Non-patent References Research Articles

Technological impact of funded research: A case study of nonpatent references

Abstract Research funding is essential to expand knowledge, foster innovation, and address the complex challenges that shape our future. The scientific literature has extensively addressed the relationship between research funding and academic impact. More recently, several studies have analyzed the technological impact of funded research as measured through citations in patents, known as nonpatent references (NPRs). But there remains much to know about NPRs and the multiplication of case studies is necessary to characterize them. Here we analyze a sample of 7,065 publications funded by the French Foundation for Medical Research (FRM) and the citations of these publications in patents. This study shows the high scientific and technological impacts of FRM funding. Indeed, the publications funded by FRM that are cited in patents are 3.5 times more frequently cited by other publications than the global average (for funded publications in the entire database, this ratio is 2.6). Furthermore, our results also indicate that USPTO patents citing these publications exhibit high-quality indicators. Moreover, five of these patents have led to approved drug products by the United States Food and Drug Administration (FDA). This study provides further evidence of the positive influence that research funding can have on both scientific and technological advancements.

Potential Value of Patents With Provisional Applications: An Assessment of Bibliometric Approach

This article used nine patent value indicators to compare the differences in the characteristics of U.S. patents for which provisional applications (PAs) were submitted and other patents without PAs (NPAs). The findings revealed significant differences in the average numbers of backward and forward citations, patent and nonpatent references, and patent claims, patent family size (number of patents and number of countries), and duration of examination between the two groups of patents granted between 2005 and 2017 by United States Patent and Trademark Office. Increasing trends were observed in the average number of backward citations and average percentage of patents renewed per year in both PA and NPA groups, whereas decreasing trends were observed in the average numbers of forward citations, countries, claims, and average duration of examination per patent and per year in PA and NPA groups. Differences in patent characteristics based on type and field were observed. This article also revealed more significant differences in PAs than in NPAs because of a larger number of pairs of fields. The results of this study confirmed that PAs have a stronger association with patent value and provided further justification for filing PAs, in addition to obtaining an early effective date for new inventions.

Quantifying the influence of Open Access on innovation and patents

Background: Open Access aims at improving the discovery, access and re-use of research not only within the scientific community, but also within broader society, for instance to promote innovation in industry. Yet, the extent to which openly available scientific work impacts technological inventions remains largely unknown. Methods: We combine publicly available data sources about patents and scholarly publications to explore the extent to which Open Access scientific literature is cited in patents. Results: Investigating over 22 million patent families indexed in Google Patents between 2010 and 2020, we found that around one third referenced non-patent literature. However, the number of references per patent family can vary considerably across technological sectors and inventor countries. Based on a sample of 215,962 scientific non-patent references published between 2008 and 2020, we determined the Open Access status using Unpaywall, Europe PubMed Central and arXiv. The proportion of Open Access citations grew over the years, with nearly half of cited articles being openly available. Discussion: In line with research on both technology-science linkage and Open Access, we found considerable country- and subject- specific variations. In particular, patents representing inventions from the US and the UK cited Open Access work disproportionately more often, although it is challenging to link these observations to specific science policies and incentives. We recommend that follow-up research and monitoring exercise take advantage of a growing evidence base associated with patent citations and Open Access evidence.

Understanding more about non-patent references

Understanding more about non-patent references

How Do Patent-Based Measures Inform Patent Collateral? A Holistic Analysis on All USPTO Patents Between 1986 and 2016

Technology market has been proposed to benefit technology development and increase productivity, and there is a growing interest on technology market in recent years. However, there is little specific guidance documented the link between patent-based measures and patent collateral. Therefore, the purpose of this article attempts to investigate how patent-based measures and patents used as collateral are related. This article employed eight frequently used patent-based measures categorized into three dimensions, i.e., knowledge flow, technological diversity, and legal protection, to analyze which factors increase patent collateral likelihood and patent collateral speed. The results of this article shows that reference count, times cited count, originality index, generality index, claim count, and family size all increase patent collateral likelihood. On the other hand, reference count, nonpatent reference count, originality index, claim count, and family size accelerate patent collateral speed.

