Intellectual Property

This article, while not directly addressing intellectual property (IP) law, raises broader constitutional and administrative law concerns that could indirectly influence IP jurisprudence—particularly in areas where executive overreach or statutory interpretation intersects with IP policy. In the **U.S.**, where IP law is primarily statutory (e.g., the Patent Act, Copyright Act) and subject to judicial interpretation, a precedent of executive reinterpretation of foundational legal principles could embolden administrative agencies (e.g., USPTO, Copyright Office) to push boundaries in IP rulemaking without clear congressional authorization. By contrast, **South Korea**—where IP enforcement is heavily centralized under the Korean Intellectual Property Office (KIPO) and courts defer to statutory text—might resist such executive aggrandizement, though recent trends toward "regulatory sandbox" approaches in innovation policy could blur lines. **Internationally**, the WIPO framework and TRIPS Agreement emphasize legal certainty in IP rights, suggesting that arbitrary executive reinterpretations could face scrutiny under international trade law or investment treaties, particularly where foreign rights holders rely on stable legal regimes. The broader takeaway is that erosion of settled legal interpretations in one domain risks destabilizing the predictability essential to IP systems across jurisdictions.

This article discusses the constitutional interpretation of the **Citizenship Clause of the 14th Amendment** (*"All persons born or naturalized in the United States, and subject to the jurisdiction thereof, are citizens of the United States"*), which has been long-settled in case law (*U.S. v. Wong Kim Ark*, 169 U.S. 649 (1898)) as conferring birthright citizenship regardless of parental immigration status. The implication for patent practitioners is indirect but relevant in **claim construction and statutory interpretation**, where courts similarly rely on established precedent (*Markman v. Westview Instruments*, 517 U.S. 370 (1996)) to define terms like "inventor" or "patentable subject matter" under 35 U.S.C. § 101. A shift in constitutional interpretation—such as undermining *Wong Kim Ark*—could theoretically influence statutory construction in patent law, though no direct case law connects the two. Practitioners should monitor such constitutional shifts, as they may indirectly affect IP jurisprudence, particularly in **immigration-related patents** (e.g., inventions by non-citizens) or **government patent policies**.

The "Med-Shicheng" framework, which leverages lightweight LLMs to codify and transfer physician expertise, presents fascinating IP implications across jurisdictions. In the US, the core LLM architecture and its training methodology would likely be protectable under copyright as a software program, and potentially patentable as a business method or system if it demonstrates novel and non-obvious technical improvements in data processing or medical decision support. However, the "diagnostic-and-therapeutic philosophy" itself, being an abstract concept or medical knowledge, would generally not be directly protectable under patent or copyright law, though its specific expression within the trained model could be. In Korea, similar to the US, the software implementing Med-Shicheng would be copyrightable. Patent protection for AI-related inventions is also available, with the Korean Intellectual Property Office (KIPO) generally requiring a technical solution to a technical problem. The "standardized way" of learning and transferring expertise might be patentable if it involves a specific, inventive algorithm or system architecture, rather than merely a conceptual approach. However, the underlying medical knowledge, much like in the US, would likely remain in the public domain or be considered unpatentable abstract information. Internationally, the varying approaches to patentability of AI and software present a complex landscape. The EU, for instance, generally requires a "technical character" for patentability, meaning the invention must solve a technical problem using technical means. While software *per se*

This article, describing "Med-Shicheng" for systematizing and scaling physician expertise via LLMs, presents significant implications for patent practitioners, particularly concerning patent eligibility, obviousness, and potential infringement. **Patent Prosecution Implications:** * **Eligibility (35 U.S.C. § 101):** The core challenge for claims related to Med-Shicheng will be demonstrating patent eligibility, avoiding abstract ideas, laws of nature, and natural phenomena. Claims focused solely on "learning and transferring diagnostic-and-therapeutic philosophy" or "case-dependent adaptation rules" might be deemed abstract. Practitioners must carefully draft claims to include specific, inventive applications of the LLM, particularly how it interacts with physical systems (e.g., generating specific treatment plans for a patient, controlling medical devices, or processing physiological data). The "five stages" and the "multi-source materials" could provide concrete steps to anchor claims in a practical application. The Federal Circuit's *Alice Corp. v. CLS Bank Int'l* framework, as elaborated by cases like *Berkheimer v. HP Inc.* and *Amdocs (Israel) Ltd. v. Openet Telecom, Inc.*, will be paramount. Claims must show "significantly more" than the abstract idea, perhaps by tying the LLM's output to a tangible diagnostic or therapeutic outcome. * **Obviousness (35 U.S.C. §

