Patent research in academic literature. Landscape and trends with a focus on patent analytics

Abstract

Patent analytics is crucial for understanding innovation dynamics and technological trends. However, a comprehensive overview of this rapidly evolving field is lacking. This study presents a data-driven analysis of patent research, employing citation network analysis to categorize and examine research clusters. Here, we show that patent research is characterized by interconnected themes spanning fundamental patent systems, indicator development, methodological advancements, intellectual property management practices, and diverse applications. We reveal central research areas in patent strategies, technological impact, and patent citation research while identifying emerging focuses on environmental sustainability and corporate innovation. The integration of advanced analytical techniques, including AI and machine learning, is observed across various domains. This study provides insights for researchers and practitioners, highlighting opportunities for cross-disciplinary collaboration and future research directions.

Full text

TYPE Original Research
PUBLISHED 08 January 2025
DOI 10.3389/frma.2024.1484685
OPEN ACCESS
EDITED BY
Antonio K. W. Lau,
Kyung Hee University, Republic of Korea
REVIEWED BY
Catalina Martínez,
Spanish National Research Council
(CSIC), Spain
Shino Iwami,
NEC Corporation, Japan
*CORRESPONDENCE
Cristian Mejia
mejia.cristian@ifi.u-tokyo.ac.jp
RECEIVED 22 August 2024
ACCEPTED 18 December 2024
PUBLISHED 08 January 2025
CITATION
Mejia C and Kajikawa Y (2025) Patent research
in academic literature. Landscape and trends
with a focus on patent analytics.
Front. Res. Metr. Anal. 9:1484685.
doi: 10.3389/frma.2024.1484685
COPYRIGHT
©2025 Mejia and Kajikawa. This is an
open-access article distributed under the
terms of the Creative Commons Attribution
License (CC BY). The use, distribution or
reproduction in other forums is permitted,
provided the original author(s) and the
copyright owner(s) are credited and that the
original publication in this journal is cited, in
accordance with accepted academic practice.
No use, distribution or reproduction is
permitted which does not comply with these
terms.
Patent research in academic
literature. Landscape and trends
with a focus on patent analytics
Cristian Mejia*and Yuya Kajikawa
Institute for Future Initiatives, The University of Tokyo, Tokyo, Japan
Patent analytics is crucial for understanding innovation dynamics and
technological trends. However, a comprehensive overview of this rapidly
evolving field is lacking. This study presents a data-driven analysis of
patent research, employing citation network analysis to categorize
and examine research clusters. Here, we show that patent research is
characterized by interconnected themes spanning fundamental patent
systems, indicator development, methodological advancements, intellectual
property management practices, and diverse applications. We reveal central
research areas in patent strategies, technological impact, and patent citation
research while identifying emerging focuses on environmental sustainability
and corporate innovation. The integration of advanced analytical techniques,
including AI and machine learning, is observed across various domains.
This study provides insights for researchers and practitioners, highlighting
opportunities for cross-disciplinary collaboration and future research directions.
KEYWORDS
patent analytics, bibliometrics, text mining, tech mining, network analysis
1 Introduction
Patents serve as a repository of technical and commercial knowledge, and protect
intellectual property, playing an important role in promoting technological progress,
business development, and innovation. These legal documents grant inventors temporary
exclusive rights over their creations, incentivizing the disclosure of technical information
that might otherwise remain hidden as trade secrets (Carr, 1995). By allowing inventors to
profit from their creativity while simultaneously inspiring further technological advances
through the revelation of prior art, patents directly impact both scientific and economic
development (Hall, 2007;Langinier and Moschini, 2002;Schankerman, 1998).
Despite the rapid increase in academic literature exploring patents or leveraging patent
documents and data, a current and comprehensive overview that captures the landscape of
patent research holistically has yet to emerge. Such a panoramic perspective is invaluable
for several reasons. First, it can reveal emerging topical clusters and current research
trends, guiding scientists and practitioners toward areas of growing relevance. Second,
mapping the landscape of patent research reveals the role and potential applications of
more specialized subfields like patent analytics. This insight allows researchers developing
quantitative patent analysis methods to focus their efforts on domains that stand to benefit
most from such techniques. Finally, an academic landscape may facilitate cross-pollination
across traditionally siloed disciplines by exposing potential applications of patent analytics.
To address this gap, our study aims to provide a landscape of patent research within
academic literature. By surveying scholarly literature, we uncover major research topics,
identify their interrelationships, and track evolving trends over time. We pay particular
Frontiers in Research Metrics and Analytics 01 frontiersin.org

Mejia and Kajikawa 10.3389/frma.2024.1484685
attention to the distinct and complementary studies of patent
analytics, which have grown increasingly important for
understanding innovation and economic progress. Specifically, this
article addresses the following research questions:
1. How is patent information used in academic research?
2. What are the current trends in patent research?
3. What is the role of patent analytics methods within the larger
scope of patent research?
In this article, we refer to “patent research” as any study that
employs and leverages patents or patent data in any form and
for any purpose, while “patent analytics” is used with a narrower
scope to refer to studies with a more systematized approach to the
study of patents or patent data, especially when used to develop
metrics or methodologies (Daim et al., 2006). Patent analytics offers
valuable insights into technology development trends, key industry
players, and competitive landscapes through various approaches
and techniques designed to extract meaningful information from
patent data.
Our study employs a combination of data extraction techniques
and topic analysis methods, including citation network analysis of
scholarly articles. We present an overview of the current landscape,
focusing on research fronts characterized by recency, relevance,
and rapid growth (Rotolo et al., 2015). We expect to contribute by
providing a comprehensive map of the patent research landscape
to guide future studies and collaborations, identify emerging trends
and underexplored areas to inform research priorities and funding
decisions, provide insights into the evolving role of patent analytics
to enhance evidence-based strategic planning and innovation
policies, and develop a framework for integrating diverse patent
research methodologies to foster interdisciplinary approaches in
both academia and practitioners.
The remainder of this article is structured as follows: first, we
explore the relevance of patents and scholarly patent research in
general, while covering previous efforts in mapping the field of
patent analytics. The methods section details our data extraction
and analysis techniques. In the results section, we present our
findings on the current landscape of patent research, with a focus
on emerging trends and key areas of development. We conclude by
discussing future directions for interdisciplinary research and shifts
in methodological approaches within the field of patent research
and analytics.
2 Previous literature
The academic interest in patent data spans several decades,
evolving from early information retrieval systems to sophisticated
analytical approaches. In the 1950s, pioneering work by Mooers
(1952) laid the foundation for patent retrieval systems, initially
focusing on searching metadata fields such as author, title, and
keywords. As technology advanced, the scope expanded to include
full-text analysis of patent documents in the 1960s and 1970s.
The 1970s and 1980s marked a significant shift in patent
research, with scholars beginning to use patent statistics as a
proxy for innovation and technological change. Soete (1979) and
Pavitt (1985) examined the relationship between research and
development (R&D) investment and patent counts at the national
level, finding significant correlations. Other studies explored
patenting patterns across countries and industries to understand
differences in innovative activity (Evenson, 1984;Schiffel and Kitti,
1978;Sláma, 1981). At the firm level, Pakes and Griliches (1980)
conducted one of the first systematic analyses of the relationship
between R&D and patenting, finding a strong cross-sectional
relationship but weaker time-series correlations.
A landmark contribution came from Trajtenberg (1990),
who studied the computed tomography scanner industry. By
combining patent data with market information, Trajtenberg
demonstrated that while raw patent counts correlated poorly with
social value creation, citation-weighted patent counts showed a
strong correlation (around 0.75) with total social welfare created.
This finding has been corroborated by subsequent studies, such
as Harhoff et al. (1999) and Hall et al. (2005), establishing the
importance of patent citations as indicators of economic and
technological significance.
The analysis of patent citation networks emerged as a distinct
field of study in the latter half of the 20th century. Early work by de
Solla Price (1965) highlighted the importance of citation analysis in
understanding scientific and technological development. The 1980s
and 1990s saw the formalization of quantitative approaches, with
Narin (1994) introducing various patent metrics for the study of
Innovation. The release of the NBER Patent Citations Data File in
1990 provided researchers with a comprehensive dataset, spurring
further studies on knowledge spillovers and innovation diffusion
(Hall et al., 2001).
More recently, patent analytics has expanded its applications
across various domains of technology management and innovation
policy. Key areas include competitive intelligence, technology
forecasting, R&D planning, merger and acquisition analysis, and
policy evaluation. The exponential growth in global patent data,
with 2022 alone estimated at 3.46 million patent applications
worldwide (WIPO, 2023), has needed the development of more
sophisticated and automated methods for analysis. Thus, the field
has benefited from the integration of advanced techniques such
as text mining, natural language processing, network analysis,
and machine learning. This plurality of methodologies and scopes
has led to the emergence of various terms describing the field,
like patent bibliometrics (Narin, 1994), patinformatics (Trippe,
2003), and technology mining or tech mining (Porter, 2004),
each with nuanced scopes and target applications, reflecting its
multidisciplinary nature.
The use of patents by academics has been surveyed in the
past, with the work of Basberg (1987) being an early example.
This survey focused on the use of patents to measure technological
change. Scholars at the time were concerned with the use of
patent citations, finding “important” patents, and benchmarking
innovation across regions. A comprehensive survey by Griliches
(1990) reviewed several decades of research on patent statistics as
economic indicators. He examined multiple data sources, including
patent counts, renewal data, and stock market valuations. His
survey synthesized evidence from studies using the U.S. Patent
Office data, European patent renewal information, and firm-
level R&D expenditure data highlighting critical measurement
challenges, including the highly skewed distribution of patent
Frontiers in Research Metrics and Analytics 02 frontiersin.org

