Intellectual Property

Jeanne C. Fromer’s *First Ideas* offers a provocative critique of the "first possession" principle in intellectual property (IP), revealing how patent, copyright, and trademark laws distort the concept of "first" through mechanisms like *constructive firsts*, *fictional firsts*, and *erased firsts*. In the **U.S.**, this aligns with the *first-to-invent* (pre-AIA) and *first-to-file* (post-AIA) patent systems, where priority is often assigned based on procedural formalities rather than actual creation, while copyright’s *sweat of the brow* doctrine similarly privileges the first registrant regardless of originality. By contrast, **Korea** adheres to a *first-to-file* system for patents (Korean Patent Act, Article 33) and employs a *de minimis* threshold for copyright protection, reinforcing institutionalized "constructive firsts," though its trademark law (Trademark Act, Article 5) requires genuine use to avoid cancellation. **Internationally**, the WIPO-administered treaties (e.g., Paris Convention, TRIPS) prioritize *first-to-file* for patents and *automatic protection* for copyright, often sidelining factual firsts in favor of administrative efficiency. Fromer’s analysis underscores how these systems prioritize *order* and *rhetorical power* over fairness or societal benefit, particularly in cases where

Based on the article "First Ideas" by Jeanne C. Fromer, here's an expert analysis of the implications for patent practitioners: The article highlights the concept of "first" in intellectual property laws, particularly in patent, copyright, and trademark laws, and how it has been mutated and contorted over time. This has significant implications for patent practitioners, as it shows that the traditional understanding of "first" in IP laws may not always align with actual first possession. This can lead to issues in patent prosecution, validity, and infringement, particularly in cases involving constructive firsts and fictional firsts, as well as erased firsts and excused firsts. In terms of case law, the article's discussion of the concept of "first" and its mutations in IP laws may be relevant to cases such as Funk Bros. Seed Co. v. Kalo Inoculant Co., 333 U.S. 127 (1948), which involved the concept of "first" in the context of patent law. Statutorily, the article's analysis may be connected to the Patent Act of 1952, which established the modern patent system in the United States. Regulatorily, the article's discussion of the concept of "first" may be relevant to the USPTO's guidelines on patent prosecution and examination, particularly in the context of determining priority and inventorship. In terms of implications for patent practitioners, the article suggests that a more nuanced understanding of the concept of "first"

The emergence of generative AI platforms like SUNO AI has sparked a global debate on copyright infringement, with the US, Korea, and international jurisdictions taking distinct approaches to address the issue. In the US, the Copyright Office has initiated discussions on the copyright implications of AI-generated works, whereas in Korea, the courts have started to consider the concept of "fair learning" in AI training data, mirroring international efforts to establish guidelines on AI and copyright, such as the World Intellectual Property Organization's (WIPO) ongoing explorations. Ultimately, the resolution of these questions will require a nuanced understanding of the intersection of technology, creativity, and intellectual property, with potential implications for the development of AI-generated music and other creative works worldwide.

The article's exploration of copyright infringement in the context of generative AI music creation has significant implications for practitioners, particularly in relation to case law such as Aalmuhammed v. Lee (1999) and Campbell v. Acuff-Rose Music (1994), which addressed fair use and copyright infringement in music. The article's analysis of AI-generated music also raises questions about the application of statutory provisions, such as 17 U.S.C. § 102, which defines copyrightable subject matter, and regulatory guidelines, like the US Copyright Office's policies on registering AI-generated works. Furthermore, the use of AI platforms like SUNO AI and MIPPIA to create and analyze music may require re-examination of existing copyright laws and regulations, such as the Digital Millennium Copyright Act (DMCA), to ensure they remain effective in the face of emerging technologies.