The technical advancements in "Probabilistic Geometric Alignment via Bayesian Latent Transport" present fascinating, albeit indirect, implications for Intellectual Property practice, particularly concerning the patentability of AI-driven innovations and the protection of data-driven models. **Jurisdictional Comparison and Implications Analysis:** The abstract describes a novel framework for domain adaptation in foundation models, focusing on "uncertainty-aware probabilistic latent transport" and "stochastic geometric alignment." This involves sophisticated mathematical and computational techniques to improve model adaptability and robustness. * **United States:** In the US, the patentability of AI algorithms and software is primarily governed by *Alice Corp. v. CLS Bank International*. The key challenge lies in demonstrating that the invention constitutes significantly more than an abstract idea. This paper's framework, with its "Bayesian transport operator," "PAC-Bayesian regularization mechanism," and "theoretical guarantees on convergence stability," presents a strong case for meeting the "inventive concept" requirement. The specific formulation of domain adaptation as a "stochastic geometric alignment problem" and the empirical demonstration of improved performance (e.g., "substantial reduction in latent manifold discrepancy") could help overcome abstract idea rejections by showing a practical application and technical solution to a specific problem in the field of AI. The focus on *how* the model adapts, rather than just the outcome, strengthens the argument for patent eligibility. * **South Korea:** South Korea, while also adhering to principles that prevent the patenting

This article describes a novel approach to adapting large-scale foundation models, which could have significant implications for patentability and infringement analysis in AI/ML. The "uncertainty-aware probabilistic latent transport framework" and its specific components, such as the "Bayesian transport operator" and "PAC-Bayesian regularization mechanism," represent potentially patentable subject matter under 35 U.S.C. § 101, assuming they meet the other criteria of novelty and non-obviousness. For patent prosecution, practitioners should focus on clearly defining the inventive steps related to the *method* of stochastic geometric alignment, the *architecture* incorporating the Bayesian transport operator, and the *system* for domain adaptation that leverages PAC-Bayesian regularization. The theoretical guarantees on convergence stability, loss landscape smoothness, and sample efficiency could serve as strong evidence of unexpected results or advantages, bolstering arguments against obviousness under 35 U.S.C. § 103. In terms of infringement, the detailed description of how this framework "redistributes latent probability mass along Wasserstein-type geodesic trajectories" and "constrains posterior model complexity" provides specific technical details that could be used to identify infringing implementations. Claims drafted around these functional and structural elements would be crucial. For example, a claim covering "a method for adapting a foundation model comprising: applying a Bayesian transport operator to redistribute latent probability mass along Wasserstein-type geodesic trajectories..." would be highly relevant. Regarding validity,

The paper "Manifold Generalization Provably Proceeds Memorization in Diffusion Models" offers a fascinating theoretical lens through which to understand the generative capabilities of diffusion models, particularly their ability to produce novel outputs even with "coarse" training. This insight has significant implications for intellectual property (IP) practice, particularly in the realm of copyright and inventorship, by refining our understanding of what constitutes "originality" and "creation" in AI-generated content. The core argument – that diffusion models capture the *geometry* of data rather than merely memorizing its *distributional structure* – directly challenges the simplistic notion that AI models are merely sophisticated copy machines. If a model is indeed learning underlying manifold regularities and generating outputs based on these learned geometric principles, rather than reproducing specific training data points, it strengthens the argument for the *originality* of AI-generated works. This theoretical underpinning could influence how courts and IP offices assess copyrightability, potentially shifting the focus from direct input-output comparisons to the sophistication of the generative process and the novelty of the resulting output. **Jurisdictional Comparisons and Implications Analysis:** The implications of this research diverge across jurisdictions, reflecting their varying approaches to AI and IP. * **United States:** The U.S. Copyright Office (USCO) has, to date, taken a relatively restrictive stance, emphasizing the need for human authorship in AI-generated works. The USCO's current guidance suggests that works "produced by a machine

