Peter Lee

This Article confronts an underappreciated bias that has long pervaded U.S. legal culture: an exceedingly enthusiastic view of technology and technological progress. It adopts the term "techno-optimism" to describe an asymmetrically positive view of innovation that extols its benefits, minimizes its harms, and elides much of its complexity. As a case study of techno-optimism, this Article scrutinizes the patent system, particularly its constitutionally defined objective of promoting technological progress. By examining legislative histories of major patent acts, executive pronouncements, and judicial opinions, this Article reveals that patent policymakers exhibit roundly positive views of innovation. On the rare occasion when they identify harms from technological progress, they defer, minimize, or otherwise compartmentalize their concerns. This Article then challenges such techno-optimism by exploring several underappreciated harms of technological progress. This critique is most centrally aimed at the patent system, and it will highlight how patents exacerbate several costs related to technological progress. However, this critique has broader salience for numerous legal and policy initiatives aimed at promoting technological advancement. First, extending previous work, this Article explores how innovations (including patented inventions) from opioids to social media to pesticides can harm individuals, society, and the environment. Second, it explores how the zealous pursuit of technological progress contributes to inequality in access to innovations and their economic benefits. Third, it argues that rampant innovation often leads to substantial waste, overinvestment, and misallocation of scarce social resources away from other worthy ends. The Article concludes with calls for a cultural shift among policymakers from techno-optimism to techno-realism. Additionally, it presents several prescriptions to mitigate the harms of innovation, enhance the distributive effects of rapid technological progress, and integrate innovation policy within a broader social policy framework.

In standard accounts of international technology transfer, strong patent protection induces innovative firms to license their technology to foreign entities. Such technology transfer is particularly important to developing countries, which rely on foreign innovations to build domestic innovative capacity. Patents and licenses, however, frequently do not convey all of the technical knowledge needed to practice an invention effectively in industrial settings. In particular, patents often do not disclose invention-related tacit knowledge and trade secrets held by innovators. Conveying such knowledge to foreign entities is difficult and often requires direct organizational connections between innovators and technology adopters. Drawing on the theory of relational contracts, this symposium Article shows that rather than operating as one-off patent licenses, international transfers of patented technology often unfold as long-term relationships that enable intensive knowledge transfer. This Article examines these dynamics in the context of the global biopharmaceutical industry through case studies of transfers between innovators and contract development and manufacturing organizations (CDMOs) around the world. It concludes by exploring the theoretical and policy implications of this relational model of international technology transfer, particularly for developing countries seeking greater access to foreign technical knowledge.

