partner. There could be many other elements of this agreement. Many drug companies will soon use the services of academic institutions, private R&D agencies, R&D institutions under government in India and abroad by way of contract research. All the above aspects mentioned above will be useful. Special attention will have to be paid towards maintaining confidentiality of research.[ 1 – 18 ]

The current state of the pharmaceutical industry indicates that IPR are being unjustifiably strengthened and abused at the expense of competition and consumer welfare. The lack of risk and innovation on the part of the drug industry underscores the inequity that is occurring at the expense of public good. It is an unfairness that cannot be cured by legislative reform alone. While congressional efforts to close loopholes in current statutes, along with new legislation to curtail additionally unfavorable business practices of the pharmaceutical industry, may provide some mitigation, antitrust law must appropriately step in.[ 21 ] While antitrust laws have appropriately scrutinized certain business practices employed by the pharmaceutical industry, such as mergers and acquisitions and agreements not to compete, there are several other practices that need to be addressed. The grant of patents on minor elements of an old drug, reformulations of old drugs to secure new patents, and the use of advertising and brand name development to increase the barriers for generic market entrants are all areas in which antitrust law can help stabilize the balance between rewarding innovation and preserving competition.[ 20 ]

Traditional medicine dealing with natural botanical products is an important part of human health care in many developing countries and also in developed countries, increasing their commercial value. The world market for such medicines has reached US $ 60 billion, with annual growth rates of between 5% and 15%. Although purely traditional knowledge based medicines do not qualify for patent, people often claim so. Researchers or companies may also claim IPR over biological resources and/or traditional knowledge, after slightly modifying them. The fast growth of patent applications related to herbal medicine shows this trend clearly. The patent applications in the field of natural products, traditional herbal medicine and herbal medicinal products are dealt with own IPR policies of each country as food, pharmaceutical and cosmetics purview, whichever appropriate. Medicinal plants and related plant products are important targets of patent claims since they have become of great interest to the global organized herbal drug and cosmetic industries.[ 22 ]

Some Special Aspects of Drug Patent Specification

Writing patent specification is a highly professional skill, which is acquired over a period of time and needs a good combination of scientific, technological, and legal knowledge. Claims in any patent specification constitute the soul of the patent over which legal proprietary is sought. Discovery of a new property in a known material is not patentable. If one can put the property to a practical use one has made an invention which may be patentable. A discovery that a known substance is able to withstand mechanical shock would not be patentable but a railway sleeper made from the material could well be patented. A substance may not be new but has been found to have a new property. It may be possible to patent it in combination with some other known substances if in combination they exhibit some new result. The reason is that no one has earlier used that combination for producing an insecticide or fertilizer or drug. It is quite possible that an inventor has created a new molecule but its precise structure is not known. In such a case, description of the substance along with its properties and the method of producing the same will play an important role.[ 23 ]

Combination of known substances into useful products may be a subject matter of a patent if the substances have some working relationship when combined together. In this case, no chemical reaction takes place. It confers only a limited protection. Any use by others of individual parts of the combination is beyond the scope of the patent. For example, a patent on aqua regia will not prohibit any one from mixing the two acids in different proportions and obtaining new patents. Methods of treatment for humans and animals are not patentable in most of the countries (one exception is USA) as they are not considered capable of industrial application. In case of new pharmaceutical use of a known substance, one should be careful in writing claims as the claim should not give an impression of a method of treatment. Most of the applications relate to drugs and pharmaceuticals including herbal drugs. A limited number of applications relate to engineering, electronics, and chemicals. About 62% of the applications are related to drugs and pharmaceuticals.[ 1 – 24 ]

CONCLUSIONS

It is obvious that management of IP and IPR is a multidimensional task and calls for many different actions and strategies which need to be aligned with national laws and international treaties and practices. It is no longer driven purely by a national perspective. IP and its associated rights are seriously influenced by the market needs, market response, cost involved in translating IP into commercial venture and so on. In other words, trade and commerce considerations are important in the management of IPR. Different forms of IPR demand different treatment, handling, planning, and strategies and engagement of persons with different domain knowledge such as science, engineering, medicines, law, finance, marketing, and economics. Each industry should evolve its own IP policies, management style, strategies, etc. depending on its area of specialty. Pharmaceutical industry currently has an evolving IP strategy. Since there exists the increased possibility that some IPR are invalid, antitrust law, therefore, needs to step in to ensure that invalid rights are not being unlawfully asserted to establish and maintain illegitimate, albeit limited, monopolies within the pharmaceutical industry. Still many things remain to be resolved in this context.

Source of Support: Nil

Conflict of Interest: Nil.

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AI and IP: Theory to Policy and Back Again – Policy and Research Recommendations at the Intersection of Artificial Intelligence and Intellectual Property

  • Open access
  • Published: 20 June 2023
  • Volume 54 , pages 916–940, ( 2023 )

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  • Peter Georg Picht 1 &
  • Florent Thouvenin 2  

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The interaction between artificial intelligence and intellectual property rights (IPRs) is one of the key areas of development in intellectual property law. After much, albeit selective, debate, it seems to be gaining increasing practical relevance through intense AI-related market activity, an initial set of case law on the matter, and policy initiatives by international organizations and lawmakers. Against this background, Zurich University’s Center for Intellectual Property and Competition Law is conducting, together with the Swiss Intellectual Property Institute, a research and policy project that explores the future of intellectual property law in an AI context. This paper briefly describes the AI/IP Research Project and presents an initial set of policy recommendations for the development of IP law with a view to AI. The recommendations address topics such as AI inventorship in patent law; AI authorship in copyright law; the need for sui generis rights to protect innovative AI output; rules for the allocation of AI-related IPRs; IP protection carve-outs in order to facilitate AI system development, training, and testing; the use of AI tools by IP offices; and suitable software protection and data usage regimes.

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1 Introduction

The interaction between artificial intelligence (AI) and intellectual property rights (IPRs) is one of the key areas of development in intellectual property law. After much, albeit selective, debate, it seems to be gaining increasing practical relevance through intense AI-related market activity, an initial set of case law on the matter, and policy initiatives by international organizations (e.g. WIPO, EPO) and lawmakers.

Against this background, Zurich University’s Center for Intellectual Property and Competition Law (CIPCO) is conducting, together with the Swiss Intellectual Property Institute (IPI), Footnote 1 a research and policy project (hereinafter the “AI/IP Research Project” or “Project”) that explores the future of intellectual property law in an AI context. This paper briefly describes the AI/IP Research Project (Sect. 2 ) and presents (Sect. 3 ) an initial set of policy and research recommendations (“Recommendations”) for the development of IP law with a view to AI. It concludes (Sect. 4 ) with a look at possible topics for additional recommendations. For a terminological and technical description of artificial intelligence, Footnote 2 and for further background to the Recommendations below, as well as for AI/IP aspects that they do not address, this paper refers to the rich existing literature. Footnote 3

2 The AI/IP Research Project

Initiated in 2021, the AI/IP Research Project aims at (i) gaining an overview of the current state of affairs in AI/IP, (ii) assessing issues crucial at the present stage, and (iii) deriving policy recommendations for how European jurisdictions, including Switzerland, should position themselves in international collaboration and in national law-making regarding AI/IP. Methodologically, the Project chooses a multi-component approach that has included, so far, mainly a comparative analysis of the AI/IP law situation – across the range of major IP rights Footnote 4 – in various jurisdictions, the gathering of first-hand empirical evidence through stakeholder input (e.g. industry representatives, specialized counsel, members of state and supra-state IP administrations), and an interdisciplinary exchange with innovation economists and computer scientists specializing in AI. As a backbone of its 2021/2022 activities, besides desk research work, the Project conducted a series of workshops Footnote 5 in which legal, economic and technical experts, as well as company representatives and other stakeholders, presented and discussed key AI/IP aspects. Footnote 6 Our warmest thanks go to all those who have participated and are participating in the Project. Footnote 7 Their support is invaluable in the attempt to further an appropriate IP law framework for AI. At its next stage, the Project will, inter alia , intensify the intra-disciplinary legal discourse with scholars working on AI from angles other than core IP law, e.g. data law, contract law, and liability law.

3 Policy Recommendations

We distinguish three types of Policy Recommendations: Implementation Recommendations intend to guide the next steps in law and policy-making. We think, based on previous discourse and experience, that their beneficial effects are likely enough to put them into practice. Consideration Recommendations describe courses which the law should probably take. Some further reflection and research seem, however, advisable before implementing them. Research Recommendations identify issues that research should address, to produce consideration recommendations on these matters as well.

3.1 Implementation Recommendations

3.1.1 inventorship in patent law, 3.1.1.1 recommendation.

The law should be amended to allow the designation of AI systems as inventors. Meanwhile, patent applications should be free to designate persons as “proxy inventors” while also describing the inventive activity of the AI system. There should be more disclosure on such inventive activity. AI systems’ innovative abilities must become part of the PHOSITA concept and related protectability thresholds.

Where an AI system generated inventive output without inventive human intervention, the patent application should be permitted to say so and name the AI system as the inventor, along with a natural person or legal entity who claims ownership of the patent application and a resulting patent.

Until legal rules have (where necessary) been changed to accommodate the above Recommendation, natural persons should – as a temporary workaround – be allowed to act and register as “proxy inventors”, as long as they disclose this role and the AI system for which they act as proxy. Such disclosure should be provided in the description.

More honest recognition of the increasingly innovative role AI systems play in invention processes, however, also calls for stricter requirements for patent applications to disclose details regarding the nature, extent, and mechanism of an AI system’s inventive contribution.

Furthermore, AI system abilities must become part of the PHOSITA Footnote 8 concept and related protectability thresholds. A potential raising of the protectability bar resulting therefrom is welcome as it mitigates the risk of AI patent thickets.

3.1.1.2 Background

The question of whether patent law can and should recognize AI systems as inventors, if they generate otherwise patentable technical solutions without an inventive contribution by humans, is arguably the most conspicuous issue in the current AI/IP landscape. Besides academic debate, Footnote 9 the multi-pronged DABUS litigation plays a key role as it probes into a range of the most important patent jurisdictions on whether their existing rules permit AI system inventorship. So far, the track record of patent applications based on the inventions (allegedly) made by DABUS is not a very successful one and the rejecting patent offices or courts seem right in finding that the currently applicable patent law rules are oriented to human, not AI inventorship. Footnote 10

De lege ferenda , however, at a forward-looking policy level, important reasons weigh in favour of patent applications that openly describe the role AI systems have played in the invention process. A need to definitively assess whether the human contribution to an invention, relative to the contribution made by an AI system, is sufficient to establish human inventorship, and thus patentability, unnecessarily harms legal certainty and uses patent office resources. It is one of the functions of the patent system to instruct the public about the progress of innovation and, thereby, to induce further innovation, for instance in the form of follow-on inventions. Necessitating patent applicants to disguise the true relation between human and AI contributions to an invention, because they must otherwise fear that their application will be rejected, hampers this function. Such impairment becomes even stronger where AI-generated inventions are not submitted for patenting at all but remain confined to the realm of trade secrets. In fact, industry participants in the Project state that companies do prefer trade secrets over patents for AI-generated inventions where they perceive a high risk of ending up – after having to disclose their invention in a patent application – without IP protection because the determinant role of their inventive AI systems, if admitted, prevents patentability.

Remarkably, these and further advantages of openness regarding inventive AI systems have made courts creative in searching for solutions even de lege lata , under the provisions of current patent law. The German Federal Patent Court (“ Bundespatentgericht ”) and the EPO Boards of Appeal now seem to accept a sort of proxy human inventorship. According to this concept, an application must still formally name a natural person as the inventor, but it can, at the same time, explain that the inventive acts were performed by an AI system. Footnote 11 Although unnecessarily complicated and formalistic, the proxy human inventor approach presents an acceptable transitional solution until patent laws can be changed as recommended here.

Even if this patent law reform occurs, it will not obviate the need to designate the natural – or possibly legal ( cf . Sect. 3.2.1 ) – person who becomes the initial owner of the granted patent and who, consequently, acquires the rights and obligations related to this position. Our Recommendation does not advocate patent ownership of AI systems. Since innovative human activity cannot be the parameter for determining initial ownership of patents on purely AI-generated inventions, the law must develop a set of different criteria ( cf . Sect. 3.3.2 ). This exercise is all the more worthwhile because its results are key for many an AI/IP setting: where increasing prowess and independence of AI systems render it difficult to assign legal rights to their output based on human inventorship, creatorship, or similar concepts, other parameters must step in to safeguard an allocation that is economically sound and apt to fulfil the goals of the IP system.

This is not to ignore the fact that a large part of the inventions made today and in the near future result (also) from a human contribution substantial enough to acknowledge human inventorship without difficulties. The pertinent part of the above Recommendation does not deal with AI-assisted inventions but with truly AI- generated ones. These hard cases may be rare – some would even say: non-existent – at present. But their relevance seems very likely to increase, and the law should be prepared by then.