An analysis of the evolution of science-technology linkage in biomedicine

Demonstrating the practical value of public research has been an important subject in science policy. Here we present a detailed study on the evolution of the citation linkage between life science related patents and biomedical research over a 37-year period. Our analysis relies on a newly-created dataset that systematically links millions of non-patent references to biomedical papers. We find a large disparity in the volume of science linkage among technology sectors, with biotechnology and drug patents dominating it. The linkage has been growing exponentially over a long period of time, doubling every 2.9 years. The U.S. has been the largest producer of cited science for years, receiving nearly half of the citations. More than half of citations goes to universities. We use a new paper-level indicator to quantify to what extent a paper is basic research or clinical medicine. We find that the cited papers are likely to be basic research, yet a significant portion of papers cited in patents that are related to FDA-approved drugs are clinical research. The U.S. National Institute of Health continues to be an important funder of cited science. For the majority of companies, more than half of citations in their patents are authored by public research. Taken together, these results indicate a continuous linkage of public science to private sector inventions.

Gatekeepers in knowledge transfer between science and technology: an exploratory study in the area of gene editing

Gene editing is an emerging technology that is promising for the prevention and treatment of human diseases. This paper reports an exploratory study of networks of authors of scientific research publications and of inventors involved in patents during the years 2000–2019 in the area of gene editing. We use patents to represent technological output and their non-patent references to represent scientific output that transferred knowledge to the technological output. We apply social network analysis to identify gatekeepers on the boundary of science and technology. We find that author–inventors are crucial for network-wide knowledge transfer as they connect parts of the network that are otherwise disconnected, and can thus be considered gatekeepers, they occupy prominent positions in the co-authorship and co-invention networks. In the area of gene editing, gatekeepers emerged during 2007–2013 and increased significantly in number during 2014–2019. Our results suggest that there are differences in the brokerage role of gatekeepers identified by different indicators. Top gatekeepers identified by betweenness and Q-measure are in areas with intensive knowledge flow among authors and inventors, whereas gatekeepers occupying more structural hole may be in areas with sparse knowledge flow.

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.

A novel method for topic linkages between scientific publications and patents

It is increasingly important to build topic linkages between scientific publications and patents for the purpose of understanding the relationships between science and technology. Previous studies on the linkages mainly focus on the analysis of nonpatent references on the front page of patents, or the resulting citation‐link networks, but with unsatisfactory performance. In the meanwhile, abundant mentioned entities in the scholarly articles and patents further complicate topic linkages. To deal with this situation, a novel statistical entity‐topic model (named the CCorrLDA2 model), armed with the collapsed Gibbs sampling inference algorithm, is proposed to discover the hidden topics respectively from the academic articles and patents. In order to reduce the negative impact on topic similarity calculation, word tokens and entity mentions are grouped by the Brown clustering method. Then a topic linkages construction problem is transformed into the well‐known optimal transportation problem after topic similarity is calculated on the basis of symmetrized Kullback–Leibler (KL) divergence. Extensive experimental results indicate that our approach is feasible to build topic linkages with more superior performance than the counterparts.

How do patent-based measures inform product commercialization? —The case of the United States pharmaceutical industry

Abstract Patents have frequently been used to analyze innovation and technological trajectory in the economic literature. The present study explored how 13 frequently used patent indicators inform product approval probability and product commercialization speed. A total of 2943 drugs approved by the FDA and the corresponding 4660 patents were analyzed to understand how patent-based indicators inform 1) which factors influence the likelihood of product approval and 2) which factors influence product commercialization speed. The results show that inventor count, nonpatent reference count, foreign reference count, originality index, claim count, and litigation probability index increase product commercialization probability and speed.

A new approach to explore the knowledge transition path in the evolution of science & technology: From the biology of restriction enzymes to their application in biotechnology

Abstract In this contribution, we develop a new approach to explore the process of knowledge transition from discovery-oriented science to technological fields, via applications-oriented research, including a mediator set. This trajectory is referred to as the D-A-T trajectory. It is shown how it can be constructed and measures are proposed to characterize the relational strength among different environments (discovery oriented research, applications-oriented research and patents) and the speed of evolution. Our approach is illustrated by a case study of three fundamental restriction enzymes articles. Among other results we found that 387 patents cited 124 of the 988 articles (a share of 12.55%) in the mediator set. Defining the non-patent references (NPR) transition rate as the number of citing patents divided by the number of articles in the mediator set yields a value 0.392. Our results suggest that the D-A-T path acts as a backbone and reveals important “invisible contributions” of an original scientific work during its evolution from discovery oriented research to outside academia. Our contribution provides a useful tool for bridging the existing gap in detecting the transition of knowledge between science and technology.