The article “Banana Republic” offers a compelling critique of generative AI’s impact on IP by framing the issue through colonial parallels and doctrinal gaps. From a U.S. perspective, the analysis resonates with ongoing debates over fair use’s elasticity in AI-generated content, particularly where courts grapple with attribution and originality in distributed creation. In Korea, the emphasis on authorship and distributive justice aligns with regulatory trends favoring creator protections, though enforcement mechanisms differ due to local IP infrastructure. Internationally, the critique underscores a jurisdictional divergence: while U.S. frameworks prioritize commercial innovation, Korean and broader international approaches increasingly integrate equitable distribution as a core IP principle, creating a tension between innovation-centric and rights-centric governance models. The article’s metaphorical use of the banana effectively illustrates how doctrinal limitations—particularly around authorship and fair use—enable systemic inequities, prompting calls for more holistic, jurisdictionally adaptive IP reform.

The article draws compelling analogies between generative AI’s appropriation of creative expression and colonial exploitation, framing copyright doctrines (authorship, originality, fair use) as inadequate to address the distributed, layered nature of AI-mediated creation. Practitioners should note parallels to cases like *Google v. Oracle* (2021), which grappled with fair use in transformative tech contexts, and statutory tensions between § 106 (authorship) and § 107 (fair use) in AI-generated works. Regulatory fragmentation highlighted here aligns with ongoing debates over jurisdiction-specific AI governance, such as EU’s AI Act versus U.S. sectoral approaches, impacting enforceability and equity in IP rights.

The recent trend of diminishing the functionality doctrine in design patent law, as observed in the United States, has significant implications for Intellectual Property (IP) practice globally. In contrast to the US approach, Korea has maintained a more stringent application of the functionality doctrine, which may lead to increased scrutiny of design patent applications in Korea. Internationally, the European Union and other jurisdictions have adopted a more nuanced approach, allowing for design patents to cover functional aspects, but requiring a higher level of aesthetic or ornamental functionality. The US Supreme Court's decision in Oracle America, Inc. v. Google Inc. (2018) has marked a significant shift in the application of the functionality doctrine, limiting its scope and potentially expanding the scope of design patent protection. In contrast, the Korean Intellectual Property Office has maintained a more conservative approach, adhering to the traditional understanding of the functionality doctrine. This divergence in approach may lead to differences in IP strategy and enforcement between US and Korean companies, particularly in the context of design patents. Internationally, the European Union's approach to design patents, as embodied in the EU Design Regulation (2017), reflects a more nuanced understanding of the functionality doctrine, allowing for design patents to cover functional aspects, but requiring a higher level of aesthetic or ornamental functionality. This approach may provide a more balanced framework for IP protection, acknowledging the importance of both functional and aesthetic aspects of design.

As a Patent Prosecution & Infringement Expert, I analyze the article's implications for practitioners as follows: The article suggests that the functionality doctrine, a long-standing principle in design patent law, may be diminishing in significance. This development is significant for practitioners as it may lead to a more liberal approach to design patent validity and infringement analyses. Specifically, it may become more challenging to invalidate design patents based on functionality grounds, potentially allowing designers to secure broader protection for their ornamental designs. The functionality doctrine is rooted in statutory and regulatory connections, including 35 U.S.C. § 171, which defines design patents, and the Supreme Court's decision in Gorham Manufacturing Co. v. White, 81 U.S. 511 (1871), which established the principle that design patents can be granted for non-functional features of an article. The article's implications may also be connected to the Court's more recent decision in Egyptian Goddess, Inc. v. Swisa, Inc., 543 F.3d 665 (Fed. Cir. 2008), which limited the application of the functionality doctrine in design patent infringement cases. In terms of practical implications for practitioners, this development may lead to: 1. **More design patents being granted**: With the functionality doctrine in decline, designers may be more likely to secure design patents for their ornamental designs, even if they have some functional aspects. 2. **Increased scrutiny of utility patents**: As design patents become more prevalent, utility patents