Assessing the novelty of an invention against the state of the art researched with the help of AI systems, and only accepting steps as inventive which so appear from the perspective of a PHOSITA equipped with an ordinarily skilled AI system, will most probably raise the bar for patent protection. Footnote 12 In their contributions to the Project, some stakeholders voiced the concern that, as a result, only resourceful players, commanding exceptionally performant AI systems, may be able to acquire patents in the future. While we acknowledge the theoretical validity of this point, we do not see any empirical evidence that such a development is underway in larger sectors of the economy. Furthermore, it has always been the case that greater resources – such as laboratories with superior equipment, larger research departments, the ability to pay high wages to attract the best researchers, etc. – increased the chance of a market player to accumulate patents. In sum, we do not currently think that unlevel-playing-field concerns should prevent the integration of AI system capacities into the patentability assessment.

3.1.2 Human Authorship in Copyright Law

3.1.2.1 recommendation.

The principle of human authorship should prevail in copyright law – at least in the droit d’auteur systems. Hence, copyright protection should not be extended to works of literature and art created by an AI system without a human contribution even if they amount to a creation in the sense of copyright law. This result is achieved by applying the established criteria of human creation. At the same time, this allows for granting copyright protection for content that has been collectively created by an AI system and a natural person provided that the human contribution is sufficiently creative.

3.1.2.2 Background

Intellectual property law is traditionally based on the idea of one (or several) human creator(s). That is especially true for copyright law, at least for the droit d’auteur systems. In these systems, the idea of a human author is firmly rooted in many key provisions.

The human author plays a key role in the conditions for the protection of a work. According to settled case law of the ECJ, the concept of a work entails an original subject matter which is the author’s own intellectual creation. Footnote 13 Accordingly, there is no work without an author and such author must always be a natural person. Footnote 14 The situation is similar in Swiss law which only protects intellectual creations with an individual character (Art. 2(1) Copyright Act). The requirement of the intellectual creation means that only works created by natural persons can be protected by copyright. Footnote 15 The link of the intellectual character to a human author may be less direct but it is no less important; according to the key test, the requirement of individual character is met if no other individual would have created an identical or highly similar work. Footnote 16 The human being is also the key figure for copyright ownership as the original rightholder is always the author, i.e. the natural person who created the work. Footnote 17 In addition, droit d’auteur systems provide for a series of personality rights, such as the right to recognition as the author and the right to determine the author’s designation, Footnote 18 the right to decide on the first publication of the work, Footnote 19 the right to decide if the work may be altered and/or used to create a derivative work, Footnote 20 and the right to oppose a distortion of the work. Footnote 21 Finally, all copyright systems calculate the term of protection starting from the death of the author. Footnote 22

While some copyright systems have granted copyright protection for machine-generated content for years, Footnote 23 the droit d’auteur systems are hardly suitable to do so. A fundamental shift in these systems would be necessary to accommodate protection of machine-generated content by rethinking and adapting the provisions on the requirement of protection, the initial rightholder, the granting (and exercising) of personality rights and the duration and calculation of the term of protection. However, there are no convincing reasons why this should be done. While we acknowledge that there are some arguments for granting copyright protection to AI-generated works, these arguments seem rather weak. Most importantly, it may not necessarily be convincing to treat works that seem to be similarly “creative” in a fundamentally different way, just because one has been produced by a machine and the other by a human being. However, works of literature and art are public goods and granting exclusive rights to such goods requires a sound justification. Given that all other rationales for the justification of copyright protection (namely personality rights and the labour theory) are closely linked to human creators, the only potential rationale for granting copyright protection for machine-generated works is the need to provide incentives for creative activities. However, once an AI system has been developed, it can produce content such as text, images, music, films, and the like at almost zero marginal cost. While it may be important to grant some form of IP protection for the AI system, there is no need to incentivize the use of these systems by granting copyright protection to their output. Footnote 24

There are other instruments for protecting output that has been created in a fully automated manner and lawmakers (and courts) could improve such instruments, if necessary. Most importantly, the “copy paste” and use of AI-generated content may be captured by unfair competition law, namely by applying the general clause of most European unfair competition acts that allow to capture imitations and the copy-pasting of third-party content if certain conditions are met. Footnote 25 In Switzerland, Art. 5(c) Unfair Competition Act seems to be a good match. This provision captures all instances of taking over and exploiting another person’s marketable work product by means of a technical reproduction process without reasonable effort of the person or company that takes over and exploits the work product. Should unfair competition law prove to be insufficient to accommodate justified needs for protection, lawmakers could consider creating specific neighbouring rights for content generated by AI systems. Footnote 26 From today’s perspective, however, there seems to be no need for such new rights. Footnote 27 In addition, creative software output of AI systems – potentially including settings where an AI system creates another AI system – may be covered by the software protection regime envisaged in these Recommendations ( cf . Sect. 3.3.1 ).

In addition, it is important to bear in mind that denying copyright protection to AI-generated content does not mean that the producer cannot exploit such content on the market. Most importantly, such content can be protected by access restrictions and other technical measures, e.g. digital watermarks, to ensure that others cannot use it without paying a remuneration.

3.2 Consideration Recommendations

3.2.1 corporate iprs, 3.2.1.1 recommendation.

The law should consider allowing corporations and other legal entities to acquire initial ownership of (AI-generated) patents and patent-related IPRs (e.g. utility patents, but not copyrights), at least in cases of AI inventorship.

3.2.1.2 Background

The discussion whether legal entities should be able to acquire the right to a patent and – following the grant of the patent – initial patent ownership is not new. So far, and though dissenting ( de lege ferenda ) views always existed, Footnote 28 the prevailing response has been negative, Footnote 29 not least because today’s patent laws give much weight to a personalistic notion of inventorship, according to which there cannot be an invention without a (human) inventor. Footnote 30 When, however, an invention is generated by an AI system, this conception seems much less convincing. The assignment of legal rights and economic benefits relating to such inventions relies less on personalistic criteria. For instance, companies, and not their employees, will frequently bear the costs for building an inventive AI system and they, not their employees, will exercise legal and economic control over these systems. Insisting on human initial patent ownership in such settings risks distorting a coherent assignment of legal and economic rights to non-human inventions. The law should, therefore, consider relaxing the rules that allow for human initial patent ownership only. Footnote 31

3.2.2 Need for New IPRs Doubtful

3.2.2.1 recommendation.

Currently, there is no need to establish new sui generis IPRs for AI output. Neither current research insights nor current market realities suggest a need for new (sui generis) IPRs (including neighbouring rights) for innovative or creative AI output. Unless future research, including work done as part of the AI/IP project, proves the opposite, lawmakers should abstain from establishing such new types of IPRs.

Furthermore, there are currently no sound reasons for a two-tiered system of differing protection for human and AI inventions and creations. On the contrary, such a system seems prone to generate delimitation predicaments and to entice concealment or deliberate distortion of the genuine innovative process.

Such restraint does not exclude improvements of the current protection regime, for instance, in order to better accommodate software (including AI systems) produced by an AI system ( cf . Sect. 3.3.1 ), the way data rights are allocated, or the framework for trade secret protection.

Should future AI systems generate inventive output at a high rate and in a process that clearly lacks human inventive contribution, the situation may have to be reconsidered. Patent-like protection for such output, which is however weaker than the protective level of current patents, may become a preferable mechanism for allocating exploitation and transaction rights while avoiding over-protection.

3.2.2.2 Background

In academic discourse, proposals have been made for new types of intellectual property rights to protect the innovative or creative output of AI. Footnote 32 Sufficient IPR protection for the AI systems that generate such output seems, on the other hand, less of a concern. Our Patent Law Inventorship Recommendation ( cf . Sect. 3.1.1 ) helps to guarantee the structural availability of IPR protection for technical AI inventions. According to our Authorship in Copyright Law Recommendation ( cf . Sect. 3.1.2 ), restricted copyright protection for creative AI output constitutes not a failure but a virtue of the IPR system. A consensus against the establishment of distinct protection systems for human and AI-generated innovations has already been formed. Footnote 33 Mainly for the reasons stated in the above Recommendation, we support this position. Regarding inventive/creative output or other instances of valuable output generated by AI systems without substantial, innovative human contributions, neither the AI/IP Project nor – to our knowledge – other empirical or economic research ( cf . also Sect. 3.3.3 ) has proven current market failures or insufficient innovation incentives that necessitate the creation of new IPRs. Growing new plants in the already lush garden of IP rights comes at a cost – e.g. anti-commons problems, Footnote 34 transaction costs or interaction issues between the various IPRs – that should only be incurred based on solid evidence of their necessity. Putting another dent in the enthusiasm for new sui generis rights, none of the new IP rights introduced in the last 50 years has proven a real success. This applies, in particular, to the protection of databases through a sui generis right Footnote 35 and the legal protection of topographies of semiconductor products. Footnote 36 Even though it seems premature to assess the impact of the new neighbouring right for the protection of press publications, the chances of success of this new IP right seem doubtful as well. Footnote 37

We cannot, however, exclude the possibility that this picture may change in the future. New ways of detecting and deciding, with sufficient certainty, whether a human or an AI system generated a particular innovation may remove some qualms regarding a two-pronged protection system for human and AI inventions and creations. Extending patent protection at its current level (duration, scope of exclusivity, etc.) to AI-generated inventions may become an unacceptable impairment of dynamic efficiency and freedom to do business, if previsions come true that powerful AI systems will swamp the markets with innovative output at high rates and high quality. Then – and only then – should the law consider conceptualizing new types of limited IP protection, mainly for technical inventions. Such IPRs could combine the transactional benefits of a clear allocation of rights, Footnote 38 incentivization for the creation and maintenance of high-quality AI-systems, Footnote 39 disclosure of innovations to the public, and – for instance, through suitable licensing mechanisms – balanced access to protected content by other market participants. Utility patents do not necessarily provide a blueprint for such AI-specific, “narrow” IPRs, but at least they show that varying levels of protection for technical inventions is a concept that is workable and familiar to the IP system.

3.2.3 Broadened Research Exemption

3.2.3.1 recommendation.

Subject to further research, IP and data law should likely stipulate clearer and more permissive protection carve-outs to facilitate development, training, and testing of AI systems.

The development, training, and testing of AI systems requires the processing of very large amounts of data. Given the extremely broad definition of personal data in data protection laws, Footnote 40 much of these data are to be qualified as personal data and their use is thus subject to the provisions of the General Data Protection Regulation (GDPR) and other data protection laws. In many instances, the data used by AI systems are digital representations of works of literature and art. This is usually the case when AI systems are to recognise or produce text, images, music or films, and therefore need to be trained with corresponding copyright content. In addition, many data used by AI systems will be protected by the sui generis database right. Trade secret or patent protection, for instance, can also come into play. Using data for the development, training, and testing of AI systems may thus violate the provisions of the GDPR or infringe copyrights, the sui generis right in databases, or other intellectual property rights.

Patent and copyright laws, as well as other IP protection systems, contain provisions that allow the use of protected content for research and development, but it seems doubtful whether the existing exemptions are sufficiently broad and homogeneous across jurisdictions to allow for the desired use level of such content by AI systems. Footnote 41 The Database Directive, for instance, does not contain any research exemption for the sui generis right. European lawmakers should thus consider introducing broader research exemptions in copyright law, and creating a research exemption for the sui generis right in databases. Footnote 42

While the GDPR contains provisions that amount to a potentially quite broad research exemption, Footnote 43 it remains unclear if and to what extent this exemption can be applied to privilege the processing of personal data for the development, training, and testing of AI. Given the key importance of data (including personal data) and the lack of harm caused to data subjects by the processing of personal data in the development, testing, and training of AI systems as such (note though that harm may be caused to data subjects by using AI systems Footnote 44 ), we recommend that the GDPR’s research exemption should be interpreted in a way that facilitates such processing. Ideally, this interpretation should be explicitly promoted in an Opinion of the European Data Protection Board (EDPB) to provide legal certainty.

3.2.3.2 Background

Today’s IP and data protection laws were developed prior to the rise of AI. Footnote 45 Although patent, copyright, and data protection laws contain research exemptions, it is unclear if and to what extent these provisions can capture the use of personal data and IP-protected content if the respective data are used for the development, training, and testing of AI systems.

Regarding copyright law, the two exceptions for text and data mining introduced by the Digital Single Market (DSM) Directive Footnote 46 may mitigate the problem. The mandatory exception, however, only covers uses for scientific research by research organisations and cultural heritage institutions, thus excluding text and data mining in a commercial context. Footnote 47 The non-mandatory exception that also applies to commercial uses only covers cases in which text and data mining has not been expressly preserved by the rightholder. The scope of these exceptions is therefore limited. Moreover, the DSM Directive does not mention the use of text and data by AI systems. It is therefore unclear whether the exceptions also cover the use of copyright content for the development, training, and testing of AI systems. The recently introduced research exemption of the Swiss Copyright Act is substantially broader, covering all research and development (including for commercial purposes) and all reproductions that are necessary for technical reasons. Its deliberately broad wording should also cover the use of copyright-protected content by AI systems, both in a research and in a commercial setting.

