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RESEARCH ARTICLE
Mapping technological innovation dynamics
in artificial intelligence domains: Evidence
from a global patent analysis
Na Liu
1☯
, Philip ShapiraID
2,3☯
*, Xiaoxu Yue
4
, Jiancheng Guan
5
1School of Management, Shandong Technology and Business University, Yantai, China, 2Manchester
Institute of Innovation Research, Alliance Manchester Business School, University of Manchester,
Manchester United Kingdom, 3School of Public Policy, Georgia Institute of Technology, Atlanta, Georgia,
United States of America, 4School of Public Policy and Management, Tsinghua University, Beijing, China,
5School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China
☯These authors contributed equally to this work.
*pshapira@manchester.ac.uk
Abstract
Artificial intelligence (AI) is emerging as a technology at the center of many political, eco-
nomic, and societal debates. This paper formulates a new AI patent search strategy and
applies this to provide a landscape analysis of AI innovation dynamics and technology evo-
lution. The paper uses patent analyses, network analyses, and source path link count algo-
rithms to examine AI spatial and temporal trends, cooperation features, cross-organization
knowledge flow and technological routes. Results indicate a growing yet concentrated, non-
collaborative and multi-path development and protection profile for AI patenting, with cross-
organization knowledge flows based mainly on interorganizational knowledge citation links.
Introduction
Artificial intelligence (AI) involves the creation of machines or agents that seek to simulate
human rationality [1–3]. AI may use machine learning, neural networks, deep learning, natu-
ral language processing, and other information technologies to imitate or augment human
capabilities, through logical calculation or through cognitively modelling human conscious-
ness [4,5].
AI is a rapidly growing and cross-disciplinary domain [4,6]. There is an expectation that
AI will be a key driver of future economic development [7]. The global AI market size, valued
at US $ 27.2 billion in 2019, is projected to reach US $ 266.9 billion by 2027 [8]. Bundled with
other information and automation technologies, AI is a key enabler of what is seen as a rapidly
evolving digital transformation phenomenon that is now disrupting and challenging multiple
aspects of business and society and driving organizational transformation and strategic change
[9–11]. As a general-purpose technology, AI is applied increasingly in areas as diverse as
power electronics, transportation, healthcare, manufacturing, finance, and education [2,5,9,
12]. Governments are actively advancing AI technologies for economic, societal, environmen-
tal, and security purposes, with the OECD [13] identifying more than 600 policy initiatives in
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OPEN ACCESS
Citation: Liu N, Shapira P, Yue X, Guan J (2021)
Mapping technological innovation dynamics in
artificial intelligence domains: Evidence from a
global patent analysis. PLoS ONE 16(12):
e0262050. https://doi.org/10.1371/journal.
pone.0262050
Editor: Fu Lee Wang, The Open University of Hong
Kong, HONG KONG
Received: August 6, 2021
Accepted: December 15, 2021
Published: December 31, 2021
Copyright: ©2021 Liu et al. This is an open access
article distributed under the terms of the Creative
Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in
any medium, provided the original author and
source are credited.
Data Availability Statement: The data underlying
the results presented in the study are available
from LexisNexis PatentSight (https://www.
patentsight.com/en/) and can be extracted using
the search approach in the study.
Funding: NL: grant number 72174112,
National Natural Science Foundation of China,
http://www.nsfc.gov.cn; grant number
Tsqn201909149, Taishan Scholars Program of
Shandong Province. PS: grant number BB/
M017702/1, Biotechnology and Biological Sciences
60 countries and jurisdictions. Measures adopted include national AI strategies, increased
R&D and training, the formation of public-private intermediary organizations, and (with vari-
ations by country) new governance and regulatory guidelines. Accompanying these private
and governmental efforts are concerns about AI’s ethical, personal privacy, employment, soci-
etal, distributional, and political dimensions [14,15].
The growth of AI—and the multiple consequences and concerns associated with its devel-
opment—make it important to probe the innovation dynamics and evolution of the domain.
Among the various ways of understanding AI trends, one approach is to investigate patent
activity to ascertain efforts (at national and organizational scales) to exploit AI intellectual
property rights and fields and applications of interest. While patents are primarily intended to
protect inventions in return for disclosure, the use of patents as a proxy for innovation activity
is a well-established practice in innovation management and policy studies [16]. Not all inven-
tions are patented and not all granted patents are of equal economic value or necessarily lead
to successful innovations. Nonetheless, indications of technological development and insights
about the direction and pace of innovation efforts can be obtained through the quantitative
analysis of patent applications including through the profiling of inventors, owners, organiza-
tional types, locations, technological content, citations, collaborations, and other information
contained in records of patenting activity [17–21].
Among examples in the extant literature of studies that use patent data to probe AI technol-
ogy and innovation developments, Tseng and Ting [22] used patent quantity and quality mea-
sures to explore AI technology trends. Just over 5,200 US AI patent grants were identified
from 1976 through to 2010, using a single technological class (Data Processing: AI) sub-
divided into four AI fields (problem reasoning and solving, machine learning, network struc-
ture, and knowledge processing). China was not discussed as, in this early study, it did not
rank among the top ten leading countries for AI patenting (in the US data). Fujii and Managi
[23] examined AI technology shifts using a patent decomposition framework. This study cov-
ered global developments from 2000 through to 2016, identifying about 13,500 AI patent
grants. There is again a narrowly targeted search approach on a single patent group (computer
systems based on specific computation models), with the finding of a shift in AI patenting
from biological and knowledge-based approaches to mathematical and other models. Van Roy
et al. [4] used text-mining to map the global AI patent landscape, highlighting emerging AI
technologies and hotspots across the world. Using a keyword approach (49 AI-related terms),
this study identified 155,000 patent family applications worldwide from 2000 through to 2016,
of which 36.4% (about 56,000) had been granted. The search strategy captured AI-related pat-
ent records beyond those classified in computing or data processing patent groups. The growth
of AI patenting in China through to 2016 is described. However, the keyword search strategy
is not thoroughly explained.
In recent years, as we will see, the domain of AI—and AI patenting—has seen a further
acceleration in scale and scope, with new technological and functional approaches emerging.
Hence, while the existing work provides useful insights, the rapidly evolving nature of AI cre-
ates both a need and an opportunity for search approach refinement and further updated anal-
ysis. In this paper, we advance a new search strategy for identifying AI-oriented patent
documents that captures this rapidly multiplying domain with high recall and precision. After
explaining its construction, we apply this search strategy to build a comprehensive picture at
country, organization, and technology levels. We integrate patent analytics with network and
main path analysis to address questions about where AI technological knowledge is located,
who are the leading organizations developing AI over successive time periods, and what are
the most active technologies and technological development routes in the AI domain. Our aim
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Research Council, https://bbsrc.ukri.org/; grant
number 895-2018-1006, Partnership for the
Organization of Innovation and New Technologies
(Social Science and Humanities Research Council
of Canada), https://www.4point0.ca. JG: grant
number 71874176, National Natural Science
Foundation of China, http://www.nsfc.gov.cn. The
funders had no role in study design, data collection
and analysis, decision to publish, or preparation of
the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
is to offer a public AI patent document search approach and to provide insights for policy and
management.
Materials and methods
Data collection
A key task in quantitatively profiling the development of patenting in an emerging technology
is to identify, with high recall and precision, which patent documents relate to the focal field
[24,25]. However, there is no agreed definition as to the composition of AI patenting. Indeed,
the emerging AI domain, which intrinsically involves novelty, boundary-crossing, and fast
growth, generates challenges in identifying patents in this field [4,6].
Some researchers have adopted AI patent search strategies based on designated patent clas-
sification codes including the International Patent Classification (IPC) [22,23]. While easily
implemented, this approach overlooks AI-related patents in classes outside of the designated
codes and, as patent classifications tend to change slowly, does not match AI’s rapid technolog-
ical evolution especially in recent years. To address such challenges, other researchers have
used a keyword-based search [4], or a combination of these two search approaches [5,26]. A
combined approach has the advantage of improving recall for patents in patent classes that are
predominantly AI-related, but which might not otherwise be captured by specific keywords.