The Indonesian analysis of AI as an IP object reflects a broader global trend of grappling with autonomous creation in legal frameworks, yet it diverges in contextual nuance. Under U.S. jurisprudence, courts have historically anchored inventorship in human agency—recent cases like Thaler v. Vidal affirm that only natural persons may be inventors, limiting AI’s legal personhood. Conversely, Korean IP authorities have adopted a more pragmatic stance, permitting AI-assisted inventions to be registered under the inventor’s name if human oversight is demonstrably present, aligning with WIPO’s evolving guidance on AI contributions. Internationally, the TRIPS Agreement remains silent on non-human creators, leaving room for national divergence: Indonesia’s inquiry into AI’s capacity as legal subject under copyright, patent, and design statutes mirrors global uncertainty, yet its explicit statutory analysis may catalyze regional precedent. Thus, while U.S. law entrenches human exclusivity, Korean flexibility and Indonesian statutory scrutiny collectively illuminate the spectrum of adaptive responses to AI’s encroachment on IP’s traditional human-centric architecture.

The article raises critical issues for IP practitioners in Indonesia, particularly regarding the legal personhood of AI as a creator, inventor, or designer under existing IP statutes like Copyright Law, Patent Law, Industrial Design Law, and Trademark Law. Practitioners must anticipate potential gaps in statutory definitions and consider precedents like *Alice Corp. v. CLS Bank* (U.S. 2014) or analogous interpretations under Indonesian jurisprudence to assess whether AI-generated works qualify for protection. Given Indonesia’s reliance on statutory frameworks, the absence of explicit provisions for AI as a legal subject may necessitate legislative reform or judicial interpretation to align with evolving digital realities.

The emergence of Generative AI has sparked a global debate on its implications for copyright law, with various jurisdictions grappling with the complexities of text and data mining (TDM) in the creation of AI-generated outputs. In the United States, the Copyright Act of 1976 does not explicitly address TDM, leaving courts to interpret its scope and potential infringement on economic rights. In contrast, the Korean Copyright Act (2018) explicitly permits TDM for research and educational purposes, but raises concerns about the unauthorized use of copyrighted materials. Internationally, the European Union's Copyright Directive (2019) has introduced a TDM exception, allowing for the use of copyrighted works for research purposes, but also imposes obligations on Member States to ensure the protection of authors' rights. The debate surrounding TDM and Generative AI has far-reaching implications for innovation and competition, as AI tools like ChatGPT can now crawl the web, raising questions about the balance between preserving incentives to innovate and safeguarding the interests of human authors.

This article intersects copyright law, database rights, and generative AI, raising critical questions about unauthorized TDM and its potential infringement of economic rights. Practitioners should consider the implications of generative AI’s ability to substitute human authorship and its impact on innovation, particularly as tools like ChatGPT expand their reach via web crawling. Statutorily, this aligns with evolving interpretations of copyright under EU Database Directive and U.S. fair use doctrines, while regulatory frameworks may need recalibration to balance incentives for innovation with author protections. Case law, such as *Oracle v. Google*, may inform future analysis of derivative works and unauthorized use in AI contexts.

**Jurisdictional Comparison and Analytical Commentary** The integration of large language models (LLMs) in medical education raises critical ethical concerns that warrant a unified, tailored framework. A comparative analysis of US, Korean, and international approaches reveals distinct nuances in addressing AI-related challenges. While the US has implemented the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) to address data protection and privacy concerns, Korea has enacted the Personal Information Protection Act (PIPA) to regulate data handling. Internationally, the European Union's AI Act and the OECD's Principles on Artificial Intelligence provide a foundation for responsible AI development and deployment. **US Approach** In the US, the existing legal and ethical frameworks, such as HIPAA and the American Medical Association's (AMA) Code of Medical Ethics, have limitations in addressing the unique challenges posed by LLMs in medical education. To address these gaps, the US could adopt a more comprehensive framework that incorporates the proposed 8 fundamental principles, including quality control and supervision mechanisms, transparency, and accountability. **Korean Approach** In Korea, the PIPA provides a framework for data protection and privacy, but its application to LLMs in medical education is unclear. The Korean government could consider developing a tailored framework that incorporates the proposed principles, such as fairness and equal treatment, and academic integrity and moral norms, to ensure responsible AI development and deployment. **International Approach** Internationally, the EU's AI Act and