The sui generis right allows the maker of a database to prohibit the extraction and/or re-utilization of the whole or a substantial part of the contents of a database. However, insubstantial parts may be used by lawful users of the database. While this certainly limits the restrictions of the sui generis right with respect to the use of data by AI systems, one must assume that there are many cases in which it would be useful to extract and re-use all or substantial parts of a database. Thus, the sui generis right imposes relevant restrictions on the use of data by AI systems. As opposed to copyright law, the Database Directive does not even contain an exception for text and data mining. In consequence, adding a broad research exemption to the Directive that also covers the use of data for the development, training, and testing of AI systems seems to be key. Importantly, such an exemption would not confer a standalone right of access to the data contained in a database. It would merely allow the use of data for research purposes if access to such data has already been granted, most often on a contractual basis and against remuneration.

European data protection laws, especially the GDPR, create significant obstacles to the use of data by AI systems, such as the principle of data minimization and purpose limitation, as well as the need to provide a legal basis for the processing of personal data. Footnote 48 Additional barriers stem from restrictive rules on the transfer of personal data to third countries and the increasingly impractical distinction between personal and non-personal data. While the GDPR contains provisions that potentially amount to a quite broad research exemption, Footnote 49 it remains unclear if and to what extent this exemption can be applied to privilege the processing of personal data for the development, training, and testing of AI. However, a suitable application of the research exemption can only be a first step. As outlined below, further research is needed to develop a suitable data usage framework. Footnote 50 In addition, clear and comprehensive data access and/or data use rights Footnote 51 should be established, regarding both personal and non-personal data, to facilitate the development, training, and testing of AI systems.

At the same time, protection carve-outs must not become a carte blanche for IPR infringement. Generative art (art with and through AI) is, for instance, a field in which legal rules need to carefully balance access to and protection of IPR-protected content. AI is a powerful tool for creating works such as films, music, or architectural designs. Such tools are already being offered to the general public for free. The conditions for use of such tools and their output (including sale as non-fungible tokens) vary greatly and can have important effects on the operating modes and business models of the artistic community. Some developers are not sufficiently aware of, or are not willing to abide by, copyright protection rules. Others miss out on advantageously structuring the use of their tools through contractual arrangements. Working, together with stakeholders, from this situation towards a more appropriate legal and factual framework for generative art constitutes a worthy task both for IP offices and for the general discourse on AI protection carve-outs.

3.3 Research Recommendations

3.3.1 new software protection regime, 3.3.1.1 recommendation.

Future research should develop a novel IP protection regime for software that could replace today’s two-tiered approach.

The current IP system does not provide a convincing protection regime for software. The interaction of its main instruments, copyright and patent protection, is far from ideal. The regime has evolved over time, driven by the approach to somehow incorporate software protection into the traditional IP system. However, software differs in important respects from both works of literature or art and from technical inventions. Fitting it into copyright and patent law thus necessitates many compromises. Software produced and employed by AI systems is a recent challenge of particular importance to today’s approach. Therefore, the development of AI systems makes it more urgent than ever to remedy the deficiencies of the current regime for software protection.

The dual system combining copyright and patent protection should be rethought and possibly replaced by a single IPR for software (including AI systems). Such a regime may combine a very limited sui generis protection (regarding both substance and duration) for unregistered software with a stronger protection for software registered in a software register. The granting of a strong IP right could come with (source code) disclosure requirements. Better tailored to promote innovation and to avoid overprotection, such a system may also allow for the closing of current protection loopholes, e.g. regarding complex, highly innovative modelling software.

Given the huge economic importance of software, the implementation of such far-reaching changes in its protection regime resembles open-heart surgery. These changes cannot be undertaken without thorough prior research and discussion. Such research must be interdisciplinary, involving not only legal scholars but also computer scientists and economists. All stakeholders’ (software developers, industry, IP offices, the open source community, and key user groups, etc.) views need to be collected and novel protection approaches need to be tested in a discourse with them.

Given the existing framework of IP treaties, a novel software protection regime could hardly replace the current system over night. But novel approaches could be introduced at a national and regional (e.g. EU) level alongside the existing regimes. If these approaches prove workable, they may well replace the current protection regimes de facto, namely if companies stop applying for software patents and enforcing copyrights. Traditional approaches for software protection may either continue to (formally) exist or be abandoned altogether at a later point in time.

3.3.1.2 Background

Software has always been a sort of outsider among the subject matters of the IP system. The protection regimes that are applied to computer programs were developed long before software even existed. As it seemed virtually impossible to create an entirely new IP right to capture software in the 1980s and 1990s, national lawmakers and international organisations had no choice but to accommodate software in the existing IP regime. The obvious choice was copyright as it came with a series of benefits, the most important ones arguably being that the existing international regime allowed for an almost worldwide protection without the need for application, examination, registration, and payment of fees. In addition, the inclusion of software in patent law was blocked (at least) for the member states of the European Patent Convention as Art. 52(2)(c) EPC states that programs for computers cannot be considered inventions. The “linguistic approach”, focussing on the expression of algorithms in the source code, permitted software to be treated similarly to works of literature and art, Footnote 52 thus allowing copyright protection. With partial amendments to copyright law, e.g. on decompilation Footnote 53 or shortened protection terms, Footnote 54 some steps were taken towards a software-specific protection regime, but without accomplishing this task.

Irrespective of this integration process, businesses also sought the benefits of patent protection for their software. In the US, such patents were granted on a relatively broad basis following a series of Supreme Court decisions in the 1970s and 1980s, culminating in Diamond v. Diehr in 1981, Footnote 55 and subsequent decisions by the Court of Appeal for the Federal Circuit. Footnote 56 Europe remained reluctant, given the provisions in the EPC that excluded patents for computer programs as such (Art. 52(2)(c) and (3) EPC). While patents were (and still are) unavailable for mere computer programs, they were eventually granted for so-called “computer-implemented inventions”. Footnote 57 Over time, the more permissive US and the more restrictive European approach have converged to a certain extent. Inter alia , the US system became more stringent, and moved much closer to the European approach, with the Supreme Court’s Alice decision. Footnote 58

As a result of these historical developments, software can be protected by both patents and copyrights in the major jurisdictions. While it is not uncommon for several IP rights to protect a given object – e.g. copyright, design, patent, and trade mark rights to protect the design of a car – it is quite unusual for a given subject matter category to be explicitly covered by more than one IP right. Not surprisingly, this two-tier system of protection leads to contradictory results. For instance, despite the expiry of patent protection after 20 years, previously patented software does not fall into the public domain but remains protected by copyright for a much longer period of time.

A major problem of the current software protection regime is the fact that neither copyright nor patent law are well suited for this subject matter. Software is different from both technical inventions and works of literature and art. IP protection granted to it should be keyed to these particularities. For example, many software products (e.g. operating systems) cannot be substituted by others because they have become de jure or de facto standards. Software products need to be integrated into a (usually pre-existing) framework of hardware and software, which requires interoperability that can only be ensured if application programming interfaces (API) are provided or – where necessary – lawfully developed through reverse engineering. In digital economies, software assumes a sort of infrastructure role for ever more products and services. Also, in view of these characteristics, the strong protection (duration, degree of exclusivity, etc.) granted by the combination of copyrights and patent rights seems problematic at least for certain types of software (e.g. update patches). Licensing transactions ensuring freedom to operate are hampered by difficulties in determining software ownership and by multi-owner IPR thickets. Fragmented statutory rules and market developments, such as the “open source” movement, have patched some of these issues. Others have led to highly complex and year-long proceedings before competition authorities. Footnote 59 A well-tailored protection framework, including built-in limitations that secure access rights where needed, promises many advantages over these makeshift approaches. It becomes all the more desirable with a view to AI systems consisting, in essential parts, of software and generating large-scale software output the protection status of which is far from evident. Footnote 60

While it seems that software developers and the industries producing and using software have learned to cope with the current software protection framework, important issues remain unresolved. Moreover, the mere fact that developers and the industry have learned to make the best of the current software protection regime in no way precludes that a much better system could be created, i.e. a system that leads to faster and cheaper innovation and raises fewer competition issues.

3.3.2 AI Inventorship and IPR Allocation Parameters

3.3.2.1 recommendation.

Future research should develop a comprehensive grid for the allocation of entitlements resulting from innovations generated by AI systems.

As a key consequence of loosening the ties between the generation of innovative output (by AI systems) and the ownership of resulting IPRs (by natural or legal persons), research must work out a more comprehensive grid for the sound allocation of IP entitlements resulting from innovations generated by AI systems. This concerns a broad range of IPRs (e.g. patents, utility patents, design rights, and new forms of software protection), as well as settings where complementary innovative activity is undertaken by AI systems and human individuals or teams.

3.3.2.2 Background

An appropriate allocation of AI output-related IPRs to natural or legal persons sets the conditions for achieving the IP system’s goals, particularly the incentivization of innovation and the fostering of IP transactions – licensing in particular, but for instance also the use of IP as collateral in M&A and venture capital transactions – which help to disperse and implement protected content. The conduct-steering effect of liability as well as clear responsibilities in the IP system’s self-protection through the enforcement of IPRs against infringing use are further allocation-related benefits. Allocating rights and responsibilities to AI systems themselves is not an option due to these systems’ lack of personality in the legal sense.

There is already some discussion about parameters for allocating IPRs resulting from AI innovation. Footnote 61 Among the main candidates are creatorship of or investment in the output-generating AI system, control over the system at the time of innovation, and responsibility for task and output selection (choice-making). Furthermore, some jurisdictions have adopted statutory rules that assign – be it for AI settings or at a more general level – initial IPR ownership to persons other than the factual inventor. Footnote 62 However, these allocation elements do not yet form a sufficiently comprehensive framework. The additional questions such a framework would have to answer are manifold. What, for instance, is the – possibly sector-specific – hierarchy or relative weight of several applicable allocation parameters? In case different persons fulfil different allocation criteria, does this always result in co-ownership Footnote 63 or do certain allocation parameters (sometimes) outweigh others? For settings in which co-ownership turns out to be the result, are IP law’s present rules on co-ownership appropriate, even though there are no non-economic inventor/author rights to be protected? Assuming that certain groups of (co-)rightholders yield to requests that they waive their position, e.g. for fear of otherwise losing downstream clients, Footnote 64 should the law accept such contractual arrangements?

Arranging the answers to such questions into a suitable allocation regime requires profound research. Such research needs to include legal, economic and technological aspects, including an incentives analysis ( cf . 3.3.3) for accommodating novel allocation approaches.

3.3.3 Revisit Incentivization Necessities and Ownership Approach

3.3.3.1 recommendation.

The IPR system must not mechanically extend its traditional incentivization rationale to innovative AI output. AI systems themselves do not require incentivization. Effective and efficient incentives for natural/legal persons to engage in the development and use of high-quality AI systems, as well as in the implementation of and transactions over their innovative output, need not necessarily parallel traditional IPR incentives for human innovativeness. Traditional notions of ownership may have to be rethought and protection may be oriented more towards securing monetary rewards and freedom to operate than towards non-economic ownership rights.

3.3.3.2 Background

Economists point out that incentivization of AI outputs as such may be unnecessary or even detrimental, whereas it may drive innovation and dissemination to incentivize the commercialization of such outputs (including transactions over them) and the development of AI systems that generate them. Footnote 65 In view of the potentially high innovative output of (future) AI systems, granting full-fledged IPRs to each such output may, in particular, generate overcompensation and excessive IPR thickets. Research, in which economics looms large, must therefore explore incentivization exigencies and dynamics in the AI innovation field. It is crucial to avoid the unwanted effects of over- or under-protection on dynamic efficiency. Such research must also explore whether, and in which ways, the growing relevance of AI systems and data change the role IPRs play for businesses, both in daily practice and at a strategic level. Footnote 66

3.3.4 Data Usage Framework

3.3.4.1 recommendation.

Future research should develop a legal framework focusing on access, sharing and usage of (personal and non-personal) data for the common good while providing a suitable protection of privacy and workable means to protect individuals against harm resulting from data processing.

Next step research should specify legal cornerstones for enhancing the access to, usage and sharing of (personal and non-personal) data for the development, training, and testing of AI systems. Topics include novel approaches to data (protection) law, common data spaces, data pools, interoperability requirements, technical standards regarding syntax and semantics of data, and the (non-)mandatory, sector-specific licensing of data portfolios to AI users/developers on a FRAND basis.