However, to be most effective, there needs to be systematic selection and testing of designated
keywords and patent classifications.
In this study, we combine a systematic keyword-based search with IPC and CPC (Coopera-
tive Patent Classification) codes to select AI-related patents. (See Table 1.) The keywords are
from our previous peer-reviewed bibliometric definition of AI constructed from benchmark
AI publications; this approach has relatively high recall and precision in capturing AI-related
publications [6]. We then manually selected and tested AI-specific CPC and IPC codes
(Table 1). We identify a patent document as AI-related if its title, abstract or claims matched at
least one AI keyword, or it was assigned at least one of the CPC or IPC AI codes. Keyword
searching in the patent title, abstract and claims has been found to be an effective strategy for
ensuring optimal recall and precision [19] and is consistent with other patent studies of tech-
nological trends [20]. (For further details, including text descriptions of the selected patent
codes, see S1 Appendix).
We applied our AI patent search strategy to PatentSight—a comprehensive worldwide pat-
ent database covering more than 100 million patent documents from over 95 authorities
including the European Patent Office, the US Patent and Trademark Office, and national pat-
ent offices around the world [27]. Our search (based on the approach in Table 1) identified
383,168 AI patent families (all years through to May 7, 2020) comprising applications and
grants (excluding design and plant patents and utility models from China).
Methodological approach
We track AI patent documents by their application years. Since patent granting takes time (up
to two years for US patents), the application year is closer to the period when the patent was
developed [28]. To avoid double counting, we analyze patent families rather than individual
patent documents. A patent family is a collection of patents filed in several patent offices to
protect the same invention [4,29]. While we summarily report on patent grants, noting where
there are major differences compared with applications, our analysis uses patent applications
which are useful for detailed consideration of topical technological trends (as opposed to
assessments of patent economic value) [30]. Patents applied for reflect R&D efforts, new tech-
nological opportunities, innovative capacities, and future performance potentials [31,32]. We
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use patents applications in the analysis of country and organizational performance and tech-
nological trends and development routes. Drawing on Frietsch and Schmoch [33], we use
counts of transnational patent applications filed at the European Patent Office (EPO) or inter-
national applications filed under the Patent Cooperation Treaty (PCT) to capture high-quality
patents. To assign patents to countries, we use inventor location. We fractionally count patent
Table 1. Search strategy for artificial intelligence patents.
No. Search terms
# 1 keyword
search
Title Abstract Claims = (artificial intelligen�OR neural net�OR machine�learning OR expert system% OR natural language processing OR deep
learning OR reinforcement learning OR reinforced learning OR learning algorithm% OR �supervised learning OR intelligent agent�OR
back_propagation learning OR Bp learning OR back_propagation algorithm�OR long short-term memory OR (Pcnn% AND NOT Pcnnt) OR
pulse coupled neural net�OR Perceptron% OR neuro_evolution OR liquid state machine�OR deep belief net�OR radial basis function net�OR
Rbfnn�OR Rbf net�OR deep net�OR autoencoder�OR committee machine�OR training algorithm% OR back_propagation net�OR bp network�
OR Q learning OR convolution�net�OR actor-critic algorithm% OR feed_forward Net�OR hopfield net�OR neocognitron�OR xgboost�OR
boltzmann machine�OR activation function% OR neuro_dynamic programming OR learning model�OR neuro_computing OR temporal
difference learning OR echo state�net�OR transfer learning OR gradient boosting OR adversarial learning OR feature learning OR generative
adversarial net�OR representation learning OR multi_agent learning OR reservoir computing OR co-training OR Pac learning OR probabl�
approximate�correct learning OR extreme learning machine�OR ensemble learning OR machine�intelligen�OR neuro_fuzzy OR lazy learning
OR multi�instance learning OR multi_instance learning OR multi�task learning OR multi_task learning OR computation�intelligen�OR neural
model�OR multi�label learning OR multi_label learning OR similarity learning OR statistical relation�learning OR support�vector�regression
OR manifold regulari?ation OR decision forest�OR generali?ation error�OR transductive learning OR neuro_robotic�OR inductive logic
programming OR natural language understanding OR adaboost�OR adaptive boosting OR incremental learning OR random forest�OR metric
learning OR neural gas OR grammatical inference OR support�vector�machine�OR multi�label classification OR multi_label classification OR
conditional random field�OR multi�class classification OR multi_class classification OR mixture of expert�OR concept�drift OR genetic
programming OR string kernel�OR learning to rank�OR machine-learned ranking OR boosting algorithm% OR robot�learning OR relevance
vector�machine�OR connectionis�OR multi�kernel% learning OR multi_kernel% learning OR graph learning OR naive bayes�classifi�OR rule-
based system% OR classification algorithm�OR graph�kernel�OR rule�induction OR manifold learning OR label propagation OR hypergraph�
learning OR one class classifi�OR intelligent algorithm�)
# 2 CPC search CPC = (A61B 5/7264, A61B 5/7267, A63F 13/67, B23K 31/006, B25J 9/161, B25J 9/163, B29C 66/965, B29C2945/76946, B29C2945/76949,
B29C2945/76979, B60G2600/1876, B60G2600/1878, B60L2260/46, B60T 8/174, B60T2210/122, B64G2001/247, B65H2557/38, B66B 7/043, B66B 7/
045, E21B2041/0028, F01N2900/0402, F02D 41/1405, F03D 7/046, F05B2270/709, F05D2270/709, F16H2059/086, F16H2061/0084, F16H2061/0087,
G01N 29/4481, G01N 30/8662, G01N 33/0034, G01N2201/1296, G01R 31/2846, G01R 31/3651, G01S 7/417, G05B 13/027, G05B 13/028, G05B 13/
0285, G05B 13/029, G05B 13/0295, G05B 23/0229, G05B 23/024, G05B 23/0254, G05B 23/0281, G05B2219/13111, G05B2219/13166, G05B2219/
21002, G05B2219/23253, G05B2219/23288, G05B2219/24086, G05B2219/25255, G05B2219/31351, G05B2219/31352, G05B2219/31353, G05B2219/
31354, G05B2219/32193, G05B2219/32327, G05B2219/32329, G05B2219/32334, G05B2219/32335, G05B2219/33002, G05B2219/33013, G05B2219/
33014, G05B2219/33015, G05B2219/33021, G05B2219/33024, G05B2219/33025, G05B2219/33026, G05B2219/33027, G05B2219/33028, G05B2219/
33029, G05B2219/33033, G05B2219/33034, G05B2219/33035, G05B2219/33038, G05B2219/33039, G05B2219/33041, G05B2219/33044, G05B2219/
33056, G05B2219/33065, G05B2219/33066, G05B2219/33295, G05B2219/33303, G05B2219/33321, G05B2219/33322, G05B2219/34066, G05B2219/
34081, G05B2219/34082, G05B2219/36039, G05B2219/36456, G05B2219/39071, G05B2219/39072, G05B2219/39095, G05B2219/39268, G05B2219/
39271, G05B2219/39276, G05B2219/39282, G05B2219/39283, G05B2219/39284, G05B2219/39286, G05B2219/39292, G05B2219/39294, G05B2219/
39297, G05B2219/39298, G05B2219/39311, G05B2219/39312, G05B2219/39352, G05B2219/39372, G05B2219/39374, G05B2219/39376, G05B2219/
39385, G05B2219/40107, G05B2219/40115, G05B2219/40408, G05B2219/40494, G05B2219/40496, G05B2219/40499, G05B2219/40528, G05B2219/
40529, G05B2219/41054, G05B2219/42018, G05B2219/42135, G05B2219/42141, G05B2219/42142, G05B2219/42149, G05B2219/42287, G05B2219/
49065, G05D 1/0088, G05D 1/0221, G06F 7/023, G06F 11/1476, G06F 11/2257, G06F 11/2263, G06F 15/18, G06F 16/243, G06F 16/24522, G06F 16/
3329, G06F 16/3344, G06F 16/90332, G06F 17/20, G06F 17/2282, G06F 17/28, G06F 17/2881, G06F 17/289, G06F 17/30401, G06F 17/3043, G06F
17/30654, G06F 17/30684, G06F 17/30976, G06F 19/24, G06F 19/345, G06F 19/707, G06F2207/4824, G06K 7/1482, G06K 9/6256, G06K 9/6264,
G06K 9/6269, G06K 9/627, G06K 9/6273, G06N 3/004, G06N 3/008, G06N 3/02, G06N 3/0427, G06N 3/0445, G06N 3/0463, G06N 3/0481, G06N 3/
