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Weak signals in Science and Technologies 2019

Analysis and recommendations

Technologies at a very early stage of development that could impact the future

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JRC 119395

EUR 30061 EN

PDF | ISBN 978-92-76-15134-0 | ISSN 1831-9424 | doi:10.2760/61303 | KJ-NA-30-061-EN-N
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Luxembourg: Publications Office of the European Union, 2020

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How to cite this report: Joanny G., Giraldi J., Perani S., Fragkiskos S., Rossi D., Eulaerts O., Weak signals in Science and Technologies 2019 - Analysis and recommendations,EUR 30061 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-15134-0, doi:10.2760/61303, JRC119395

Abstract

JRC has developed a quantitative methodology to detect very early signs of emerging technologies, so called ”weak signals of technology development”. Using text mining and scientometric indicators, 256 of these weak signals have been identified on the basis of scientific literature and have been reported earlier this year in a JRC technical report. The purpose of this follow-up report is to provide a European perspective and to provide recommendations for policy makers. Europe shows vulnerabilities in 179 of these weak signals, further analysed in the present report.

Executive summary

This report presents the results of an analysis performed on 256 weak signals in technology development detected through applying text mining techniques to a dataset of scientific publications. The aim of the present analysis is to identify points of focus for policy-makers, the research community, and stakeholders of technological development.

The main conclusions from the analysis are:

There is a risk of technology dependency of Europe in 98 of the weak signals.

There is no or little R&D activities in Europe for ¼ of the weak signals (64).

Research in Europe should be reinforced for ¼ of the weak signals (66).

Research ecosystems in Europe are fragmented in more than a third of the weak signals (97).

Patenting incentives should be put in place for more than half (136) of the weak signals.

42 research ecosystems might be impacted by the Brexit.

There are knowledge monopolies or duopolies in 8 weak signals.

It also appears that Europe is strong in weak signals related to social sciences, health, and environment, but shows some vulnerabilities in Physics & Engineering, Biology, ICT, Viruses, Energy, and Materials. Looking at sub-topics, Europe is strong in weak signals related to biomaterials, microbiology, neurosciences, new medicines, and digital social sciences but vulnerable in weak signals related to the 27 other sub-topics, including perovskite, solar cells, 5G, Internet of Things, 3D printing, Neural Networks, Viruses, new materials (2D materials, nanomaterials, metasurfaces, new catalysts).

Organisations leading R&D are most often from China and the US. Overall, a reasonable proportion of European companies are taking part in research and development efforts in the various weak signals. There are as many small industrial players in Europe than in the other main territories, but fewer large size companies. Organisations from China and the US are the most frequent in the ranking of the top 10 organisations for each weak signals. Except for the weak signals related to Environment, European organisations do not appear often in these top 10.

Finally, Europe suffers from a low capacity of transformation of knowledge into patents compared to the US, China, Japan, or South Korea. This is observed for many weak signals, even when European companies are very active. Chinese organisations are very active in patenting but only a small number of the patents filed in China (6.22%) are extended to another patent jurisdiction. This proportion of patents extended is much higher for South Korea, the US, Japan, and for the EU (up to 80%).

1 Introduction

For an organisation like the Joint Research Centre acting at the interface between science, policy making and society, the early identification of emerging technologies is key to support the design and implementation of European policies, which need to take into account the potential of these new technologies to create opportunities or disruptions. To complement its current foresight capacity, which is mostly based on qualitative technology monitoring and assessment, JRC is developing new analytic techniques based on text mining applied to large datasets.

The present report analyses the 256 weak signals in technology development identified with the new quantitative methodology developed by JRC and presented in the report “Weak signals in Science and Technologies - 2019 Report”¹. This quantitative methodology relies on text mining algorithms and was conceived to identify early signals of new technological developments, as well as new applications of existing technologies. The weak signals of the 2019 exercise (see figures 1 and 2) were detected by applying this new text mining methodology on a corpus of scientific publications covering the period 01/01/1996 to 31/12/2018.

The 256 weak signals can be examined in detail here:
https://www.timanalytics.eu/TimTechPublic/main.jsp?dataset=s_1231

The aim of the analysis is to better understand the positioning of Europe for each of the weak signals and to make recommendations. The following types of questions are considered: Does Europe produce knowledge? Are European organisations active in innovation and patenting? Is there a risk for future technology dependency? Will Brexit have an impact on the research environment? The weak signals can be sorted in one of the four following categories: 1) Europe is weak in producing knowledge, 2) Europe is weak in patenting, 3) Europe is strong in producing knowledge, and 4) Europe is strong in patenting. In particular, the analysis will focus on the weak signals where Europe is producing little knowledge or patents (categories 1 and 2) and those which could be impacted by Brexit.

In a next step, the results of the present exercise will be submitted to experts in Europe and more widely in other regions of the world. Through an iterative and structured process, the significance and future impacts of the identified weak signals on the European economy and society will be evaluated. The objective is to combine the results of the new JRC quantitative methodology to detect weak signals with expert consultations to reach an assessment of the European positioning and competitiveness in these technologies, their possible widespread development, including needs for further research and investments, and their social and ethical acceptance. The ultimate aim is to identify a set of key policy recommendations and legislative opportunities for European stakeholders and policymakers to act upon.

