Topology and Evolution of Technology Innovation Networks

The web of relations linking technological innovation can be fairly described in terms of patent citations. The resulting patent citation network provides a picture of the large-scale organization of innovations and its time evolution. Here we study the patterns of change of patents registered by the US Patent and Trademark Office (USPTO). We show that the scaling behavior exhibited by this network is consistent with a preferential attachment mechanism together with a Weibull-shaped aging term. Such attachment kernel is shared by scientific citation networks, thus indicating an universal type of mechanism linking ideas and designs and their evolution. The implications for evolutionary theory of innovation are discussed.Comment: 6 pages, 5 figures, submitted to Physical Review

Mapping the importance of the real world: the validity of connectivity analysis of patent citations networks

[EN] Recent empirical findings have questioned the use of patent citations as a measure. This points to the need of validation of patent citations methodologies, which we address by testing a recent methodology for studying technological evolution, namely connectivity analysis of citation networks. We find connectivity analysis to be a valid tool to identify the reliable knowledge which opens the way to further technological evolution of a surgical prosthesis, the artificial spinal disc. We also illustrate how connectivity analysis represents how this reliable knowledge differs depending on the stage of technological evolution. The corroborated validity of connectivity analysis of patent citations may trigger a renaissance in the use of this kind of patent data. (C) 2010 Elsevier B.V. All rights reserved.We would like to thank Arianna Martinelli, Gerald Silverberg, Lee Davies, Francesco Rulliani, Davide Consoli and three anonymous reviewers for their helpful comments on earlier versions of this manuscript. Remaining errors and omissions are entirely our own.Barberá Tomás, JD.; Jiménez Saez, F.; Castelló Molina, I. (2011). Mapping the importance of the real world: the validity of connectivity analysis of patent citations networks. Research Policy. 40(3):473-486. doi:10.1016/j.respol.2010.11.002S47348640

Determining the Life Cycle Phase of a Technology Based on Patent Data

Developing new technologies is one of the most important goals of today’s scientific and industrial research. Understanding how technology evolves, as well as its current state, is invaluable in an ecosystem where technology is evolving at an increasingly rapid pace. In this paper, patent data is used to determine a technology’s life cycle. Two patent maps are created, one based on patent citations and one based on keywords. The citation patent map visualizes how patents cite each other, while the keyword patent maps visualize keywords used to describe patents and their relations. Both of these patent maps are dynamic, meaning they change over time thus giving insight into an examined technology’s evolution. A growth analysis of both networks is conducted as well as a degree distribution analysis. Both of these analyses are used to help determine the technology’s lifecycle phase as well as its patterns of growth. This insight is invaluable to stakeholders tasked to make strategic decisions related to technology development

Tracing knowledge diffusion

Scientometrics, 59 (2): pp. 199-211.Knowledge diffusion is the adaptation of knowledge in a broad range of scientific and engineering research and development. Tracing knowledge diffusion between science and technology is a challenging issue due to the complexity of identifying emerging patterns in a diverse range of possible processes. In this article, we describe an approach that combines complex network theory, network visualization, and patent citation analysis in order to improve the means for the study of knowledge diffusion. In particular, we analyze patent citations in the field of tissue engineering. We emphasize that this is the beginning of a longer-term endeavor that aims to develop and deploy effective, progressive, and explanatory visualization techniques for us to capture the dynamics of the evolution of patent citation networks. The work has practical implications on resource allocation, strategic planning, and science policy

Indirect ties in knowledge networks:a social network analysis with ordered weighted averaging operators

This PhD thesis analyses networks of knowledge flows, focusing on the role of indirect ties in the knowledge transfer, knowledge accumulation and knowledge creation process. It extends and improves existing methods for mapping networks of knowledge flows in two different applications and contributes to two stream of research. To support the underlying idea of this thesis, which is finding an alternative method to rank indirect network ties to shed a new light on the dynamics of knowledge transfer, we apply Ordered Weighted Averaging (OWA) to two different network contexts. Knowledge flows in patent citation networks and a company supply chain network are analysed using Social Network Analysis (SNA) and the OWA operator. The OWA is used here for the first time (i) to rank indirect citations in patent networks, providing new insight into their role in transferring knowledge among network nodes; and to analyse a long chain of patent generations along 13 years; (ii) to rank indirect relations in a company supply chain network, to shed light on the role of indirectly connected individuals involved in the knowledge transfer and creation processes and to contribute to the literature on knowledge management in a supply chain. In doing so, indirect ties are measured and their role as means of knowledge transfer is shown. Thus, this thesis represents a first attempt to bridge the OWA and SNA fields and to show that the two methods can be used together to enrich the understanding of the role of indirectly connected nodes in a network. More specifically, the OWA scores enrich our understanding of knowledge evolution over time within complex networks. Future research can show the usefulness of OWA operator in different complex networks, such as the on-line social networks that consists of thousand of nodes

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

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

Measure of Design M&As: Exploratory investigations of IP analysis in design

Despite active participation of design firms in M&A markets, there has been little research measuring the value of design M&As. It is partially due that design has been seen to be an intangible asset. This paper seeks to the understanding of the value of design M&As and provides a possible metric for measuring the value using patent analysis. The value of design M&A was investigated at two levels: Design as differentiator (i.e., new product development) and Design as coordinator or integrator (i.e., organizational growth). The evolution of patenting quantity (e.g., the number of design patent applications, Locarno classes) and quality (e.g., forward citation, coinventor networks) in pre- and post-acquisition deals was suggested. We conducted a case study using the design and utility patents of Adobe Systems Inc. The results show the dynamics of innovation area and the presence of the high values of inventors holding design-tech linkage, which could be a potential intangible source of company growth. This study further provides implications for companies which might consider design M&As as new ways of design investment

Early identification of important patents through network centrality

One of the most challenging problems in technological forecasting is to identify as early as possible those technologies that have the potential to lead to radical changes in our society. In this paper, we use the US patent citation network (1926-2010) to test our ability to early identify a list of historically significant patents through citation network analysis. We show that in order to effectively uncover these patents shortly after they are issued, we need to go beyond raw citation counts and take into account both the citation network topology and temporal information. In particular, an age-normalized measure of patent centrality, called rescaled PageRank, allows us to identify the significant patents earlier than citation count and PageRank score. In addition, we find that while high-impact patents tend to rely on other high-impact patents in a similar way as scientific papers, the patents' citation dynamics is significantly slower than that of papers, which makes the early identification of significant patents more challenging than that of significant papers.Comment: 14 page