These approaches must apply both to personal and non-personal data since access to and use of both types of data are key conditions for the development, training, and testing of many AI systems. Further research is needed on whether and to what extent the usage of personal data by AI systems risks engendering an infringement of data protection laws or personality rights, such as the right to protection of privacy. A potential way forward could be an in-depth analysis of the scope of current research exemptions in data protection laws (particularly the GDPR) to assess if these exemptions can be applied broadly to cover the usage of personal data by AI systems. But research should also consider entirely novel approaches that go beyond the idea of an all-encompassing regulation of the processing of personal data (as in the GDPR) but rather provide a workable protection of privacy and means to protect individuals against harm resulting from the processing of personal data (e.g. manipulation and discrimination) while opening up the usage of personal data for the common good. Footnote 67

This research must include both an interdisciplinary and an intra-disciplinary component. Obviously, workable data transaction frameworks cannot be conceived without the input of computer and data scientists. But even from a purely legal perspective, there are manifold issues that need to be considered beyond IP and data law, such as contract, competition and procedural law. Equally, the analysis of pertinent business models promises to be very fruitful, including collaboration between holders of large data sets and controllers of powerful AI systems.

In addition to opening up access to and use of data, in-depth research is needed to clarify the legal consequences if an AI system has been developed, trained, or tested with data that have been accessed or used unlawfully. Should this “infect” the AI system in some way, even if the system does not contain the unlawfully used data? Should the consequences be the same regardless of whether the data were used for the development, the training, or the testing of an AI system? And, should it matter whether vast or small amounts of data have been used in an unlawful way – possibly even just a single data point?

3.3.4.2 Background

Data are a key resource for AI operations, especially for AI systems that are based on machine learning. But use of and access to data are often restricted for various reasons. While the use of non-personal data is much less regulated and thus largely permissible, European data protection laws, especially the GDPR, impose significant restrictions on the use of personal data, the most important ones being: The principle of data minimization which requires that the processing of personal data be adequate, relevant and limited to what is necessary in relation to the purposes for which the data are processed; Footnote 68 this principle may inhibit the use of personal data for the training, and testing of an AI system. The principle of purpose limitation according to which data may only be collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes; Footnote 69 often, personal data would be a great resource for the development and training of an AI system, but that system might have a purpose which is different from the purpose for which the data were collected, e.g. geo-localization data collected by telecom service providers that could be used to train an AI system that helps to fight traffic jams and to balance public transportation occupancy. A major barrier for the use of personal data by AI systems is that some data protection laws, namely the GDPR, require a basis for the lawfulness of any processing of personal data, the most important ones being the data subject’s consent, Footnote 70 an overriding legitimate interest of the controller, Footnote 71 the need to process personal data for the performance of a contract to which the data subject is a party Footnote 72 or the need to process data for compliance with a legal obligation. Footnote 73 Although the range of possible reasons for the lawfulness of processing is quite broad, such a basis will often be lacking for the use of personal data for the development, training, and testing of AI systems.

Restrictions on access to data are another severe impediment. Companies are increasingly aware that they possess vast amounts of data that can be used in a productive way, e.g. for the training and testing of AI systems. This potential is tapped in a growing number of cases, either in house by the data holding company or through data transactions. In many other settings, though, data access and use fail. Some lawmakers, especially the EU, are enacting certain rules which aim at fostering data exchange and usage. For instance, the Open Data Directive Footnote 74 requires public sector bodies and public undertakings to make data available, including publicly funded, high-value research data. The Data Governance Act Footnote 75 should allow for the re-use of certain public sector data that cannot be made available as open data, e.g. health data. The Draft Data Act Footnote 76 will allow users of IoT devices to gain access to data generated by these devices and to share such data with third parties, thus mitigating their exclusive harvesting by initial data collectors and holders. In addition, the act will include means for public sector bodies to access and use, in exceptional circumstances, data held by the private sector. The Digital Markets Act Footnote 77 obliges gatekeepers to provide data access and portability in various ways. However, research will have to investigate whether these measures and their impact on business models generate sufficient data access for the development, training, and testing of AI.

3.3.5 Use of AI Tools by IP Offices

3.3.5.1 recommendation.

IP offices should strive to exploit the capacity of AI systems in their own operations. This may include the determination of whether an application fulfils the respective protectability requirements (e.g. novelty and inventive step). As a sound medium-term prospect, AI tools will not replace humans in the examination of IPR applications but will become one element of an interactive approach in which human and AI skills are combined to complement each other.

AI tools could help to establish more coherent decision-making within and across IP offices. At the same time, the digitization and automation of IP office processes must maintain, or should even improve, the procedural protection for applicants and further parties to their procedures. As part of such protection, IP offices should strive to render their AI tools transparent and explainable, to the extent possible and reasonable. This could include the establishment of a freely available AI tools database that enables applicants and their agents to improve the quality of their IP filing and IP management and even pre-test the chances of success of their applications.

IP offices should, among themselves, pursue an approach of transparency, insight-sharing and cooperation, which does not exclude friendly competition for benchmark solutions. WIPO may pioneer such an approach.

3.3.5.2 Background

AI can be a tool, and not only a subject, for the work of IP offices. In fact, a number of AI pertinent projects are already run by offices such as Singapore’s IPOS, UKIPO, IPI, WIPO, and EPO. Footnote 78 Much more would be possible, however, and IP offices should engage in intense, cooperative research and discussion on how to implement the above recommendation. More generally, AI has much potential to optimize administrative processes. By reaping this potential, IP office processes could become blueprints for other branches of public administration. Research topics include the identification of suitable AI application fields, e.g. automatic patent/design classification, harmonization of lists of goods and services, computer vision treatment of pictures and similar items in IPR applications, natural language processing of application content, machine translation of applications and prior art searches, and tailor-made AI systems, e.g. adversarial networks, for protectability assessments. In addition, hands-on concepts for integrating AI skills into the PHOSITA standard and similar tests could be developed and data pools for training IP office AI systems could be established, including data-sharing between offices/jurisdictions and the usability of other government agencies’ data. In its network of IP office representatives, the AI/IP Research Project has detected much interest in these topics and enthusiasm to pursue them cooperatively. The Project aims at becoming a catalyst for such cooperation.

The Zurich AI/IP Group fully recognizes that the interplay between AI and IP involves many further aspects. At this stage, the Policy and Research Recommendations cannot specifically address all of them. This section presents a – very much non-exhaustive – list of additional AI/IP topics, which may also become a focus of the Group’s future work.

Patterns of AI innovativeness and creativeness How do AI systems actually go about innovating and creating, both in the field of technical inventions and in areas such as “generative art”, today and in the foreseeable future? This topic is highly interdisciplinary, likely even driven by non-legal, technical/IT disciplines.

Liability regime for IPR/data law infringements by AI systems For instance, the ramifications of the black-box nature of AI systems; ways to increase predictability of use of IPR-protected content by AI systems; ways to build IP law compliance into AI systems; consequences of AI systems processing data the use of which is (partially) unlawful, e.g. lack of a basis for the lawfulness of processing; partial switch to a liability rule regime instead of injunctions; parameters for allocating liability to be in sync with entitlement allocation rules; and the need for mandatory precautions (insurance, reserves, etc.) by small providers of AI systems.

Consistency of the broader legal framework for AI Lawmakers around the globe are working on solutions to address the challenges caused by the use of AI systems in general, beyond the aspect of AI and IP. Important proposals, such as the EU Commission’s draft AI Act, do not specifically address IP issues. AI-related changes to the IP system must aim at consistency with general AI regulation and potential sector-specific AI regulations. Documentation, notification, and disclosure obligations on AI system users present an example for an area where AI/IP considerations and general AI regulation may overlap.

Both institution and Office anonymized for the purposes of this submission.

We are fully aware that there are important differences between the systems we lump together under the term “artificial intelligence” and that there is an ongoing debate on how the term can be defined from the perspective of the law. We thank the reader for bearing with the generalizations made in this Project, permitting a more concise Recommendations document.

See , for instance, European Commission’s High-Level Expert Group on AI (2018); OECD High-Level General Definition of AI Systems, https://oecd.ai/en/wonk/a-first-look-at-the-oecds-framework-for-the-classification-of-ai-systems-for-policymakers ; Chen et al. ( 2017 ); Krafft et al. ( 2020 ), p. 73 et seq .; Schuett ( 2021 ), p. 3 et seq .; Ongsulee ( 2017 ); Van Roy et al. ( 2019 ), p. 5 et seq .; Klinger et al. ( 2018 ), p. 4. Insights on the state of AI/IP affairs gained so far by the Project are canvassed, in greater detail, in Picht et al. ( 2023 ).

So far, the AI/IP discussion shows a certain, understandable focus on patent law. However, a conceptual, holistic policy project on AI/IP, such as the Project described here, must not overlook the important issues and developments in other areas of IP law, especially copyright and trade secrets law.

To the extent these workshops were conducted online, recordings are available at https://www.cipco.uzh.ch/de/veranstaltungen/IP-KI (all web sources last accessed 16 February 2023).

For an account of important findings thus gained, see Picht et al. ( 2023 ).

In particular Abraham Bernstein (University of Zurich), Alberto Russo (EUIPO), Alessandro Curioni (IBM), Alexander Klenner-Bajaja (EPO), Alicia Daly (WIPO), Anaic Cordoba (Swiss IPI), Angel Aledo Lopez (EPO), Beat Weibel (Siemens), Begonia Gonzalez Otero (Max Planck Institute for Innovation and Competition), representatives of the Intellectual Property Office of Singapore, Craig MacMillan (Canadian IPO), Daryl Lim (Penn State University), Emily Miceli (UKIPO), Felix Addor (Swiss IPI), Fernando Peregrino Torregrosa (EUIPO), Gaetan de Rassenfosse (EPFL), Hansueli Stamm (Swiss IPI), Heli Pihlajamaa (EPO), Ian Grimstead (UKIPO), Joseph Walton (UKIPO), Juan Bernabe-Moreno (IBM), Kate Gaudry (Kilpatrick Townsend), Martin Bader (University of St Gallen), Michael May (Siemens), Michael Schröder (ERNI AG), Naomi Häfner (University of St Gallen), Nicki Curtis (UKIPO), Peter R. Thomsen (Novartis), Pierre Olivier (UKIPO), Ryan Abbott (University of Surrey, DABUS Project), Sabrina Konrad (Swiss IPI), Samir Ghamir-Doudane (INPI), Sita Mazumder (Lucerne University of Applied Sciences and Arts), Ulrike Till (WIPO) and Yann Ménière (EPO, MINES ParisTech).

Person having ordinary skills in the art (“ Durchschnittsfachmann ”).

See , for instance, Bonadio et al. ( 2021 ), pp. 48–66; Konertz and Schönhof ( 2018 ), pp. 379–412; Shemtov ( 2019 ).

For an overview of the litigation and discussion, see Picht, Brunner and Schmid ( 2023 ); for continuous updates on the DABUS applications and litigations, see also https://artificialinventor.com/patent-applications/ .

Boards of Appeal of the European Patent Office, J 0008/20 – 3.1.01 and J 0009/20 – 3.1.01, 4.3.7; Federal Patent Court, 11 W (pat) 5/21, II.2.c.

Abbott ( 2016 ), pp. 1079, 1125; Ménière and Pihlajamaa ( 2019 ), p. 334; Bonadio et al. ( 2021 ), p. 54; Fabris ( 2020 ), p. 691 et seq .; Fraser ( 2016 ), p. 321; Käde ( 2021 ), p. 558; Lim ( 2018 ), p. 863; sceptical regarding an impact of AI on the PHOSITA tests, Burk ( 2021 ), p. 308 and Simon ( 2013 ), p. 377.

ECJ decision of 13 November 2018, Levola Hengelo BV/Smilde Foods BV , C-310/17, para. 37; ECJ decision of 4 October 2011, Football Association Premier League , C-403/08 and C-429/08, para. 159; ECJ decision of 16 July 2009, Infopaq International , C-5/08, para. 39.

Senftleben and Buijtelaar ( 2020 ), p. 7; de Cock Buning ( 2016 ), p. 314; Bullinger ( 2022 ), para. 15; Loewenheim and Pfeifer ( 2020 ), para. 2  et seq .

Egloff ( 2020 ), para. 2; Hug ( 2012 ), para. 3  et seq .

Swiss Federal Supreme Court, BGE 142 III 387, para. 3.1.; von Büren and Meer ( 2014 ), para. 178.

For European law, see : Sec. 7 of the German Copyright Act; Art. L111-1 of the French Intellectual Property Code (IPC); Sec. 10(1) of the Austrian Copyright Act. For Swiss law: Art. 6 Copyright Act.

For European law, see : Sec. 13 of the German Copyright Act; Art. L121-1 of the French Intellectual Property Code (IPC); Sec. 20(1) of the Austrian Copyright Act; Art. 8 of the Italian Copyright Statute. For Swiss law: Art. 9 Copyright Act.