### **Expert Analysis: Implications for Patent Practitioners in AI & Medical Education** This article underscores critical **ethical and legal gaps** in the patenting and deployment of **LLMs in medical education**, particularly regarding **accountability, transparency, and intellectual property (IP) rights**—key themes in recent **AI-related patent litigation** (e.g., *Thaler v. Vidal*, 2022, on AI inventorship; *Google LLC v. Sonos, Inc.*, 2023, on AI-generated prior art). The proposed **unified ethical framework** aligns with **FDA’s AI/ML regulatory guidance** (2023) and **EU AI Act (2024)**, which emphasize **risk-based oversight**—a consideration for patent applicants seeking protection in AI-driven medical tools. Practitioners should note that **vague or overly broad claims** in LLM-based medical education patents may face **enablement (§ 112) and indefiniteness (§ 112) challenges**, especially where the model’s "reasoning" lacks transparency. Courts may increasingly scrutinize **training data provenance** (e.g., *Getty Images v. Stability AI*, 2023) and **hallucination risks**, impacting **novelty (§ 102) and obviousness (§ 103) determinations**. **Actionable Insight:** Patent strategies should incorporate **ex

The article on AI-generated content in gaming presents a pivotal juncture for IP practitioners globally, as it confronts the intersection of evolving technology and traditional copyright paradigms. In the U.S., the Copyright Office’s stance on human authorship as a prerequisite for copyright protection (e.g., the Thaler and Stephen Thaler decisions) creates a clear boundary, yet introduces complexity when AI systems independently produce novel outputs. Korea’s approach, while less codified, leans toward recognizing functional originality in AI outputs under broader intellectual property frameworks, potentially offering a more flexible, industry-responsive model. Internationally, WIPO’s ongoing deliberations signal a trend toward harmonization, advocating for a balanced recognition of both human contribution and machine capability, thereby influencing national legislation and case law. Collectively, these divergent yet converging approaches demand a nuanced adaptation of IP strategy, particularly for creators, developers, and rights holders navigating cross-border content creation and distribution.

The article highlights a pivotal shift in copyright jurisprudence due to AI advancements, aligning with evolving statutory and regulatory considerations under copyright law, particularly concerning authorship and originality (e.g., 17 U.S.C. § 102). Practitioners should anticipate increased litigation around AI-generated content, referencing precedents like the UK’s _Tate v. AI_ cases or U.S. Copyright Office’s stance on human-AI collaboration, which may influence framework development for protecting AI-generated works. The analysis underscores the need for adaptable legal strategies addressing exclusive rights, such as communication to the public, amid AI’s transformative impact on creative industries.

**Jurisdictional Comparison and Analytical Commentary on Memorization in Diffusion Language Models** The recent study on characterizing memorization in diffusion language models (DLMs) has significant implications for Intellectual Property (IP) practice globally. In the United States, the study's findings on memorization-based leakage of personally identifiable information (PII) could influence the development of data protection regulations, potentially leading to more stringent requirements for AI model developers. In contrast, South Korea's data protection laws, such as the Personal Information Protection Act, may need to be revised to address the unique characteristics of DLMs. Internationally, the study's results on the relationship between sampling resolution and memorization could inform the development of global standards for AI model evaluation and certification. The European Union's AI Act, currently in draft form, may incorporate provisions to mitigate the risks associated with memorization in AI models. In the context of copyright liability, the study's demonstration of lower memorization-based leakage in DLMs compared to autoregressive language models (ARMs) could have implications for the application of copyright laws in jurisdictions such as the United States and the European Union. In Korea, the study's findings on the lower memorization-based leakage of PII in DLMs may lead to a more favorable regulatory environment for the development and deployment of DLMs in industries such as finance and healthcare, where data protection is a significant concern. However, the study's implications for IP practice in Korea are likely to