Mejia and Kajikawa 10.3389/frma.2024.1484685
values and variations in patenting propensity across industries
and time. Griliches’ synthesis helped establish methodological
frameworks for evaluating patent quality and understanding the
limitations of patent statistics as innovation indicators. More recent
efforts have adopted computer-assisted methods to bring a more
systematized understanding of the field by using bibliometrics
(Mejia et al., 2021). Mikova (2016) analyzed Global TechMining
conference proceedings from 2011 to 2015, identifying trends
such as the integration of multiple approaches (e.g., bibliometrics,
NLP, statistical analysis) and the use of novel data sources
(e.g., web data, social media). Aristodemou and Tietze (2018)
reviewed 57 articles on applying AI, machine learning, and
deep learning to intellectual property data, categorizing them
into knowledge management, technology management, economic
value, and information extraction/management. The study found
a growing interest in intellectual property (IP) analytics but
called for more research on use cases and firm-level applications.
Karata et al. (2024) analyzed 1,006 papers on “patent analysis,”
revealing trough a descriptive approach that “technology” was
the most common keyword and that top journals included
“Technological Forecasting and Social Change” and “Information
Processing & Management.” Hu et al. (2024) explored the
foundations and frontiers of technology mining using co-citation
analysis, bibliographic coupling, and content analysis of 277
articles. The study identified text analysis, bibliometrics, patent
analysis, and strategic technology management as foundational
areas, with technology topic analysis, roadmapping, component
analysis, opportunity analysis, and management/decision support
as frontier clusters.
While these previous studies offer valuable insights, they are
limited in providing a comprehensive understanding of patent
research given their narrower econometric or methodological
focus. Our study aims to address these gaps by analyzing a larger
and more recent dataset to capture the latest developments. It takes
a holistic view of patent research, considering all aspects rather than
focusing on specific methods or applications.
3 Materials and methods
The bibliographic data for this study was sourced from the Web
of Science (WOS) Core Collection. To identify relevant articles,
a topical search was conducted using the query TS =“patent∗”,
where the asterisk serves as a truncation symbol to accommodate
variations of the term (e.g., patents). The search was performed
without time constraints, retrieving articles from all available years
in the database. Data were retrieved on May 31, 2024, yielding
103,738 articles.
While comprehensive, the query also retrieves articles unrelated
to the target topic due to the various meanings of the term “patent”.
In addition to referring to intellectual property documents, “patent”
may be used as an adjective to denote open, unobstructed, or
accessible, particularly in biomedical research. For example, there
is extensive research on patent ductus arteriosus, a congenital heart
defect (Schneider and Moore, 2006). Other deviating meanings
include its use as a synonym for obvious, clear, or apparent. From
a document retrieval standpoint, it may be tempting to generate a
list of banned keywords (e.g., to be used with the NOT operator),
but this would result in neglecting patent analytics papers on those
alternative meanings [for instance, patent analysis of patent ductus
research (Hsieh et al., 2004)]. Therefore, to focus on our target
topic, citation networks were employed as both a data-cleaning
mechanism and a means to extract thematic clusters.
Academic articles are positioned within a research field by
citing previous related research. Articles that do not cite nor are
cited by other articles were excluded from the study, as these
are the papers that used the keyword “patent” without belonging
to the patent research domain. A direct citation network was
constructed, establishing linkages between articles when one cites
the other (de Solla Price, 1965). Direct citation networks are known
to surface research field taxonomies (Klavans and Boyack, 2017)
and help identify research fronts (Shibata et al., 2008), making them
suitable for long-term bibliometric research. However, this network
would also contain papers in other fields of research, such as in
biomedicine, that may cover other meanings of patents. To exclude
these, thematic clusters were extracted, and after human inspection,
unrelated clusters were pruned from the citation network.
Identifying topics from a citation network involves grouping
nodes with denser connections compared to other groups. An
optimal partition is achieved when the link density is higher at
the intra-cluster level than the inter-cluster level, maximizing the
network’s modularity (Newman, 2006). We applied the Louvain
method, a computationally efficient algorithm for partitioning large
networks, to obtain the clusters (Blondel et al., 2008). For large
networks, the first pass of the clustering algorithm may result
in relatively large clusters. To obtain a more granular view, we
applied the resolution limit theorem (Fortunato and Barthélemy,
2007) to further split clusters into subclusters, resulting in a topical
hierarchy that facilitates the analysis, as the topics become smaller
and more coherent. The authors named the clusters based on an
assessment of the titles of the most connected articles, the most
frequent keywords, and relevant metadata such as journal names,
countries, or authors. During this step, the final cleaning was
conducted, and unrelated clusters were removed from the study.
Figure 1 represents a summary of the methodology.
Summary statistics of the publication years and citations
received by the articles within each cluster and subcluster were
calculated. Concretely, we apply metrics related to “size” being
the number of documents; “relevance” the standardized cluster
citations; “emergence” the average publication year; and “fast
growth” the largest delta increase of publication counts within
each subcluster over the past 10 years. These metrics are known
to be useful in defining emerging research (Rotolo et al., 2015).
Subclusters that are outliers in any of those metrics are separated
for detailed descriptions.
Bibliographic data, including the full record and cited
references, were exported as tab-delimited files from the WOS
website. The dataset was processed using the statistical software R
version 3.6.3 (R Core Team, 2019), with the igraph package version
1.2.5 (Csárdi et al., 2024) for network creation and clustering and
the tm package version 0.7.7 for text processing (Feinerer et al.,
2008). The citation network was visualized using the large graph
layout (Adai et al., 2004), selected for its computational efficiency.
The choice of layout has no impact on the research results.
Frontiers in Research Metrics and Analytics 03 frontiersin.org

Mejia and Kajikawa 10.3389/frma.2024.1484685
FIGURE 1
Overview of the methodology. (A) Data acquisition. (B) Citation network. (C) Clustering. (D) Remove unrelated clusters. (E) Cluster analysis.
4 Results
From the original dataset, 53,668 articles used alternative
meanings of the term “patent” that are not part of the core of
patent research. These papers are disconnected from the main
corpus of knowledge as they do not cite or receive citations from
other patent-related literature. The rest of the articles compose
the citation network of patents-related literature, covering 50,070
articles. Figure 2 shows the citation network forming a 2-group
divide of clusters, one being those from biomedicine fields where
patent is used in its medical meaning, and the other group of
clusters studying patents as the IP document. Patent as the IP
document has the largest share with 27,119 (54%) articles. In
Figure 2, this group looks smaller due to its more cohesive nature,
as the citations seem to be shared across the clusters in the group.
In the remaining of this article, we refer as the citation network of
patents research to this group of 27,119 documents and the results
presented hereafter are based on this dataset.
The study of patents in academic literature has been steadily
growing over the past decade, with an average 7% yearly increase in
publications. The earliest records in the dataset of the divide date
back to 1909, when Baekeland (1909) called for revisions to the
United States (US) patent law. Since then, the trend of publications
has been on the rise, with 2,139 publications in 2023 alone. The
US and China have been the largest contributors to patent-related
literature, with China consolidating its position as the country
with the most publications since 2020. In 2023, more than 35% of
publications came from China, while the US followed at 17%. The
United Kingdom (UK) has consistently maintained its position as
the third-largest contributor.
The study of patents spans across various fields, with
Economics, Management, and Law being the most prominent. The
journals that have been at the forefront of publishing patent-related
literature include Research Policy, Reviews on Therapeutic Patents,
Scientometrics, Technological Forecasting and Social Change, and
Sustainability. Supporting figures for these descriptive statistics are
presented in the Supplementary material.
4.1 Clusters
Our analysis of the citation network yielded 15 distinct
clusters, along with an additional grouping of very small
clusters aggregated as “others”. These clusters represent the
primary themes in patent research. Figure 3 presents a visual
representation of these clusters, illustrating their relative size,
emergence, and relevance. The underlying data is presented
as Table 1.
The field exhibits a clear evolution over time, with clusters
spanning, on average, from 2007 to 2019. Larger clusters, such
as “Patent Analytics and Innovation Dynamics” (1), “Patent
Systems and Biomedical Innovations” (2), and “Advanced Methods
in Patent Analytics and Technology Forecasting” (3), dominate
the field, indicating areas of extensive research. The chart also
highlights emerging trends, with clusters like “Environmental
Innovation and Sustainable Development” (9) and “Patenting
and Traditional Medicine in Modern Healthcare” (12) positioned
toward the right, signifying more recent areas of focus. Matured
research areas, represented by clusters such as “Computational
Methods in Drug Discovery and Patent Analysis” (11), “Emerging
Trends in Drug Development and Therapeutics” (13), and “Patent
Systems and Biomedical Innovations” (2) are found on the left
side of the chart. This also signals that, on average, the prevalent
use of patent data in pharma and biomedical fields precedes that
of innovation studies. The vertical axis reveals varying levels of
citation impact, with clusters 1, 10, and 15 showing the highest
relevance. Notably, “Patent Analytics and Innovation Dynamics”
(1) stands out as a large, recent, and highly cited cluster, suggesting
its dominant and influential role in current research. The lso
captures a shift from general patent system and policy-related
research toward more specialized and application-oriented topics
over time. A granular view of the evolution in the vocabulary
related to each cluster and their most frequent fields of research
based on the Web of Science classification is offered in Figure A3
and Figure 7 in the Appendix, respectively.
4.2 Subclusters
We identified 93 distinct subclusters derived from the main
clusters. These provide a more granular view of the research
landscape within patent research. Figure 4 replicates Figure 3 at the
subcluster level. The underlying data is available in the Table A1.
The naming convention for subclusters follows a two-part code,
where the first number represents the main cluster, and the second
number indicates the subcluster’s position within that main cluster.
For instance, subcluster “1-2” denotes the second largest subcluster
Frontiers in Research Metrics and Analytics 04 frontiersin.org

