Analysis of the relationship between citation frequency of patents and diversity of their backward citations for Japanese patents

Conventional patent citation analyses have focused mainly on the presence of citation relationships, the number of patents cited by the subject patent, and the number of times the subject patent is cited by others (i.e., the numbers of backward and forward citations of the subject patent). However, most of them have not focused on patent classifications. Assuming that a patent based on a variety of technological bases tends to be an important patent that is cited more often, this study examines and clarifies the relationship between the diversity of classifications assigned to backward citations and the number of forward citations for Japanese patents. The results show notable differences in the number of classifications assigned to backward citations between the often cited and less frequently cited groups. It is considered that the diversity of backward citations can be utilized in the evaluation criteria for grouping that roughly identifies the often cited patents or eliminates a large part of less frequently cited patents.     

Validation study: Patent citations as indicators of science and foreign dependence

The purpose of this study was to test whether the distribution of citations from issued U.S. patents could be used to measure the science dependence and the foreign dependence of patented technologies. The citations considered were front page references from U.S. patents citing to U.S. and foreign patents, to research papers and to other publications. Rankings based on the number of citations per patent to the scientific literature were compared to peer rankings of the science dependence of the technologies. Rankings based on the number of citations to foreign origin material, including foreign origin U.S. patents, foreign patents and foreign priority statements, were compared to peer rankings of the foreign dependence of the technological areas.For the analysis a total of 24 technologies were chosen. Twelve of these were judged in advance to be science dependent and twelve were judged in advance to be foreign dependent. A peer group of 19 high level R&D managers was asked to rank all 24 technologies in terms of both their science and their foreign dependence. The bibliometric rankings of the technologies, based on their citations, were then compared with the peer rankings of the technologies.Overall, a high degree of agreement was found between the experts' opinion as to the science and foreign dependence of the areas and the corresponding bibliometric rankings. For example, the eight technologies judged most science dependent by experts averaged 0.92 cites per patent to scientific journal papers, while the eight technologies judged least science dependent had only 0.05 references per patent to journal papers. Similarly, large and statistically significant differences were found in the number of cites to foreign origin material for the eight technologies judged most foreign dependent by the experts when compared with the eight technologies judged least foreign dependent by the experts. These findings provide support for the hypothesis that patent citation data can be used in technological indicators development, and in technological policy analysis. They imply that citation-based location and analysis of science and foreign dependent technologies is a valid research tool when applied to the U.S. patent system.     

Analyzing the value of technology based on the differences of patent citations between applicants and examiners

Patent applicants and examiners do not always have the same point of view when conducting a prior-art search. Although several studies have suggested differences between citations by applicants and examiners, the data and range of empirical studies are too incomplete to generalize the characteristics of relationships between citation types and the value of a technology or invention. To overcome this limitation, it is crucial to compare citations by applicants and by examiners in depth, with diverse perspectives and data, to determine the value of patent information for technological innovation. Thus, this paper suggests that the differences in the composition of technical information and patent quality in patent-level investigations as well as the locus of the knowledge source and knowledge recentness in knowledge-level investigations according to patent citation type (by applicants and examiners) reflect Pavitt’s perspective on the nature, impact, and source of technological innovation. We found that the quality of patents cited by applicants is higher than that of those by examiners in four industries, excluding a supplier-dominated industry. The citation types are related to the locus of the knowledge source in four industries, excluding the supplier-dominated industry. In particular, the patents cited by examiners tended to be more recently issued in all sectoral fields. This research contributes to confirming the technological value of patents based on the citation behaviors of applicants and examiners through empirical analysis. The results can be utilized to investigate signals or noise in technological innovation and improve processes or systems of patent examination. In addition, it can help applicants conduct more thorough prior-art searches by comprehending the examiner’s perspective toward citations to increase the probability of patent registration.     

Patents cited in the scientific literature: An exploratory study of 'reverse' citation relations

This paper reports on a new approach to study the linkage between science and technology. Unlike most contributions to this area we do not trace citations of scientific literature in patents but explore citations of patents in scientific literature. Our analysis is based on papers recorded in the 1996-2000 annual volumes of the CD-Edition of Science Citation Index (SCI) of the Institute for Scientific Information (ISI) and patent data provided by the US Patent and Trademark Office. Almost 30,000 US patents were cited by scientific research papers. We analysed the citation links by scientific fields and technological sectors. Chemistry-related subfields tended to cite patents more than other scientific area. Among technological sectors, chemical clearly dominates followed by drugs and medical patents as the most frequently cited categories. Further analyses included a country-ranking based on inventor-addresses of the cited patents, a more detailed inspection of the ten most cited patents, and an analysis of class-field transfers. The paper concludes with the suggestions for future research. One of them is to compare our 'reverse' citation data with 'regular' patent citation data within the same classification system to see whether citations occur, irrespectively of their directionality, in the same fields of science and technology. Another question is as to how one should interpret reverse citation linkages.     