049, G06N 3/06, G06N 3/08, G06N 3/084, G06N 3/086, G06N 5, G06N 5/00, G06N 5/02, G06N 5/043, G06N 7/023, G06N 7/046, G06N 20, G06N
20/00, G06N 20/10, G06N 20/20, G06N 99/005, G06T 3/4046, G06T 9/002, G06T2207/20081, G06T2207/20084, G07C2009/00849, G07C2009/
00888, G07D 7/2083, G08B 29/186, G08G 1/096888, G10H2250/311, G10K2210/3024, G10K2210/3038, G10L 15/06, G10L 15/144, G10L 15/16,
G10L 15/18, G10L 17/18, G10L 25/30, G11B 20/10518, G16B 40, G16C 20/70, G16H 50/20, G21D 3/007, G21D2003/007, H01H2009/566,
H01H2047/009, H01J2237/30427, H01M 8/04992, H02H 1/0092, H02P 21/0014, H02P 21/0025, H02P 23/0018, H02P 23/0031, H03H2017/0208,
H03H2222/04, H04L 12/2423, H04L 25/0254, H04L 25/03165, H04L 41/16, H04L 45/08, H04L 45/36, H04L2012/5686, H04L2025/03464,
H04L2025/03554, H04N 21/4662, H04N 21/4663, H04N 21/4665, H04N 21/4666, H04Q2213/054, H04Q2213/13054, H04Q2213/13343,
H04Q2213/343, H04R 25/507, Y10S 128/924, Y10S 128/925, Y10S 706)
# 3 IPC search IPC = (A63F 13/67, G06F 8/33, G06F 15/18, G06F 17/20, G06F 17/21, G06F 17/27, G06F 17/28, G06F 19/24, G06K 9/66, G06N 3/02, G06N 3/04,
G06N 3/06, G06N 3/063, G06N 3/067, G06N 3/08, G06N 3/10, G06N 5/00, G06N 5/02, G06N 5/04, G06N 7/02, G06N 20, G06N 20/00, G06N 20/
10, G06N 20/20, G06T 1/40, G10L 15/06, G10L 15/16, G10L 15/18, G10L 17/04, G10L 17/10, G10L 17/18, G10L 25/30, G16B 40, G16B 40/00, G16B
40/20, G16B 40/30, G16C 20/70, G16H 50/20, H01M 8/04992)
# 4 AI patents # 4 = # 1 OR # 2 OR # 3
https://doi.org/10.1371/journal.pone.0262050.t001
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documents with multiple inventors from different countries [34]. As a proxy for multi-country
inventor collaboration, we compute international co-patent documents in a certain country as
those with at least one inventor located abroad. Based on international co-patenting, we map
collaborative network relationships among leading countries [35].
We homogenized assignee names using VantagePoint software to address syntax variants
for the same patent assignee and to identify transnational patent documents. To delineate
interorganizational knowledge flows, we identified co-patenting relationships based on patent
documents with multiple assignees [36,37] and analyzed forward citation networks [38]. For-
ward citations—the citations of an organization’s patents by other organizations—are often
used to evaluate organizational knowledge diffusion [38,39]. In interorganizational citation
networks, nodes refer to organizations and edges refer to the directed links obtained through
citing and the cited relationship between organizations.
We further developed citation-based indicators to measure the openness of an organiza-
tion’s knowledge flow in the processes of its patented innovation, which can be divided into
the dimensions of outward diffusion, inward absorption, and self-creation. An organization’s
knowledge diffusion capacity, reflecting its knowledge spillover or knowledge contribution to
other organizations, is computed through the formula:
TDCi¼X
n
j¼1
kij kii
X
m
i¼1X
n
j¼1
kij
ð1Þ
where k
ij
is the number of weighted directed citation links from node ito node j;k
ii
has a simi-
lar meaning, the numerator denotes knowledge outflow of node i, and the denominator is the
total knowledge outflow of all nodes. In the same way, knowledge absorptive capacity repre-
senting knowledge gains of an organization from other organizations can be defined as:
TACi¼X
n
j¼1
kji kii
X
m
i¼1X
n
j¼1
kji
ð2Þ
where the numerator denotes knowledge inflow to node iand the denominator is the total
knowledge inflow to all nodes. Technology absolute impact can be calculated by formula: TAI
i
=TDC
i
+TAC
i
, which is a measure of overall knowledge capacity of node i, including knowl-
edge diffusion and knowledge absorption. The greater the technology absolute impact of an
organization, the more knowledge flows through it, indicating that the organization acts as a
knowledge broker.
Finally, we performed analyses at the technology level looking at the identity of technologi-
cal building blocks and their development paths in the AI domain. We focused on the most
frequently occurring AI IPC classes at 4-digit levels and finer granularities. Each patent docu-
ment is assigned one or more IPC classes, allowing analysis of detailed AI technologies as cap-
tured by the IPC taxonomy system. We identified the patent level citation network based on
forward citations in the dataset to identify relationships from focal to other patents [40]. In
this patent citation network, nodes refer to patents and edges refer to directed links obtained
through the citing and cited relationship. We then applied a main path analysis method to the
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patent citation network to identify AI technological development trajectories. Main path anal-
ysis was introduced by Hummon and Dereian [41] and extended by Verspagen [42] and Liu
and Lu [43]. Main path analysis first calculates the traversal weight for every link in the citation
network and then seeks the main path based on the traversal weights using a specific search
algorithm [44]. Traversal weight is the times that a link is traversed through searching from
starting nodes to ending nodes of a citation network [45]. There are several algorithms avail-
able to calculate traversal weight, involving search path count (SPC), search path link count
(SPLC), search path node pair (SPNP) and node pair projection count (NPPC) [41,42]. In this
study, we applied SPLC to calculate traversal weight as it reflects knowledge diffusion scenarios
in science and technology development [44]. Algorithms available for identifying main tech-
nological trajectories include local main path, global main path, and key-route main path [43],
each of which tends to produce similar results [46]. We applied the key-route (global) search
algorithm using Pajek software for our investigation of AI technological trajectories.
Results
AI patent applications started to grow in the early 1980s, with modest yet fluctuating growth
through to the early 2010s (Fig 1). In the 2010s, AI patent applications entered a rapid expan-
sion. From 2011 to 2018, AI patent applications averaged 30 percent annual growth, with a
remarkable average annual growth of 48 percent between 2015 and 2018. Over half of total AI
patent applications were filed in the most recent five years. The apparent drop-off of patent
applications in 2019 is due to the time gap between patent filing and publishing. Patent grants
broadly follow these trends, albeit at a lower level.
Country-level landscape
Trends by AI patent productivity and quality. Assigned by inventor location (with frac-
tional counts for multi-inventor patents), ten leading countries together contributed 95% of
worldwide AI patent applications and 93% of grants. Through to 2019, patent grants com-
prised about 39% of all patent applications worldwide. China now has the greatest share of pat-
ent applications (41.2%), followed by Japan (20.5%), the USA (20.3%), South Korea (5.1%),
Germany (2.3%) and the UK (1.3%). (Table 2.) Currently, Chinese pending applications
account for nearly 73% of total pending AI patent applications worldwide, reflecting China’s
considerable efforts to develop AI in recent years [47,48]. However, China has a less signifi-
cant presence in the granted and transnational patent landscape (21.3% and 10.1% respec-
tively). The USA ranks first both in terms of granted patents (32.6%) and transnational patents
(39.2%), highlighting its established leadership and level of patent quality. Japan has a 21.3%
share of granted patents and a 13.1% share of transnational patents. Other countries with posi-
tions in the granted and transnational AI patent landscape are South Korea, Germany, and the
UK, with shares of 7.6%, 3.0% and 1.8% in granted patents and 4.3%, 6.8% and 3.9% in trans-
national patents, respectively. International collaboration among inventors is particularly high
for India, the UK, and Canada.