Mejia and Kajikawa 10.3389/frma.2024.1484685
FIGURE 2
Citation network of patents. The divide between the use of “patent” in biomedicine, and as an IP document is apparent. This article focuses in the
group of clusters in the bottom-left side.
FIGURE 3
Patent research clusters. The size of each cluster represents the number of articles, the x-axis shows the average publication year (emergence), and
the y-axis indicates the standardized average citations received (relevance). Each cluster is represented by a dierent color and numbered from the
largest size.
within main cluster one. Subclusters are ordered by size within each
main cluster.
Larger subclusters, such as “Legal Frameworks and Challenges
in Patent Systems” (2-1), “Factors Influencing Innovation and
Technological Impact” (1-1), “Geographic Mobility and Knowledge
Spillovers” (1-2), indicate areas of extensive research activity. The
figure also reveals emerging trends, particularly in environmental
sustainability and corporate innovation, as evidenced by the
concentration of recent subclusters from clusters 8 and 9, all
about sustainability and green innovation on the right side of
Frontiers in Research Metrics and Analytics 05 frontiersin.org

Mejia and Kajikawa 10.3389/frma.2024.1484685
TABLE 1 Patent research clusters summary.
ID Cluster Articles Ave. year Ave. citations
1 Patent analytics and innovation dynamics 3,660 2015.6 33.9
2 Patent systems and biomedical innovations 3,455 2009.6 13.2
3 Advanced methods in patent analytics and technology forecasting 3,183 2017.0 12.6
4 Economic implications of patent policies 2,659 2012.8 17.8
5 University-industry collaboration and knowledge transfer 2,545 2013.5 22.4
6 Strategic patent management and market dynamics 2„532 2015.4 19.3
7 Pharmaceutical patents and market access 2„059 2015.3 14.8
8 Corporate innovation and patent performance 1,284 2017.4 22.8
9 Environmental innovation and sustainable development 1,244 2019.5 16.2
10 Nanoparticle-based drug delivery systems 982 2017.5 36.5
11 Computational methods in drug discovery and patent analysis 773 2007.5 26.5
12 Patenting and traditional medicine in modern healthcare 627 2018.7 13.2
13 Emerging trends in drug development and therapeutics 402 2009.7 28.2
14 Patents and technology standards 382 2016.2 8.7
15 Carbonic anhydrase inhibitors research 345 2014.5 33.6
16 Others 987 2015.0 19.7
FIGURE 4
Patent research subclusters. The size of each subcluster represents the number of articles, the x-axis shows the average publication year
(emergence) from 2004, and the y-axis indicates the standardized average citations received (relevance). Each cluster is represented by a dierent
color and numbered from the largest size. Labels indicate subcluster codes.
the chart. An interesting case is that of “Traditional Chinese
Medicines for Viral Infections” (12-1), which also appears
as a recent subcluster due to an increase in publications
related to alternative medicine and the IP challenges triggered
due to the coronavirus disease 2019 (COVID-19) pandemic.
The upper right quadrant of the chart showcases high-impact
recent research, exemplified by subclusters “Patent Analytics
in Drug Discovery and Network Pharmacology” (11-4) and
Frontiers in Research Metrics and Analytics 06 frontiersin.org

Mejia and Kajikawa 10.3389/frma.2024.1484685
“Patent Analytics in Energy Sectors” (3-4), which have quickly
gained significant attention. Another aspect that characterizes
research fronts is that of fast-growing research, as shown
in Figure 5, depicting the subclusters with the largest decline
and growth.
Subclusters related to research on University-Industry
collaboration (Cluster 5) and Patent Systems and Biomedical
Innovations (Cluster 2) are among the research trends with
the larger decline. Growth is aligned with recency as, again,
subclusters in the areas of sustainability and corporate
innovation show rapid growth. Two subclusters from “Patent
Analytics and Innovation Dynamics” also show fast growth
even though they belong to the established cluster 1: Factors
Influencing Innovation and Technological Impact (1-1),
Patent Analytics in Regional and Technological Innovation
(1-8), meaning that these topics have been and continue to
be relevant for patent research. Subclusters with outstanding
characteristics have been selected for further description in the
following sections.
4.2.1 Biggest subclusters
Factors Influencing Innovation and Technological Impact
(1-1): This cluster explores how knowledge recombination,
organizational learning structures, and search behaviors influence
innovation effectiveness (Hausman et al., 1984;Rosenkopf and
Nerkar, 2001). The research examines the roles of knowledge
relationship intensity, neighboring knowledge concentration,
technology sourcing strategies, and team-based research in
generating high-impact patents (Wuchty et al., 2007).
Geographic Mobility and Knowledge Spillovers (1-2):
The research in this cluster examines how geographic
factors [e.g., geographical proximity (Jaffe et al., 1993),
transportation infrastructure (Cao et al., 2024)], and inventor
mobility influence the localization and dissemination of
knowledge. It explores the role of alliances and labor
networks in facilitating knowledge transfer across geographical
and technological boundaries. Additionally, this cluster
investigates spillover effects between different sectors [such
as defense to civilian (Riebe et al., 2024)] and compares
innovation patterns among different groups of inventors,
including immigrants.
Legal Frameworks and Challenges in Patent Systems (2-1):
This cluster covers the nuances of patent protection, examining
discrepancies between copyright and patent standards, particularly
in areas like design patents. For example, we find research
investigating how patents function as both profit-making tools
and mechanisms for technological foresight, using examples from
specific sectors such as hydrogen energy (Erivantseva et al., 2024). It
also addresses common pitfalls in patent protection and strategies
for effective claim drafting (Merges and Nelson, 1990). The cluster
encompasses discussions on the economic dynamics of patent
scope, the fundamental principles and purposes of the patent
system (Kitch, 1977), and the concept of rational ignorance within
patent offices (Lemley, 2001). Additionally, it explores critiques of
the current patent system, including movements advocating for
open access to innovation.
4.2.2 Highly cited subclusters
Strategic Alliance Governance and Innovation Outcomes (1-
5): This cluster focuses on using patent analytics to understand
innovation dynamics and strategic partnerships. It explores the
application of advanced techniques like machine learning in
patent analysis, the role of strategic alliances in technological
innovation, and the relationship between various innovation
indicators (Hagedoorn and Cloodt, 2003;Hanisch, 2024). The
research in this cluster has evolved from early studies on
patent statistics as innovation proxies to more sophisticated
analyses of knowledge transfers and the impact of acquisitions on
innovation performance.
Innovative Drug Development and Repurposing (7-7): This
cluster traces the evolution of drug repurposing and innovative
therapeutic development, emphasizing both natural products
and existing drugs. The research trajectory reflects a shift
from traditional drug discovery to more efficient, cost-effective
strategies (Paul et al., 2010). Research in this cluster leans toward
computational methods for drug repositioning and renewed
interest in natural products as therapeutic sources (Malla et al.,
2024;Singla et al., 2023). It emphasizes the critical role of patents
in protecting new applications of repurposed drugs and natural
compounds, addressing unmet medical needs, and improving drug
development efficiency.
Patent Analytics in Drug Discovery and Network
Pharmacology (11-4): This cluster tackles research from traditional
database-driven approaches (Gaulton et al., 2017;Wang et al.,
2020) to more sophisticated network pharmacology analyses and
machine-learning applications (Xia et al., 2024;Zheng et al., 2024).
This cluster emphasizes the growing importance of patent analytics
in identifying new drug candidates and leveraging traditional
medicine knowledge in modern pharmaceutical research.
4.2.3 Recent subclusters
Impact of Policies and Corporate Factors on Innovation (8-3):
This cluster examines how national policies, corporate structures,
and cultural factors influence innovation outcomes, using patent
data as a key metric. It explores the effects of initiatives like
“Made in China 2025” (Chen K. J. et al., 2024) and US-China
technology decoupling on firm performance and innovation (Han
et al., 2024). The research investigates how corporate risk culture
impacts innovation, particularly in innovative industries, and
how different organizational forms (such as conglomerates and
venture capital backing) affect R&D productivity. Studies in this
cluster also analyze the dynamics of mergers and acquisitions in
relation to patent portfolios and technological synergies (Bena
and Li, 2014), highlighting how patent data can inform strategic
corporate decisions.
Banking Financing R&D and Innovation (8-8): This research
examines how the development of equity and credit markets, as
well as banking deregulation, affects corporate patenting (Hsu
et al., 2014). Studies in this cluster reveal that the ability to
use patents as collateral [patent pledgeability (Dai et al., 2024)]
positively impacts corporate patenting. The cluster also explores
how financial constraints and debt financing influence innovation
Frontiers in Research Metrics and Analytics 07 frontiersin.org