This article's findings regarding diffusion models' ability to generate novel samples from coarse scores, by capturing data geometry rather than fine-scale distribution, has significant implications for patent practitioners in the AI/ML space. **Implications for Practitioners:** 1. **Claim Scope and Enablement (35 U.S.C. § 112):** The concept of "coarse scores capturing the geometry of the data" suggests that claims directed to AI models might be enabled even if they don't explicitly define every fine-grained parameter or training detail. If the core innovation lies in *how* the model learns and leverages data geometry for generalization, rather than precise density estimation, then broad claims focusing on this geometric learning could be defensible. Conversely, if an inventor claims a specific "fine-scale distributional structure," but the underlying model operates on coarse geometric principles, the claim might lack adequate written description or enablement for the *actual* invention. This connects to cases like *Ariad Pharmaceuticals, Inc. v. Eli Lilly and Co.* regarding written description, and *The Medicines Co. v. Hospira, Inc.* on enablement, where the specification must teach one of ordinary skill in the art how to make and use the invention without undue experimentation. 2. **Inventive Step/Non-Obviousness (35 U.S.C. § 103):** The article highlights that this generalization behavior "is a phenomenon not accounted

This research, while highly technical in its focus on activation function curvature and adversarial robustness in AI, carries significant implications for IP practice, particularly concerning the patentability and defensive strategies around AI models. The discovery of an optimal range for $\max|\sigma''|$ (4 to 10) for adversarial robustness suggests a potentially patentable invention in the design and training of AI systems, offering a novel method for improving model security against adversarial attacks. From an IP perspective, this could lead to a surge in patent applications claiming specific activation function designs or training methodologies that leverage this curvature insight. **Jurisdictional Comparison and Implications Analysis:** * **United States:** The U.S. Patent and Trademark Office (USPTO) would likely evaluate claims related to the RCT-AF or its application under the framework of *Alice Corp. v. CLS Bank Int'l*, scrutinizing whether the invention is merely an abstract idea or a patent-eligible application. Claims focusing on the specific mathematical relationship and its tangible impact on AI model robustness (e.g., "a method for training a neural network comprising adjusting activation function parameters to maintain $\max|\sigma''|$ between 4 and 10 to enhance adversarial robustness") would likely fare better than abstract claims. The "technical solution to a technical problem" doctrine, while not explicitly codified, often influences examiners' perspectives on software-related inventions. This research provides a clear technical solution (improved robustness) to a technical problem (adversarial attacks

This article, while focused on machine learning, has significant implications for patent practitioners dealing with AI/ML inventions, particularly concerning enablement, written description, and infringement. The finding that optimal adversarial robustness is tied to a specific range of activation function curvature ($\max|\sigma''|$ between 4 and 10) could be crucial for drafting and prosecuting claims related to robust AI systems. **Implications for Practitioners:** 1. **Enablement (35 U.S.C. § 112(a)):** For claims directed to AI models or methods designed for adversarial robustness, this research provides a concrete technical parameter that could be essential for satisfying enablement. If an inventor claims a robust AI system, merely stating "a robust neural network" might be insufficient if the claimed robustness critically depends on this specific curvature range. Practitioners should consider whether the specification adequately discloses how to achieve and/or measure this curvature, especially if the invention relies on the RCT-AF or similar curvature-tunable activation functions. Failure to disclose such details could lead to enablement rejections, as the public might not be able to make and use the invention without undue experimentation. 2. **Written Description (35 U.S.C. § 112(a)):** Similarly, for inventions where adversarial robustness is a key feature, the written description should ideally demonstrate possession of the invention by detailing how this optimal curvature range is achieved or utilized. If the invention's novelty or