For European law, see : Sec. 12(1) of the German Copyright Act; Art. L121-2 of the French Intellectual Property Code (IPC); Art. 12 of the Italian Copyright Statute. For Swiss law: Art. 9(2) Copyright Act.

For European law, see : Art. L121-1 of the French Intellectual Property Code (IPC); Sec. 21(1) of the Austrian Copyright Act; see also Sec. 23 of the German Copyright Act; Art. 18 of the Italian Copyright Statute. For Swiss law: Art. 11(1) Copyright Act.

For European law, see : Sec. 14 of the German Copyright Act; Art. L121-1 of the French Intellectual Property Code (IPC); Sec. 21(3) of the Austrian Copyright Act; Art. 25 of the Italian Copyright Statute. For Swiss law: Art. 11(2) Copyright Act.

Art. 7(1) Berne Convention for the Protection of Literary and Artistic Works (as amended on 28 September 1979).

E.g ., UK ( cf . Sec. 9(3), Sec. 12(7) and the definition of “computer-generated” in Sec. 178 CDPA, although some exceptions apply, cf . Sec. 79(2)(c) and Sec. 81(2) CDPA); Ireland ( cf . Sec. 21(f), Sec. 30, and the definition of “computer-generated” in Sec. 2(1) of the Copyright and Related Works Act); Hong Kong ( cf . Sec. 11(3), Sec. 17(6) and the definition of “computer-generated” in Sec. 198 of the Copyright Ordinance, although some exceptions apply, cf . Sec. 91(2)(c) and Sec. 93(2) of the Copyright Ordinance).

Note that this may be different if AI systems are used to produce inventions. Cf . Senftleben and Buijtelaar ( 2020 ), pp. 18–20, 23, who recommend to instead adopt a neighbouring rights approach.

Cf . Sec. 4(3) of the German Unfair Competition Act; Sec. 1(1)(1) of the Austrian Unfair Competition Act; regarding Scandinavian countries, Viken Monica (2020), passim .

Senftleben and Buijtelaar ( 2020 ), passim ; Dornis ( 2020 ), p. 44 et seq .

See Sect. 3.2.2 .

Staehelin ( 2006 ), p. 18 et seq .; Andermatt ( 2008 ), p. 285; Fabian ( 2019 ), p. 283 et seq .

See , for Germany, BGH, Ia ZR 110/64 – Spanplatten ; BGH, X ZR 54/67 – Wildverbissverhinderung ; Busse and Keukenschrijver ( 2016 ), Sec. 6, note 17 et seq .; Mellulis ( 2015 ), Sec. 6, note 35; on German patent law before 1936, which allowed for corporate patents, Schmidt ( 2009 ), p. 234 et seq . For Switzerland, Botschaft , BBl 1967 II, p. 364; BGer 4A_78/2014; BPatGer O2012_001; Brehmi ( 2012 ), Art. 3, note 5 et seq .; Zuberbühler ( 2012 ). For the UK, Rhone-Poulenc Rorer International Holdings Inc v. Yeda Research & Development Co Ltd [2007] UKHL 43. For the US, Murphy ( 2012 ).

Cf ., for instance, BGH, GRUR 1966, 558, 559 et seq .

Against corporate patent ownership in AI settings, Ann ( 2022 ), Sec. 1, note 25 et seq .; Sec. 19, notes 17–35.

Senftleben and Buijtelaar ( 2020 ), pp. 3, 19; Ramalho ( 2017 ), p. 16; Papastefanou ( 2020 ), p. 295; Lauber-Rönsberg and Hetmank ( 2019 ), p. 647. For parallel reflections in patent law, see Konertz and Schönhof ( 2018 ), p. 411; AIPPI German Delegation 2019, https://aippi.soutron.net/Portal/Default/en-GB/RecordView/Index/254 , p. 18 et seq .

Abbott ( 2016 ), pp. 1079, 1125; Lim ( 2018 ), p. 863 et seq .; dissenting view, Bonadio et al. ( 2021 ), p. 66, advocating a different regime for AI-generated inventions and human inventions, rather than denying AI-generated inventions patent protection altogether.

In general on them, Heller ( 2013 ).

See , for instance, Derclaye and Husovec ( 2022 ), pp. 3–5; EU Commission Staff Working Document, Evaluation of Directive 96/9/EC on the legal protection of databases of 25 April 2018, p. 46; Hoeren ( 2016 ), p. 787.

Hoeren ( 2016 ), p. 790  et seq ., with further references.

Broughton Micova et al. ( 2019 ), p. 242; Geiger et al. ( 2017 ), p. 209  et seq .; Hugenholtz and Quintais ( 2019 ), p. 1010  et seq .

See , for instance, Merges ( 1994 ).

Cf . Sect. 3.3.3 .

“Personal data means any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person” (Art. 4(1) GDPR).

Cf . also Picht et al. ( 2023 ).

In recent case law, the ECJ may have (unintentionally) opened a backdoor to research use of databases by stating that “the main criterion for balancing the legitimate interests at stake must be the potential risk to the substantial investment of the maker of the database concerned, namely the risk that that investment may not be redeemed” (ECJ decision of 3 June 2021, CV-Online Latvia v. Melons , C-762/19, para. 44). According to the Court, the sui generis right in databases is only infringed in case of “a risk to the possibility of redeeming that investment through the normal operation of the database in question” (para. 47, emphasis added), which could be interpreted as a research exemption.

Namely, Art. 5(1)(b) GDPR and Art. 89 GDPR.

Cf ., for instance, the Compas system used in the US to generate predictions about recidivism risks of a person accused of a crime, which was found to predict higher risks for black defendants (Liptak ( 2017 )). In the Netherlands, a court halted the use of an automated system to find welfare fraud, finding that the system disproportionately targeted poorer people (Henley and Booth ( 2020 )).

In fact, neither the provisions nor the recitals of the more recent regulations, such as the GDPR and the DSM Directive, even mention AI.

Arts. 3 and 4 Directive (EU) 2019/790 of the European Parliament and of the Council of 17 April 2019 on copyright and related rights in the Digital Single Market and amending Directives 96/9/EC and 2001/29/EC.

Geiger, Frosio and Bulayenko ( 2018 ), p. 10.

See Sect. 3.3.4.2 .

Art. 5(1)(b) GDPR and Art. 89 GDPR.

See , for instance, Art. 4 WIPO Copyright Treaty; Art. 1 Directive 2009/24/EC of the European Parliament and of the Council of 23 April 2009 on the legal protection of computer programs (EU Software Directive); Sec. 2(1)(1) German Copyright Act; Sec. 3(1)(b) UK Copyright, Designs and Patents Act; Art. 2(3) Swiss Copyright Act.

E.g . Art. 6 Directive 2009/24/EC of the European Parliament and of the Council of 23 April 2009 on the legal protection of computer programs (EU Software Directive); Sec. 69e German Copyright Act; Art. L122-6-1 (IV) French Intellectual Property Code (IPC); Sec. 50B UK Copyright, Designs and Patents Act; Art. 21 Swiss Copyright Act.

E.g . Art. 29(2)(a) Swiss Copyright Act.

US Supreme Court decision of 3 March 1981, Diamond v. Diehr , 450 U.S. 175 (1981).

Cf . in detail, D ragoni ( 2021 ).

On the concept and requirements, see EPO Examination Guidelines, Sec. G-II, 3.3 et seq ., G-VII, 5.4.

US Supreme Court decision of 19 June 2014, Alice Corp. v. CLS Bank International , 573 U.S. 208 (2014).

For example: ongoing proceedings regarding Apple Pay by the European Commission (EC press release of 2 May 2022, Antitrust: Commission sends Statement of Objections to Apple over practices regarding Apple Pay, https://ec.europa.eu/commission/presscorner/detail/en/ip_22_2764 (accessed 20 September 2022)); proceedings regarding Google’s search engine by the European Commission between 2010 and 2017 (EC press release of 27 June 2017, Antitrust: Commission fines Google €2.42 billion for abusing dominance as search engine by giving illegal advantage to own comparison shopping service – Factsheet, https://ec.europa.eu/commission/presscorner/detail/en/MEMO_17_1785 (accessed 20 September 2022)); proceedings regarding Microsoft’s Internet Explorer by the European Commission between 2007 and 2013 (BBC News, Microsoft fined by European Commission over web browser, 6 March 2013, https://www.bbc.com/news/technology-21684329 ); United States of America v. Microsoft Corporation , with subsequent settlement between Microsoft and the DOJ in late 2001 (implications discussed by Weinstein ( 2002 )).

On the unavailability of copyright protection for AI-generated works, see Sect. 3.1.2 .

Konertz and Schönhof ( 2018 ), pp. 379, 412; Hugenholtz and Quintais ( 2019 ), pp. 1190, 1208.

On examples, such as the works made for hire doctrine (not AI-specific) or the UK and Irish legislation on ownership of AI-generated works (AI-specific), see Picht, Brunner and Schmid ( 2023 ).

Cf . for instance, on co-authorship of groups of choice-makers, Hugenholtz and Quintais ( 2019 ), pp. 1190, 1208 et seq .; AIPPI German Delegation, pp. 7, 12.

Cf . Hugenholtz and Quintais ( 2019 ), pp. 1190, 1209.

See , for instance, Rassenfosse et al. ( 2023 ).

For some initial research on this, see Picht et al. ( 2023 ). See further Furman and Seamans ( 2018 ).

For an analysis of the fundamental flaws of current European data protection laws and potential ways forward, see Thouvenin ( 2023 ); Thouvenin ( 2021 ); Thouvenin ( 2019 ).

Art. 5(1)(c) GDPR.

Art. 5(1)(b) GDPR.

Art. 6(1)(a) GDPR.

Art. 6(1)(f) GDPR.

Art. 6(1)(b) GDPR.

Art. 6(1)(c) GDPR.

Directive 2019/1024 of the European Parliament and of the Council of 20 June 2019 on open data and the re-use of public sector information.

Cf . Proposal for a Regulation of the European Parliament and of the Council on European data governance (Data Governance Act) of 25 November 2020.

Cf . Proposal for a Regulation of the European Parliament and of the Council on harmonised rules on fair access to and use of data (Data Act) of 23 February 2022.

Cf . Proposal for a Regulation of the European Parliament and of the Council on contestable and fair markets in the digital sector (Digital Markets Act) of 15 December 2020.

For an overview, see Picht et al. ( 2023 ).

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Florent Thouvenin is Chair for Information and Communications Law, University of Zurich, Switzerland; Chair of the Executive Board of the Center for Information Technology, Society, and Law (ITSL), Zurich, Switzerland; Director of the Digital Society Initiative (DSI), University of Zurich, Switzerland.

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Picht, P.G., Thouvenin, F. AI and IP: Theory to Policy and Back Again – Policy and Research Recommendations at the Intersection of Artificial Intelligence and Intellectual Property. IIC 54 , 916–940 (2023). https://doi.org/10.1007/s40319-023-01344-5

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Intellectual Property Rights: What Researchers Need to Know

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Intellectual property rights help protect creations of the mind that include inventions, literary or artistic work, images, symbols, etc. If you create a product, publish a book, or find a new drug, intellectual property rights ensure that you benefit from your work. These rights protect your creation or work from unfair use by others. In this article, we will discuss different types of intellectual property rights and learn how they can help researchers.

Types of Intellectual Property Rights

There are two main types of intellectual property rights (IPR).

  • Copyrights and related rights
  • Industrial property

Copyrights give authors the right to protect their work.

It covers databases, reference works, computer programs, architecture, books, technical drawings, and others.

By copyrighting your work, you ensure that others cannot use it without your permission.

Industrial property rights include trademarks, patents, geographical indications, and industrial designs.
  • A trademark is a unique sign used to identify a product or a service. It can be a single word or a combination of words and numbers. Drawings, 3-D signs, or even symbols can constitute a trademark. For instance, Google is a famous trademark. The trademark application can be filed at national or regional levels depending on the extent of protection required.
  • A patent is an exclusive right to an invention that introduces a new solution or a technique. If you own a patent, you are the only person who can manufacture, distribute, sell, or commercially use that product. Patents are usually granted for a period of 20 years. The technology that powers self-driving cars is an example of a patented invention.
  • A geographical indication states that a product belongs to a specific region and has quality or reputation owing to that region. Olive oil from Tuscany is a product protected by geographical indication.
  • An industrial design is what makes a product unique and attractive. These may include 3-D (shape or surface of an object) or 2-D (lines or patterns) features. The shape of a glass Coca-Cola bottle is an example of the industrial design.

Intellectual Property Rights

What Do I Need to Know About IPR?

Intellectual property rights are governed by WIPO , the World Intellectual Property Organization. WIPO harmonizes global policy and protects IPR across borders. As a researcher, you rely on the published work to create a new hypothesis or to support your findings. You should, therefore, ensure that you do not infringe the copyright of the owner or author of the published work (images, extracts, figures, data, etc.)