Multiple regression analysis of a patent’s citation frequency and quantitative characteristics: the case of Japanese patents

Although many studies have been conducted to clarify the factors that affect the citation frequency of “academic papers,” there are few studies where the citation frequency of “patents” has been predicted on the basis of statistical analysis, such as regression analysis. Assuming that a patent based on a variety of technological bases tends to be an important patent that is cited more often, this study examines the influence of the number of cited patents’ classifications and compares it with other factors, such as the numbers of inventors, classifications, pages, and claims. Multiple linear, logistic, and zero-inflated negative binomial regression analyses using these factors are performed. Significant positive correlations between the number of classifications of cited patents and the citation frequency are observed for all the models. Moreover, the multiple regression analyses demonstrate that the number of classifications of cited patents contributes more to the regression than do other factors. This implies that, if confounding between factors is taken into account, it is the diversity of classifications assigned to backward citations that more largely influences the number of forward citations.     

Identifying rapidly evolving technological trends for R&;D planning using SAO-based semantic patent networks

Patents constitute an up-to-date source of competitive intelligence in technological development; thus, patent analysis has been a vital tool for identifying technological trends. Patent citation analysis is easy to use, but fundamentally has two main limitations: (1) new patents tend to be less cited than old ones and may miss citations to contemporary patents; (2) citation-based analysis cannot be used for patents in databases which do not require citations. Naturally, citation-based analysis tends to underestimate the importance of new patents and may not work in rapidly-evolving industries in which technology life-cycles are shortening and new inventions are increasingly patented world-wide. As a remedy, this paper proposes a patent network based on semantic patent analysis using subject-action-object (SAO) structures. SAO structures represent the explicit relationships among components used in a patent, and are considered to represent key concepts of the patent or the expertise of the inventor. Based on the internal similarities between patents, the patent network provides the up-to-date status of a given technology. Furthermore, this paper suggests new indices to identify the technological importance of patents, the characteristics of patent clusters, and the technological capabilities of competitors. The proposed method is illustrated using patents related to synthesis of carbon nanotubes. We expect that the proposed procedure and analysis will be incorporated into technology planning processes to assist experts such as researchers and R&D policy makers in rapidly-evolving industries.     

Prolific inventors—A bibliometric analysis

Patent information on 7392 inventors who received 9 or more U.S. Patents during 1975–84 was obtained. Analysis of the frequency distribution of patents per inventor reveals an approximately logarithmic decline from 9 to approximately 45 patents per inventor. The rate of decline decreases significantly for patent output above 45 patents per inventor. Patent citation analysis on 45 randomly selected inventors was performed. This sample included inventors who received from 9 to over 100 patents. The group received 1.79 citations per patent, 56.8% of the patents received at least 1 citation, and 2.7% of the patents received 10 or more citations. No statistically significant differences for these averages was found across the range of inventor patent output. No significant decline of patent quality with increased yearly patent output was observed.     

Indirect patent citations

Patent citations are extensively used as a measure of patent quality. However, counting citations does not account for the fact that citations come from patents of different qualities, and that citations are of variable qualities. We develop a citation index which takes into account the cumulative quality of the citing patents. We apply this index to the 2,139,314 utility patents granted in the U.S. between 1975 and 1999. We study the properties of this index by year and by technological category, and analyse the links between patents.     

Exploring the quality of environmental technology in Europe: evidence from patent citations

In this paper we carry out an empirical analysis to address some questions concerning the production and quality of technology in environmental sectors. The methodology involves patents as a measure of the generation of new knowledge, and patent citations as a proxy for the quality of a technological invention. The sample contains more than 12,000 environmental European patents from firms and government institutions from 1998 to 2004. From our econometric analysis, we found that environmental patents applied by individual inventors present on average less quality that those applied by institutional inventors. The size of family patent is relevant to explain forward patent citation. Furthermore, patents coming from abroad (out of Europe), in particular with US and Japan priority, are more cited on average than local patents (with European priority). Lastly, the specialization in environmental fields of a patent plays a negative role in determining the frequency of forward citation.     