Mejia and Kajikawa 10.3389/frma.2024.1484685
FIGURE 5
Patent research subclusters with the largest (A) decline and (B) growth over the past 10 years.
outcomes, using patent-based metrics to measure these effects
(Shahzad et al., 2024).
Regional Dynamics of Green Technology Development (9-
5): This cluster analyzes the role of digital governance and
technological innovation in driving sustainable development,
mainly in China, using patent analytics to assess the effects on
natural resource management, energy efficiency, and urban green
development (Chen K. et al., 2024;Lu and Li, 2024). Studies in
this cluster also investigate the spatial and temporal distribution of
environmental patents in China, assess the effectiveness of patent
subsidy programs, and scrutinize regional disparities in innovation
capabilities and eco-efficiency across Chinese cities. Although the
focus is on China, regional studies from other countries are
also present.
Patent Analytics in Carbon Reduction Technologies (9-3):
This cluster explores the role of technological innovation in
addressing environmental sustainability challenges, examining how
patents influence green innovation and environmental degradation
across various geographic contexts (Albino et al., 2014;Hashmi
and Alam, 2019). For instance, studies analyze the impact of
technological innovation on the ecological footprint of innovative
countries, the effectiveness of China’s green patent fast-track
system (Xu A. T. et al., 2024), and the role of environmental-
related patents in Nordic countries (Alola et al., 2024). The
research also highlights development trends in low-carbon
energy technologies and examines the dynamic interplay between
innovation, environmental regulation, CO2 emissions, population,
and economic growth in countries part of the Organization for
Economic Co-operation and Development (OECD).
Drivers of Corporate Environmental Innovation (9-4): This
cluster investigates the various factors driving green innovation
and environmental sustainability, focusing on the impact of green
finance, external resources, corporate Environmental, Social, and
Governance (ESG) ratings, place-based policies, and institutional
pressures on the development of green patents (Berrone et al., 2013;
Cainelli et al., 2015). It also explores the impact of place-based
policies, such as the revitalization of old revolutionary base areas
in China (Nie et al., 2024), on urban green technology innovation
and how institutional pressures drive environmental innovation in
polluting industries.
4.2.4 Rapidly growing subclusters
Patent Analytics in Regional and Technological Innovation
(1-8): Studies in this cluster investigate drivers of regional
diversification in industrial districts, the impact of technology flows
through patent transactions on regional specialization (Liu et al.,
2024), and the integration of AI into green technologies. The
research also examines how related and unrelated technological
variety influences innovation output at the city or state levels and
how urbanization affects economic development and knowledge
creation (Bettencourt et al., 2007;Castaldi et al., 2015).
Patent Analytics in Energy Sectors (3-4): Focusing on energy-
related technologies, this cluster utilizes patent analytics to track
innovations in hydrogen fuel cells, lithium-based batteries, CO2
capture, and redox flow batteries (Li et al., 2013;Zhou et al.,
2024). It highlights the importance of collaborative networks and
patent analysis in informing policy decisions and technological
development strategies. Studies also examine patents related to
CO2 capture technologies and the commercial development of
all-vanadium redox flow batteries for energy storage (Kear et al.,
2012).
Frontiers in Research Metrics and Analytics 08 frontiersin.org

Mejia and Kajikawa 10.3389/frma.2024.1484685
Corporate Governance and Financial Factors in Firm
Innovation (8-1): This cluster examines various factors affecting
firm-level innovation, including corporate lobbying, institutional
ownership, political alignment of executives, financial analyst
pressure, and CEO overconfidence (Hirshleifer et al., 2012;Jiao
and Lu, 2024). It uses patent metrics to measure innovation
outcomes and explore the complex interplay of these factors.
Studies investigate how corporate lobbying enhances firm
innovation outcomes, the impact of institutional stock ownership
on corporate innovation (Simeth and Wehrheim, 2024), and
the influence of political partisanship among firm executives on
innovation outcomes (Jiao et al., 2024).
Public Financing R&D and Innovation (8-7): The research
in this cluster emphasizes the importance of addressing financial
constraints and tailoring policy approaches to enhance R&D
investments and patenting activities (Czarnitzki et al., 2007).
Studies explore the role of financial technology in promoting
regional innovation and the effectiveness of public R&D subsidies
(Wang et al., 2024). The cluster also includes research on the
funding gap in R&D (Hall, 2002), the effects of government-
sponsored commercial R&D subsidies.
Environmental Regulation and Green Patent Quality (9-2):
This cluster explores the relationship between environmental
policies, pollution levels, and green innovation. It uses patent data
to analyze the impact of carbon intensity policies, air pollution,
and environmental regulations on technological innovation
and regional carbon emission reduction (Brunnermeier and
Cohen, 2003;Jaffe and Palmer, 1997). The cluster also includes
research on the relationship between environmental compliance
expenditures and R&D investment, global trends in environmental
patenting, and the impact of pollution abatement expenditures on
environmental innovation in manufacturing industries (Xu J. H.
et al., 2024;Xu S. C. et al., 2024).
4.3 Knowledge cross-sharing
The citation network revealed clusters and subclusters
representing focal topics of research. Academic articles were used
as nodes in the initial network in Figure 2. An aggregation of the
nodes at the subcluster level reveals the knowledge structure across
the topics. Figure 6 presents a network visualization that captures
the interactions and knowledge flows between different areas of
patent analytics research as represented by the subclusters, being
this a macro-level perspective on how various subclusters relate to
and influence each other.
In this network, the central position of, for instance, subclusters
6-1 (Patent Value and Citations), and 6-2 (Patent Strategies
and Innovation) indicates these are foundational areas in patent
analytics research. Their centrality suggests they serve as active
sources and sinks of insights that are widely applicable across the
field. The network also reveals thematic coherence within the main
research areas, as the subclusters tend to agglomerate near other
subclusters in the same main topic (i.e., same cluster color). We
observe that subclusters Advanced Methods in Patent Analytics and
Technology Forecasting (Cluster 3) tend to interact more with each
other, suggesting a well-integrated body, yet they share connections
to clusters 1, 6, and 9, indicating their relevance for these other
research areas.
The dense center of the network, dominated by subclusters
from the main clusters 1, 2, 4, and 6, represents the core of
patent research. The sparser periphery, including specialized topics
like specific drug delivery systems or environmental technologies,
represents more focused applications of patent analytics.
Another possible representation of the subcluster’s relationship
is through semantic analysis, for example, by measuring the text-
similarity of the contents in each cluster. Such a perspective has
been added in Figure A4. However, semantic analysis tends to place
the subclusters of the same cluster near each other, and thus, it only
has value as a confirmatory method. It confirms that the subcluster
partitions from the citation network are also semantically coherent.
5 Discussion
Through a citation network analysis of over 27,000 academic
articles, 15 main clusters and 93 subclusters were identified,
revealing the landscape of patent research. The results show a
steady increase in the use of patent documents in academic
research. We note that the interest in academia is shared by
two distinct but highly integrated groups, one that focuses
on management and innovation studies and the other that is
more applied to pharma and biomedical research. For instance,
clusters such as “Patent Analytics and Innovation Dynamics”
and “Advanced Methods in Patent Analytics and Technology
Forecasting” dominate the first, while “Patent Systems and
Biomedical Innovations” dominates the second. Emerging trends
in environmental sustainability and biomedical innovations were
identified, as evidenced by the recent and rapidly growing
subclusters in these areas. The analysis also revealed the widespread
integration of advanced analytical techniques, including AI and
machine learning, across various domains of patent research. These
findings provide a foundation for addressing the study’s core
research questions: how patent documents are used in academic
research, current trends in patent research, and the role of patent
analytics methods within the broader scope of patent research. The
following discussion examines these questions in detail, drawing
insights from the identified clusters and their interrelationships.
How is patent information used in academic research?
We found that patent bibliographic data and information
are used extensively and diversely in academic research, serving
as rich sources of information for understanding technological
innovation, knowledge flows, and economic impacts. The cluster
analysis revealed several key applications of patent data in research:
Indicators of innovation: Patents are widely used as proxies
for measuring innovative activity across different technological
fields and geographic regions. This being the most mature use of
patents in Innovation Management, as evidenced by the seminal
work of Soete (1979) and later consolidated by Narin (1994)
highlighting the similarities between patent bibliometrics and
scientific literature bibliometrics, particularly in assessing national
and inventor productivity.
Knowledge spillover analysis: The network visualization of
subclusters (Figure 6) highlighted the importance of patent
citations in tracing knowledge flows as inferred from the central
Frontiers in Research Metrics and Analytics 09 frontiersin.org

Mejia and Kajikawa 10.3389/frma.2024.1484685
FIGURE 6
Network visualization of patent analytics research subclusters. Nodes represent individual subclusters, with node color indicating the main cluster to
which each subcluster belongs. Node size reflects the size based on the number of documents. Edges represent aggregate citations
between subclusters.
place of subclusters 1-2 and 6-1, both on patent citation
flows. These build upon the seminal work of Jaffe et al.
(1993), who used patent citations to demonstrate the geographic
localization of knowledge spillovers. Our findings suggest that
this approach remains relevant, with studies on geographical
proximity and spillover still being conducted and what has
changed is the sophistication of the methods or the access to
new datasets.
Technological forecasting: Several subclusters focus on using
patent data to identify emerging technologies and predict future
trends. Some of these, like most of the subclusters in cluster
3, prime the development of methods from patent data, while
other subclusters focus on the application of such methods to
bring forward-looking views on the fields (e.g., research on patent
analytics for energy, green energy, and nano technologies) This
streams aligns with the tech mining approaches (Porter and
Newman, 2009), now being extended by the incorporation of more
advanced data analytics techniques.
Innovation quality assessment: This covers research
using patents as a benchmark to assess firms or innovation
portfolios. Although there is a prominent background in
patent counts and citations, some research emphasizes
the multidimensional nature of patent quality, challenging
simplistic metrics, thus aligning more with the concept of
patent quality (Higham et al., 2021). Our analysis supports this
view, showing diverse approaches to evaluating patent impact
across different technological domains (e.g., as in subclusters
1-1, 8-4).
Frontiers in Research Metrics and Analytics 10 frontiersin.org

