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The development and deployment of technologies depend upon collaborations concurrently relying on proximity between partners. By employing publication data of German nanotechnology, we augment former findings on the relationship between proximity and collaboration in three ways. First, we shed light on how the various forms of proximity affect different stages of collaboration. Particularly, we split geographical proximity into pure physical and systemic proximity. By doing so, we can show that pure physical proximity plays a role early on, as it positively influences the formation of collaborations. In contrast, systemic proximity affects collaborations later on by inducing higher output. Second, innovation systems shape collaboration networks. We learn that specific features of publicly funded German research organizations influence the formation and output of collaborations via organizational proximity or a lack thereof. Third, cognitive proximity has by far the strongest magnitude of effect on both the formation and the output of collaborations. Particularly, existing partnership being cognitively diverse have a lot of potential. Therefore, research policy and university management might consider to stimulating current partnerships, being cognitively different.

Emerging technologies are in the core focus of supra-national innovation policies.These strongly rely on credible data bases for being effective and efficient. However, since emerging technologies are not yet part of any official industry, patent or trademark classification systems, delineating boundaries to measure their early development stage is a nontrivial task. This paper is aimed to present a methodology to automatically classify patents as concerning service robots. We introduce a synergy of a traditional technology identification process, namely keyword extraction and verification by an expert community, with a machine learning algorithm. The result is a novel possibility to allocate patents which (1) reduces expert bias regarding vested interests on lexical query methods, (2) avoids problems with citational approaches, and (3) facilitates evolutionary changes. Based upon a small core set of worldwide service robotics patent applications we derive apt n-gram frequency vectors and train a support vector machine (SVM), relying only on titles, abstracts and IPC categorization of each document. Altering the utilized Kernel functions and respective parameters we reach a recall level of 83% and precision level of 85%.

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