The detection of weak signals using text mining techniques on large data corpuses will be integrated to existing foresight and anticipation mechanisms in JRC to support services and policy Directorate Generals of the European Commission in designing European policies.

¹ JRC 2019.5552, EUR 29900 EN, ISBN 978-92-76-12386-6, ISSN 1018-5593, doi: 10.2760/858426

2 List of weak signals as identified in JRC report “Weak signals in Science and Technologies - 2019 Report”

Figure 1: wheel of weak signals, per topic.

Figure 2: List of weak signals per topic.

3 Methodology for the analysis

The 256 weak signals detected have been reconstructed in TIM Technology, a technology monitoring system developed and operated by JRC, using semantic searches. As a result, each weak signal in TIM Technology is composed of a group of documents retrieved by the search query in a patent database (Patstat), a scientific literature database (Scopus), and a database containing R&D grants funded by the Framework Programme for Research of the European Commission (Cordis). Bibliometric indicators are then calculated on the basis of the documents retrieved to analyse and classify the weak signals.

3.1 Indicators

The following indicators are used in the analysis of the weak signals:

Total number of scientific publications worldwide;

Number of scientific publications by European organisations (including UK);

Number of publications for organisations in Europe (all Member States except UK);

Total number of patents families worldwide;

Number of patent families by organisations in Europe;

The fragmentation of the research environment in Europe, calculated by dividing the number of organisations from Europe active in research (i.e. publishing scientific publications) by the number of co-publications between these organisations. A fragmented research landscape is a research environment where research organisations do not collaborate together (few or no co-publications), while a collaborative landscape is characterised by a lot of co-publications between the organisations active in research.

It should be noted that the indicators for patents are considering only the number of patent families (a set of patents applied in various countries to protect a single invention) and do not use proxies related to their importance, such as the essential patents or the triadic patents (a series of corresponding patents for the same invention filed at the European Patent Office, the US Patent Office, and the Japan Patent Office).

3.2 Entity disambiguation

A common issue in scientometrics is the disambiguation of entity names i.e. merging all the name variants of an organisation from different documents and/or sources into one unique name. Algorithms that automatically compare the entities names with a list of known organisations are used to ensure a reasonable level of disambiguation of organisation name variants. This process is complemented by manual cleaning when necessary. Although this allows reaching high levels of disambiguation of the data related to organisations, it must be noted that the accuracy of the entity matching process in TIM never reaches 100%.

To estimate the accuracy of the entity matching process, 10 randomized samples of 100 name variants have been extracted from the raw metadata in our system (patents in Patstat, scientific publications in Scopus, EU grants in Cordis), and compared manually to their unique name as resolved by the TIM entity matcher. The average accuracy of the process as measured by this sampling technique reaches 93.8% with a standard deviation of 2.15. Although the disambiguation of entities names is not error-free, the result is accurate enough to allow the observation of trends in science and technologies.

3.3 Categories of weak signals

The focus of the present analysis is made on weak signals that are problematic from a European perspective. The following vulnerabilities have been considered:

Low knowledge production (Europe representing less than 25% of the scientific publications for the weak signal). The number of publications from European organisations is used as a proxy to knowledge production. There are of course other types of knowledge produced that are not considered in this study (number of PhD thesis, informal knowledge within organisations …). The assumption is: the higher the number of scientific publications for a country/territory, the higher the knowledge production for that country/territory.

Low transformation of knowledge into innovation (less than 25% of the patents belong to organisations in Europe). The number of patents is used as a proxy to the capacity to innovate. The following assumption is made: the higher the number of patents, the higher the capacity of a country/territory to transform academic knowledge into patents (technological products or services). Innovation is a complex mechanism and patents only reflect the capacity for technological innovations. In addition, patenting strategies can vary from one country to another (some countries have strong incentives in place to promote patenting), from one culture to another, or from one technological field to another. The number of patents should therefore be taken as an indication of the capacity to innovate, and not as a definitive and accurate measure of innovation.

A significant proportion of knowledge and/or patents originating from the UK. This is considered as a vulnerability in view of Brexit, which might isolate UK institutions and companies from their counterpart in Europe. It is likely that collaboration at a scientific level will continue, but research environments might be disturbed by the transition towards Brexit. In addition, Brexit could represent a loss of knowledge from the European perspective. We consider here that the weak signals that could be impacted by Brexit are those for which more than 40% of the scientific publications or of the patents from Europe involve at least one organisation from the UK (author or applicant). These vulnerabilities could potentially lead to technology dependency of Europe in various new emerging technological areas.

In addition, two characteristics of the research ecosystem in Europe are considered as vulnerabilities:

A low level of collaboration in knowledge production (i.e. number of co-publications). This level is calculated by the fragmentation of the network graph of publishing organisations. The network is considered as fragmented when the fragmentation indicator is above 1.