Mejia and Kajikawa 10.3389/frma.2024.1484685
Policy and economic analysis: Patents are used to assess the
effectiveness of R&D funding strategies. We observed subclusters
focused on how different funding sources (public vs. private)
influence patenting outcomes across various sectors (Shelton and
Leydesdorff, 2012) and also research areas focused on the interplay
between industry, academia, and government trough different
initiatives of polices (Ivanova et al., 2017).
Corporate strategy and competitive intelligence: Patent analysis
is extensively used to understand competitive landscapes and
inform strategic decision-making. Most of the clusters related to
innovation management are geared toward this use case.
Interdisciplinary research: We observe the integration of patent
analysis with diverse fields such as environmental sustainability,
biomedical innovations, and digital technologies, showcasing the
versatility of patent data in being a supporting data source for
multiple disciplines.
What are the current trends in patent research?
Current trends in patent research reflect a shift toward
more sophisticated, context-aware approaches in patent analysis.
This evolution addresses Meyer’s (2000) call for recognizing
the unique characteristics of patent citations compared to
scientific citations. The emergence of tech mining and AI-
driven analytics in patent research, as highlighted in recent
and growing subclusters, suggests a shift toward more data-
intensive and nuanced approaches to extracting value from
patent documents.
A prominent trend is the integration of environmental
sustainability and green innovation with patent analytics.
Subclusters focusing on “Patent Analytics in Carbon Reduction
Technologies” (9-3) and “Drivers of Corporate Environmental
Innovation” (9-4) have shown recent emergence and significant
growth. This trend reflects the broader societal focus on sustainable
development and demonstrates how patent analysis is being
applied to track and foster eco-friendly innovations.
The rise of micro-level scientometrics, focusing on detailed
interactions within organizations and among individuals,
represents another current trend. This approach, highlighted
in Zhang et al. (2017) work on scientometrics for tech mining,
is evident in growing subclusters that examine the impact of
corporate structures, funding sources, and individual inventor
characteristics on innovation outcomes.
Another notable trend is the application of advanced data
analytics and artificial intelligence in patent research. Subcluster
3-4, “Patent Analytics in Energy Sectors,” exemplifies this trend,
showcasing the use of sophisticated analytical techniques to
understand technological evolution in energy-related fields. While
this subcluster is more on the applied side, it is embedded within
cluster 3 of advanced methods, suggesting a need in the field of
energy innovation to leverage the most up-to-date methodologies
for innovation analysis. The integration of patent analytics into
unrelated but specific research has been long foreseen as part of the
discussions on tech mining (Porter and Newman, 2009).
Lastly, we observe a growing trend in analyzing the intersection
of patent data with other data sources, such as scientific
publications, market data, and policy information. This holistic
approach to innovation analysis, evident in subclusters like 1-
5 “Strategic Alliance Governance and Innovation Outcomes,”
represents an evolution from earlier, more siloed approaches to
integrated patent analysis. Advances in natural language processing
facilitate the integration of diverse data sources.
Role of patent analytics methods within the larger scope of
patent research?
The role of patent analytics methods within the larger scope
of patent research has evolved to become increasingly central and
sophisticated. Patent analytics methods serve as critical tools for
identifying and quantifying technological emergence (Carley et al.,
2018). Growing subclusters focused on advanced methods in patent
analytics indicate a shift toward more data-driven and objective
approaches to understanding innovation trajectories.
These methods facilitate the integration of diverse data sources,
allowing for a more comprehensive understanding of innovation
ecosystems. There is a trend in patent analytics methods now
routinely combining patent data with scientific publications
(Mejia and Kajikawa, 2020), market information, and even social
media (Orduna-Malea and Font-Julian, 2022) data to provide
richer insights.
Patent analytics methods play a crucial role in enhancing
the strategic value of patent research for both policymakers and
industry practitioners. The growth of subclusters focused on
competitive intelligence and strategic decision-making underscores
how these methods are bridging the gap between academic research
and practical applications.
5.1 Patent analytics: a framework
The citation network analysis revealed the “organic” structure
and evolution of topics based on authors’ research preferences
and citation patterns. This method allowed us to process a
large volume of papers and identify focal topics in the research
landscape. However, to enhance the practical utility of these
insights, we conducted a deeper analysis of the subcluster contents
and meanings. Building upon this systematic, computer-assisted
analysis of citation networks in patent research, we propose a
conceptual framework that synthesizes and organizes the field into
five core components. Table 1 shows the proposed framework,
which consists of five core components and the corresponding
subclusters in our analysis. The core components are:
(1) Fundamentals of patents systems
(2) Patents as indicators
(3) Methodological development of patent analytics
(4) IP management practice
(5) Patent analytics applications
Patent research has come a long way since the early review
attempts of the field (e.g., Basberg, 1987). Now, basis analytics like
patent counts and citations, regional innovation benchmarking,
and patent relevance assessment are just a small (yet important)
part of the big picture the field has become. By organizing
the subclusters into these components, we aim to provide a
clearer picture of how different and cutting-edge research streams
contribute to the overall understanding and practice of patent
analytics. As can be seen in Table 2, the framework is organized
around fundamental aspects of patent analytics research and
practice. This approach allows us to highlight how different streams
Frontiers in Research Metrics and Analytics 11 frontiersin.org

Mejia and Kajikawa 10.3389/frma.2024.1484685
TABLE 2 List of subclusters in the core components of patent research
and analytics.
Id Core components and subclusters
(1) Fundamentals of patents systems:
Research on the legal, economic, and policy aspects of patent systems. It includes
studies on patent laws, regulations, ethics, histories, and policies, as well as their
impacts on innovation and economic growth. The research objective of this
foundational core is to provide the context for understanding how patent systems
function and evolve and also to provide evidence for the design and legitimation of
patent systems.
2-1 Legal frameworks and challenges in patent systems
2-2 Patenting genetic data and human biological materials
2-3 Neurotechnology and cell therapy patent regulation
2-5 IP challenges in biomedical patents
2-6 Socio-cultural discussions of patents and patent systems
2-10 Patent policies and pharmaceutical innovation for vulnerable groups
2-12 Patent analytics in corporate power and policy dynamics
4-1 Impact of patent policies on innovation and technology spillover
4-2 Global patent protection analysis and determinants
4-3 Determinants of innovation and patent activity across different
economies
4-5 Patent policy and economic growth
4-6 Regional patent competition and technology diffusion
4-7 Historical and sectoral analysis of patents and innovation
7-1 Biosimilars development and regulation
7-2 Pharmaceutical patents and market exclusivity
7-3 Patent policies and access to medicines in developing countries
7-4 Pharmaceutical price dynamics and market entry
9-1 Environmental policy impact on green patents
14-2 Impact of technology standards on patenting and innovation
(2) Patents as indicators:
This component focuses on the use of patent data as indicators of innovation,
technological progress, and economic performance. It includes research on patent
citations, knowledge spillovers, and the relationship between patents and various
economic and social factors. It also includes quantitative and monetary valuation
of patents and analysis of economic and social outcomes. The main research
objective of this core is to understand innovation processes, including technological,
business, and economic development, rather than patent and patent systems.
1-1 Factors influencing innovation and technological impact
1-2 Geographic mobility and knowledge spillovers
1-3 Demographic-driven regional innovation
1-4 Structural analysis of innovation networks
1-6 Patent analytics in multinational corporations
1-7 R&D investment impact on firm innovation efficiency
1-8 Patent analytics in regional and technological innovation
5-1 Interplay between scientific research and technological innovation in
patent analytics
5-2 Efficiency and dynamics of university-industry collaboration and
technology transfer
5-3 University patenting and commercialization
5-5 Impact of university research on patent landscapes
(Continued)
TABLE 2 (Continued)
Id Core components and subclusters
5-6 Gender disparities in patenting
6-1 Patent value and citations
6-3 Role of patents in innovation and economic performance
7-9 Quality, safety, and market dynamics of generic and off-patent
pharmaceuticals
8-10 External influences on innovation and patenting activities
14-3 Role of patents and technological innovation in economic growth and
trade performance
(3) Methodological development of patent analytics:
Development and refinement of methods and tools for analyzing patent data. The
research objective of this core is to provide methodology for searching patents,
illustrating patent landscape, describing patent trends, analyzing technological
development, and identifying technological and business opportunities. The
methods include text mining, machine learning, and other advanced analytical
techniques applied to patents and databases.
3-1 Patent analytics for technological trends and innovation assessment
3-2 Text Mining and machine learning in patent analytics
3-3 Data-driven approaches in patent analytics
3-5 Patent citation networks and development pathway analysis
3-6 NLP-based patent mining for innovation gaps
3-7 Patent analytics and technology convergence
3-8 Patent-driven product design and knowledge transfer
11-1 Drug design and patent analytics
11-2 Chemical patent information retrieval
11-3 Patent search strategies
11-4 Patent analytics in drug discovery and network pharmacology
(4) IP management practice:
This component focuses on practical aspects of managing intellectual property,
particularly patents, within organizations. It includes research on R&D strategies,
patent strategies, portfolio management, licensing, financing, and the integration of
patent analytics into business decision-making processes. Case studies in the
medical and healthcare sectors are active in this core, which reflects the strong
impacts of patenting in the sectors. The research objective of this core is to derive
practical implications based on existing academic expertise and to provide feedback
to academic expertise based on practical cases.
1-5 Strategic alliance governance and innovation outcomes
2-4 Intellectual property strategies in healthcare innovation
5-8 Impact of knowledge disclosure and intellectual property strategies on
firm innovation and performance
6-2 Patent strategies and innovation
6-4 Strategic patent commercialization and licensing dynamics
6-5 Venture capital and patent-based innovation financing
6-6 Empirical analysis and trends in patent licensing and innovation
6-7 Impact of intellectual property analytics on innovation and economic
performance
8-1 Corporate governance and financial factors in firm innovation
8-2 The role of R&D and patents in economic performance and
technology acquisition
8-3 Impact of policies and corporate factors on innovation
(Continued)
Frontiers in Research Metrics and Analytics 12 frontiersin.org