By citations received to AI granted patents, which can be considered (with qualifications) as
a proxy measure of patent value and technological importance [16], the USA has a dominant
position. US inventors accounted for about 30% of granted AI patents but almost 70% of the
most cited (top 10% cited) patents. (Table 2.) Canada and the UK also have a relative higher
presence in the top 10% of cited AI patents relative to their shares of granted AI patents. China
contributes just over 2% of the top 10% of cited patents overall (compared to more than 20%
of all AI patent grants). Chinese inventors now account for 19% of the top 10% of cited patents
considering only patents granted in the period 2015–2019, with the analogous US share of the
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top 10% patents dropping to under 56.5% in this period. For 2015–2019 patents, there is still a
lower Chinese presence in the top 10% of cited AI patents relative to their shares of granted AI
patents. However, these results suggest a noticeable rise in the technological importance of
more recently granted Chinese AI patents.
Although Japan was an early first-mover in AI patent applications, it was overtaken by the
USA in the early 2000s, then China in 2010 (Fig 2a). Japan’s AI patent applications increased
again in the mid-2010s and are now just above those of South Korea. Additionally, while the
USA led in AI patent applications for much of the 2000s, China overtook the USA in 2011 and
has continued to see a rapid acceleration by quantity of AI patent applications through to 2019
(latest data year). However, the USA has consistently maintained a leading position in transna-
tional patent applications (which potentially signal higher quality inventions) between 1991
and 2019 (Fig 2b). China is less prominent in transnational patent applications, but it has
exceeded Germany and Japan since 2015.
Fig 1. AI patent applications and grants by year.
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Table 2. AI patents by countries.
Patent applications Patent grants
Country All Pending Transnational International co-pat. All Cited top 10%
N % N % N % N % % of apps N % % All % 2015–2019�
China 158.0 41.2 109.9 72.6 7.7 10.1 3.2 15.7 2.0 31.5 21.3 2.1 19.2
USA 77.8 20.3 16.8 11.1 29.9 39.2 14.9 73.9 19.1 48.1 32.6 69.5 56.5
Japan 78.6 20.5 8.0 5.3 10.0 13.1 1.2 5.9 1.5 30.0 20.3 9.9 2.6
South Korea 19.6 5.1 3.8 2.5 3.3 4.3 1.0 5.1 5.2 11.7 7.9 1.2 2.2
Germany 9.0 2.3 2.5 1.7 5.2 6.8 2.7 13.3 29.9 4.4 3.0 2.1 2.1
UK 4.8 1.6 1.3 0.8 3.0 3.9 2.7 13.5 56.4 2.6 1.8 2.7 2.7
India 4.5 1.2 1.5 1.0 1.6 2.1 2.8 13.9 61.9 2.2 1.5 1.0 1.9
Canada 4.5 1.2 1.1 0.7 2.0 2.7 2.4 11.7 52.5 2.4 1.6 2.7 3.0
Taiwan 4.0 1.0 0.5 0.3 0.2 0.3 0.9 4.4 21.9 2.6 1.8 0.3 0.5
France 3.6 1.0 0.7 0.5 2.3 3.1 1.4 6.8 37.5 2.4 1.6 1.4 1.2
Total 383.2 100.0 151.4 100.0 76.3 100.0 20.1 100.0 5.3 147.8 100.0 100.0 100.0
Source: Analysis of PatentSight patent documents as of May 7, 2020, using patent search approach (see text).
Numbers (N) in thousands.
Note:
�Granted in years 2015–2019.
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International co-patenting relationships. There are about 20,000 patent applications
with inventors from multiple countries, accounting only for about 5% of total AI patent appli-
cations worldwide (Table 2). This limited level of international cooperation is influenced by
China, Japan, and South Korea, which have low rates of international cooperation (2.0%, 1.5%
Fig 2. a. AI all patent applications, top 10 countries, 1991–2019. b. AI transnational patent applications, top 10 countries, 1991–2019.
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and 5.2%, respectively). The comparable rates for the USA and Taiwan are 19.1% and 21.9%,
respectively. The UK, Germany, France, and Canada have higher rates of international co-pat-
enting, with the highest level reached in India (61.7%). However, although its rate of interna-
tional co-patenting is at a middling level, the USA remains the predominant node in
international cooperation due to its absolute count of international cooperation patent appli-
cations, accounting for more than 70% of total international cooperation patent applications
worldwide. The USA is the hub of the global AI patenting cooperation network and the leading
partner for most other countries including China, Canada, India, the UK, and Germany (Fig
3). A European sub-cluster is also evident (with the UK, Germany, and France as key nodes),
with China the ley node for a sub-cluster with Taiwan, South Korea, and Japan.
Organization-level landscape
Productive and high-quality organizations. The leading assignees of AI patent applica-
tions over the 1991–2019 period are primarily corporations and are concentrated in a relatively
small number of countries. Twenty-one of the top 25 AI patent application assignees are com-
panies, led by the USA (3), China (6) and Japan (10), with a further three based in South
Korea, Germany, and Taiwan. Four of the top 25 assignees are Chinese universities or insti-
tutes. Additionally, all 25 top assignees for transnational patent applications are companies,
with 9 for the USA, 7 for Japan, 4 for China, 2 for Germany and each one for South Korea,
Netherlands, and Finland, respectively (Table 3). Up to 2004, AI patent applications were con-
centrated in companies from Japan (led by Canon, NEC, Toshiba, Fujitsu, and Hitachi) and
the USA (including IBM, Microsoft, and Alphabet). The top Chinese assignees (including the
Chinese Academy of Sciences, the State Grid Corporation, Baidu, Tencent, Ping An Insurance,
Alibaba, and Tsinghua, Zhejiang, and Xidian Universities) are more recent entrants, develop-
ing AI patent applications particularly from 2005 through to 2019. Samsung (South Korea),
Siemens (Germany), and Foxconn (Taiwan) are also consistent AI patent applicants (Table 3).
There are 14 companies listed as top assignees for both total and transnational applications,
led by Microsoft, Alphabet (the parent company of Google), Siemens, and Samsung, followed
by NEC, Sony, IBM, Tencent and Alibaba, which suggests these organizations are both pro-
ductive and high-quality performers in AI patenting. Eleven organizations are listed only as
Fig 3. AI co-patenting relationships, top 20 countries (by inventor addresses).
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top assignees for total patent applications (including the Chinese Academy of Sciences, the
State Grid Corporation, Baidu, and NTT), while another 11 companies are listed only as top
assignees for transnational applications (including Philips, Nokia, Huawei, Intel, General Elec-
tric, and Qualcomm) (Table 3).
Interorganizational co-patenting and citation networks. At a consolidated level of
assignees, organizations mostly filed their AI patent applications as single assignees, with only
about 2% of 1991–2019 AI patent applications involving multiple organizations. Yet, the co-
patenting network across the 150 most AI patent-intensive organizations is informative
Table 3. Leading assignees, AI patent applications, 1991–2019.
