**1. Introduction**

Today, scientific research is expected not only to create knowledge but also to contribute to the development of industrial technology and the solution of social problems. Citations of scientific papers from patents (hereafter patent-paper citations) are rare data representing knowledge flows between coded scientific knowledge (scientific papers) and coded technological knowledge. Although there have been controversies over what is meant by patentpaper citations, it is deemed as data representing knowledge flows and used in the public statistics at present (e.g., see [1–3]).

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

As an indicator representing the relationship between science and technology, the number of cited scientific documents per patent (it is known as "science linkage") has been widely used. It is relatively straightforward to introduce science linkage, since it does not require identification of each scientific paper cited in patents and match to a specific record in databases of academic papers, such as Web of Science (WoS) and Scopus. However, science linkage only provides information on vicinity of science from technology, not vicinity of technology from science.

of contributions to technological development, citations of scientific papers from "highimpact" patents seem to be good indicators of scientific papers. As far as my survey, I could

Exploring Characteristics of Patent-Paper Citations and Development of New Indicators

http://dx.doi.org/10.5772/intechopen.77130

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According to the aforementioned problem consciousness, I develop the new impact indicators of papers in the aspect of patent-paper citations. To secure the validity of new indicators, we investigate the nature of patent-paper citations in the dataset prior to the development of the indicators. This article consists of the following sections. In Section 2, I explain data and time scheme of the study. I analyze relationships between probabilities of occurrence of paper citations from the patents and feature values of the scientific papers, using logistic regression analysis in Section 3. Based on the result of the analysis in Section 3, I improve the patent-paper citation index which we developed recently [9] (Section 4) and develop a set of new indicators from the aspect of

patents' feature values (Section 5). Then, issues to be tackled are discussed in Section 6.

I utilized data sources and decided time scope in the study in the following process.

January 2016 and publications published until February 2016.

I used worlds' patent data contained in the 2016 spring edition of the Patstat database produced by European Patent Office (EPO). The database contains patent applications filed until

To avoid overrating the same inventions, patent data were counted by the DOCDB patent family. Only patent families which contain published patents, neither utility models nor design patents, were included in the dataset for securing consistencies of their statistic natures. Patent families are counted by their application year. The application year of the patent family was defined as the earliest filing year of the applications that constituted the family. Patent families which no application belonged to any of technology field defined in [10] were excluded, since percentiles of patent-patent citations were calculated by technology field.

The Science Citation Index Expanded collection of the WoS database was used for this study. The WoS database contained bibliographic records of scientific papers which were published between 1981 and 2015. Each scientific paper in the WoS was classified to 1 of 22 scientific disciplines of the Essential Science Indicators. As for journals classified in "Multidisciplinary" by Clarivate Analytics, each of their papers was classified into 1 of the other 21 disciplines using their information on both forward and backward citations. Papers which were not classified into any of the 21 disciplines by the process were classified into "Multidisciplinary." They were excluded from the study because most of them obtained no or only a few citations and tended to be overestimated in the calculation of percentiles in the "Multidisciplinary" discipline. Disciplinary classification used in the study is shown in **Table 1**. Hereafter, I designated the codes for disciplines in the figures in this article.

**2. Data and their process**

**2.2. Data of scientific papers**

**2.1. Patent data**

not find any empirical study of indicators from the view mentioned above.

Along with the research utilizing science linkage as an index as described above, the nature of patent-paper citations itself has been studied. Such studies needed identification of bibliography of papers which appeared in patent documents. For example, Branstetter and Ogura [4] used data of patent-paper citations provided by CHI Research and analyzed the relationship between probabilities of occurring patent-paper citations and some variables obtained from both patents and papers for California. Such research had been relatively scarce, since they required a large-scale data set with identified paper data. However, in recent years, Ahmadpoor and Jones analyzed a large citation network, which consisted of patent-patent, paper-paper, and patent-paper citations, based on a large data set of US patents and scientific papers indexed in the Web of Science database provided by Clarivate Analytics and comprehensive patentpaper citation data [5]. They dealt with both patent-patent and paper-paper citations symmetrically and handled patent-paper citations like it bordered between these two networks and then uncovered differences in various aspects of them. Fukuzawa and Ida [6] analyzed the features of patent-paper citations from the paper side for 100 top researchers who were awarded the twenty-first-century COE. They found some important characteristics of patent-paper citations, such as the time lag of the former was longer than the latter, and the more the papers were cited from other papers, the more they tended to be cited from patents.

While these findings are important for practical use of patent-paper citations, there are almost no existing studies on the development of impact indicators of papers cited in patents.

On the other hand, the demand for methods of analysis and empirical indicator data of "papers cited in patents" in practical context has been expanded recently. For example, the Fifth Science and Technology Basic Plan which is the current Japanese five-year national plan for the promotion of science and technology between FY 2016 and 2020 requires monitoring of the performance. "Scientific papers cited in patents" is one of the key performance indicators of the plan. However, an effective method for showing performance using patent-paper citations is still unclear; therefore, it is indispensable to develop valid indicators of patent-paper citations.

My motivation is to develop impact indicators for scientific papers to show technological impact at meso (institutional sector in a country, research funding, and so on) to macro levels (country), based on the statistical nature of patent-paper citations. In the field of bibliometrics, many indicators have been developed and verified by many researchers (see [7]) and practical uses such as Leiden Ranking and Scimago Journal & Country Rank. Therefore, by developing robust impact indicators based on patent-paper citations symmetrical to existing bibliometric impact indicators, it should be possible to overview both the scientific and technological impacts of researches at the same time.

Moreover, from the view of patents, there have been many indicators for measuring patent quality (major indicators were written in [8]). For evaluating scientific papers from the aspect of contributions to technological development, citations of scientific papers from "highimpact" patents seem to be good indicators of scientific papers. As far as my survey, I could not find any empirical study of indicators from the view mentioned above.

According to the aforementioned problem consciousness, I develop the new impact indicators of papers in the aspect of patent-paper citations. To secure the validity of new indicators, we investigate the nature of patent-paper citations in the dataset prior to the development of the indicators.

This article consists of the following sections. In Section 2, I explain data and time scheme of the study. I analyze relationships between probabilities of occurrence of paper citations from the patents and feature values of the scientific papers, using logistic regression analysis in Section 3. Based on the result of the analysis in Section 3, I improve the patent-paper citation index which we developed recently [9] (Section 4) and develop a set of new indicators from the aspect of patents' feature values (Section 5). Then, issues to be tackled are discussed in Section 6.