Mejia and Kajikawa 10.3389/frma.2024.1484685
TABLE 2 (Continued)
Id Core components and subclusters
8-4 Innovation efficiency and benchmarking
8-5 Organizational and environmental factors in corporate innovation
8-6 Impact of patent analytics and R&D on firm innovation and financial
performance
8-7 Public financing R&D and innovation
8-8 Banking financing R&D and innovation
8-9 Patent analytics in innovation and economic policy
14-1 Evolving dynamics of SEP licensing and litigation
(5) Patent analytics applications:
Application of patent analytics in specific fields or industries. The research objective
of this core is to apply existing analytical methods in eachtechnological topic and to
gain insights for driving technological development and innovation in various
sectors, from pharmaceuticals to green technologies.
2-7 Patent analytics in natural and genetic resources
2-8 Patent analytics in agricultural biotechnology
2-9 CRISPR and precision agriculture patents
2-11 Patent analytics in life sciences innovation
3-4 Patent analytics in energy sectors
3-9 Sector-specific applications of patent analytics in innovation
3-10 Patent analysis in environmental and health sciences
4-4 Pharmaceutical patents and global healthcare access
5-4 Patent analytics in nanotechnology
5-7 Industry-specific innovation networks
7-5 Patent challenges and opportunities in biosimilars
7-6 Drug pricing policy analysis
7-7 Innovative drug development and repurposing
7-8 Patents and access to diabetes medications
9-2 Environmental regulation and green patent quality
9-3 Patent analytics in carbon reduction technologies
9-4 Drivers of corporate environmental innovation
9-5 Regional dynamics of green technology development
9-6 Green Innovation systems and economic transformation
9-7 Impact of patenting activities on employment and innovation
dynamics
10-1 Pharmaceutical formulations and solubility enhancement
10-2 Patent analytics in biomedical innovations and drug delivery systems
10-3 Innovative drug delivery systems and patent analytics
10-4 Nanoparticle drug delivery systems
10-5 Nanocarrier-based drug delivery systems
12-1 Traditional Chinese medicines for viral infections
15-1 Development and applications of carbonic anhydrase inhibitors
15-2 Patent landscape of carbonic anhydrase inhibitors
of research contribute to explaining various aspects of the patent
analytics process and its applications. The framework can provide a
practical and actionable structure for researchers and practitioners
in the field of patent analytics. It will also work as a guidance
for authors, reviewers, and editors in Patent Analytics section of
the journal.
5.2 Future research directions
Despite the comprehensive nature of this study, there are
limitations that present opportunities for future research. In
the area of patent systems fundamentals, future research could
explore the evolving nature of patent systems in the digital age,
such as the potential integration of blockchain technology for
improved transparency and efficiency. Studies on the impact of
harmonization efforts in global patent systems, particularly in
emerging economies, are needed. Research on the effectiveness
of patent policies in promoting innovation in specific sectors,
such as green technologies or artificial intelligence, could provide
valuable insights.
Regarding patents as indicators, developing more sophisticated
indicators that combine patent data with other data sources
could provide a more holistic view of innovation dynamics.
Future research could explore the use of patents as indicators
of technological convergence and the potential for using patent
indicators to predict emerging technologies or market trends,
leveraging machine learning techniques.
In methodological development, advancements in natural
language processing and machine learning offer exciting
possibilities for patent analytics. Future research could
focus on developing more accurate, efficient, and effective
text mining techniques for patent documents, improving
visualization techniques for large-scale patent data, and developing
methodologies for real-time patent analytics.
Cross-cutting themes for future research include exploring how
AI can enhance various aspects of patent research, examining how
patent systems and analytics can promote sustainable innovation,
studying how patent analytics can inform global innovation
strategies, researching ethical implications and responsible use of
patent analytics tools, developing tools and methodologies that
make patent analytics more accessible to smaller organizations and
individual inventors, exploring how patent data can be effectively
combined with other data sources for more comprehensive
innovation analysis, and conducting more sector-specific studies in
rapidly evolving fields.
6 Conclusion
This study provided an overview of the current state and
future directions of patent analysis in academic research. We
identified 93 research streams from academic literature that
use the patent document in any form; these topics were
Frontiers in Research Metrics and Analytics 13 frontiersin.org

Mejia and Kajikawa 10.3389/frma.2024.1484685
evaluated in terms of size, recency, citation impact, and growth,
revealing relevant trends. These include an increased focus
on AI methods and the application of patent analytics for
sustainability and evaluation of corporate performance. We
further organized the topics to reach a five-core component
framework encompassing fundamentals of patent systems, patents
as indicators, methodological developments, IP management
practices, and applications. By proposing an integrated framework
and identifying key trends and challenges, we contribute to
both the theoretical understanding and practical application of
patent analytics.
As patent data becomes increasingly accessible and analytical
techniques more sophisticated, the field of patent analysis is poised
to play an even more crucial role in informing innovation policy,
guiding corporate strategy, and advancing our understanding of
technological progress.
The evolution of patent analysis from simple citation counts
to complex, AI-driven analyses reflects the growing recognition of
patents as rich sources of technological and economic information.
However, this evolution also brings new challenges in terms of data
interpretation and methodological rigor. Future research should
focus on addressing these challenges while continuing to explore
novel applications of patent analytics across various domains
of science and technology and also in various sectors, such as
academia, business, and policy, to empower innovation.
Data availability statement
The original contributions presented in the study are included
in the article/Supplementary material, further inquiries can be
directed to the corresponding author.
Author contributions
CM: Conceptualization, Data curation, Formal analysis,
Investigation, Methodology, Project administration, Resources,
Software, Validation, Visualization, Writing – original draft,
Writing – review & editing. YK: Conceptualization, Data
curation, Formal analysis, Funding acquisition, Investigation,
Methodology, Project administration, Resources, Supervision,
Validation, Writing – original draft, Writing – review
& editing.
Funding
The author(s) declare that no financial support was
received for the research, authorship, and/or publication of
this article.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/frma.2024.
1484685/full#supplementary-material
References
Adai, A. T., Date, S. V., Wieland, S., and Marcotte,E. M. (2004). LGL: creating a map
of protein function with an algorithm for visualizing very large biological networks. J.
Mol. Biol. 340, 179–190. doi: 10.1016/j.jmb.2004.04.047
Albino, V., Ardito, L., Dangelico, R. M., and Petruzzelli, A. M. (2014).
Understanding the development trends of low-carbon energy technologies: a patent
analysis. Appl. Energy 836:12. doi: 10.1016/j.apenergy.2014.08.012
Alola, A. A., Victor, B. F., Obekpa, H. O., and Adebayo, T. S. (2024). Explaining
the environmental efficiency capability of energy mix innovation among the nordic
countries. Energy Rep. 11:233. doi: 10.1016/j.egyr.2023.11.051
Aristodemou, L., and Tietze, F. (2018). The state-of-the-art on Intellectual Property
Analytics (IPA): a literature review on artificial intelligence, machine learning and
deep learning methods for analysing intellectual property (IP) data. World Pat. Inf. 55,
37–51. doi: 10.1016/j.wpi.2018.07.002
Baekeland, L. H. (1909). The United States patent system, its use and abuse - the
inventor s standpoint. J. Ind. Eng. Chem. 1, 204–205. doi: 10.1021/ie50003a017
Basberg, B. L. (1987). Patents and the measurement of technological change: a
survey of the literature. Res. Policy 16, 131–141. doi: 10.1016/0048-7333(87)90027-8
Bena, J., and Li, K. (2014). Corporate innovations and mergers and acquisitions. J.
Finan. 1923:12059. doi: 10.1111/jofi.12059
Berrone, P., Fosfuri, A., Gelabert, L., and Gomez-Mejia, L. R. (2013). Necessity as the
mother of “green” inventions: institutional pressures and environmental innovations.
Strat. Manag. J. 891:2041. doi: 10.1002/smj.2041
Bettencourt, L. M. A., Lobo, J., Helbing, D., Kuhnert, C., and West, G. B. (2007).
Growth, innovation, scaling, and the pace of life in cities. Proc. Natl. Acad. Sci. U. S. A.
104, 7301–7306. doi: 10.1073/pnas.0610172104
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008).
Fast unfolding of communities in large networks. J. Stat. Mech. 2008:P10008.
doi: 10.1088/1742-5468/2008/10/P10008
Brunnermeier, S. B., and Cohen, M. A. (2003). Determinants of environmental
innovation in us manufacturing industries. J. Environ. Econ. Manage. 45, 278–293.
doi: 10.1016/S0095-0696(02)00058-X
Cainelli, G., De Marchi, V., and Grandinetti, R. (2015). Does the development
of environmental innovation require different resources? Evidence from spanish
manufacturing firms. J. Clean. Prod. 94, 211–220. doi: 10.1016/j.jclepro.2015.02.008
Frontiers in Research Metrics and Analytics 14 frontiersin.org