Imbalances in strength for the different actors in Europe (e.g. monopolistic situation when one organisation dominates)

The analysis is designed to investigate the above mentioned vulnerabilities. In a first step, the indicators described at point 3.1 are calculated for each weak signal, which allows for their classification into the four categories described in figure 3: 1) EU is weak in knowledge production (and in patenting), 2) EU is weak in patenting, 3) EU is strong in knowledge production, 4) EU is strong in knowledge production and in patenting. In a second step, weak signals belonging to the categories “EU weak in KW” and “EU weak in IP” are analysed further in order for recommendations to be made (see chapter 4), while the weak signals of the categories “EU strong in KW” and “EU strong in KW and IP” are only analysed further if a possible impact from Brexit is detected. The report also contains an additional analysis of some of the so-called “persistent” weak signals (for which documents have been retrieved over many different years), which may seem counterintuitive for technologies that are supposed to emerge. In total, 190 weak signals are looked at in this report.

Figure 3: Categories chosen for the analysis of weak signals

3.4 Recommendations

The Joint Research Centre acts at the interface between science, policy making and society, and supports the design and implementation of European policies. Within the context of that mission, early identification of emerging technologies is an important step to ensure economic opportunities for Europe or mitigate technological disruptions. Recommendations are therefore made to stakeholders of the European science and innovation ecosystem to support adequate decision-making regarding for example funding of research, support mechanisms to innovative industry, or investment opportunities.

5 types of recommendations have been made for the weak signals for which Europe shows some vulnerabilities:

Measures should be put in place to create R&D activities: For weak signals where Europe lags behind in knowledge production.

Measure to further reinforce R&D activities should be considered for weak signals where there is production of knowledge in Europe but for which there is fierce competition with other countries/territories.

Measures to develop the network of research organisations and increase scientific collaboration should be put in place. For weak signals where actors from European are not collaborating.

Measures to foster innovation and encourage innovation and patenting should be put in place for weak signals where Europe lags behind in patenting. Patenting of innovations developed in Europe are essential if Europe wants to remain technologically independent in strategic technologies.

Measures to support the R&D ecosystem in Europe during Brexit: for weak signals for which the research ecosystem in Europe might be disturbed by the departure of UK organisations from the European Union.

In addition, two risks are flagged:

Risk of technological dependency when there is very little knowledge developed in Europe, or very few patents owned by European organisations, or very few organisations from Europe active in R&D compared to other countries.

Risk related to “Knowledge imbalance” when knowledge in Europe is concentrated in one or two organisations.

3.5 Considerations on the patent data

A steep increase in the number of patents from China is observed for many of the weak signals detected. Without negating the reality of Chinese innovation capacity in the various weak signals that have been detected, some particularities related to patent data call for considering the number of patents for the last 24 months with some caution. First, and for most of the patent authorities, a systematic decrease in the number of patents for years 2017 and 2018 can be observed. This is inherent to the patent system and related to the 18 month delay between the first filing of a patent and its publication. An extra delay can be caused by the frequency at which national patent offices send their patent data to the European Patent Office (EPO) for integration in the Patstat database. Figure 4 shows the effect of these two factors on the database of patents used for the analysis.

Figure 4: number of patents families per year and related relative proportions for the 5 biggest territories (EU = patents filed in the 28 Member States).

The higher proportion of patents from China observed for the years 2017 and 2018 is the exception to the abovementioned decrease. This is confirmed by figures 5 and 6, using data compiled by the EPO² for the present report and shown below.

Figure 5: total number of patents applications for the year 2018 contained in PATSTAT Global - 2019 Spring Edition. Figures for the Korean, Japanese, US, Chinese, and European patent offices

² Courtesy of Geert Boedt, European Patent Office.

Figure 5 shows that the numbers of patents filed at the Chinese patent office in 2018 is at least ten times higher than in the four other territories considered here. This might be a direct result of the National Patent Development Strategy of the Chinese National Intellectual Property Administration which has incentives in place to increase the number of patents filed in China³. This results in numerous patents being awarded for small design improvements and incremental changes, protected by utility models and design patents. Contrary to patents for inventions, utility models and design patents are delivered faster (less than 1 year) and are not subject to rigorous examination. 77% of the patents filed in China in 2017 are utility models or design patents and about 60% of them are abandoned within five years of their filing⁴. This could partly explain both the increase in patenting observed in China and the skewing of data towards China in 2017-2018 observed in figure 4. This trend to multiply design patents and utility models is not observed in other economies like Japan, the EU, or the US, where patents are still mostly granted for entirely new inventions (”patents for inventions”).

Figure 6 clearly shows that China applies an early publication strategy for invention patents as well. Around 80% of invention patents are already published 6 months after their filing at the Chinese patent office. Early publications are also observed for the other territories, but later and to a lesser extent. For example, for patents registered at month 6, while 80% of Chinese patents are already published, only 10% of the patents have been through early publication in Japan and South Korea and around 40% in Europe and the US.

Figure 6: proportion of invention patents published as a function of the number of months after filing for South Korea, Japan, US, China, and Europe (courtesy of the EPO). Data from August 2019, excluding PCT applications.

Considering the three elements explained above, i) incentives in China to patent, ii) design patents and utility models included in the numbers of patent filed, and iii) the trend for Chinese organisation to request early publication of their patent, it is not surprising to observe more Chinese patents over the last years for many of the weak signals detected (see details in annex 1). The steep increase in patenting from Chinese organisations over the last 2-3 years should therefore be nuanced, considering further elements explained below.