Total patent applications Transnational patent applications
Assignee AOC 1991–
2019
1991–
94
1995–
99
2000–
04
2005–
09
2010–
14
2015–
2019
Assignee AOC 1991–
2019
1991–
94
1995–
99
2000–
04
2005–
09
2010–
14
2015–
19
IBM USA 9444 228 416 896 1265 1969 4670 Microsoft USA 3175 20 92 371 583 771 1338
Microsoft USA 6900 47 275 1023 1731 1587 2237 Alphabet USA 1887 6 30 77 178 466 1130
Chinese
Acad Sci
China 4322 4 9 52 191 784 3282 Siemens Germany 1595 74 162 212 223 216 708
Canon Japan 4232 931 787 659 690 542 623 Samsung South
Korea
1415 5 7 61 100 361 881
NEC Japan 4216 1187 655 360 628 590 796 Philips Netherlands 1270 35 70 150 164 237 614
Toshiba Japan 4141 1386 925 448 585 384 413 NEC Japan 1080 12 29 26 214 270 529
State Grid
Corp
China 3814 0 1 0 25 506 3282 Sony Japan 1044 9 83 183 142 143 484
Alphabet USA 3627 13 92 180 395 1367 1580 IBM USA 819 133 65 96 118 158 249
Fujitsu Japan 3483 732 527 387 415 518 904 Nokia Finland 795 38 101 126 144 170 216
Hitachi Japan 3424 1109 730 346 312 328 599 Huawei China 756 0 2 8 47 194 505
Baidu China 3406 0 0 0 3 299 3104 Intel USA 737 9 13 56 38 154 467
NTT Japan 3264 496 489 407 398 650 824 General
Electric
USA 627 4 34 97 86 144 262
Samsung South
Korea
3257 39 111 219 396 791 1701 Tencent China 627 0 0 0 9 220 398
Panasonic Japan 3213 1302 712 420 194 114 471 Alibaba
Group
China 598 0 1 0 11 124 462
Tencent China 2908 0 0 1 42 350 2515 Panasonic Japan 597 34 50 85 80 79 269
Ping An
Insurance
China 2861 0 0 0 0 1 2860 HP Inc. USA 581 19 30 92 90 179 171
Fujifilm Japan 2829 449 394 457 764 340 425 Qualcomm USA 552 0 8 22 63 253 206
Siemens Germany 2644 123 258 315 461 463 1024 Hitachi Japan 545 32 28 33 50 162 240
Alibaba
Group
China 2326 1 1 2 19 229 2074 Ping An
Insurance
China 543 0 0 0 0 0 543
Foxconn Taiwan 2273 772 424 276 309 338 154 Fujitsu Japan 539 19 27 66 81 104 242
Sony Japan 2232 153 302 463 408 335 571 Mitsubishi
Electric
Japan 530 16 57 46 52 117 242
Ricoh Japan 2120 653 320 435 304 180 228 Canon Japan 491 74 44 89 95 85 104
Tsinghua
Univ
China 1928 1 0 23 79 213 1612 Bosch Germany 424 10 20 43 70 66 215
Zhejiang
Univ
China 1909 6 0 12 139 305 1447 Apple USA 394 36 17 9 43 112 177
Xidian
Univ
China 1710 0 0 0 42 279 1389 Nuance USA 389 13 80 83 90 91 32
Note: AOC = Assignee original country.
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(Fig 4). The most obvious co-patenting clusters are among companies and research institu-
tions in China, suggesting interorganizational knowledge flows through social capital [35].
More loosely coupled networks are observable in the USA, South Korea, and Japan.
Interorganizational knowledge flows can also occur indirectly through citations in patents
to other organizations. For AI, such citation relationships are dense, as illustrated in a mapping
of links across the top 150 citing and cited organizations (Fig 5). Compared to the dispersed
co-patenting network across organizations, this highlights how technological innovation in AI
builds upon citation-based rather than collaboration-based knowledge links. We observe
Fig 4. Collaboration networks, top 150 AI patenting applicant organizations, 1991–2019.
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Fig 5. Citation networks, top 150 citing and cited AI patenting organizations, 1991–2019.
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several citation clusters. The first cluster (green) mainly involves companies from the USA,
including Microsoft, Alphabet, and IBM. At the edges of this cluster are companies from other
countries, such as Samsung, Nokia, SAP, Blackberry and Lenovo. The second cluster (blue)
involves companies from Japan, such as Sony, Canon, Fujitsu, and Toshiba. The third cluster
(red) comprises a dispersed Chinese group including not only firms but also many universities
and institutes. Overall, the USA and Japan express an industry-oriented innovation model,
while China expresses an industry-university-institute-oriented innovation model in the AI
domain.
Based on forward citation relationship among patenting organizations in the AI field, we
calculated the technological impacts of organizations worldwide. High-impact organizations
mostly originated in the USA (11) and Japan (9), with one each for South Korea, Finland, Ger-
many, China, and Netherlands (Table 4). The higher the technology impact of an organization,
the more the organization serves as a knowledge broker. Microsoft, whose technological
knowledge for AI has diffused (via forward citations) to more than 5000 organizations, has the
highest technology diffusion capacity worldwide (6.2%), followed by IBM, Alphabet and
Nuance. IBM, which absorbed technological knowledge (via backwards citations) from more
than 1000 organizations, has the highest technology absorptive capacity worldwide (2.3%),
Table 4. Top 25 technological high-impact organizations, based on AI patent citations, 1991–2019.
Assignee AOC TAI (%) TDC (%) TAC (%) Outdegree Indegree WDL WAL WPR (%)
Microsoft US 8.39 6.23 2.17 5157 1052 57201 19904 5.43
IBM US 7.02 4.40 2.62 4747 1191 40436 24054 4.21
Alphabet US 4.23 2.58 1.65 3424 939 23711 15171 2.26
Samsung South Korea 2.77 1.24 1.53 2246 1085 11432 14051 1.00
Sony Japan 2.37 1.40 0.97 2122 812 12880 8903 1.28
Nuance US 2.24 1.72 0.53 1918 413 15793 4829 1.66
Canon Japan 2.11 1.22 0.89 1774 733 11207 8196 1.19
Fujitsu Japan 1.93 0.98 0.95 1837 840 9035 8693 0.96
Toshiba Japan 1.90 1.10 0.79 1851 718 10146 7293 1.09
Apple US 1.88 1.13 0.74 2044 628 10400 6841 1.02
NEC Japan 1.79 0.98 0.81 1797 718 8981 7467 0.93
Nokia Finland 1.77 1.09 0.68 1797 679 10045 6236 1.14
HP Inc. US 1.72 0.96 0.76 1912 712 8826 7018 0.98
Siemens Germany 1.66 1.01 0.65 2177 860 9254 6012 1.06
Hitachi Japan 1.62 1.05 0.57 1968 731 9649 5243 1.07
Panasonic Japan 1.60 1.05 0.55 1850 679 9684 5048 1.03
Oracle US 1.51 0.90 0.61 1782 566 8263 5598 0.86
Fujifilm Japan 1.50 0.90 0.60 1535 573 8231 5555 0.88
Xerox US 1.49 1.05 0.45 1922 529 9641 4090 1.04
Verizon US 1.47 0.88 0.59 1728 571 8073 5413 0.79
Mitsubishi Electric Japan 1.43 0.89 0.54 1704 614 8170 4984 0.95
Intel US 1.41 0.69 0.71 1568 792 6373 6544 0.63
Chinese Acad Sci China 1.38 0.82 0.56 1485 967 7535 5184 0.45
General Electric US 1.37 0.84 0.53 1982 771 7742 4852 0.90
Philips Netherlands 1.32 0.86 0.46 2061 634 7903 4208 0.95
Note: AOC = Assignee original country; TAI = Technology absolute impact; TDC = Technology diffusion capacity; TAC = Technology absorptive capacity;
WDL = Weighted diffusion links; WAL = Weighted absorptive links; WPR = Weighted PageRank.
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followed by Microsoft, Alphabet and Samsung. The weighted PageRank (WPR) for each top
organization also indicates their importance in knowledge transmission.
There is an apparent “Matthew effect” [49] in AI knowledge flows (Fig 6). A few organiza-
tions possess high levels of technology diffusion and absorptive capacity; most others are at a
lower level in technology impact. Leading US companies, such as Microsoft, IBM, and Alpha-
bet, have maintained a high level of technology impact throughout the 1991–2019 period; the
same is true for Samsung (South Korea). Until 2009, Japanese companies, including Sony,
Fujitsu, and Toshiba, showed noticeable technology impact, but were less visible from 2010.