Mejia and Kajikawa 10.3389/frma.2024.1484685
Cao, S. W., Ma, G. R., and Mao, H. (2024). Is distance really dying? Transportation
and knowledge spillovers. J. Reg. Sci. 64, 355–405. doi: 10.1111/jors.12675
Carley, S. F., Newman, N. C., Porter, A. L., and Garner, J. G. (2018). An
indicator of technical emergence. Scientometrics 115, 35–49. doi: 10.1007/s11192-018-
2654-5
Carr, F. K. (1995). Patents Handbook: A Guide for Inventors and Researchers to
Searching Patent Documents and Preparing and Making an Application. McFarland
Publishing.
Castaldi, C., Frenken, K., and Los, B. (2015). Related variety, unrelated variety
and technological breakthroughs: an analysis of us state-level patenting. Reg. Stud.
767:940305. doi: 10.1080/00343404.2014.940305
Chen, K., Li, Q. Y., Shoaib, M., Ameer, W., and Jiang, T. (2024). Does
improved digital governance in government promote natural resource management?
Quasi-natural experiments based on smart city pilots. Resour. Policy 90:104721.
doi: 10.1016/j.resourpol.2024.104721
Chen, K. J., Meng, Q. S., Sun, Y. T., and Wan, Q. Q. (2024). How does industrial
policy experimentation influence innovation performance? A case of ¡i¿made in
china¡/i¿ 2025. Human. Soc. Sci. Commun. 11:40. doi: 10.1057/s41599-023-02497-x
Csárdi, G., Nepusz, T., Müller, K., Horvát, S., Traag, V., Zanini, F., et al. (2024).
igraph for R: R Interface of the igraph Library for Graph Theory and Network Analysis
(v2.0.1). doi: 10.5281/zenodo.10595645
Czarnitzki, D., Ebersberger, B., and Fier, A. (2007). The relationship between r&d
collaboration, subsidies and r&d performance: empirical evidence from finland and
germany. J. Appl. Econ. 1347:jae.992. doi: 10.1002/jae.992
Dai, Y. K., Du, T., Gao, H. S., Gu, Y., and Wang, Y. Q. (2024). Patent
pledgeability, trade secrecy, and corporate patenting∗.J. Corp. Finan. 85:102563.
doi: 10.1016/j.jcorpfin.2024.102563
Daim, T. U., Rueda, G., Martin, H., and Gerdsri, P. (2006). Forecasting emerging
technologies: use of bibliometrics and patent analysis. Technol. Forecast. Soc. Chang.
73, 981–1012. doi: 10.1016/j.techfore.2006.04.004
de Solla Price, D. J. (1965). Networks of scientific papers. Science 149:510.
doi: 10.1126/science.149.3683.510
Erivantseva, T. N., Tuzova, S. Y., Lyskov, N. B., Salnikov, M. Y., Tuzhilkina,
E. A., Palamarchuk, M. S., et al. (2024). Hydrogen energy vector in the sphere
of intellectual property in russian federation. Int. J. Hydrog. Energy 60:147.
doi: 10.1016/j.ijhydene.2024.02.086
Evenson, R. (1984). “International invention: implications for technology market
analysis,” in R&D, Patents,and Productivity, NBER Chapters. Cambridge, MA: National
Bureau of Economic Research, Inc, 89–126.
Feinerer, I., Hornik, K., and Meyer, D. (2008). Text mining infrastructure in R. J.
Stat. Softw. 25, 1–54. doi: 10.18637/jss.v025.i05
Fortunato, S., and Barthélemy, M. (2007). Resolution limit in community detection.
Proc. Nat. Acad. Sci. U. S. A. 104, 36–41. doi: 10.1073/pnas.0605965104
Gaulton, A., Hersey, A., Nowotka, M., Bento, A. P., Chambers, J., Mendez,
D., et al. (2017). The chembl database in 2017. Nucl. Acids Res. D945:gkw1074.
doi: 10.1093/nar/gkw1074
Griliches, Z. (1990). Patent statistics as economic indicators: a survey. J. Econ. Lit.
28, 1661–1707. doi: 10.3386/w3301
Hagedoorn, J., and Cloodt, M. (2003). Measuring innovative performance:
is there an advantage in using multiple indicators? Res. Policy 32, 1365–1379.
doi: 10.1016/S0048-7333(02)00137-3
Hall, B. H. (2002). The financing of research and development. Oxf. Rev. Econ. Policy
18, 35–51. doi: 10.1093/oxrep/18.1.35
Hall, B. H. (2007). Patents and patent policy. Oxf. Rev. Econ. Policy 23, 568–587.
doi: 10.1093/oxrep/grm037
Hall, B. H., Jaffe, A., and Trajtenberg, M. (2005). Market value and patent citations.
R J. Econ. 36, 16–38.
Hall, B. H., Jaffe, A. B., and Trajtenberg, M. (2001). The NBER Patent Citation
Data file: Lessons, Insights and Methodological Tools (NBER Working Papers No. 8498).
Cambridge, MA: National Bureau of Economic Research, Inc.
Han, P. F., Jiang, W., and Mei, D. Q. (2024). Mapping US-China technology
decoupling: policies, innovation, and firm performance. Manage. Sci. 98:2057.
doi: 10.1287/mnsc.2022.02057
Hanisch, M. (2024). Strategic alliance governance through termination
provisions: safeguard and incentive, flexibility and commitment. J. Manage.
doi: 10.1177/01492063241247495
Harhoff, D., Narin, F., Scherer, F. M., and Vopel, K. (1999). Citation
frequency and the value of patented inventions. Rev. Econ. Stat. 81, 511–515.
doi: 10.1162/003465399558265
Hashmi, R., and Alam, K. (2019). Dynamic relationship among environmental
regulation, innovation, CO2 emissions, population, and economic growth
in oecd countries: a panel investigation. J. Clean. Prod. 231, 1100–1109.
doi: 10.1016/j.jclepro.2019.05.325
Hausman, J., Hall, B. H., and Griliches, Z. (1984). Econometric-models for count
data with an application to the patents r and d relationship. Econometrica 909:1911191.
doi: 10.2307/1911191
Higham, K., de Rassenfosse, G., and Jaffe, A. B. (2021). Patent quality: towards
a systematic framework for analysis and measurement. Res. Policy 50:104215.
doi: 10.1016/j.respol.2021.104215
Hirshleifer, D., Low, A., and Teoh, S. H. (2012). Are overconfident ceos better
innovators? J. Finan. 1457:1598021. doi: 10.2139/ssrn.1598021
Hsieh, W. H., Chiu, W. T., Lee, Y. S., and Ho, Y. S. (2004). Bibliometric
analysis of patent ductus arteriosus treatments. Scientometrics 60, 205–215.
doi: 10.1023/B:SCIE.0000027793.12866.58
Hsu, P. H., Tian, X., and Xu, Y. (2014). Financial development
and innovation: cross-country evidence. J. Financ. Econ. 112, 116–135.
doi: 10.1016/j.jfineco.2013.12.002
Hu, X., Gu, H., Tang, Y., and Wang, B. (2024). Mapping the field: a
bibliometric literature review on technology mining. Heliyon 10:e23458.
doi: 10.1016/j.heliyon.2023.e23458
Ivanova, I., Strand, O., Kushnir, D., and Leydesdorff, L. (2017). Economic and
technological complexity: a model study of indicators of knowledge-based innovation
systems. Technol. Forecast. Soc. Chang. 120, 77–89. doi: 10.1016/j.techfore.2017.04.007
Jaffe, A. B., and Palmer, K. (1997). Environmental regulation and innovation: a panel
data study. Rev. Econ. Stat. 79, 610–619. doi: 10.1162/003465397557196
Jaffe, A. B., Trajtenberg, M., and Henderson, R. (1993). Geographic localization
of knowledge spillovers as evidenced by patent citations∗.Q. J. Econ. 108, 577–598.
doi: 10.2307/2118401
Jiao, A. Q., and Lu, J. T. (2024). Buffing firm innovation by lobbying. J. Corp. Finan.
85:102557. doi: 10.1016/j.jcorpfin.2024.102557
Jiao, A. Q., Lu, J. T., and Ren, H. L. (2024). Partisanship, optimism, and firm
innovation. Finan. Manag. 53, 543–577. doi: 10.1111/fima.12455
Karata,¸s, A. R., Kazak, H., Akcan, A. T., Akka¸s, E., and Arik, M. (2024). A
bibliometric mapping analysis of the literature on patent analysis. World Patent Inf.
77:102266. doi: 10.1016/j.wpi.2024.102266
Kear, G., Shah, A. A., and Walsh, F. C. (2012). Development of the all-vanadium
redox flow battery for energy storage: a review of technological, financial and policy
aspects. Int. J. Energy Res. 36, 1105–1120. doi: 10.1002/er.1863
Kitch, E. W. (1977). Nature and function of patent system. J. Law Econ. 265:466903.
doi: 10.1086/466903
Klavans, R., and Boyack, K. W. (2017). Which type of citation analysis generates
the most accurate taxonomy of scientific and technical knowledge? J. Assoc. Inf. Sci.
Technol. 68, 984–998. doi: 10.1002/asi.23734
Langinier, C., and Moschini, G. C. (2002). “The economics of patents,” in ed.
M. Rothschild Scott Newman and Max Rothschild, Intellectual Property Rights and
Patenting in Animal Breeding and Genetics (New York, NY: CABI Publishing), 31–50.
Lemley, M. A. (2001). Rational ignorance at the patent office. Northwest. Univ. Law
Rev. 1495:35. doi: 10.2139/ssrn.261400
Li, B. Y., Duan, Y. H., Luebke, D., and Morreale, B. (2013). Advances
in CO2 capture technology: a patent review. Appl. Energy 102, 1439–1447.
doi: 10.1016/j.apenergy.2012.09.009
Liu, X. J., Liu, C. L., and Piao, J. X. (2024). Unpacking technology flows based
on patent transactions: does trickle-down, proximity, and siphon help regional
specialization? Ann. Reg. Sci. 73, 433–458. doi: 10.1007/s00168-024-01277-y
Lu, J., and Li, H. (2024). Can digital technology innovation promote total
factor energy efficiency? Firm-level evidence from China. Energy 293:130682.
doi: 10.1016/j.energy.2024.130682
Malla, R., Viswanathan, S., Makena, S., Kapoor, S., Verma, D., Raju, A. A., et al.
(2024). Revitalizing cancer treatment: exploring the role of drug repurposing. Cancers
16:81463. doi: 10.3390/cancers16081463
Mejia, C., and Kajikawa, Y. (2020). Emerging topics in energy storage based
on a large-scale analysis of academic articles and patents. Appl. Energy 263:114625.
doi: 10.1016/j.apenergy.2020.114625
Mejia, C., Wu, M., Zhang, Y., and Kajikawa, Y. (2021). Exploring topics in
bibliometric research through citation networks and semantic analysis. Front. Res.
Metrics Anal. 6:742311. doi: 10.3389/frma.2021.742311
Merges, R. P., and Nelson, R.R. (1990). On the complex economics of patent scope.
Columb. Law Rev. 90:839. doi: 10.2307/1122920
Meyer, M. (2000). What is special about patent citations? Differences
between scientific and patent citations. Scientometrics 49, 93–123.
doi: 10.1023/A:1005613325648
Mikova, N. (2016). “Recent trends in technology mining approaches: quantitative
analysis of GTM conference proceedings,” in Anticipating Future Innovation Pathways
Through Large Data Analysis, Innovation, Technology, and Knowledge Management,
eds. T. U. Daim, D. Chiavetta, D. L. Porter, and O. Saritas (Cham: Springer
International Publishing), 59–69.
Frontiers in Research Metrics and Analytics 15 frontiersin.org