What makes the first forward citation of a patent occur earlier?

Identifying the economic value of a patent is crucial to technology management. It also facilitates the commercialization and transactions of patents. As a proxy of patent value, forward citation counting is widely used, but it takes a long time for a patent to be sufficiently cited. In this context, we suggest the first citation lag, namely the time taken until the first citation, as a proxy of patent value, as it is positively correlated with the patent value and considers the fact that important patents with a high economic value tend to receive many citations in a short space of time. We explore the influential patent attributes related to the first citation lag to build a model to predict the patent value. By using the Cox proportional hazard model on green inventory patent data, we find that patents with a shorter technology cycle time and a larger number of IPC four-digit classes, claims, patent family, and backward citations are associated with the shorter first citation lag, while a patent’s science linkage has an inverse U-shaped relationship with the first citation lag. Further, patents having an unconventional technological core have a longer first citation lag and among patents having an unconventional technological core, the presence of a novel element makes the first citation earlier. Our study is expected to help patent evaluation in the early stage of technology transfer.     

Bibliometric performance measures

Three different types of bibliometrics — literature bibliometrics, patent bibliometrics, and linkage bibliometric can all be used to address various government performance and results questions. Applications of these three bibliometric types will be described within the framework of Weinberg's internal and external criteria, whether the work being done is good science, efficiently and effectively done, and whether it is important science from a technological viewpoint. Within all bibliometrics the fundamental assumption is that the frequency with which a set of papers or patents is cited is a measure of the impact or influence of the set of papers. The literature bibliometric indicators are counts of publications and citations received in the scientific literature and various derived indicators including such phenomena as cross-sectoral citation, coauthorship and concentration within influential journals. One basic observation of literature bibliometrics, which carries over to patent bibliometrics, is that of highly skewed distributions — with a relatively small number of high-impact patents and papers, and large numbers of patents and papers of minimal impact. The key measure is whether an agency is producing or supporting highly cited papers and patents. The final set of data are in the area of linkage bibliometrics, looking at citations from patents to scientific papers. These are particularly relevant to the external criteria, in that it is quite obvious that institutions and supporting agencies whose papers are highly cited in patents are making measurable contributions to a nation's technological progress.     

The growing number of patent citations to scientific papers: Changes in the world,nations, and fields

This study analyzes USPTO patents in the period 1998–2017. The number of science-related patents has increased twice as fast as the number of patents and scientific publications, and the number of cited papers per patent has almost doubled. These results vary substantially from one scientific and technological field to another. The proportion of the research papers cited by a patent has doubled. It refers to papers that are mostly published by the countries that have developed both scientific and technological capability and, surprisingly, are mainly used by inventors abroad. However, a weak relationship between the number of citations received from patents and papers reveals that the assessment of research performance needs some changes as the percentage of papers related to the innovations has grown over time.     

Network structure of innovation: can brokerage or closure predict patent quality?

Patents are important intellectual assets for companies to defend or to claim their technological rights. To control R&D cost, companies should carefully examine their patents by patent quality. Approaches to evaluating patent quality are mostly a posteriori uses of factual information of patent quality. This paper examined whether patent quality can be predicted a priori, i.e., during the early years after a patent is granted, by analyzing information embedded in a network of patent citations. Social network analysis was applied to analyze two network positions occupied by a patent, brokerage and closure to determine whether either position is a good predictor of patent quality. Patent renewal decisions and forward citations were adopted as surrogates of patent quality. The analytical results showed that forward citations can be positively predicted by the brokerage position and negatively predicted by the closure position in the early and mature stages. Renewal decisions can be negatively predicted by the brokerage position in the early stage, and the closure position influences the renewal decision in a different way in the early and mature stages. These analytical results imply that a company should focus on developing patents that bridge different technologies as its technological developments reach maturity.     

Using Essential Patent Index and Essential Technological Strength to evaluate industrial technological innovation competitiveness

The aim of this article is to develop new patent indicators for evaluating technological innovation competitiveness between companies. A novel indicator representing an industrial’s patent performance, Essential Patent Index (EPI), was developed by incorporating information on who cited these patents and when these patents were cited, based on the assumption that both contribute to meaningful quality assessment. By combining EPI and Chi’s well known Technological Strength (TS) indicator, a second novel indicator Essential Technological Strength (ETS) was developed to represent the innovation competitiveness of an individual company. In this study, patent performance of three high-tech industries in Taiwan were analyzed using ETS as well as the traditional TS for comparison. Results from this analysis demonstrated that ETS provided better insights by clearly verifying the latent influence of citations, reinforcing the impact of essential patents, and aggrandizing the differences of innovation competitiveness between companies.     