Conversely, several Chinese organizations have emerged as high-impact organizations since
2010, including the Chinese Academy of Sciences, Tencent, Baidu, Alibaba, and Huawei.
Technology-level landscape
Distribution of technology fields. AI patent applications cover a wide range of techno-
logical and application areas. More than 600 IPC 4-digit subclasses are represented in our AI
patent data. However, the top 20 IPC subclasses account for over 90% of all AI patent applica-
tions, while the more disaggregated top 20 IPC subgroups captured 65% of patent applications
(Table 5).
The largest AI patenting class is “computing, calculating, or counting” (G06) classifying
almost 78% of all AI patent applications (1991–2019). At the subclass level, nearly a half of AI
patents are classified under “electric digital data processing” (G06F), although with a decline
from about 70% of AI patent applications in 1991–1999 to about 40% in 2010–2019. This tech-
nology subclass is followed by “computer systems based on specific computational models”
(G06N, 24.8%), “recognition and presentation of data” (G06K, 19.2%), “data processing
Fig 6. The distribution of technology diffusion capacity and absorptive capacity of organizations.
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systems or methods for special purposes” (G06Q, 13.7%) and “image data processing or gener-
ation” (G06T, 10.2%), whose relative shares increased significantly over time (Table 5). These
classifications are fundamental AI technologies associated with improvements in computing
capabilities.
For specific subgroups of AI technologies, “methods or arrangements for recognition using
electronic means” (G06K 9/62) ranks first with 12.1% of all patent applications. “Information
retrieval; Database structures therefor” (G06F 17/30) ranks second with 12.9% of AI patent
applications. “Recognition patterns” (G06K 9/00) ranks third at 9.4%. There are several top
IPC subgroups related to “handling natural language data, including text processing” (G06F
17/21, 8.92%; G06F 17/24, 4.94%; G06F 17/22, 4.49%), “automatic analysis” (G06F 17/27,
8.55%), “processing or translating of natural language” (G06F 17/28, 4.34%). “Architecture”
(G06N 3/04) and “learning methods” (G06N 3/08) related to “using neural network models”
account for 8.3% and 8.1% of AI patent applications, respectively (Table 5).
Key technological development routes. This section analyzes the main technological tra-
jectories of AI technologies through extracting key technological routes from citation networks
[50]. We use the Louvain method for community detection [51] available in the Pajek software
package [52]. We extracted the key technological routes using the SPLC algorithm from the
subnets of the six largest communities of citation networks for AI technologies. These six com-
munities represent 41% of all nodes and 45% of all arcs in the citation network for AI technol-
ogies. The results are shown in Fig 7a–7f, where nodes refer to patents and arcs refer to links
obtained through forward patent citations, indicating knowledge flow between patents.
Table 5. Top IPCs for AI patent applications, 1991–2019.
IPC subclass Patent applications IPC subgroup Patent applications
Count 1991–2019 1991–1999 2000–2009 2010–2019 Count 1991–2019
G06F 173644 48.30% 73.98% 67.70% 40.66% G06K9/62 43514 12.10%
G06N 89198 24.81% 19.05% 14.41% 27.90% G06F17/30 43096 11.99%
G06K 68991 19.19% 6.03% 8.66% 23.23% G06K9/00 33746 9.39%
G06Q 49308 13.71% 5.70% 12.40% 15.01% G06F17/21 32073 8.92%
G06T 36666 10.20% 8.77% 6.63% 11.19% G06F17/27 30752 8.55%
G10L 24654 6.86% 8.56% 10.20% 5.88% G06N3/04 29847 8.30%
H04L 24124 6.71% 2.41% 6.24% 7.35% G06N3/08 29133 8.10%
A61B 17096 4.75% 1.74% 4.88% 5.10% G06F17/24 17749 4.94%
H04N 14096 3.92% 3.98% 5.35% 3.59% G06F17/22 16131 4.49%
G05B 13137 3.65% 5.88% 3.96% 3.31% G06F17/28 15598 4.34%
G01N 11400 3.17% 2.08% 4.35% 3.04% G06T7/00 13806 3.84%
G16H 10641 2.96% 0.17% 0.65% 3.84% G06F19/00 13639 3.79%
G05D 8830 2.46% 1.03% 0.88% 2.99% G06N99/00 13490 3.75%
H04W 7364 2.05% 0.37% 1.57% 2.37% G06F17/00 12690 3.53%
G08G 5902 1.64% 0.77% 1.04% 1.89% G06N5/04 10210 2.84%
H04M 5574 1.55% 1.44% 3.03% 1.23% G06F15/18 10179 2.83%
G01R 4745 1.32% 0.93% 1.21% 1.39% G06K9/46 9975 2.77%
B25J 4638 1.29% 0.51% 0.73% 1.51% G06N5/02 9372 2.61%
G09B 4570 1.27% 1.43% 1.71% 1.15% A61B5/00 9335 2.60%
G01C 4513 1.26% 0.53% 1.09% 1.39% H04L29/08 9061 2.52%
Note: For explanation of IPC codes, see https://www.wipo.int/classifications/ipc/en/ and S1 Appendix. AI patent search approach.
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We analyze the first application country and priority authorities of patents in these techno-
logical routes. For four technological routes (Fig 7a–7c and 7e), most patents are applied and
prioritized in the USA, with 79%, 76%, 90% and 74%, respectively. Japan is also a leading
country in technological routes shown in Fig 7a, with 14% prioritized patents. China is signifi-
cant in the technological route shown in Fig 7b, with 12% prioritized. Russia is significant in
the technological route shown in Fig 7e, with 10% prioritized patents. The technological route
shown in Fig 7d is jointly dominated by the USA, Germany, and Japan, respectively with 57%,
21% and 14% patents applied and prioritized. The technology routes shown in Fig 7a–7e are
industry-dominated, with 73%-94% of all patents assigned to firms. This contrasts with the
university-institute-industry-oriented technological route depicted in Fig 7f, where all patents
are applied and prioritized in China, with 43% of patents assigned to firms and 48% assigned
to universities or research institutes.
Analyzing interorganizational co-ownership for patents involved in key technological
routes, we find that Fig 7a has no patents with co-ownership across organizations, Fig 7b has
only five patents with co-ownership across organizations, and Fig 7c–7f each have just one pat-
ent of this type. In other words, for AI technological routes, there is a preference by organiza-
tions for exclusive technology development.
By technology topics (analyzed by IPC codes and keywords in patent abstracts), Fig 7a
mainly covers technologies handling natural language data (G06F 17/20, G06F 40/00), specifi-
cally including text processing (G06F 17/21, G06F 17/22, G06F 17/24) and automatic analysis
(G06F 17/27). Patents in the technological routes shown in Fig 7b are mainly classified into
handling natural language data (G06F 17/27, G06F 17/28), information query (G06F 17/30)
and speech recognition (G10L 15/02, G10L 15/06, G10L 15/14, G10L 15/18, G10L 15/22, G10L
15/24, G10L15/26, G10L 15/28). Patents in the technological route shown in Fig 7c mainly tar-
get technologies of digital computing or data processing equipment or methods that are spe-
cially adapted for specific applications (G06F 19/00) or for specific functions (G06F 17/00) and
measuring for diagnostic purposes (A61B 5/00, A61B 5/0476). The technological route shown
in Fig 7d involves technologies about adaptive control systems (G05B 13/02, G05B 13/04) and
learning machines (G06F 15/18) such as sensors and controllers, which basically use biological
models (G06N 3/00), especially neural network modeling (G06N 3/02, G06N 3/04). Patents in
the technological route shown in Fig 7e cover technologies of machine learning (G06F 15/18,
G06N 20/00), information retrieval and data structures (G06F 17/30), knowledge representa-
tion (G06N 5/02) and inference methods or devices (G06N 5/04). This technological route
addresses problems of prediction, ranking and recommendation involved in administration,
management, business, or financial fields by using machine learning technologies. Patents in
the technological routes shown in Fig 7f are mainly classified into recognizing patterns (G06K
9/00, G06K 9/20, G06K 9/46, G06K 9/60, G06K 9/62, G06K 9/78), image analysis (G06T 7/00,
G06T 7/11, G06T 7/12, G06T 7/13, G06T 7/33, G06T 7/246, G06T 7/73) and using neural net-
work models (G06N 3/08, G06N 3/04, G06N 3/02).