³ “International Patenting Strategies of Chinese Residents: An Analysis of Foreign-Oriented Patent Families”, Mila Kashcheeva,

⁴ Sacha Wunsch-Vincent, Hao Zhou, Economic Research Working Paper No. 20, WIPO, 2014. Source: National Intellectual Property Administration of China.

4 Analysis of the weak signals in technology and science

4.1 Overview

In total, 60193 scientific publications and 6922 patents have been retrieved for the 256 weak signals detected. From the distribution of publications and patents into the 9 topics of weak signals (figure 7), one can see that more than half of the scientific publications have been retrieved for three topics: ICT, Materials, and Health. Half of the patents have been retrieved for the topic ICT.

Figure 7: number of patents and publications per topic of weak signals (number of WS per category on top of each column).

According to the categories of weak signals defined at point 3.2 above, Europe lacks knowledge and patents in 45% (114) of the weak signals detected. This means that European organisations are involved in less than 25% of the publications and of patents worldwide. There are 44 weak signals for which European organisations are strong in producing knowledge but fail to transform this knowledge into patents. For these weak signals, European organisations are involved in more than 25% of the scientific publications worldwide but applied to less than 25% of the patents. In addition, Brexit could impact 10 of these weak signals. Finally, Europe is strong in 80 weak signals (72 EU strong in KW, 8 EU strong in patents). In total, Europe leads in 98 of the weak signals detected but Europe’s strength resides in UK organisations for 18 of them (more than 40% of the publications and/or 40% of the patents involve UK organisations). There are therefore only 80 weak signals (72 EU strong in KW, 8 EU strong in patents) out of 256 for which Europe is strong.

Figure 8: proportion of weak signals per categories.

The proportion of weak signals for which Europe is vulnerable or strong varies with the technical topic of the weak signal (figure 9). For example, Europe leads in all the weak signals related to Social Sciences but underperforms for those related to Materials.

Figure 9: proportion of weak signals per categories and by topics.

Figure 10 shows the sub-topics of weak signals where:

Europe leads: European organisations lead in terms of patenting and or scientific publications in a majority of the weak signals (the majority of weak signals in the sub-topics belong to the categories “EU strong in KW” and “EU strong in KW and IP”);

Europe follows: European organisations lead in some weak signals and follows in others (a high proportion of weak signals in the sub-topics belonging to the categories “EU weak in KW” and “EU weak in KW and IP”);

Europe lags behind: European organisations take a leading role in very few or none of the weak signals for the sub-topics (the majority of weak signals in the sub-topics belong to the categories “EU weak in KW” and “EU weak in KW and IP”).

Figure 10: Positioning of Europe in sub-topics of weak signals With [x, y, z] = [#WS in categories “EU strong in KW” and “EU strong in KW and IP”, #WS in category “EU weak in IP”, #WS in category “EU weak in KW and IP”]

Although Europe is strong in 80 of the 256 weak signals detected (31%), it appears clearly from figure 10 that there are only few sub-topics of weak signals where Europe is the leader. While EU is a clear leader in weak signals related to e.g. neurosciences, medicines & diseases, and digital aspects of social sciences, Europe shows vulnerabilities in topics like batteries, perovskite, solar cells, 3D printing, or 2D materials, to name but a few.

4.2 Risk of technology dependency

Based on the analysis presented in the annex, the possibility of a future technology dependency has been identified for 98 weak signals (figure 11). In the present report, we considered a risk of technology dependency when:

Modest research activities is ongoing in Europe while intense research is being performed elsewhere in the world,

Little patenting activity is detected in Europe while patents are being filed in the main competing countries.

Figure 11: weak signals presenting a risk of technology dependency for Europe

4.3 Knowledge and patent imbalances

Figure 12 lists the 8 weak signals that have been flagged for a knowledge or patent imbalance i.e. when research activities or patenting in Europe is mainly due to one or two organisations. R&D ecosystems that are characterised by a monopoly or a duopoly are considered to be more fragile than ecosystems where research and patenting are performed by a large number of actors.

Figure 12: weak signals with a knowledge or patents imbalance

4.4 Creation or reinforcement of research and development activities in Europe

R&D activities were very low in Europe for approximately one quarter of the weak signals detected (64 WS). Figure 13 lists them by topics. Depending on the strategic importance of these technologies, Europe could decide to support the development of these burgeoning research and development activities.

For another quarter of the weak signals (66 WS, listed in figure 14), R&D activities were detected but were at levels below the R&D intensity detected for the main competitors of Europe. Measures to support R&D could be considered for these weak signals, possibly building on the R&D capacity of some identified knowledgeable organisations in Europe.

Figure 13: weak signals with little or no R&D activity in Europe

Figure 14: weak signals for which support to existing R&D activities in Europe could be considered

4.5 Transformation of academic knowledge into patents

Figure 15 shows, by topics, the percentage of scientific publications and patents to which organisations from Europe, China, South Korea, Japan, and the US are contributing. While European organisations are contributing significantly to scientific publications (EU is in the top 3 contributors for each topic of weak signal with the US and China), it can be observed that the leading role of European organisations in production of knowledge does not translate into a strong position in patenting, as they are indeed significantly less European organisations contributing to patents.