Structural model of patent quality applied to various countries

PurposeIn the current knowledge-based economy era, national innovation ability is crucial. Abundant information can be obtained through patent analysis, and such information can help in the formulation of policies and the making of R&D decisions; numerous researchers thus continue to make patent analyses. The quality of patents possessed by a country indicates the level of innovation and technology in the country, and this study aims to assess the quality of patents possessed by various countries.Design/methodology/approachIn this study, the authors determined patent quality in various countries from the perspective of the reflective measurement model and used a novel method to construct a structural model of patent quality.FindingsThis study discovered that patent family, number of claims, number of international patent classifications, forward citations, nonpatent references and maintenance time are the structural factors that affect patent quality. Forward citations and the number of claims are particularly highly explained by patent quality, which is a latent construct.Originality/valueThe results of this study provide valuable information to the government and help in the assessment of patent quality in various countries. In addition, the assessment model proposed in this study can be used in the investigation of patent quality in academic research and can predict patent quality, which will be of interest to the government and industry.

Extracting metadata of scientific references in patents based on combination of representation learning and machine learning

ABSTRACTThe scientific references in patent (SRPs), one type of non‐patent references (NPRs), is the best representative of scientific knowledge cited in patents (SKCP). However, SKCP is often indicated by NPRs rather than SRPs currently, which may reduce the accuracy of the analysis of science & technology transfer and transformation. In the other hand, the content of SRPs is important for subsequent in‐depth patent analysis, which requires more accurate SRPs' metadata extraction. In order to enhance the accuracy of SRPs identification and SRPs' metadata extraction, this paper uses the representation learning method to learn vectors of the NPRs and the parts separated by delimiters with each SRP (metadata), respectively. These vectors conclude the context information and the same type of metadata may appear in the same position in vector space. Then the problem of SRPs identification and SRPs' metadata extraction are transferred as the automatic categorization with the machine learning method. The experiment is conducted in the field of gene where patents cite lots of SRPs. The result shows that in 1000 patent citations, the accuracy of SRP identification reaches 99.27% and the accuracy of SRP title extraction reaches 90.57% which is 4.51% higher than the pure machine learning method.

Top management team’s innovation-related characteristics and the firm’s explorative R&D: an analysis based on patent data

This study investigates the relationship between characteristics of the firm's top management team (TMT) and its research and development (R&D) activities. Specifically, this research analyzes how observable characteristics of the TMT, such as functional experiences or educational background, and average tenure affect the firm's proportion of explorative R&D activities. From the perspective of the upper-echelon theory, we hypothesize that the TMT's functional experiences with R&D or science or engineering educational backgrounds increase the firm's tendency towards explorative R&D. Moreover, we propose that the average tenure of TMT members with innovation-related experiences would have a positive moderation effects on these relationships. The hypotheses are tested using a dataset containing biographical information of the TMT members, financial, and patent data of 89 firms in U.S. high-tech industries from 2006 to 2009. Firm's explorative R&D activities are analyzed using data on patent citations, patent classes, and non-patent references. The empirical analysis shows that the top managers' educational background in science or engineering as well as their previous functional experiences with R&D have a positive effect on the firm's explorative innovation activities. We also find that the size of these effects increases with a longer tenure of these TMT members. Our findings provide implications related to the effects of organizational characteristics on the establishment of a R&D strategy and highlight the role of TMT members with innovative experiences in directing a firm's R&D activities and outcomes.

Patent Citations Analysis and Its Value in Research Evaluation: A Review and a New Approach to Map Technology-relevant Research

Abstract Purpose First, to review the state-of-the-art in patent citation analysis, particularly characteristics of patent citations to scientific literature (scientific non-patent references, SNPRs). Second, to present a novel mapping approach to identify technology-relevant research based on the papers cited by and referring to the SNPRs. Design/methodology/approach In the review part we discuss the context of SNPRs such as the time lags between scientific achievements and inventions. Also patent-to-patent citation is addressed particularly because this type of patent citation analysis is a major element in the assessment of the economic value of patents. We also review the research on the role of universities and researchers in technological development, with important issues such as universities as sources of technological knowledge and inventor-author relations. We conclude the review part of this paper with an overview of recent research on mapping and network analysis of the science and technology interface and of technological progress in interaction with science. In the second part we apply new techniques for the direct visualization of the cited and citing relations of SNPRs, the mapping of the landscape around SNPRs by bibliographic coupling and co-citation analysis, and the mapping of the conceptual environment of SNPRs by keyword co-occurrence analysis. Findings We discuss several properties of SNPRs. Only a small minority of publications covered by the Web of Science or Scopus are cited by patents, about 3%–4%. However, for publications based on university-industry collaboration the number of SNPRs is considerably higher, around 15%. The proposed mapping methodology based on a “second order SNPR approach” enables a better assessment of the technological relevance of research. Research limitations The main limitation is that a more advanced merging of patent and publication data, in particular unification of author and inventor names, in still a necessity. Practical implications The proposed mapping methodology enables the creation of a database of technology-relevant papers (TRPs). In a bibliometric assessment the publications of research groups, research programs or institutes can be matched with the TRPs and thus the extent to which the work of groups, programs or institutes are relevant for technological development can be measured. Originality/value The review part examines a wide range of findings in the research of patent citation analysis. The mapping approach to identify a broad range of technology-relevant papers is novel and offers new opportunities in research evaluation practices.