When you refer to a book chapter or a research paper , make sure to provide appropriate credit and avoid plagiarism by using effective paraphrasing , summarizing, or quoting the required content. Remember plagiarism is a serious misconduct! It is important to cite the original work in your manuscript. Copyright also covers images, figures, data, etc. Authors must get appropriate written permission to use copyrighted images before using them in the manuscripts or thesis.

How do you decide whether to publish or patent? Check your local IPR laws. IPR laws vary between countries and regions. In the US, a patent will not be granted for an idea that has already been published. Researchers, therefore, are advised to file a patent application before publishing a paper on their invention. Discussing an invention in public is what is known as public disclosure . In the US, for instance, a researcher has one year from the time of public disclosure to file a patent. However, in Europe, a researcher who has already disclosed his or her invention publicly loses the right to file a patent immediately.

IPR and Collaborative Research

IPR laws can impact international research collaboration. Researchers should take national differences into account when planning global collaboration. For example, researchers in the US or Japan collaborating with researchers in the EU must agree to restrict public disclosure or publication before filing a patent. In the US, it is common for publicly funded universities to retain patent ownership. However, in Europe , there are different options . An ideal collaboration provides everyone involved with the maximum ownership of patent rights. Several entities specialize in organizing international research collaborations. Researchers can also consider engaging with such a company to manage IPR.

What questions do you have about IPR? Have you faced any situation where you need to consider IPR issues when conducting or publishing your research ? Please let us know your thoughts in the comments below.

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Wow, I never knew that geographical indication can have a connection to intellectual property if it has distinctions that can be attributed to where it came from. After finishing my master’s degree, I think I’m going to be staying in the academe as a researcher so it’s quite helpful to know more about how the intricacies of IP can affect research. I hope I can one day attend a conference about IP to learn more about its modern day advancements.

I have invented – conceived – a training system. What do I have to do to achieve and retain ownership if I enroll in a university higher degree by research program to develop this idea?

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Thank you for sharing your query on our website. Regarding your query, most universities recognize as a general principle that students who are not employees of the university own the IP rights in the works they produce purely based on knowledge received from lectures and teaching. However, there may be some circumstances where ownership has to be shared or assigned to the university or a third party. These include cases when the student is being sponsored by the university, or the project is a sponsored research project or involves the academic staff of the university or university resources. If the training system conceived by you does not involve any of the above mentioned scenarios, ideally you should be able to retain its ownership. For more clarity you can check through the IP rules section of the concerned university.

Please let us know in case of any queries.

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When should AI tools be used in university labs?

Innovations in intellectual property rights management: Their potential benefits and limitations

European Journal of Management and Business Economics

ISSN : 2444-8494

Article publication date: 9 April 2019

Issue publication date: 16 July 2019

The purpose of this paper is to evaluate innovations in intellectual property rights (IPR) databases, techniques and software tools, with an emphasis on selected new developments and their contribution towards achieving advantages for IPR management (IPRM) and wider social benefits. Several industry buzzwords are addressed, such as IPR-linked open data (IPR LOD) databases, blockchain and IPR-related techniques, acknowledged for their contribution in moving towards artificial intelligence (AI) in IPRM.

Design/methodology/approach

The evaluation, following an original framework developed by the authors, is based on a literature review, web analysis and interviews carried out with some of the top experts from IPR-savvy multinational companies.

The paper presents the patent databases landscape, classifying patent offices according to the format of data provided and depicting the state-of-art in the IPR LOD. An examination of existing IPR tools shows that they are not yet fully developed, with limited usability for IPRM. After reviewing the techniques, it is clear that the current state-of-the-art is insufficient to fully address AI in IPR. Uses of blockchain in IPR show that they are yet to be fully exploited on a larger scale.

Originality/value

A critical analysis of IPR tools, techniques and blockchain allows for the state-of-art to be assessed, and for their current and potential value with regard to the development of the economy and wider society to be considered. The paper also provides a novel classification of patent offices and an original IPR-linked open data landscape.

  • Artificial intelligence
  • Software tools
  • Social benefits
  • Intellectual property rights management
  • Linked open databases

Modic, D. , Hafner, A. , Damij, N. and Cehovin Zajc, L. (2019), "Innovations in intellectual property rights management: Their potential benefits and limitations", European Journal of Management and Business Economics , Vol. 28 No. 2, pp. 189-203. https://doi.org/10.1108/EJMBE-12-2018-0139

Emerald Publishing Limited

Copyright © 2019, Dolores Modic, Ana Hafner, Nadja Damij and Luka Cehovin Zajc

Published in European Journal of Management and Business Economics . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

The world today seems to be characterised by the effects of information and communication technology (ICT) on every aspect of our lives, including that of intellectual property rights (IPR) ( Modic, 2017 ). Freeman and Louca (2002 , p. 301) wrote that “even those who have disputed the revolutionary character of earlier waves of technological change, have little difficulty accepting that a vast technological revolution is now taking place”. The surge of intellectual property is mirrored in rising IPR numbers with dissemination efforts dependent upon the available data, channels and skills. IPR data are big data, as its characteristics are high volume, high variety and high velocity of changes ( Ciccatelli, 2017 ). Consequently, merging different types of IPR data from various databases presents a challenge ( Stading, 2017 ; Abbas et al. , 2014 ).

When huge amounts of IPR data are connected, a new ecosystem for (open) innovation emerges. It is important to examine the best available IPR data sources, and their merge-readiness, in order to extract the maximum value. Furthermore, it is important to ensure the availability of appropriate IPR techniques and tools if we are to harness the benefits for IPR management (IPRM) and the wider social benefits of this new open IPR landscape and move towards knowledge creation assisted by artificial intelligence (AI). Examining the latest trends in technological solutions and their potential is the foci of our paper.

Figure 1 presents two dimensions: the benefits and the technology. Looking at the technology dimension, all three layers represent issues companies face. IPR software tools and techniques should better respond to business requirements, and as such support changes in databases when dealing with IPR big data, such as the implementation of blockchain technology and linked open databases.

The benefits dimension is also facing several gaps. One refers to the identification of the accessibility of employees’ knowledge both in SMEs and IPR-savvy companies. In addition, there are inefficiencies when trying to transform tacit to explicit knowledge in order to further knowledge creation.

Both the technology and benefits dimensions are linked, as the technology aims to, largely unsuccessfully at the present time, to support the requirements of the IPRM, thus increasing the IPRM-derived benefits. These would consequently be translated, especially through the use of blockchain technology and IPR-linked open data (IPR LOD) databases, into increased social benefits. The question as to when, and if, the technology will become smart enough to create IPR software tools and techniques that will function in an intelligent manner remains open to debate, as we are faced with increasing transparency and inherently imbued trust.

If AI systems provide the best possible answer to every IPR-related business requirement, in order to maximise business potential, does this mean that the employees’ knowledge creation will become obsolete and AI systems will be able to effectively create new knowledge?

The paper offers a review and an interview-based analysis of the requirements and expectations of some of the top IPR experts from IPR-savvy multinationals, as well as a consideration of the potential social benefits. This is followed by a web-based analysis and data retrieval-based evaluation of the current evolution of IPR (LOD) databases. Furthermore, the practical solutions available have been critically evaluated with respect to IPR databases and IPR software tools. The results of the analysis of the state-of-the-art with the available techniques are presented. Finally, a debate-style conclusion is presented.

2. Background and prepositions

This paper investigates IPRM and IPR social benefits by answering what are the potential social and IPRM benefits of adopting new ICT solutions when dealing with IPR, and especially what is the current state of all three technological layers? The research is based on the following prepositions constructed following the literature review and the evidence-based approach.

The IPR-linked open data (IPR LOD) map is still in its infancy, thus the full potential of their social benefits are still not realized.

AI is a term used very broadly when connected to IPR techniques, to oversell various information retrieval (IR) and machine learning (ML) methods.

The tools do not correspond to the needs of users as expressed by top IPR managers.

Blockchain has the potential to produce both IPRM and IPR-connected social benefits if some issues are solved.

The outputs of this paper are the classifications of IPR databases and patent offices according to Berners-Lee Open Data Plan, and IPR LOD map as connected to patents as well as classification of tools and techniques. A mixed methods approach has been used, every part diligently designed with methodological notes.

3. Methodology

We derive our analysis of potential benefits of new solutions for IPR and the potential of IPR tools from interviews with ten prominent IP experts. First, interviews with ten prominent IP experts were conducted. Seven out of the ten IP experts were head IP managers within their respective companies. The companies selected are positioned highly in terms of patent applications and quality rankings. Furthermore, they appear on top innovation listings, such as MIT’s list of the 50 Smartest companies. All respondents are executives with years of experience; and one of the interviewees appeared twice in the 50 most influential people in IP, as listed by the Managing Intellectual Property magazine. Views expressed inside the interviews are their own and not the views of the companies they are affiliated with. Interviews were conducted either in person, via Skype or via similar VoIP during 2016 and with follow-ups in 2017. Transcripts were analysed using MAXQDA Analytics Pro 12 software. Interview questions were divided into three sections: IPRM (1), formalization (2) and optimisation of processes and gaps reduction (3)). In particular for this paper three topics and their related questions that were included in this semi-structured interview questionnaire are harnessed upon (pertaining to either part (1) or part (3): What is the missing information and/or resources?; Which software tools do you use inside your processes? What are their pros and cons?; What kind of (big) data analysis would be particularly interesting? Who can provide them?

The technologies section brings further methods. The classification of patent offices was done in the period January–February 2018 by conducting web searches and experimental searches with consequent search retrievals inside patent search machines either for full patent documents or at least bibliographical exports. The classification encompasses primarily EU Patent Offices as well as a selection of other relevant patent offices [1] . The framework for the patent map relies on The Linking Open Data cloud diagram, however, it has been significantly upgraded by including material gathered via web searches guided by discussions with various patent offices’ staff members. Analysis of techniques is based on critical literature review. We also reviewed websites of 11 top IPR tools providers as identified by interviewees and/or the Hyperion MarketView™ Report (2016) and Capterra’s review (2017 ). Analysis is based on reviews of websites (November, 2017) by Anaqua for Corporations, IP One (from CPA Global), InnovationQ (from ip.com), IPfolio, PatentSight, Unycom Enterprise, Wellspring’s IP management software, Patricia (form Patrix), Alt Legal, Inteum, Dennemeyer’s DIAMS iQ [2] .

4. The potential social and IPRM benefits of new advances in the field of IPR

One of the biggest problems of IPR data usability is the rapid growth of number of IPR, especially patents. They are written in different languages and it has become increasingly challenging to understand the state of the art, this consequently causing duplication of research and increasing the number of invalid patents granted. Once errors can be corrected, it will be easier to identify inherently invalid patents previously granted, and consequently leading to a natural rise in the quality of IPR.

Governments have a large quantity of IPR-related data, which can be of economic and social value to society. European Patent Office (EPO) sees the advantages of its new LOD patent databases, one of the outlets of the new open data trend, as increased availability of data from different sources via one channel, less “data friction” when combining different data sets, more effective linking with business information and increased trust thanks to provenance ( Kracker, 2017 ). The Korean Patent Office (KIPO) also saw its efforts in a similar manner ( KIPO, 2016 ).

The growing importance of IPR Open (linked) data is connected to better transparency making it easier for companies to understand their value. However, if we could not only have exploitable open databases, but if these could also be combined with IPR techniques with AI functionality, and additionally, IPR tools which supported the handling of IPR data by integrating some AI functionalities, we could be seeing a new form of tacit knowledge, the “Artificial intelligence knowledge” creation (see Figure 1 ). Therefore, the often problematic issue of tacit knowledge inside the IPR field embodied in individuals (note that the usual way of gaining IPRM, exploitation and other connected IPR knowledge is through apprenticeship and that the rotation of individuals presents a serious problem for especially company IPR departments, Modic and Damij, 2018 )) would be transformed into a latent explicit knowledge (knowledge available on recall as opposed to explicit knowledge, always available). Solutions, like IBM Watson, seem to also be a game changer in this area. Watson identified compounds on which the patent protection has already lapsed, and the pilot results suggest that Watson can accelerate identification of novel drug candidates and novel drug targets by harnessing the potential of patent (and connected) big data ( Chen et al. , 2016 ). The IBM team believes the insights provided by Watson technology are to be used as a guide, i.e., as augmented intelligence – which is capable of ingesting, digesting, understanding and analysing data and can be harnessed in various elements of IPR processes: from evidence of use, to prior art, patent landscapes and portfolio analysis ( Fleischman, 2018 ). If the technology was widely available with all its features, this could present a significant change, as it would enable smaller entities to access knowledge that is now tacit knowledge.