Discussions and conclusions
In this study, we put forward an approach to identify AI patents and applied this to offer a
broad global analysis of the AI patenting landscape at country, organization, and technology
levels. We discussed the details of the methodological approach to operationalize a new search
approach which captures AI-related patents with recall and precision. To assess patent produc-
tivity and quality, we examined total patent applications and transnational patent applications
respectively, using cross-sectional and longitudinal views. This analysis highlighted AI patent
developments in leading countries and organizations. The study also examined co-patenting
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collaborations and forward citation relationships, to understanding knowledge sharing among
organizations. Based on forward citation relationships among organizations, we analyzed tech-
nological knowledge inflows and outflows in interorganizational citation networks and identi-
fied patent-based technological routes to identify development trajectories and core
technologies in the AI domain.
Our analysis of patent application data over two decades (1991–2020) finds that activity in
AI patenting is currently in a phase of rapid growth, driven significantly (although not exclu-
sively) by a recent increase of AI patenting in China. From a geographical perspective, AI pat-
enting efforts remain highly concentrated, with inventors in the top 10 countries contributing
almost 96% of all worldwide AI patent applications. The USA, China and Japan are among the
most patent-intensive and specialized countries in the AI domain, followed by South Korea,
Germany, and the UK. Over the past three decades, the USA has maintained a leading posi-
tion, with China growing most rapidly in recent years and Japan seeing relative loss of its early
leadership position.
The analysis of top assignees showed that most AI patents are owned by large private com-
panies. Public organizations and universities are less prominent in the ownership of AI patent
applications, except in China where public research organization and universities are frequent
AI patent assignees. In general, the USA and Japan each exhibit an industry-oriented innova-
tion model, while China expresses an industry-university-institute-oriented innovation model
in AI patent development. AI co-patenting collaborations through joint ownership are not
common. However, there are many interorganizational knowledge citation relationships,
although they are geographically bounded. Matthew effects of accumulated advantage are
apparent, with a few organizations acting as high-impact technology disseminators and
acceptors.
We demonstrated that AI patents cover a wide range of technological areas, not only in
information technologies and computing but also in applied fields such as medicine, health-
care, finance, and education. The main path analysis highlighted multiple AI technological
development paths. However, we found that flows of technological knowledge tend to be con-
centrated among patents developed in the same country, mostly dominated by the USA
although there were some technological routes led by China. This analysis confirmed the
strong presence of independent assignees and a lack of collaborative patent assignments
among organizations in the AI domain.
There are a series of implications that can be drawn from the study for management and
policy. AI is emerging as a general-purpose technology with applications, as we have shown,
across multiple fields. The current boom in AI patenting reminds us of the need for continuing
attention to the challenges of AI applications, including societal consequences. For managers
and analysts in all sectors, our study highlights the strategic relevance of tracking (if not engag-
ing in) AI-enabled developments, opportunities, and competitive challenges. A way to do this
is through monitoring rapidly increasing efforts to secure intellectual property in AI through
patent applications. Our search approach can be used (and further modified or refined) to
assist monitoring of AI patents. Patent applications do vary in quality, as we have shown, and
not all patent applications are granted. Nonetheless, this growing body of codified information
(especially if searched appropriately) provides a basis for tracking developments and further-
ing AI knowledge progression and innovation as potential approaches and processes are dis-
closed in published patent applications. Inventors and organizations in the USA, Japan and
selected other developed countries continue to be well-represented in high quality AI patent-
ing activity, but we also find that AI patenting efforts in China are growing not only in terms
of patent quantity but are also (especially recently) increasing in patent quality.
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While AI patenting trends by countries, organizations, and technologies are of interest to
policymakers, our findings about the dominant roles of a subset of leading multi-national
organizations in AI patenting also raise ongoing policy questions about the control and man-
agement of emerging AI technologies and applications. Indeed, the observed concentration of
AI patenting in a few countries and organizations raises multiple policy issues, including how
to moderate information and power asymmetries between developers and users of the technol-
ogies and between incumbents and new entrants. Additionally, the current weaknesses of col-
laborative development highlighted in AI patenting may cause problems in technological and
market reach and for standardization and interoperability.
We acknowledge limitations that should be kept in in mind when interpreting the results of
the study. As we have discussed, although patent data is widely used as a proxy for innovation,
it has shortcomings. To avoid disclosure, some firms may choose not to patent their techno-
logical innovations but to hold them as business secrets. Although patent application data do
signal inventive trends and assignee interests, it should be noted that not all patent applications
are granted, nor are granted patents necessarily used, maintained, or enforced. Our research
has taken a broad landscape perspective. Further research is needed to pinpoint trends in spe-
cific sub-technological AI fields and to probe implications by specific application areas.
Supporting information
S1 Appendix. AI patent search approach. Summary of bibliometric search term method and
selection of AI-related CPC and IPC patent codes.
(PDF)
Author Contributions
Conceptualization: Na Liu, Philip Shapira.
Data curation: Na Liu, Xiaoxu Yue.
Formal analysis: Na Liu, Philip Shapira, Xiaoxu Yue.
Funding acquisition: Na Liu, Philip Shapira.
Investigation: Na Liu, Philip Shapira, Xiaoxu Yue.
Methodology: Na Liu, Philip Shapira, Xiaoxu Yue, Jiancheng Guan.
Project administration: Philip Shapira.
Resources: Philip Shapira.
Software: Na Liu, Philip Shapira, Xiaoxu Yue.
Supervision: Philip Shapira, Jiancheng Guan.
Validation: Na Liu, Philip Shapira.
Visualization: Na Liu.
Writing – original draft: Na Liu, Philip Shapira, Xiaoxu Yue, Jiancheng Guan.
Writing – review & editing: Na Liu, Philip Shapira, Xiaoxu Yue.
References
1. Goralski MA, Tan TK. Artificial intelligence and sustainable development. International Journal of Man-
agement Education. 2020, 18, 100330.
PLOS ONE
Mapping technological innovation dynamics in artificial intelligence domains
PLOS ONE | https://doi.org/10.1371/journal.pone.0262050 December 31, 2021 18 / 20
2. Miller T. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence.
2019, 267, 1–38.
3. Russell S, Hauert S, Altman R, Veloso M. Ethics of artificial intelligence. Nature. 2015, 521, 415–416.
https://doi.org/10.1038/521415a PMID: 26017428
4. Van Roy V, Vertesy D., Damioli G. AI and robotics innovation: A sectoral and geographical mapping
using patent data. GLO Discussion Paper 433. Global Labor Organization; 2019.
5. WIPO. Technology trends 2019: Artificial intelligence. Geneva: World Intellectual Property Organiza-
tion; 2019.
6. Liu N, Shapira P, Yue X. Tracking developments in artificial intelligence research: Constructing and
applying a new search strategy. Scientometrics. 2021, 126, 3153–3192. https://doi.org/10.1007/
s11192-021-03868-4 PMID: 34720254
7. Dwivedi YK, Hughes L, Ismagilova E, Aarts G, Coombs C, Crick T, et al. Artificial Intelligence (AI): Multi-
disciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and
policy. International Journal of Information Management. 2019, 57, 101994.
8. FortuneBusinessInsights. Artificial intelligence (AI) market size, share & COVID-19 impact analysis, by
component, by technology, by deployment, by industry and regional forecast, 2020–2027. 2020. https://
www.fortunebusinessinsights.com/industry-reports/artificial-intelligence-market-100114
9. Vial G. Understanding digital transformation: A review and a research agenda. The Journal of Strategic
Information Systems. 2019, 28, 118–144.
