Data-Mining the Foundational Patents of Photovoltaic Materials: An Application of Patent Citation Spectroscopy

Abstract

Keeping abreast of most technologically important patents is also a challenging task for patent granting organizations and patenting inventors and firms. As a consequence, patent offices have invested in developing novel automated approaches for identifying landmark patents in technology areas. Patent Citation Spectroscopy (PCS) provides a web-application to answer this question within minutes.Patents branch out in tree-like structures along trajectories. The historical root, or seminal, patent can be followed using sequences of patent citations. The algorithmic method of PCS presented in this study provides a solution to the problem where to begin the analysis of a technological development. PCS enables the user to retrieve the fundamental patent in any technological domain using a topical search. This application thus orients the user strategically.The online data-mining method of PCS is based on Reference Publication Year Spectroscopy (RPYS) technique, a methodology developed for use on academic literature. However, PCS includes additional normalization calculations to disentangle citation outliers based on the outstanding performance of a single document as compared to a group of documents.To illustrate the value of PCS, we provide the results of a search for the seminal patents of the nine CPC subclasses pertaining to photovoltaic solar cells, a key area of technological innovation. Research and development (R&D) in photovoltaic devices continues to yield greater efficiencies, offering the potential to lower the cost of solar energy (Chu et al, 2016). As these advances in solar technology become primed for penetrating the global energy system, an understanding of the key patents and inventors in photovoltaic materials will assist decisionmakers in understanding the R&D landscape (Polman et al, 2016). We demonstrate that such searches are easily completed via PCS in each of the nine CPC subclasses. Searches of scholarly article databases validated the results obtained through PCS in five of the nine classes.

The innovation effects of renewable energy policies and their interaction: the case of solar photovoltaics

“…This is of course an important limitation given the fact that equipment innovation has largely been responsible for driving down the cost of solar PV (Huenteler et al 2016). Still, the chosen focus is motivated in part by the fact that we position our paper in relation to the studies by Johnstone et al (2010) and Peters et al (2012), and these have similar (albeit not identical) approaches to solar PV patent data selection (see also the recent paper by Comins and Leyersdorff 2018). Furthermore, the purpose of our paper is not to investigate the determinants of observed cost reductions, but, instead, the statistical correlation between various policies and patenting activity in the solar PV field.…”

Which are the influential publications in the Web of Science subject categories over a long period of time? CRExplorer software used for big-data analyses in bibliometrics

“…climate change [17]). The method has already been successfully applied in identifying papers with outstanding performance [18–20] and landmark patents [21].…”

“…The method is based on counting cited references (instead of citations) to assess the impact of publications on a topic-or field-specific publication set (e.g., climate change, see Marx, Haunschild, Thor, & Bornmann, 2017). The method has already been successfully applied in identifying papers with outstanding performance (Comins & Leydesdorff, 2018;Thor, Bornmann, Marx, & Mutz, 2018) and landmark patents (Comins, Carmack, & Leydesdorff, 2017). Thor, Marx, Leydesdorff, and Bornmann (2016) introduced the CRExplorera program for undertaking RPYS.…”