Figure 15: contribution to scientific publications and patents by territory and for each topic of weak signals (percentage of publications and patents to which EU organisations contribute are displayed on the graph).

On figure 16, the ratio #patents/#publications is indicated for each topic and for each of the main territories. This ratio is an indirect indication of the capacity of a country to transform knowledge acquired through research (proxy: # of scientific publications) into innovations (proxy: # of patents). Comparing this ratio for the main territories confirms that Europe is less efficient in transforming knowledge into innovations. China, South Korea, and Japan have the highest ratio patents/publications for the various categories of weak signals. The US are either third or fourth (except for the topic viruses where it is second), and Europe has the lowest patents/publications ratio for every topic. This is confirmed by the graphic shown in figure 17 where one can observe the proportion of companies versus public institutions active in the nine weak signals topics.

Figure 16: Knowledge transformation ratio (#patents/#scientific publications) per territory and WS topic.

Figure 17: Proportion of companies in the nine weak signals topics and per main territory.

It can be observed from figure 17 that for Europe the proportion of companies versus universities and research centres varies between 13,5% (Biology) and 36% (Materials). The highest percentage of European companies compared to other countries is observed for “Health”. For the other topics, the proportion of European companies is always above the average ratio companies/(universities + research centres). This shows that European companies are active in research and development related to the 256 weak signals. The lower efficiency in transforming knowledge into innovations observed for Europe can therefore not be attributed to a lower number of active companies.

Figure 18 sheds some light on this observation by plotting the ratio [#patents/#scientific publications] against the ratio [#companies/(# universities and research centres)] for each of the 9 topics and for each territory. A very high capacity to transform knowledge into innovations would locate data points at the bottom right of the graph: the proportion of patents is high, and the proportion of companies is low. A low capacity to transform knowledge into patents and innovations would locate the data points at the top left of the graph: the proportion of patents is low, but the proportion of companies is high. The capacity to transform knowledge into patents varies for China, Japan, South Korea, and the US: these countries have a high transformation capacity for some topics of weak signals, and lower capacity for others. It is striking to observe that Europe has a low capacity of transformation of knowledge into patents for all categories of weak signals (blue dots, all located left of figure 18), and independently of the number of companies active in the various weak signals. It does not matter how high the proportion of companies is (from 13,5% to 36%, see figure 17), the transformation ratio for Europe is always low (low proportion of patents compared to scientific publications). In other words, even when European companies are active in R&D related to one of the weak signals, they do not file as many patents as companies in other of the main territories. As we saw above (point 3.5), this cannot be directly related to differences in the European patenting system and the causes for the low capacity of European companies to transform knowledge into patents are to be found elsewhere.

Figure 18: correlation between the proportion of patents and the proportion of companies for the main territories and for each of the 9 topics of weak signals.

Considering the above, it is not surprising that measures to support patenting could be implemented for 136 weak signals. Europe is indeed lagging behind in patenting for the weak signals listed in figure 19. This could evolve in technological dependency for some of these weak signals, which could be detrimental to Europe should these technologies become strategically or commercially important.

Figure 19: weak signals for which incentives to patenting should be considered

4.6 Multiple countries patents

Figure 20 below shows the number and proportion of patents first filed in the Chinese, American, Japanese, South Korean, European patent offices (UK patent office excluded) or at the European Patent Office that are then extended to another patent jurisdiction: China, US, South Korea, Japan, EPO, or in one of the patent offices of the 27 Member States. The extension of a patent in other territories than the one where it was originally filed is considered to be an indirect indication of its economic value. Indeed, it is costly and risky to extend a patent to other countries and applicants will only engage in it if they expect a return on investment. Although Chinese organisations have filed significantly more patents than other large territories for all of the weak signals, patents that are first filed in China and then extended to other territories are actually less numerous than their US equivalent (patents first filed in US, then extended).

Figure 20: numbers of first patents filed by patent jurisdiction (left) extended to another patent jurisdiction (right).

Figure 20 (left) shows that the number of patents first filed by Chinese organisations in China for all of the weak signals is around 10 times higher than patents first filed in the US (second after China). We have seen above that this striking difference is partly related to the particularities of the Chinese patent system (see 3.5). To complement this analysis, it is interesting to compare the number of patents that have been subject to an extension in at least one additional territory different than the territory of first filing. This number of extended patents for the main territories is shown in figure 20 (right). It can be observed that only around 300 patents first filed in China have been extended to at least one other territory (less than 6,2% of all the patents first filed in China). This proportion is much higher for all the other territories (figure 21).

Figure 21: Proportion of non-extended patents and extended patents by patent jurisdiction (% extended patents shown on top of the columns).

Despite a much higher number of patents first filed in China, only 6.22% are extended to another patent jurisdiction. China files a lot of patents but a small number of them are then extended to another country. The proportion of extended patents is much higher for South Korea, the US, Japan, and for the EU where 80% of the patents first filed in one of the patent office of an EU Member State is then extended to another jurisdiction. European organisations file a small number of patents but many of them are extended to at least one other country.