Chinese TranslatorZuwei DuChinese  摘 要: 人工智能(AI) 未来最突出的技术和法律挑战源自于AI 需要大 量现实世界数据用于训练机器学习(Machine Learning, ML) 模型。收集这些 数据极为困难, 并且可能会威胁隐私、引发自动化决策偏见、大范围侵犯版 权等。本文探讨了一种看似矛盾的技术创新———合成数据, 其虽不能消除但 却可以缓解这些担忧。越来越多的数据科学家正在使用模拟驾驶、虚拟医疗 记录、虚拟图像及其他形式的合成数据来训练ML 模型, 即人工数据( artificial data) 正被用于训练人工智能。合成数据带来了诸多技术与法律的红利, 包括大幅度降低数据获取成本、规避隐私风险、减少自动化歧视、避免侵害 版权等。然而, 利好与风险并存, 合成数据开发和部署过程中的瑕疵同样可 能加剧人工智能危险并造成重大社会损害。鉴于合成数据蕴含的重要价值, 本文尝试勾勒出一个创新生态系统框架以促使其稳健可靠发展。合成数据供 给、公开和获取民主化是相关法律和政策构建的三个基本目标, 在理想情况 下, 该框架应能够激励高质量合成数据生成、鼓励生成过程公开和丰富创新 来源多样性。本文讨论了一系列有利于这些目标的“创新机制”, 从开源生成 到专利、商业秘密、版权保护, 不一而足。法律和政策对塑造人工智能未来 具有关键作用, 本文始终建议要以促进创新、提高透明度和强化民主化访问 为方向进行政策和理论变革English The future of artificial intelligence (AI) is synthetic. Several of the most prominent technical and legal challenges of AI derive from the need to amass huge amounts of real-world data to train machine learning (ML) models. Collecting such real-world data can be highly difficult and can threaten privacy, introduce bias in automated decision making, and infringe copyrights on a massive scale. This Article explores the emergence of a seemingly paradoxical technical creation that can mitigate—though not completely eliminate—these concerns: synthetic data. Increasingly, data scientists are using simulated driving environments, fabricated medical records, fake images, and other forms of synthetic data to train ML models. Artificial data, in other words, is being used to train artificial intelligence. Synthetic data offers a host of technical and legal benefits; it promises to radically decrease the cost of obtaining data, sidestep privacy issues, reduce automated discrimination, and avoid copyright infringement. Alongside such promise, however, synthetic data offers perils as well. Deficiencies in the development and deployment of synthetic data can exacerbate the dangers of AI and cause significant social harm.In light of the enormous value and importance of synthetic data, this Article sketches the contours of an innovation ecosystem to promote its robust and responsible development. It identifies three objectives that should guide legal and policy measures shaping the creation of synthetic data: provisioning, disclosure, and democratization. Ideally, such an ecosystem should incentivize the generation of high-quality synthetic data, encourage disclosure of both synthetic data and processes for generating it, and promote multiple sources of innovation. This Article then examines a suite of “innovation mechanisms” that can advance these objectives, ranging from open source production to proprietary approaches based on patents, trade secrets, and copyrights. Throughout, it suggests policy and doctrinal reforms to enhance innovation, transparency, and democratic access to synthetic data. Just as AI will have enormous legal implications, law and policy can play a central role in shaping the future of AI.

While modern science and technology have produced astounding advances, their contributions to social welfare often fall short of expectations. This Article provides a holistic framework for promoting “socially responsive” innovation that increases the social benefits, decreases the social harms, and improves the distributional effects of massive investments in science and technology. Traditionally, policymakers have relied on “centralized” governance regimes such as mission-based science funding and technological regulation to guide the development and use of new innovations. While valuable, centralized approaches feature several shortcomings, and this Article proposes a “distributed” model of innovation governance to supplement them. In so doing, it draws on the CHIPS and Science Act of 2022, which reformed the National Science Foundation’s longstanding practice of considering social impacts in peer-review assessments of research grant proposals. Building on these reforms, this Article proposes a model of distributed innovation governance that shifts greater authority to promote socially responsive innovation away from central policymakers and toward a wide range of public and private stakeholders, peer reviewers, scientists, and engineers. This Article argues for orienting various innovation “gatekeepers”—including federal funding agencies, scientific journals and conferences, and the Patent and Trademark Office—toward considering the broader social impacts (not just technical merit) of innovative projects when awarding key resources. By tying research funds, publication and presentation opportunities, and patents to considerations of social impact, this proposal creates a powerful incentive for researchers and inventors to consider and modify the contributions of their work to social welfare. This Article shows how government action can shift norms in the innovative community—including among private actors—to embrace socially responsive science and technology. This distributed governance model offers several benefits, including preserving scientific autonomy, guarding against the overpoliticization of innovation policy, and exploiting significant amounts of private information distributed among scientists and engineers.

The best mode requirement is a criterion of patentability in some jurisdictions requiring that a patent applicant disclose subjective knowledge of the best way to practice a claimed invention. To satisfy the requirement, a patent applicant must do more than simply “enable” a functional version of an invention. For instance, under U.S. patent law, if a patent applicant “develops specific instrumentalities or techniques which are recognized at the time of filing as the best way of carrying out the invention, then the best mode requirement imposes an obligation to disclose that information to the public as well.” Spectra-Physics Inc. v. Coherent Inc., 827 F2d 1524, 1532 (Fed. Cir. 1987). Relatively few patent systems possess a best mode requirement, and in several that do, enforcement is weak. This entry in the Encyclopedia of Intellectual Property Law provides a brief overview of the best mode requirement.