Mejia and Kajikawa 10.3389/frma.2024.1484685
Mooers, C. N. (1952). “Information retrieval viewed as temporal signaling,” in
Proceedings of the International Congress of Mathematicians. Amsterdam.
Narin, F. (1994). Patent bibliometrics. Scientometrics 30, 147–155.
doi: 10.1007/BF02017219
Newman, M. E. J. (2006). Modularity and community structure in networks. Proc.
Natl. Acad. Sci. U. S. A. 103, 8577–8582. doi: 10.1073/pnas.0601602103
Nie, C. F., Ye, S. S., and Feng, Y. (2024). Place-based policy and urban green
technology innovation: evidence from the revitalization of old revolutionary
base areas in China. Econ. Anal. Policy 81:1257. doi: 10.1016/j.eap.2024.
02.020
Orduna-Malea, E., and Font-Julian, C. (2022). Are patents linked on
Twitter? A case study of Google patents. Scientometrics 127, 6339–6362.
doi: 10.1007/s11192-022-04519-y
Pakes, A., and Griliches, Z. (1980). Patents and R&D at the firm level: a first report.
Econ. Lett. 5, 377–381. doi: 10.1016/0165-1765(80)90136-6
Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H.,
Lindborg, S. R., et al. (2010). How to improve R&D productivity: the pharmaceutical
industry’s grand challenge. Nat. Rev. Drug Discov. 9, 203–214. doi: 10.1038/nrd3078
Pavitt, K. (1985). Patent statistics as indicators of innovative activities - possibilities
and problems. Scientometrics 7, 77–99. doi: 10.1007/BF02020142
Porter, A. (2004). Tech Mining: Exploiting New Technologies for Competitive
Advantage. Wiley. Available at: https://www.wiley.com/en-br/Tech+Mining%3A+
Exploiting+New+Technologies+for+Competitive+Advantage-p- 9780471475675
(accessed Januart 25, 2024).
Porter, A. L., and Newman, N. C. (2009). “Tech mining: a key tool to bolster
innovation,” in Proceedings of International Forum on Technological Innovation and
Competitive Technical Intelligence 2008. Presented at the Proceedings of International
Forum on Technological Innovation and Competitive Technical Intelligence 2008
(Beijing: Peking Univ Press), 254–270.
R Core Team (2019). R: A Language and Environment for Statistical Computing.
Vienna: R Foundation for Statistical Computing.
Riebe, T., Schmid, S., and Reuter, C. (2024). Measuring spillover effects from defense
to civilian sectors - a quantitative approach using linkedin. Defen. Peace Econ. 32,
773–785. doi: 10.1007/978-3-658-41667-6_8
Rosenkopf, L., and Nerkar, A. (2001). Beyond local search: boundary-spanning,
exploration, and impact in the optical disk industry. Strat. Manag. J. 22, 287–306.
doi: 10.1002/smj.160
Rotolo, D., Hicks, D., and Martin, B. R. (2015). What is an emerging technology?
Res. Policy 44, 1827–1843. doi: 10.2139/ssrn.2743186
Schankerman, M. (1998). How valuable is patent protection? Estimates by
technology field. RAND J. Econ. 29, 77–107. doi: 10.2307/2555817
Schiffel, D., and Kitti, C. (1978). Rates of invention: international patent
comparisons. Res. Policy 7, 324–340. doi: 10.1016/0048-7333(78)90010-0
Schneider, D. J., and Moore, J. W. (2006). Patent ductus arteriosus. Circulation 114,
1873–1882. doi: 10.1161/CIRCULATIONAHA.105.592063
Shahzad, U., Luo, F. K., and Liu, J. (2024). Debt financing and technology
investment kuznets curve: evidence from China. Int. J. Finan. Econ. 28, 751–765.
doi: 10.1002/ijfe.2448
Shelton, R. D., and Leydesdorff, L. (2012). Publish or patent: bibliometric evidence
for empirical trade-offs in national funding strategies. J. Am. Soc. Inf. Sci. Technol. 63,
498–511. doi: 10.1002/asi.21677
Shibata, N., Kajikawa, Y., Takeda, Y., and Matsushima, K. (2008).
Detecting emerging research fronts based on topological measures in
citation networks of scientific publications. Technovation 28, 758–775.
doi: 10.1016/j.technovation.2008.03.009
Simeth, M., and Wehrheim, D. (2024). On “innovation and institutional
ownership.” J. Corp. Finan. 86:102569. doi: 10.1016/j.jcorpfin.2024.102569
Singla, R. K., Joon, S., Sinha, B., Kamal, M. A., Simal-Gandara, J., Xiao, J. B.,
et al. (2023). Current trends in natural products for the treatment and management
of dementia: computational to clinical studies. Neurosci. Biobehav. Rev. 147:105106.
doi: 10.1016/j.neubiorev.2023.105106
Sláma, J. (1981). Analysis by means of a gravitation model of international
flows of patent applications in the period 1967–1978. World Patent Inf. 3, 2–8.
doi: 10.1016/0172-2190(81)90016-8
Soete, L. L. G. (1979). Firm size and inventive activity: the evidence reconsidered.
Eur. Econ. Rev. 12, 319–340. doi: 10.1016/0014-2921(79)90024-2
Trajtenberg, M. (1990). A penny for your quotes: patent citations and the value of
innovations. Rand J. Econ. 21, 172–187. doi: 10.2307/2555502
Trippe, A. J. (2003). Patinformatics: tasks to tools. World Patent Inf. 25, 211–221.
doi: 10.1016/S0172-2190(03)00079-6
Wang, Y. X., Zhang, S., Li, F. C., Zhou, Y., Zhang, Y., Wang, Z. W., et al.
(2020). Therapeutic target database 2020: enriched resource for facilitating research
and early development of targeted therapeutics. Nucl. Acids Res. D1031:gkz981.
doi: 10.1093/nar/gkz981
Wang, Z. W., Xue, X. D., Ding, W. Q., and Feng, M. K. (2024). The
promotion effect and spillover effect of financial technology on regional innovation:
evidence from China. Technol. Anal. Strat. Manag. 1–13. doi: 10.1080/09537325.2024.
2328144
WIPO (2023). World Intellectual Property Indicators Report: Record Number of
Patent Applications Filed Worldwide in 2022. Avaailable at: https://www.wipo.int/
pressroom/en/articles/2023/article_0013.html (accessed August 14, 2024).
Wuchty, S., and Jones, B. F., Uzzi, B. (2007). The increasing dominance of teams in
production of knowledge. Science 316, 1036–1039. doi: 10.1126/science.1136099
Xia, W., Xiao, J., Bian, H. W., Zhang, J. J., Zhang, J. Z., and Zhang, H.
P. (2024). Deep learning-based construction of a drug-like compound database
and its application in virtual screening of hsdhodh inhibitors. Methods 225:44.
doi: 10.1016/j.ymeth.2024.03.008
Xu, A. T., Song, M. Y., Xu, S. Y., and Wang, W. P. (2024). Accelerated
green patent examination and innovation benefits: an analysis of private economic
value and public environmental benefits. Technol. Forecast. Soc. Change 200:123105.
doi: 10.1016/j.techfore.2023.123105
Xu, J. H., Ye, F. S., and Li, X. X. (2024). Carbon intensity constraint policy and
firm green innovation in china: a quasi-did analysis. Sustain. Account. Manag. Policy J.
15:704. doi: 10.1108/SAMPJ-08-2023-0572
Xu, S. C., Meng, X. N., Wang, H. N., Zhang, J. N., and Feng, C. (2024). The costs
of air pollution: how does air pollution affect technological innovation? Environ. Dev.
Sustain. doi: 10.1007/s10668-023-04371-x
Zhang, Y., Porter, A. L., and Chiavetta, D. (2017). Scientometrics for tech mining:
an introduction. Scientometrics 111, 1875–1878. doi: 10.1007/s11192-017-2344-8
Zheng, S. H., Xue, C. Y., Li, S. Z., Zao, X., Li, X. K., Liu, Q. Y.,
et al. (2024). Chinese medicine in the treatment of non-alcoholic fatty liver
disease based on network pharmacology: a review. Front. Pharmacol. 15:1381712.
doi: 10.3389/fphar.2024.1381712
Zhou, H. J., Dai, J., Chen, X. H., Hu, B., Wei, H. R., and Cai, H. H. (2024).
Understanding innovation of new energy industry: observing development trend and
evolution of hydrogen fuel cell based on patent mining. Int. J. Hydrogen Energy 52:548.
doi: 10.1016/j.ijhydene.2023.07.032
Frontiers in Research Metrics and Analytics 16 frontiersin.org

Mejia and Kajikawa 10.3389/frma.2024.1484685
Appendix
The most frequent Web of Science categories show
correspondence to the clusters’ topical focus with dominance
of Management, Law, and Economics for the largest clusters
as shown in Figure 7. Pharma and Chemistry fields have also
strong presence due to prevalence of journals on patent reviews in
these fields. Cluster 3′s distinctive focus on patent analytics and
forecasting is led by publications in Information Science. The most
frequent categories per subcluster are shown in the Table A2.
FIGURE 7
Most frequent 5 Web of Science categories by number of articles per cluster.
Frontiers in Research Metrics and Analytics 17 frontiersin.org