A Network Theory of Patentability

Patent law is built upon a fundamental premise: only significant inventions receive patent protection while minor improvements remain in the public domain. This premise is indispensable for maintaining an optimal balance between incentivizing new innovation and providing public access to existing innovation. Despite its importance, the doctrine that performs this gatekeeping role—nonobviousness— has long remained indeterminate and vague. Judicial opinions have struggled to articulate both what makes an invention significant (or nonobvious) and how to measure nonobviousness in specific cases. These difficulties are due in large part to the existence of two clashing theoretical frameworks, cognitive and economic, that have vied for prominence in justifying nonobviousness. Neither framework, however, has generated doctrinal tests that can be easily and consistently applied. This Article draws on a novel approach—network theory—to answer both the conceptual question (what is a nonobvious invention?) and the measurement question (how do we determine nonobviousness in specific cases?). First, it shows that what is missing in current conceptual definitions of nonobviousness is an underlying theory of innovation. It then supplies this missing piece. Building upon insights from network science, we model innovation as a process of search and recombination of existing knowledge. Distant searches that combine disparate or weakly connected portions of social and information networks tend to produce high-impact, new ideas that open novel innovation trajectories. Distant searches also tend to be costly and risky. In contrast, local searches tend to result in incremental innovation that is more routine, less costly, and less risky. From a network theory perspective, then, the goal of nonobviousness should be to reward, and therefore to incentivize, those risky distant searches and recombinations that produce the most socially significant innovations. By emphasizing factors specific to the structure of innovation—namely, the risks and costs of the search and recombination process—a network approach complements and deepens current economic understandings of nonobviousness. Second, based on our network theory of innovation, we develop an empirical, algorithmic measure of patentability—what we term a patent’s “network nonobviousness score” (NNOS). We harness data from US patent records to calculate the distance between the technical knowledge areas recombined in any given invention (or patent), allowing us to assign each patent a specific NNOS. We propose a doctrinal framework that incorporates an invention’s NNOS to nonobviousness determinations both at the examination phase and during patent litigation. Our use of network science to develop a legal algorithm is a methodological innovation in law, with implications for broader debates about computational law. We illustrate how differences in algorithm design can lead to different nonobviousness outcomes, and discuss how to mitigate the negative impact of black box algorithms

Anti-Innovation Norms

Intellectual property (IP) scholars have recently turned their attention to social norms—informal rules that emerge from and are enforced by nonhierarchically organized social forces—as a promising way to spur innovation in communities as diverse as the fashion industry and the open-source software movement. The narrative that has emerged celebrates social norms’ ability to solve IP’s free-rider problem without incurring IP’s costs. But this account does not fully consider the dark side of social norms. In fact, certain social norms, when overenforced, can create substantial barriers to the most socially beneficial creative pursuits. Because IP scholars have left unexplored how social norms can hinder innovation in this way, the harm they cause has gone unmitigated. This Article sheds light on the dark side of innovation norms. It coins the term “anti-innovation norms” to label these counterproductive social forces. Using the double lens of sociology and psychology, it gives a full theoretical account of three types of anti-innovation norms: research priority, methodology, and evaluation norms—all of which interfere with socially beneficial boundary-crossing innovation. Our elucidation of anti-innovation norms has both theoretical and policy implications. On the theory side, it suggests that IP scholars to date have been too focused on addressing the free-rider problem. This has caused them to overlook other barriers to innovation, like those posed by the set of anti-innovation norms we describe here. This focus on free riding may also help explain why innovation and norms scholars have paid little attention to debates within the broader literature on law and social norms concerned with identifying situations in which social norms are welfare reducing. On the policy side, it points to innovation dilemmas that IP is not fully equipped to solve. While changes to the IP doctrines of attribution and fair use in copyright and nonobviousness in patent law can counteract anti-innovation norms at the margin, a comprehensive solution requires innovation scholars to broaden their vision beyond the IP toolkit. We take the first steps in this direction, proposing a number of interventions, including novel funding regimes and tax credits

Business Method Patents Following Alice v. CLS Bank

This panel will discuss Alice v. CLS Bank, 134 S. Ct. 2347 (2014)

Business Method Patents Following Alice v. CLS Bank

This panel will discuss Alice v. CLS Bank, 134 S. Ct. 2347 (2014)

Transport of neutral IgG2 versus anionic IgG4 in PD : implications on the electrokinetic model

Background: It is debated whether transperitoneal membrane transport of larger (charged) molecules in peritoneal dialysis can be partially governed by the electrokinetic model. In this model, it is postulated that streaming potentials are generated across the capillary wall by forced filtration of an ionic solution, for example transcapillary ultrafiltration induced by osmotic forces as in peritoneal dialysis. We investigated the presence of streaming potentials in the process of transperitoneal transport in Peritoneal Dialysis (PD) patients by measuring ratios of dialysate concentrations of IgG2 (neutral) and IgG4 (negative), both 150kD, under different conditions of transcapillary ultrafiltration. Methods: Adult PD patients randomly got two consecutive dwells of 120 min each, with either 2 L Physioneal 1.36% or 3.86% glucose dialysis fluid (Baxter, USA) as their first dwell. A blood sample was taken at the test start, and dialysate samples were taken at 5, 15, 30, 60 and 120 min. IgG2 and IgG4 concentrations were measured (ELISA) and ratios calculated. Results: In 10 patients (65 +/- 17 years, 2017 months on dialysis), drained volume after 120 min was different between the 1.36% (1950 [1910; 2020] mL) and 3.86% (2540 [2380; 2800] mL) glucose dwells (P = 0.007). At none of the time points and irrespective of glucose concentration, a significant difference was found between the IgG2/IgG4 ratios at any time point. Conclusion: Our data failed to demonstrate a difference in the transport ratios of two macromolecules with same molecular weight but different charge, as would be expected by the electrokinetic model, and this despite sufficient differences in transcapillary ultrafiltration

Spill Your (Trade) Secrets: Knowledge Networks as Innovation Drivers

Theories of intellectual property take the individual inventor or the firm as the unit of innovation. But studies in economic sociology show that in complex fields where knowledge is rapidly advancing and widely dispersed among different firms, the locus of innovation is neither an individual nor a single firm. Rather, innovative ideas originate in the informal networks of learning and collaboration that cut across firms. Understanding innovation in this subset of industries as emerging out of networks of informal information-sharing across firms challenges traditional utilitarian theories of trade secret law—which assume trade secret protection is needed to prevent excessive private, self-help efforts to preserve secrecy. Doctrinally, knowledge network research suggests that the scope of trade secret protection in these industries should be narrow. In these industries, strong trade secret rights that grant managers tight control over employee-inventors’ informal information-sharing practices are bad innovation policy. Rather, optimizing trade secret law requires tailoring the strength of protection to match industry characteristics, narrowing trade secret scope in those industries where informal information-sharing networks are predicted to enhance innovative output. In turn, because industry types tend to cluster around geographic centers, the importance of tailoring cautions against current trends towards uniformity by federalizing trade secret law and favors state experimentalism in designing trade secret law and policy