Even if the proportion of extended patents from China is low, it should be noted that all the patents filed by China (i.e. also those that are not extended to other countries or abandoned after a few years) will be part of what is called in the patenting system the “prior art”, which consists of all the published knowledge related to an invention prior to the filing of a patent for said invention. One essential condition for patentability is novelty i.e. no publication describing an invention in whole or in part should exist prior to the filing date of said invention. If the novelty condition is not met, the invention cannot be protected by patent. By filing a massive amount of patents (even if they are not extended), China is restricting the possibilites for further patenting (by themselves or others) in many technological fields.

4.7 Research ecosystems in Europe

In science, cross-border collaboration is an important contributing factor to papers with a high scientific impact⁵. There is a clear correlation between a nation’s scientific influence and the links it fosters between researchers from different countries. The implementation of measures to increase collaboration among European organisations performing R&D would be beneficial for the research ecosystem of 97 of the weak signals (Figure 22). These environments are indeed characterised by a rather high fragmentation which, for the purpose of this report, is defined as the simple ratio (number of organisations)/(number of collaborations), with a collaboration between two organisations being defined as either a co-publication, a co-patenting, or a joint participation in an EU project. Incentives to collaborations among EU organisations could therefore contribute to strengthen the impact and influence of Europe in these weak signals.

⁵ “Open countries have strong science”, CS Wagner, K Jonkers, Nature News 2017 550 (7674), 32.

Figure 22: weak signals with a fragmented research ecosystem.

Various research ecosystems might also be impacted by Brexit. As organisations from the UK will formally leave the European Union, some knowledge will leave the EU and the collaboration patterns might also be affected. Organisations from the UK and the rest of Europe that were collaborating before Brexit could cease their collaborations. Although it is difficult to estimate the real consequences of Brexit on the various research ecosystems for the weak signals analysed here, figure 23 shows the research ecosystems that are more likely to be impacted by Brexit.

Figure 23: weak signals with research ecosystems that could be impacted by Brexit.

4.8 Productivity of European R&D organisations

Although Europe is the territory with the highest proportion of small organisations with less than 10 documents retrieved, this proportion is of the same order of magnitude for the other main territories considered (figure 24). There is about the same number of small actors in Europe in terms of production of documents than in the other main territories.

Differences between Europe and the other territories appear when looking at organisations at the other end of the spectrum, the ones that produce many documents. As can be observed in figure 25, Europe has less organisations of high productivity (in terms of publishing or patenting) active in the weak signals than South Korea, the US, and China.

Figure 24: percentage of organisations for the main territories per number of documents produced (range 1 to 10 documents). Only the percentage for Europe is shown on the graphic.

Figure 25: percentage of organisations for the main territories per number of documents produced.

4.9 Top organisations

The top 10 organisations in terms of number of documents has been extracted for each weak signal and the results aggregated by topics. Figure 26 shows the organisations that appear the most frequently in the top 10 organisations for each weak signal (grouped by topics). For example, in the topic Energy, the Chinese academy of Sciences appears in the top 10 organisations for 16 of the 24 weak signals in this topic. Chinese and US organisations are the most frequent organisations in the top 10 organisations for each weak signals i.e. they are the most prolific in terms of documents published or patents registered and they perform R&D for most of the weak signals. European organisations do not appear as often in the top 10 organisations performing research in the various weak signals. Although it is difficult to establish a direct causality, this might be related to the fact that the most productive organisations in Europe are not the most productive worldwide (see chapter about the size of organisations at the previous page).

Figure 26: Organisations most frequently present in the top 10 organisations engaged in R&D in the 256 weak signals, grouped by topics.

Figure 27 shows the distribution of the country of origin of the top 10 organisations and confirms the top organisations are most often from China and the US. The only exception is the massive presence of organisations from Europe in the weak signals related to Environment. This could indicate that European organisations are more prone to perform edge research in the field of environment, but this should be nuanced by the fact that none of these organisations are active in more than 2 weak signals on the 18 in the topic Environment, and that the more diverse organisations are from China and the US.

Figure 27: percentage of organisations from the main territories per weak signal topic.

5 Synopsis of the recommendations for the weak signals

A summary of the recommendations is presented in the next pages for each of the scientific topic. A first table summarises the key indicators for the weak signals, and a second table then summarises the recommendations and risks.

Two types of risk are flagged, respectively on the risk of dependency to non-EU technologies and on the risk related to a knowledge imbalance, and 5 types of recommendations are considered:

“Risk of technological dependency”: crossed when there is very little knowledge developed in Europe, or very few patents owned by European organisations, of very few organisations from Europe active in R&D compared to other countries.

Risk related to “Knowledge imbalance”: checked when knowledge in Europe about the weak signal is concentrated in one or two organisations.

“Create R&D” is checked when there is little or no R&D ongoing in Europe for the weak signal considered.

“Reinforce R&D” is crossed when there is R&D ongoing in Europe but relatively less than in other countries.

“Develop network” is checked when the research landscape in Europe is fragmented i.e. when there is R&D in Europe but little collaboration between organisations in Europe.