10. Magistretti S, Dell’Era C, Messeni Petruzzelli A. How intelligent is Watson? Enabling digital transforma-
tion through artificial intelligence. Business Horizons, 2019, 62, 819–829.
11. Warner KSR, Wager M. Building dynamic capabilities for digital transformation: An ongoing process of
strategic renewal. Long Range Planning, 2019, 52, 326–349.
12. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: Past, present and
future. Stroke and Vascular Neurology. 2017, 2, 230–243. https://doi.org/10.1136/svn-2017-000101
PMID: 29507784
13. OECD. AI Policy Observatory. Paris: Organisation for Economic Cooperation and Development; 2021,
June 1. https://www.oecd.ai/
14. Tegmark M. Life 3.0: Being human in the age of artificial intelligence. New York: Knopf; 2017.
15. Crawford K. Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. New Haven and
London: Yale University Press; 2021.
16. OECD. OECD Patent Statistics Manual. Paris: OECD Publishing; 2009.
17. Porter AL, Newman NC. Patent Profiling for Competitive Advantage. In: Moed HF, Gla
¨nzel W, Schmoch
U. (eds) Handbook of Quantitative Science and Technology Research. Dordrecht: Springer; 2004,
587–612.
18. Habibollahi Najaf Abadi H, Pecht M. Artificial Intelligence Trends Based on the Patents Granted by the
United States Patent and Trademark Office. IEEE Access, 2020, 8, 81633–81643.
19. Xie Z, Miyazaki K. Evaluating the effectiveness of keyword search strategy for patent identification.
World Patent Information, 2013, 35, 20–30.
20. Evangelista A, Ardito L, Boccaccio A, Fiorentino M, Messeni Petruzzelli A, Uva AE. Unveiling the tech-
nological trends of augmented reality: A patent analysis. Computers in Industry, 2020, 118, 1–15.
21. Jaffe AB, de Rassenfosse G. Patent citation data in social science research: Overview and best prac-
tices. Journal of the Association for Information Science and Technology, 2017, 68, 1360–1374.
22. Tseng C-Y, Ting P-H. Patent analysis for technology development of artificial intelligence: A country-
level comparative study. Innovation. 2013, 15, 463–475.
23. Fujii H, Managi S. Trends and priority shifts in artificial intelligence technology invention: A global patent
analysis. Economic Analysis and Policy. 2018, 58, 60–69.
24. Huang Y, Schuehle J, Porter AL, Youtie J. A systematic method to create search strategies for emerg-
ing technologies based on the Web of Science: illustrated for ‘Big Data’. Scientometrics. 2015, 105,
2005–2022.
25. Shapira P, Kwon S, Youtie J. Tracking the emergence of synthetic biology. Scientometrics. 2017, 112,
1439–1469. https://doi.org/10.1007/s11192-017-2452-5 PMID: 28804177
26. Abadi HHN, Pecht M. Artificial intelligence trends based on the patents granted by the United States
Patent and Trademark Office. IEEE Access. 2020: 8, 81633–81643.
27. PatentSight. Excellent data quality is the foundation of reliable analyses.2021, June 1. https://www.
patentsight.com/excellent-patent-data
PLOS ONE
Mapping technological innovation dynamics in artificial intelligence domains
PLOS ONE | https://doi.org/10.1371/journal.pone.0262050 December 31, 2021 19 / 20
28. Albino V, Ardito L, Dangelico RM, Petruzzelli AM. Understanding the development trends of low-carbon
energy technologies: A patent analysis. Applied Energy. 2014: 135, 836–854.
29. Nagaoka S, Motohashi K, Goto A. Patent statistics as an innovation indicator. In: Hall BH, Rosenberg
N, editors, Handbook of the economics of innovation. Vol 2. North-Holland, Elsevier, 2010, pp. 1083–
1127.
30. Hinze S, Schmoch U. Opening the Black Box. Analytical Approaches and their Impact on the Outcome
of Statistical Patent Analysis. In: Moed HF, Gla
¨nzel W, Schmoch U. (eds) Handbook of Quantitative Sci-
ence and Technology Research. Dordrecht: Springer; 2004, 215–235.
31. Griliches Z, Pakes A, Hall BH. The Value of Patents as Indicators of Inventive Activity. Working Paper
No. 2083. National Bureau of Economic Research. Cambridge, MA; 1986.
32. Ernst H. Patent applications and subsequent changes of performance: evidence from time-series
cross-section analyses on the firm level. Research Policy, 2001, 30, 143–157.
33. Frietsch R, Schmoch U. Transnational patents and international markets. Scientometrics. 2010, 82,
185–200.
34. de Rassenfosse G, Dernis H, Guellec D, Picci L, de la Potterie BvP. The worldwide count of priority pat-
ents: A new indicator of inventive activity. Research Policy. 2013: 42, 720–737.
35. Guan J, Liu N. Exploitative and exploratory innovations in knowledge network and collaboration net-
work: A patent analysis in the technological field of nano-energy. Research Policy. 2016, 45, 97–112.
36. Liu N, Guan J. Policy and innovation: Nanoenergy technology in the USA and China. Energy Policy.
2016, 91, 220–232.
37. Ardito L, D’Adda D, Petruzzelli AM. Mapping innovation dynamics in the Internet of Things domain: Evi-
dence from patent analysis. Technological Forecasting and Social Change. 2018: 136, 317–330.
38. Duguet E, MacGarvie M. How well do patent citations measure flows of technology? Evidence from
French innovation surveys. Economics of Innovation and New Technology. 2005: 14, 375–393.
39. Jeong HJ, Ko Y. Analysing the structure of bioplastic knowledge networks in the automotive industry.
International Journal of Technology Management. 2020, 82, 132–150.
40. Suh Y, Jeon J. Monitoring patterns of open innovation using the patent-based brokerage analysis.
Technological Forecasting and Social Change. 2019, 146, 595–605.
41. Hummon NP, Dereian P. Connectivity in a citation network: The development of DNA theory. Social
Networks. 1989, 11, 39–63.
42. Verspagen B. Mapping technological trajectories as patent citation networks: A study on the history of
fuel cell research. Advances in Complex Systems. 2007, 10, 93–115.
43. Liu JS, Lu LY. An integrated approach for main path analysis: Development of the Hirsch index as an
example. Journal of the American Society for Information Science and Technology. 2012, 63, 528–
542.
44. Liu JS, Lu LY, Ho MH-C. A few notes on main path analysis. Scientometrics. 2019, 119, 379–391.
45. Lu LY, Liu JS. A novel approach to identify the major research themes and development trajectory: The
case of patenting research. Technological Forecasting and Social Change. 2016, 103, 71–82.
46. Martinelli A. An emerging paradigm or just another trajectory? Understanding the nature of technologi-
cal changes using engineering heuristics in the telecommunications switching industry. Research Pol-
icy. 2012, 41, 414–429.
47. O’Meara S. Will China lead the world in AI by 2030? Nature. 2019, 572, 427–428. https://doi.org/10.
1038/d41586-019-02360-7 PMID: 31435064
48. Wu F, Lu C, Zhu M, Chen H, Zhu J, Yu K, et al. Towards a new generation of artificial intelligence in
China. Nature Machine Intelligence. 2020, 2, 312–316.
49. Merton RK. The Matthew effect in science. The reward and communication systems of science are con-
sidered. Science. 1968, 159, 56–63. PMID: 5634379
50. de Paulo AF, Nunes B, Porto G. Emerging green technologies for vehicle propulsion systems. Techno-
logical Forecasting and Social Change. 2020, 159, 120054.
51. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks.
Journal of Statistical Mechanics: Theory and Experiment, 2008: P10008.
52. Mrvar A, Batagelj V. Analysis and visualization of large networks with program package Pajek. Complex
Adaptive Systems Modeling. 2016, 4:6.
PLOS ONE
Mapping technological innovation dynamics in artificial intelligence domains
PLOS ONE | https://doi.org/10.1371/journal.pone.0262050 December 31, 2021 20 / 20