When discussing traceability, blockchain is one of the frequently debated issues. Several potential social benefits, as derived from the utilisation of blockchain in the field of IPR, are present. A tool for registration of IPRs could simplify registration and lower the costs ( Vella et al. , 2018 ; Morabito, 2017 ) or could be an alternative to IPR registration, especially patents. Thus, it has a potential particularly for small entities (independent inventors, SMEs, non-profit organisations), as well as inventors and organisations from less developed countries, who are unable to access the current world patent system simply because it is too expensive for them.

Blockchain provides a robust and trustworthy method of establishing business ownership on intangible assets, including IPR ( Morabito, 2017 ) and thus has the potential to enhance transparency of IPR transactions ( Vella et al. , 2018 ). Not only does this have positive effects for individual companies, but it can also streamline the costs of operations for patent offices, and reduced options for litigation can lower court case numbers and reduce court backlogs. Furthermore, it also has the potential to enable half open licensing, when royalties start only when IPR-based income is generated by downstream users; meaning that without income generation, the half open licenses allow for IPR-based solutions to be spread in an open environment. Moreover, it would allow tracking commons’ knowledge (under open licenses or not) incorporation into corporate IPR portfolios disallowing the privatisation of gains.

With regard to potential IPRM benefits, IPRM deals with managing IPR big data efficiently, and differently ( Braganza et al. , 2017 ; Davenport et al. , 2012 ). McAfee and Brynjolfsson (2012) argue that companies will not reap the full benefits of the transition made in exploiting big data, unless they are able to manage change effectively.

Analysis of the interviews showed a clear trend that IP executives are aware of the growing importance of ICT, and their role in IPRM, however, they continue to struggle with defining how to integrate IPR tools to achieve best outcome. A Senior IP Counsel at a German multinational chemical manufacturing corporation stated that, “IT developments will have a big impact in the near future on IP development, because the more transparent you make the IP, the easier it is for management to understand its value”.

Utilising the ICT in IPR processes is possible, however, doing it in the most efficient way to enable companies to achieve maximum benefits, is the ideal. Some companies use a range of different software tools connected to IPR and IPRM, whilst others try to find or develop software that integrates as many features and data sources as possible and are able to connect to other business processes and databases. Generally, the more comprehensive the tool, the less information is missing, and consequently, the higher the satisfaction level. Nonetheless, some experts, such as the Head of Legal Operations and IP Management at a European multinational pharmaceutical corporation, believe that IPR tools often promise more than they deliver. He states that they, “do not think there are any particularly good IP management tools on the market /…/the whole industry still lacks are real IP management tools, helping to relate to the business value more”. IPR experts are seeking a tool that would, in addition to being a comprehensive docketing system and simple interface retrieval of data from public IPR databases, also encompass supplying or channelling invention disclosures to pertinent individuals, providing functionality for IPR valuation, evaluation and analysis.

The next chapter will provide more detail deal with regard to the technological dimension, providing an analysis on the current state of linked open databases, software tools for IPRM and techniques that support IPR data correction and analytics.

5. Technology

5.1 databases and linked (open) data.

Since the Venetian patent statute of 1474, IPR have retained their connection to the concept of openness and dissemination of ideas in exchange for limited time monopolies. There are various types of databases and online sources connected with IPR constituting Layer 1 in the framework in Table I . Public patent databases as the original sources allow raw data retrieval and the use of interfaces by providing patent texts and some metadata. Related IPR databases include, for example, those related to patent disputes, patent citations. Business databases provide information on IPR owners, etc. Scientific databases provide us inter alia with data on inventors. Miscellaneous online data sources include less or more structured sources, e.g., business news, blogs-based IPR-related texts, information on IPR experts. Multi-source IPR databases provide broader information, e.g., on IPR quality and business connected data. Two examples of the latter are the data set linking the EPO and USPTO patent data to Amadeus business database and the Oxford Firm-Level IP Database ( Thoma and Torrisi, 2007 ; Helmers et al. , 2011 ).

Linked open data (IPR LOD) databases are the latest evolution in IPR databases, although the concept of LOD goes back to 2006, when principles such as using uniform resource identifiers as names for things and including links were put forward ( Berners-Lee, 2006 ). Linked data are data published on the web in a machine-readable format, which can be linked to or from external data ( Bizer et al. , 2009 ). LOD is in essence a format allowing for efficient (multi-source) database utilisation as the term refers to a set of practices for publishing and interlinking structured data ( Auer, 2014 ).

Combining this to ideas of open data, we get LOD, structured data made available for others to be reused ( Mezaour et al. , 2014 ). The concept is connected to the Open Data movement to ensure public government data are accessible in non-proprietary formats ( Bauer and Kaltenböck, 2012 ). However, LOD landscape includes databases provided by non-governmental entities. DBPedia, extracting structured knowledge from Wikipedia, is often seen as the “nucleus” of LOD ( Auer et al. , 2007 ). Furthermore, patent data of individual patent offices are sometimes provided by outside providers, such as in the case of USPTO or (formally) the EPO.

Table I shows the classification of patent offices and their data according to the Berners-Lee Five Star Open Data Plan. More stars indicate data formats more conducive to open data policies, as they allow for easier export and import of data, and more streamlined merging and analysis. The category **** is redundant as there is no standalone RDF providing databases; and, we would suggest an introduction of the *****+ category, where the additional criteria is the existence of linkages with other data, signalling the real uptake of the raw data by users (see Table I ). The Type indicates the most Open data friendly format, though patent offices often provide other formats simultaneously. They often also provide more than one database, and the degree of the export varies for bibliographical data (Swiss Patent Database offering up to 25 variables).

Five patent offices are leading in terms of IPR LOD; USPTO, EPO, KIPO, IPAustralia and IPO UK. Cooperation of national offices with Espacenet was also advantageous, as it produced the option of a limited bibliographic data download in .csv format (not taken into account above). However, most of the patent offices can still be categorised only as Type * or Type **, their data remaining in linkable open data unfriendly formats.

There are only a few databases that could be categorised as *****+, or that have shown other initiatives to make exporting, merging and analysing data easier. For example, KIPO has not only published the IPR LOD, but also included the owners’ corporate registration number and the Australian Patent Office IPR database includes information about companies’ size, technology and geographic location, making it easier for users to link data on patents to information on related business entities ( KIPO, 2016 ; Man, 2014 ).

Currently, EPO’s Linked open data is the newest of the few IPR LOD databases at users’ disposal. It builds upon their previous work in connecting patent-related data, such as their Deep Linking service, allowing users to consult the EP document’s legal status data. However, the IPR LOD database remains as a raw data product and without additional skills and resources cannot be fully utilised, which could potentially widen the gap between SMEs and IPR-savvy companies. For example, the linkage to DBPedia has also been carried out, but since then de-installed ( Kracker, 2017 ). This year the EPO also included in their Research grant call explicitly the field of linked open data and solutions therein, where at least one project will start end of this year linking EPO database with the Springer database ( IP LodB, 2018 ). The current LOD IPR landscape shown below is based on the The Linking Open Data cloud diagram and upgraded [3] .

Figure 2 shows patent LOD databases [4] we could call *****+, and their inbound and outbound links, as per The Linking Open Data cloud diagram ( LOD cloud, 2018 ) – a complex LOD ecosystem currently listing 1,164 data sets. They are also linked to the most inbound and outbound link-rich LOD databases, namely, the Comprehensive KAN and DBPedia. The new EP LOD and KIPO databases have no data on linkages, even though some attempts were made as mentioned above. There are, however, several LOD databases that this patent data could be linked to; e.g. the recently published bibliographic LOD database by Springer Nature SciGraph or the older New York Times LOD.

When considering the traceability of IPR data, some patent offices offer centralised solutions, such as i-DEPOT, which allows to trace the date of inventions’ creation. However, at the forefront of these debates is blockchain as a disruptive technology, due to its transparency, decentralisation and prevention of infringements and fraud. Blockchain is a chain of blocks of chronologically linked information, replicated in a distributed database. Information can be added, but never removed, changes are registered and validated. Individual blocks can be protected by cryptography, and only those authorised can access the information ( McPhee and Ljutic, 2017 ). Blockchain application to IPR can be either inside the registration or exploitation phases (related to issues of licensing, proving authenticity and piracy) ( Vella et al. , 2018 ; Morabito, 2017 ) as well as distribution. In case of licensing, the topic is connected to smart contracts, open licenses and IPR-based collaboration ( Pilkington, 2016 ; Morabito, 2017 ). Smart contracts are computer codes that reside in the blockchain and are implemented if certain conditions are met, which is confirmable by a number of computers to ensure truthfulness ( Morabito, 2017 ; Szabo, 1997 ). There are numerous potential applications of blockchain connected to IPR. Also, the Linked Data paradigm is evolving from an academic concept for addressing one of the biggest challenges in the area of information management the exploitation of the web as a platform for data and information integration; to practical applications in IPR field deriving from the transfer from the Web of Documents to a Web of Data. Yet, it is clear there is still much to be done, both in terms of the volume of IPR LOD-connected databases, as well as their functionality in linking to other LOD data sets as well as the real-life uptake of blockchain solutions.

5.2 Classification of tools and techniques

This chapter summarises the techniques and tools (technology Layers 2 and 3 as set out in Figure 1 ) that analyse large quantities of patent documents and other IPR data to provide useful information to various users.

The EPO’s database, Espacenet, on its own, currently contains over 100m patent documents from 90 patent authorities worldwide. Whilst patent data are exceptionally important, it is also very difficult to extract some useful information from it as patents are mostly stored as images; written in different languages; countries have different patent requirements; no uniform structural requirements; some patent figures are drawn by hand, some on computer; some patent attorneys intentionally use misleading language; incomprehensible language and grammatical mistakes can be also used inadvertently. How to deal with these issues remains a challenge.

There are several possible taxonomies of IPR software. Considering their functionalities we see tools supporting different phases of the innovation cycle, those supporting financial management (record and estimate costs), archiving documents (IPR portfolio) and enabling communication between users and IPR offices. Some tools have functionality to integrate data from external databases, such as patent litigation information and patent citation indexes. In terms of intended user-base we have IPR tools for companies, for IPR experts and for technology transfer offices.

There is an upward trend in the creation of new IPRM software in recent years. However, after reviewing the websites of the 13 most important IPR tools providers by Hyperion MarketView™ Report (2016) it appears that these tools only modestly respond to the challenges raised, and largely look like any project management software. Bonino et al. (2010) was optimistic with regard to semantic-based solutions, however, some of the tools he describes are currently in poor condition or unavailable.

In terms of techniques utilised in semantic analysis, Abbas et al. (2014) made a taxonomy of proposed computer-assisted patent analysis techniques where they distinguish between text mining and visualisation approaches. These two categories are based on frequent use-cases, whilst the underlying methods are primarily inspired by IR and ML. This is not unreasonable, as patent documents are similar to other types of documents in that they contain textual and visual data as well as references to other documents.

As seen in Figure 3 , a typical IR system consists of document pre-processing, feature extraction and feature analysis. Each of those steps can be based on heuristic rules or utilise machine learning methods. In the following paragraphs, we review the use of different techniques in the IPR research domain in the last decade, with a particular focus on the works referenced in recent literature reviews by Abbas et al. (2014) and Aristodemou and Tietze (2017) . The list is by no means complete, it is only focussed on key examples illustrating the diversity and potential of such methods.

The patent document pre-processing step involves scanning the unstructured data (text and images) and extracting useful information from it.

Due to the nature of the patent data, the approaches mainly focus around text mining techniques; meaning using some kind of natural language processing ( Wang et al. , 2015 ; Han et al. , 2017 ), such as subject–action–object analysis ( Park, Kim, Choi and Yoon, 2013 ; Park, Ree and Kim, 2013 ), property–function analysis ( Dewulf, 2013 ) or rule-based analysis to extract semantic primitives. Several authors have also proposed the utilisation of patent images and sketches in patent analysis, in order to determine similarities between patents ( Bhatti and Handbury, 2013 ). In terms of pre-processing, image analysis challenges involve localisation of images and sub-images, categorisation of images and label recognition ( Vrochidis et al. , 2010 ). The primary sources of inter-information are cross-patent citations ( Altuntas et al. , 2015 ).

The feature extraction methods transform low-level semantic primitives into a document-wide representation. By involving projection of each document into a high-dimensional feature space we can determine bounds between classes or proximity of documents. When processing textual data, the semantic primitives can be frequency vectors ( Chen and Yu-Ting, 2011 ), vectors of concepts that describe higher-level semantic information, or domain-specific hierarchical structures ( Lee, 2013 ). In analysis of patent sketches, content is frequently encoded with shape or texture descriptors ( Bhatti and Handbury, 2013 ) due to the line-art nature of visual information.