“Foster innovation” gets a cross when patents have been retrieved for the weak signal but very little of them have been registered by European organisations.

“Support during Brexit” is checked when Brexit will disrupt the landscape of R&D in Europe for a weak signal (when a significant proportion of knowledge (publications or patents) or of organisations are from the UK). Brexit was not considered as an issue for weak signals where there is no or very little R&D in Europe (there is nothing to disturb…).

The detailed analysis for each weak signal is presented in the annex to this report.

5.1 Energy

21 of the 24 weak signals detected are problematic: 14 in the category “EU weak in KW”, 3 in the category “EU weak in IP”. In addition, 4 weak signals in the two categories where EU is strong show a Brexit risk.

Figure 28: weak signals in Energy (Brexit risk highlighted in orange).

Figure 29: Summary of the recommendations for the weak signals in Energy (Brexit risk highlighted in orange).

5.2 Information and Communication Technologies

36 weak signals detected show some vulnerabilities for EU: 18 in the category “EU weak in KW”, 17 in the category “EU weak in IP”, and 1 is analysed for the specific risks related to Brexit.

Figure 30: weak signals in ICT (Brexit risk highlighted in orange).

Figure 31: Summary of the recommendations for the weak signals in ICT (Brexit risk highlighted in orange).

5.3 Health

17 weak signals are analysed: 8 are related to a lack of knowledge in Europe, 7 are related to a weakness in IP, and 2 are related to Brexit.

Figure 32: weak signals in Health (Brexit risk highlighted in orange).

Figure 33: Summary of the recommendations for the weak signals in Health (Brexit risk highlighted in orange).

5.4 Biology

16 weak signals are analysed: 11 are related to a lack of knowledge in Europe, 2 are related to a weakness in IP, and 3 are related to Brexit.

Figure 34: weak signals in Biology (Brexit risk highlighted in orange).

Figure 35: Summary of the recommendations for the weak signals in Biology (Brexit risk highlighted in orange).

5.5 Viruses

11 weak signals are analysed: 7 are related to a lack of knowledge in Europe, 1 is related to a weakness in IP, and 3 are related to Brexit.

Figure 36: weak signals in Viruses (Brexit risk highlighted in orange).

Figure 37: Summary of the recommendations for the weak signals in Viruses (Brexit risk highlighted in orange).

5.6 Materials

47 weak signals are analysed: 40 are related to a lack of knowledge in Europe, 5 is related to a weakness in IP, and 2 are related to Brexit.

Figure 38: weak signals in Materials (Brexit risk highlighted in orange).

Figure 39: Summary of the recommendations for the weak signals in Materials (Brexit risk highlighted in orange).

5.7 Physics and Engineering

19 weak signals are analysed: 11 are related to a lack of knowledge in Europe, 7 are related to a weakness in IP, and 1 is related to Brexit.

Figure 40: weak signals in Physics and Engineering (Brexit risk highlighted in orange).

Figure 41: Summary of recommendations for weak signals in Physics & Engineering (Brexit risk highlighted in orange).

5.8 Social Sciences

Europe is strong in knowledge and in IP for all of the weak signals related to Social Sciences. Four weak signals are analysed for the risks related to Brexit.

Figure 42: weak signals in Social Sciences (Brexit risk highlighted in orange).

Measures to support organisations active in “algorithmic fairness”, “civic technologies”, “digital humanitarism”, and “drug cryptomarkets” could be put in place during the Brexit transition.

5.9 Environment

7 weak signals are analysed: 5 are related to a lack of knowledge in Europe, 2 are related to a weakness in IP.

Figure 43: weak signals in Environment (Brexit risk highlighted in orange).

Figure 44: Summary of the recommendations for the weak signals in Environment (Brexit risk highlighted in orange).

5.10 Persistent weak signals

Documents (patents, scientific publications, EU projects) have been accumulating over many years for some of the weak signals. Figure 45 below shows the distribution of the weak signals per number of years in which documents were produced.

Figure 45: Distribution of weak signals per number of years for which documents were retrieved

Most of the weak signals show a number of years with documents between 3 and 12. Even though it is field dependant, this time range could correspond to some standard speed of research and development. But for some of these weak signals, this number of years for which documents were retrieved is higher (up to 25 years). The “persistent” weak signals with documents retrieved for more than 14 years are listed in figure 46.

This constitutes some kind of paradox: how to explain that R&D spreads over 25 years for weak signals i.e. new technologies? The indicators used by JRC to detect the weak signals have been used precisely to include weak signals of this sort (with many years of documents accumulation)⁶. Although it was not the main objective of the study, the aim was to also detect weak signals that correspond to a new application/product/service related to an existing and well-known technology. These signals would be characterised both by a peak of documents over the last years (signature of a typical weak signal) and by an accumulation of documents over many years (signature of a known technology).

There are various reasons for such accumulations of documents over a long period of time. For most of the persistent weak signals, the research phase is simply longer than for others. For some, it is the retrieval of false positive documents (noise) during the semantic queries that explains the persistence in whole or in part. Although semantic queries are optimised, they are never perfect. For some, it could be explained by the discovery of a new application of a known technology or by a longer R&D phase than average, for example due to some technological barrier. For some others, it could be related to a policy decision that triggered a renewed interest for a particular known technology or topic of research.