Accumulated stock of knowledge and current search practices: The impact on patent quality

The paper suggests a multidimensional patent-based framework that investigates the relationships among accumulated stock of knowledge, current technological strategies, knowledge search practices and quality of innovation output. More specifically, the work intends to examine how the previous open innovation adoption, the recourse to specific technological strategies, the use of external sources of inspiration and the engagement of external actors for knowledge search affect both the market and the technological value of patented inventions. The methodology is applied to 25,583 patents filed by 68 R&D intense bio-pharmaceutical and technology hardware & equipment companies. Results show that focalization leads to technological acknowledgement and is stimulated by the use of non-patent references as sources of inspiration and the engagement of external scientific actors. In general, the use of patent and non-patent references is positively related to the quality of innovation output. Moreover, when companies have largely employed open innovation to accumulate knowledge, they continue to adopt similar practices in current R&D efforts.

A new model for measuring the impact of patent value growth trajectory

Patents play a crucial role in determining the economy, industrial development, and R&D of a country. Extant patent literature has focussed on the decision model of the econometric method, which enables researchers to understand the short-term explanatory power of various factors for patent value. However, this method cannot be used to observe the degradation rate and growth trajectory of the patent value. In this study, we proposed a dynamic time-series design to further explore the growth trajectory of patent value by using the latent growth curve model. The results show that backward citation, non-patent reference, and number of claims positively affect the patent value growth rate. Patent disclosure information and characteristics are crucial for exploring the value of patents. Patent value is typically established on the basis of knowledge stock, patent breadth and international linkage. Therefore, this study applied an experimental map to validate the linkage relationship of the studied dimensions to provide governments, industries and patent assignees with some practical suggestions.

Measuring science-based science linkage and non-science-based linkage of patents through non-patent references

Abstract This paper analysed the citations of patents to science- and non-science-based references as an agency of the linkage between technology and science. A review of the literature identified a variety of techniques of measuring science linkage (SL) with various results. Therefore, this study aimed to compare the differences between science-based SL and non-science-based linkage (NSL). Patent data were collected from the United States Patent and Trademark Office database for the past two decades. Results showed a phenomenon of rapidly growing NSL of patents at different levels of technological fields and firms. In addition, field- and firm-specific differences in the linkages between science and technology were identified. This study analysed various types of SL performances of the top 20 firms in the Computers and Communications field and found that science–technology linkages were stronger in Lucent, Mitsubishi and Microsoft. It is worth noting that Texas Instruments (TI) was ranked thirteenth in science-based SL but third in Relative SL Ratio. Based on the Relative SL Ratio, TI's science-based SL was much higher than its NSL.

Can the technological impact of academic journals be evaluated? The practice of non-patent reference (NPR) analysis

Journal rankings and journal ratings are important to governments, research institutes, and scientific research in general, and they frequently serve as the criteria for evaluating research performance to determine whether specific researchers will receive promotions and/or earn research grants. However, the only widely adopted journal assessment method is known as impact factor (IF), which focuses on citations in academic journals. However, IF disregards the technological applications and value of academic journals. In this article, we propose a method to rank academic journals that utilizes non-patent references in patent documents. We also compare the differences between journal rankings derived by using IF with those derived from the Intellectual Property Citation Index (IPCI) across different fields; moreover, some fields contain positive and significant correlations between IF and the IPCI. The results of this study offer a new perspective from which to assess the technological value of academic journals, particularly those in the technological and scientific fields. This study considers linkages among science and technology and the needs of the stakeholders in journal assessment to shed light on journal assessment and journal ranking methods.

Identifying SCI covered publications within non-patent references in U.S. utility patents

In order to evaluate approaches for identifying science citation index (SCI) covered publications within non-patent references (NPRs), the author employs a computer science method that uses two key...