This Article identifies and describes a crucial source of innovation failure-linked not to the market but to the structure of social relations that underlie market transactions-that this Article terms social network innovation failures. This source of innovation failure, however, has been obscured by two assumptions in traditional market failure models of innovation. First, market failure models frequently assume that public, non-secret knowledge (or information) will flow freely among communities of innovators and be put to its optimal use. Second, market failure models pay little attention to how good ideas emerge, assuming that good ideas will follow from investment in research and development. Social network failures are failures of social interaction. Drawing on studies from the sociology of networks as well as original ethnographic research in innovator communities, this Article develops a taxonomy of social network innovation failures: (1) lack of social ties; (2) cognitive distance; and (3) different (or clashing) evaluative frames. It then illustrates how these social network innovation failures are endemic in a wide variety of fields, including computer science, mathematics, public health, and medicine, allowing key pieces of publicly available knowledge and expertise needed to solve complex problems to remain trapped in communities of innovators that do not interact with each other. Understanding social barriers to information flow is especially important in light of findings in the sociology literature that breakthrough ideas arise from the work of teams that bring together knowledge from cognitively-distant communities

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-nonobviousnesshas 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

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.

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