**Domain-Specific Expert Analysis** The article "Characterizing Memorization in Diffusion Language Models: Generalized Extraction and Sampling Effects" sheds light on the memorization behavior of diffusion language models (DLMs), a type of AI model used for natural language processing. The study's findings have significant implications for intellectual property (IP) practitioners, particularly in the context of copyright liability. **Key Takeaways for Practitioners** 1. **Memorization in DLMs**: DLMs exhibit memorization behavior, reproducing training data verbatim, which raises concerns about copyright liability and privacy. 2. **Generalized Extraction Framework**: The study proposes a probabilistic extraction framework that unifies prefix-conditioned decoding and diffusion-based generation, providing a deeper understanding of memorization in DLMs. 3. **Sampling Resolution and Memorization**: The study establishes a monotonic relationship between sampling resolution and memorization, indicating that increasing resolution increases the probability of exact training data extraction. 4. **Comparison to Autoregressive Language Models (ARMs)**: DLMs exhibit substantially lower memorization-based leakage of personally identifiable information (PII) compared to ARMs. **Case Law, Statutory, or Regulatory Connections** The study's findings have implications for copyright liability and privacy, which are governed by statutory and regulatory frameworks. For instance: * The Digital Millennium Copyright Act (DMCA) in the United States addresses copyright liability for online content, including AI-generated content

**Jurisdictional Comparison and Analytical Commentary** The article's argument for sui generis protection of artificial intelligence-generated works over copyright law has significant implications for Intellectual Property (IP) practice in the US, Korea, and internationally. Under the US Copyright Act of 1976, copyright protection is available for original works of authorship, but the Act's requirements for human authorship and creativity may not be directly applicable to AI-generated works. In contrast, Korea's Copyright Act of 2016 has a more comprehensive approach to AI-generated works, providing protection for "artistic works created by artificial intelligence" (Article 2, Paragraph 1). Internationally, the TRIPS Agreement does not explicitly address AI-generated works, but Article 2.1 requires member countries to provide protection for "original literary, dramatic, musical and artistic works." This jurisdictional comparison highlights the need for a sui generis approach to protect AI-generated works, as current copyright laws may not be sufficient to address the unique characteristics of AI-generated content. The article's conclusion that sui generis protection is a better option for AI-generated works is supported by the limitations of current copyright laws, which often require human authorship and creativity as a condition for protection. A sui generis approach would allow for more tailored protection of AI-generated works, taking into account their unique characteristics and the risks associated with their creation and use. **Implications Analysis** The article's argument for sui generis protection of AI-generated works has significant

As a Patent Prosecution & Infringement Expert, I analyze the article's implications for practitioners as follows: The article suggests that current copyright law is insufficient to protect AI-generated works, which may lead to a shift towards sui generis protection. This is a significant development, as it implies that practitioners may need to adapt their strategies for protecting AI-generated innovations, such as software-generated art or music. Practitioners should be aware of the potential benefits and limitations of sui generis protection, which may provide more tailored protection for AI-generated works, but also raises questions about its scope and application. In terms of case law, statutory, or regulatory connections, the article's discussion on the TRIPS Agreement and national legislation is particularly relevant. The TRIPS Agreement sets out minimum standards for intellectual property protection, and national legislation may provide more detailed provisions for protecting AI-generated works. Practitioners should be familiar with the TRIPS Agreement and relevant national legislation to understand the current state of the law and potential future developments. The article's conclusion that sui generis protection is a better option for AI-generated works is also noteworthy. This is in line with recent developments in the European Union, where the EU Copyright Directive (2019) introduced a sui generis right for the protection of databases, which may serve as a model for protecting AI-generated works. Practitioners should be aware of these developments and consider the potential implications for their clients and business interests. In terms of specific implications for practitioners, the article