The citation impact of social sciences and humanities upon patentable technology

This paper examines the citation impact of papers published in scientific-scholarly journals upon patentable technology, as reflected in examiner- or inventor-given references in granted patents. It analyses data created by SCImago Research Group, linking PATSTAT’s scientific non-patent references (SNPRs) to source documents indexed in Scopus. The frequency of patent citations to journal papers is calculated per discipline, year, institutional sector, journal subject category, and for “top” journals. PATSTAT/Scopus-based statistics are compared to those derived from Web of Science/USPTO linkage. A detailed assessment is presented of the technological impact of research publications in social sciences and humanities (SSH). Several subject fields perform well in terms of the number of citations from patents, especially Library and Information Science, Language and Linguistics, Education, and Law, but many of the most cited journals find themselves in the interface between SSH and biomedical or natural sciences. Analyses of the titles of citing patents and cited papers are presented that shed light upon the cognitive content of patent citations. It is proposed to develop more advanced indicators of citation impact of papers upon patents, and ways to combine citation counts with citation content and context analysis.

     

Analyzing the nonlinear effects of firm size,profitability, and employee productivity on patent citations of the US pharmaceutical companies by using artificial neural network

This study utilizes neural network to explore the nonlinear relationships between corporate performance and the patent traits measured from Herfindahl-Hirschman Index of patents (HHI of patents), patent citations, and relative patent position in the most important technological field (RPP_MIT) in the US pharmaceutical industry. The results show that HHI of patents and RPP_MIT have nonlinearly and monotonically positive influences upon corporate performance, while the influence of patent citations is nonlinearly U-shaped. Therefore, pharmaceutical companies should raise the degrees of the leading position in their most important technological fields and the centralization of their technological capabilities to enhance corporate performance.     

Indicators for measuring the impact of scientific citations in patents

Scientific publications cited in patents are fundamental elements to assess the transfer of science to technology. Numerous studies evaluate the impact of references in patents and scientific publications and various measurement methods and indicators are proposed. This article reviewed the existing literature on the indicators used to date to determine their suitability and effectiveness to evaluate the impact of patent citations. For this purpose, we analyzed the characteristics of the studies examined and proposed a qualitative classification of indicators from both a technological (patents) and scientific-academic (scientific articles) perspective. Among the results we find that the use of scientometric indicators is primarily focused on analyzing their relevance for patents through the inclusion of scientific citations. Conversely, the same is not true from the academic point of view where gaps still persist in terms of what the impact is when scientific articles are cited in patents. Among the key conclusions is the diversity of proposals to measure scientific citations in patents, though these are conditioned on bibliographic standardization and metadata management of the patents themselves, making it possible to quantify aspects similar to the immediacy index, impact index, or h-index for authors/inventors, albeit from a technological dimension.     

Patent activity by date of application — Estimating recent applications in the U.S. patent system

Using the number of patents applied for each year can be a useful indicator of the level of inventive effort and a more accurate measure than one based on patent issue date. In the U.S. patent system, this indicator is hampered by the closed examination process, which results in an undercounting of applications field in the recent past.This article describes a method for overcoming this problem by estimating recent patent activity by date of application for U.S. patents. The method uses the historical data for the time between application and issue and can be used to estimate activity by application date up to one year before the date of the most recently issued patents.     

The nonlinear nature of the relationships between the patent traits and corporate performance

This study utilizes neural network to explore the nonlinear relationships between corporate performance and the patent traits measured from Herfindahl-Hirschman Index of patents (HHI of patents), patent citations, and relative patent position in the most important technological field (RPP_MIT) in the US pharmaceutical industry. The results show that HHI of patents and RPP_MIT have nonlinearly and monotonically positive influences upon corporate performance, while the influence of patent citations is nonlinearly U-shaped. Therefore, pharmaceutical companies should raise the degrees of the leading position in their most important technological fields and the centralization of their technological capabilities to enhance corporate performance.     

Prediction of emerging technologies based on analysis of the US 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 (1) identifies actual clusters of patents: i.e., technological branches, and (2) 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.