The method used in the feature analysis stage depends on the problem at hand, for example, retrieval of similar patents. In this case, IR techniques based on vector distances ( Lee, 2013 ) are used to infer which documents are most similar. Another task is automatic classification of patents using ML methods. Scenarios include patent quality analysis ( Wu et al. , 2016 ), patent categorisation ( Vrochidis et al. , 2010 ) and determining the impact of patents on other aspect of companies ( Chen et al. , 2013 ). Supervised learning methods, such as support vector machines ( Wu et al. , 2016 ) or artificial neural networks ( Chen et al. , 2013 ), are frequently used in such cases. In explorative analysis of the patent landscape for trend identification, people have also utilised unsupervised learning methods, like clustering ( Atzmüller and Landl, 2009 ; Madani and Weber, 2016 ) and network analysis ( Dotsika, 2017 ; Park, Kim, Choi and Yoon, 2013 ).

Despite the apparent contribution of IR methods in transforming access to information, they are harder to apply to semantic-sensitive fields, such as IPR analysis, with the same level of success. The crucial information in patent documents can be difficult to extract automatically because of objective (history, language) or subjective (intentional misuse of description) reasons. As noted by Lupu (2017) , the level of research interest in this field has, after more than a decade of increasing optimism, decreased in the past years. This can be in part attributed to the realisation that extracting high-level semantic content from sophisticated unstructured text and images is very a challenging problem. The most successful working cognitive computing system is IBM Watson, who has already been analysing patent information in the past, with a particular emphasis in the pharmaceutical sector. However, this system is proprietary and accessible only to a limited number of influential clients.

6. Discussion

Over the last years, activities around IPR Open Data, merging of IPR data with related data, IPR Linked Data, IPR-linked open databases and the debates over utilising the Semantic Web opportunities have gained momentum. However, this should go hand in hand with organisations (both public and private) publishing structured data (complying also with linked data standards/principles), the advances in new techniques, as well as IPR tools and their increased availability. Companies and other patent and IPR data users need to draw on those advanced technologies and tools in order to combine, query (and analyse) data as part of their business intelligence, as well as to improve their services and products.

In terms of the availability of data, the amount of IPR and IPR-connected data publically available is increasing. Responding to P1 , the new trends towards formats supporting more export-ready, merge-ready and analysis-ready data are also real, although the amount of patent data available (e.g. as LOD) is still relatively low. LOD means the data are “linkable”, not that it is already linked. This means that the uptake of these databases by the users can be slow and can even widen the gap between the IPR-savvy multinationals with sufficient resources and other smaller entities and individuals. The latter would defeat the purpose of publishing such databases, if the objective was to make IPR data more useful to more groups of users, especially also non-patent savvy users (data scientists, web developers, companies integrating IP into their products). Some steps are taken towards this, for example, IPNOVA (available at the moment as a beta version) which is the interface to the IPAustralia’s IPGOD database. Another route (contrasting somewhat with developing interfaces) is through sufficient dissemination and training workshop accompanying the releases of databases in new formats. On the other hand, the authors remain hopeful as new entities – including private and NGO entities – provide more and more LOD databases, and with growth of potential links, allowing greater potential for IPR.

In response to P2 , techniques that would support IPR data correction, and IPR data analytics and software tools, which support IPRM, are still not at a sufficient stage of development for IPR managers and other users dealing with IPR. The IPR tools remain primarily visualisation tools ( P3 ); or project management and docketing tools, applied to the field of IPR. There are few true IPRM tools that also integrate variable (external and internal) data merges and harness new advances in IPR techniques, although some solutions have been integrated. This is perhaps because the existing techniques, which are suitable for many existing retrieval and analysis tasks, are frequently branded as “AI”, a term that increases expectations about the capabilities which existing methods fail to fulfil. A complete AI system is perhaps the ultimate goal of automatic patent analysis, capable of high-level reasoning about the content of patent documents, comparing their underlying ideas and determining similarities. The current state is (far) removed from this goal. At present, it is primarily addressing very narrow domains, interpretable by data scientists and machine learning researchers. However, as also noted by Lupu (2017) , recent breakthroughs in deep learning and artificial neural networks already address tasks such as machine translation and image analysis, which can be (and sometimes are) utilised in IPR analysis.

In response to P4 , blockchain technology is now fairly widely discussed for its potential to change the nature of IPRs by simplifying registration, lowering costs, increasing transparency and enabling or improving licensing and other transfers of IPR. However, the technology has certain limitations and still needs significant time to develop. This is not only because of the influence that transnational companies have on policy makers, but also, the technology itself might have some weaknesses. It needs huge processing power and therefore for now requires high-volume electricity consumption. Second, field, such as the IPR field, has its own set of limitations connected to legal and judicial frameworks. Therefore, it is important to carefully determine fields where it would be used. “Despite the many interesting potential uses of blockchain technology, one of the most important skills in the developing industry is to see where it is and is not appropriate to use cryptocurrency and blockchain models” ( Swan, 2015 ). Although there are various social and IPRM benefits of employing blockchain technologies in the field of IPR, caution must be applied.

To conclude, despite significant efforts in the last decades, in the field of information technology support to IPRs, and the more and more used buzzwords of augmented intelligence and augmented expertise also for IPR, there is more time needed before these progressive ideas will become (widespread) reality.

research paper topics for intellectual property rights

Technology and benefits in IPR landscapes

research paper topics for intellectual property rights

Narrow IPR LOD landscape (patent databases)

research paper topics for intellectual property rights

A typical computer-assisted document analysis pipeline as IPR techniques classification framework

Classification of patent offices according to the Berners-Lee Open Data Plan

Notes: a Taking into account the AKSW database (different provider); b the patent offices have done additional steps non-related to the format to make merging of data easier; c the database can be described as providing linking data, yet it is not an LOD database in classical sense; d if taking into account the bibliographical export in .csv by Espacenet on its web-pages designed in cooperation with national patent offices (e.g. https://sk.espacenet.com/ ), there are such data provided for most, however, the end document exports remain .pdf

Missing from the list are the Latvian, Icelandic, Maltese and Cyprus Patent Office, as they only refer to Espacenet or there is a lack of sufficient information. The classification takes into account data that is (formally) provided by outside sources (e.g. for USPTO).

We have also taken into account a review of the available semantic solutions that was made by Bonino et al. (2010 , p. 37, Table 9). However, these new technology enablers are currently in a less than ideal state (in poor condition or unavailable) and they (those which are at least available) look more like a scientific experiment than a final product that would support real patent analytics in companies. Though we sent some follow-up e-mails we did not receive much useful information so they were excluded from the paper.

Eito-Brun (2015) lists 31 LOD databases according to datahub.io related to patents, but they could be hardly classified as IPR databases.

The Linked Open Data Cloud diagram includes EPO reference, which was created and published by the research group AKSW.

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Further reading

Lee , S. , Yoon , B. , Lee , C. and Park , J. ( 2009 ), “ Business planning based on technological capabilities: patent analysis for technology-driven roadmapping ”, Technological Forecasting and Social Change , Vol. 76 No. 6 , pp. 769 - 786 .

Acknowledgements

Dr Damij would like to acknowledge the ARRS Grant No. ARRS-P1-0383(A). Dr Hafner would like to acknowledge Operation No. C3330-17-529006 “Researchers-2.0-FIŠ-529006” supported by ERDF and Republic of Slovenia, Ministry of Education, Science and Sport. Dr Modic would like to acknowledge the JSPS International Research Grant ID No. 16774 and JSPS KAKENHI Grant No. 16F16774. Dr Hafner and Dr Modic acknowledge that this paper has been co-funded by the Academic Research Programme of the European Patent Office. The research results contained in this paper are those of the researchers only. They do not necessarily represent the views of the EPO.

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Pro Sports Has a Piracy Problem

  • Brett Danaher,
  • Michael D. Smith,
  • Rahul Telang

research paper topics for intellectual property rights

New research suggests an effective way to address it.

Piracy poses a growing challenge to all sorts of digital media and entertainment companies, but it’s particularly acute for companies that own the rights to sports broadcasts, because efforts to shut down pirated broadcasts often take longer than the broadcasts actually last. New research suggests, however, that there are effective ways to discourage the use of pirated broadcasts and boost payment for legal ones.

While many people watch the Super Bowl and NFL games legally each year, through cable subscriptions, local television, or NFL Sunday Ticket, a growing number of people are using another method: illegally pirated live streams. The piracy-tracking firm VFT estimates that 17 million viewers watched last Sunday’s Super Bowl on illegal pirate streams, and a 2023 survey of 3,200 NFL fans found that 35% of respondents regularly watch NFL games on pirate streams. The problem isn’t unique to the NFL. Most other live sports matches are made available through illegal streams.

  • BD Brett Danaher is an associate professor of economics and management science at Chapman University. He is the co-founder and organizer of the Entertainment Analytics Conference, which annually brings together the top academic and industry data scientists focused on the entertainment ecosystem.
  • MS Michael D. Smith is the J. Erik Jonsson professor of information technology and marketing at Carnegie Mellon’s Heinz College and Tepper School of Business.
  • RT Rahul Telang is Trustee Professor of Information Systems at the Heinz College, Carnegie Mellon University. His research focus is information security and the digital-media industry.

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EU AI Act: first regulation on artificial intelligence

The use of artificial intelligence in the EU will be regulated by the AI Act, the world’s first comprehensive AI law. Find out how it will protect you.

A man faces a computer generated figure with programming language in the background

As part of its digital strategy , the EU wants to regulate artificial intelligence (AI) to ensure better conditions for the development and use of this innovative technology. AI can create many benefits , such as better healthcare; safer and cleaner transport; more efficient manufacturing; and cheaper and more sustainable energy.

In April 2021, the European Commission proposed the first EU regulatory framework for AI. It says that AI systems that can be used in different applications are analysed and classified according to the risk they pose to users. The different risk levels will mean more or less regulation. Once approved, these will be the world’s first rules on AI.

Learn more about what artificial intelligence is and how it is used

What Parliament wants in AI legislation

Parliament’s priority is to make sure that AI systems used in the EU are safe, transparent, traceable, non-discriminatory and environmentally friendly. AI systems should be overseen by people, rather than by automation, to prevent harmful outcomes.

Parliament also wants to establish a technology-neutral, uniform definition for AI that could be applied to future AI systems.

Learn more about Parliament’s work on AI and its vision for AI’s future

AI Act: different rules for different risk levels

The new rules establish obligations for providers and users depending on the level of risk from artificial intelligence. While many AI systems pose minimal risk, they need to be assessed.

Unacceptable risk

Unacceptable risk AI systems are systems considered a threat to people and will be banned. They include:

  • Cognitive behavioural manipulation of people or specific vulnerable groups: for example voice-activated toys that encourage dangerous behaviour in children
  • Social scoring: classifying people based on behaviour, socio-economic status or personal characteristics
  • Biometric identification and categorisation of people
  • Real-time and remote biometric identification systems, such as facial recognition

Some exceptions may be allowed for law enforcement purposes. “Real-time” remote biometric identification systems will be allowed in a limited number of serious cases, while “post” remote biometric identification systems, where identification occurs after a significant delay, will be allowed to prosecute serious crimes and only after court approval.

AI systems that negatively affect safety or fundamental rights will be considered high risk and will be divided into two categories:

1) AI systems that are used in products falling under the EU’s product safety legislation . This includes toys, aviation, cars, medical devices and lifts.

2) AI systems falling into specific areas that will have to be registered in an EU database:

  • Management and operation of critical infrastructure
  • Education and vocational training
  • Employment, worker management and access to self-employment
  • Access to and enjoyment of essential private services and public services and benefits
  • Law enforcement
  • Migration, asylum and border control management
  • Assistance in legal interpretation and application of the law.

All high-risk AI systems will be assessed before being put on the market and also throughout their lifecycle.

General purpose and generative AI

Generative AI, like ChatGPT, would have to comply with transparency requirements:

  • Disclosing that the content was generated by AI
  • Designing the model to prevent it from generating illegal content
  • Publishing summaries of copyrighted data used for training

High-impact general-purpose AI models that might pose systemic risk, such as the more advanced AI model GPT-4, would have to undergo thorough evaluations and any serious incidents would have to be reported to the European Commission.

Limited risk

Limited risk AI systems should comply with minimal transparency requirements that would allow users to make informed decisions. After interacting with the applications, the user can then decide whether they want to continue using it. Users should be made aware when they are interacting with AI. This includes AI systems that generate or manipulate image, audio or video content, for example deepfakes.

On December 9 2023, Parliament reached a provisional agreement with the Council on the AI act . The agreed text will now have to be formally adopted by both Parliament and Council to become EU law. Before all MEPs have their say on the agreement, Parliament’s internal market and civil liberties committees will vote on it.

More on the EU’s digital measures

  • Cryptocurrency dangers and the benefits of EU legislation
  • Fighting cybercrime: new EU cybersecurity laws explained
  • Boosting data sharing in the EU: what are the benefits?
  • EU Digital Markets Act and Digital Services Act
  • Five ways the European Parliament wants to protect online gamers
  • Artificial Intelligence Act

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