26,207 research outputs found

    Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network

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    The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (i) identifies actual clusters of patents: i.e. technological branches, and (ii) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the {citation vector}, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action

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    Department of Management EngineeringFirms participating in printer industries have invested their constrained resources into technology development in order to sustain their competitiveness in the industry. Considering the fast-changing market circumstances, each firm???s own investment decisions on technology portfolio may directly affect their performance. In this study, we analyzed patent data, namely number of forward citations and technological classification data (CPC). Using this data, the technological portfolio of a specific firm can be identified, which can further help our understanding on firms??? R&D investment strategies. Number of studies mainly focused on patent class combinations of individual technology level, but portfolios of patent class at a firm level have been understudied. In this study, we tracked the change of class composition within each firms??? technological patents??? portfolio and attempted to identify practical and theoretical implications to portfolio management. We utilized Entropy Index, Co-occurrence and cosine similarities measurements for each indicating diversification, patent scope and portfolio similarities within each patents??? classification subclasses. Additionally, performance evaluation of each portfolio is conducted using forward citation data. This paper shows that in-depth patent data analysis can allow us to explore deeper insights at various levels, individual technology, products and product lines, and firms sufficing different stories.ope

    Long-run dynamics of the U.S. patent classification system

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    Almost by definition, radical innovations create a need to revise existing classification systems. In this paper, we argue that classification system changes and patent reclassification are common and reveal interesting information about technological evolution. To support our argument, we present three sets of findings regarding classification volatility in the U.S. patent classification system. First, we study the evolution of the number of distinct classes. Reconstructed time series based on the current classification scheme are very different from historical data. This suggests that using the current classification to analyze the past produces a distorted view of the evolution of the system. Second, we study the relative sizes of classes. The size distribution is exponential so classes are of quite different sizes, but the largest classes are not necessarily the oldest. To explain this pattern with a simple stochastic growth model, we introduce the assumption that classes have a regular chance to be split. Third, we study reclassification. The share of patents that are in a different class now than they were at birth can be quite high. Reclassification mostly occurs across classes belonging to the same 1-digit NBER category, but not always. We also document that reclassified patents tend to be more cited than non-reclassified ones, even after controlling for grant year and class of origin

    An Introduction to the Patstat Database with Example Queries

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    This paper provides an introduction to the Patstat patent database. It offers guided examples of ten popular queries that are relevant for research purposes and that cover the most important data tables. It is targeted at academic researchers and practitioners willing to learn the basics of the database.Comment: To appear in the Australian Economic Revie

    Fostering innovation in a small open economy: The case of the New Zealand biotechnology sector

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    The New Zealand Biotechnology sector is worthy of study for several reasons. While there is a large and growing international literature on economic aspects of biotechnology innovation these studies concentrate on the United States and Europe. The New Zealand biotechnology sector may be expected to develop along a different trajectory as a consequence of a markedly different set of initial and framework conditions. Government has indicated a strong interest in fostering innovation and aims to concentrate on selected areas where New Zealand may be able to develop a new comparative advantage. One such area is biotechnology, which would build on New Zealand’s existing comparative advantage in the primary sector (dairy, forestry, meat, wool and horticulture). This paper describes the preliminary results of an ongoing study that aims to fill some of the gaps in our knowledge of innovation processes in New Zealand while using the international literature as a benchmark. The paper focuses on the drivers of innovation in the biotechnology sector; the role of networks and other linkages; the role of government and industry, the role of human and venture capital, and data from patenting

    Knowledge management, innovation and big data: Implications for sustainability, policy making and competitiveness

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    This Special Issue of Sustainability devoted to the topic of “Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness” attracted exponential attention of scholars, practitioners, and policy-makers from all over the world. Locating themselves at the expanding cross-section of the uses of sophisticated information and communication technology (ICT) and insights from social science and engineering, all papers included in this Special Issue contribute to the opening of new avenues of research in the field of innovation, knowledge management, and big data. By triggering a lively debate on diverse challenges that companies are exposed to today, this Special Issue offers an in-depth, informative, well-structured, comparative insight into the most salient developments shaping the corresponding fields of research and policymaking

    Technological capability building through networking strategies within high-tech industries

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    Learning through networks has been considered as an important research topic for several years now. Technological learning is more and more based on a combination of internal and external learning and firms need to develop both technological and social capital for that purpose. This paper analyses the relationship between both types of capital and their impact on the technological performance of companies in high-tech industries. We claim and find empirical evidence for decreasing marginal returns on social capital. Technological capital and social capital mutually reinforce each other's effect on the rate of innovation for companies with small patent and alliance portfolios. However, when the patent portfolio and network of alliances are extensive, companies risk to over-invest since optimal levels of social capital become smaller at higher levels of technological capital and the marginal benefits of investing in technological capital decreases the higher the levels of social capital. Finally, we find empirical evidence that companies that explore novel and pioneering technologies have higher levels of innovation performance in subsequent years than companies that solely invest in incremental innovations.Strategic Alliances, Networks, Innovation

    The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools

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    This paper describes the database on U.S. patents that we have developed over the past decade, with the goal of making it widely accessible for research. We present main trends in U. S. patenting over the last 30 years, including a variety of original measures constructed with citation data, such as backward and forward citation lags, indices of 'originality' and 'generality', self-citations, etc. Many of these measures exhibit interesting differences across the six main technological categories that we have developed (comprising Computers and Communications, Drugs and Medical, Electrical and Electronics, Chemical, Mechanical and Others), differences that call for further research. To stimulate such research, the entire database about 3 million patents and 16 million citations is now available on the NBER website. We discuss key issues that arise in the use of patent citations data, and suggest ways of addressing them. In particular, significant changes over time in the rate of patenting and in the number of citations made, as well as the inevitable truncation of the data, make it very hard to use the raw number of citations received by different patents directly in a meaningful way. To remedy this problem we suggest two alternative approaches: the fixed-effects approach involves scaling citations by the average citation count for a group of patents to which the patent of interest belongs; the quasi-structural approach attempts to distinguish the multiple effects on citation rates via econometric estimation.

    The relative importance of home and host innovation systems in the internationalisation of MNE R&D: a patent citation analysis

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    This paper examines the phenomenon of home base augmenting (HBA) R&D and home base exploiting (HBE) R&D. It has three novelties. First, we argue that any given R&D facility’s capacity to exploit and/or augment technological competences is a function not just of its own resources, but the efficiency with which it can utilise complementary resources associated with the relevant local innovation system. Just as HBA activities require proximity to the economic units (and thus the innovation system) from which they seek to learn, HBE activities draw from the parent’s technological resources as well as from the other assets of home location’s innovation system. Furthermore, we argue that most firms tend to undertake both HBE and HBA activities simultaneously. Second, we use patent citation data from the European Patent Office to quantify the relative HBA vs. HBE character of foreign-located R&D. Third, we do so for European MNEs located in the US, as well as US MNEs located in Europe. Our results indicate that both EU (US) affiliates in the US (EU) rely extensively on home region knowledge sources, although they appear to exploit the host country knowledge base as well. The HBA component of US R&D in Europe in chemicals, electronics and petroleum refining is stronger than their European counterparts, as is the case for European R&D activities in the US in engineering.economics of technology ;
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