One of the most striking and undertheorized aspects of fields that commercialize patented technologies is the dynamic interplay of structural forces pushing toward consolidation. Of course, technological industries are complex ecosystems featuring numerous players of different sizes along the value chain spanning “upstream” research and development and “downstream” commercialization. However, when focusing on downstream industry segments that bring patented technologies to market—“innovation” in an economic sense—a relatively small number of large companies frequently play outsize roles. Technological industries tend to be patent-intensive, and legal and economic theory has long explored the role of patents in shaping industry structure—the number, size, and character of firms in an industry. However, such theory only explains one facet of industry structure. This Article provides a more holistic account of the forces shaping the commercialization of patented technologies by examining three industries: biopharmaceuticals; agricultural biotechnology, seeds, and agrochemicals; and software. This Article argues that the commercialization of patented technologies is subject to several “concentration drivers” pushing toward consolidation, including direct barriers to entry based on exclusive rights and cost, indirect barriers to entry based on efficiencies of size, and significant merger and acquisition activity. It also argues that several common “fragmentation drivers” push in the opposite direction to increase the number of participants in these industries, including technology-based entry, voluntary divestures, and antitrust enforcement. While such forces are significant, the formidable strength of concentration drivers results in substantial consolidation in the commercialization of drugs, genetically modified seeds, and software. Turning to normative analysis, this Article argues that such consolidation can be salutary to a point but that undue concentration ultimately harms innovation, efficiency, consumer welfare, and democratic representation. It argues that patent law should more fully consider the myriad forces beyond exclusive rights that shape technological commercialization, and it provides prescriptions for enhancing industry entry through private ordering, federal innovation policy, and antitrust enforcement.

Traditional conceptions of university-industry technology transfer typically focus on patenting and licensing of academic inventions. However, effective technology transfer often requires significant knowledge exchange between academic and commercial entities in parallel to patent licensing. Although patents on university technologies nominally disclose those inventions, a significant amount of knowledge related to practicing and commercializing them remains tacit or uncodified, residing in the mind of the faculty inventor. This Chapter explores the nature of tacit knowledge and mechanisms for transferring it. It notes that the “tacit dimension” of university inventions can be quite high given the embryonic nature of such technologies. It further reveals that human and institutional connections play a critical role in transferring tacit knowledge between universities and commercial firms. In particular, networks, consulting engagements, sponsored research, proof of concept centers and incubators, and university spinoffs facilitate direct interactions between academic and commercial entities, thus promoting tacit knowledge exchange.

Design – which encompasses everything from shape, color, and packaging to user interface, consumer experience, and brand aura – is the currency of modern consumer culture and increasingly the subject of intellectual property claims. But the law of design is confused and confusing, splintered among various doctrines in copyright, trademark, and patent law. Indeed, while nearly every area of IP law protects design, the law has taken a siloed approach, with separate disciplines developing ad hoc rules and exceptions. To address this lack of coherence, this Article provides the first comprehensive assessment of the regulation of consumers’ aesthetic experiences in copyright, trademark, and patent law – what we call “the law of look and feel.” We canvas the diverse ways that parties have utilized (and stretched) intellectual property law to protect design in a broad range of products and services, from Pac-Man to Louboutin shoes to the iPhone. In so doing, we identify existing doctrines and principles that inform a normatively desirable “law of look and feel” that courts and Congress should extend throughout IP law’s protection of design. We argue that design law should protect elements of look and feel but remain sensitive to eliminating or mitigating exclusive rights in response to evolving standardization, consumer expectations, and context. Notably, our normative conception of design protection sometimes departs quite starkly from how courts have expansively conceptualized “look and feel” as protectable subject matter. Going further, we argue that the new enclosure movement of design, if not comprehensively reformed and grounded in theory, can erode innovation, competition, and culture itself.

This Article advances two novel claims about the internalization of academic science within patent law and the concomitant evolution of “academic exceptionalism