Out of these 46 persistent weak listed in figure 46, 9 have been linked to new applications of known technologies or materials (in green), 2 were linked to technological barriers that have been resolved or about to be resolved (in blue), and 1 was linked to a policy making decision that triggered a surge in R&D (in orange).

Figure 46: List of persistent weak signals.

⁶ Various “activeness” indicators have been used. See report EUR 29900 EN

6 Conclusions

1. Europe is:

Strong in 80 of the weak signals detected (30%);

Not transforming its scientific leadership into patents in 44 of the weak signals (17%);

Lacking scientific knowledge and lagging behind in patenting in 114 weak signals (45%).

2. Europe is strong in weak signals related to social Sciences, Health, and Environment. Europe shows vulnerabilities in Physics & Engineering, Biology, ICT, Viruses, Energy, and Materials.

3. Looking at sub-topics, Europe is strong in weak signals related to biomaterials, microbiology, neurosciences, new medicines, and digital social sciences. Europe is vulnerable in weak signals related to the 27 other sub-topics, including perovskite, solar cells, 5G, Internet of Things, 3D printing, Neural Networks, viruses, new materials (2D materials, nanomaterials, metasurfaces, new catalysts).

4. Overall, European companies are participating in research and development efforts in the various weak signals. The proportion of companies versus universities and research centres varies between 13,5% (Biology) and 36% (Materials). Europe has the highest percentage of companies active in the topic “Health” and this proportion for the other topics is always above the average ratio of company participation.

5. Europe suffers from a low capacity of transformation of knowledge into patents for many of the weak signals. Even when European companies are very active, there is not as many patents filed in Europe than in the other main territories like the US, China, Japan, or South Korea.

6. Europe has less large size organisations (publishing or patenting) involved in the weak signals than South Korea, the US, and China. There is about the same number of small actors in Europe than in the other main territories.

7. Despite a much higher number of patents first filed in China (10 times more than the US which is second in patenting), a small number of these patents (6.22%) are extended to another patent jurisdiction. The proportion of patents extended is much higher for South Korea, the US, Japan, and for the EU.

8. European organisations file a smaller number of patents than organisations in the US, Japan, South Korea or Japan, but many of them are extended to at least one other country (80% of the patents first filed in one of the patent office of an EU Member State is then extended to another jurisdiction).

9. In terms of absolute numbers of patents, organisations in the US are the ones extending the most patents to other jurisdictions. China is second ahead of South Korea, Japan, and the EU.

10. Leading organisations are most often from China and the US, with the exception of weak signals in Environment where the majority of the top organisations are form Europe.

11. Recommendations:

98 weak signals present a risk of future technology dependency of Europe.

There is an imbalance of knowledge distribution in Europe for 8 of the weak signals (knowledge monopoly or duopoly).

There is no or little R&D activities in Europe for 64 weak signals, requiring the creation of R&D activities (very few or no active EU organisations).

The research ecosystems in 66 weak signals should be reinforced (there are EU actors but they are not leading and there is a risk of Europe losing grip).

Research ecosystems in Europe are fragmented in 97 of the weak signals and measures to develop collaborations among EU organisations should be implemented.

Incentives to innovation and patenting should be put in place for more than half (136) of the weak signals.

Measures to support European R&D ecosystems during Brexit are suggested for 42 of the weak signals.

7 Annex

Figure 47 shows the information displayed for each weak signal that is analysed in the annex of the present report.

Figure 47: Information contained in the analysis of the weak signals.

7.1 Energy

7.2 Information and Communication

7.3 Health

7.4 Biology

7.5 Viruses

7.6 Materials

7.7 Physics and Engineering

7.8 Social Sciences

7.9 Environment

7.10 Persistent weak signals

List of figures and tables

Figure 1: wheel of weak signals, per topic.

Figure 2: List of weak signals per topic.

Figure 3: Categories chosen for the analysis of weak signals

Figure 4: number of patents families per year and related relative proportions for the 5 biggest territories

Figure 5: total number of patents applications for the year 2018 contained in PATSTAT

Figures for the Korean, Japanese, US, Chinese, and European patent offices

Figure 6: proportion of invention patents published as a function of the number of months after filing

Figure 7: number of patents and publications per topic of weak signals

Figure 8: proportion of weak signals per categories.

Figure 9: proportion of weak signals per categories and by topics.

Figure 10: Positioning of Europe in sub-topics of weak signals

Figure 11: weak signals presenting a risk of technology dependency for Europe

Figure 12: weak signals with a knowledge or patents imbalance

Figure 13: weak signals with little or no R&D activity in Europe

Figure 14: weak signals for which support to existing R&D activities in Europe could be considered

Figure 15: contribution to scientific publications and patents by territory for each topic of weak signals

Figure 16: Knowledge transformation ratio (#patents/#scientific publications) per territory and WS topic.

Figure 17: Proportion of companies in the nine weak signals topics and per main territory.

Figure 18: correlation between proportion of patents and proportion of companies for the main territories and for each of the 9 topics of weak signals.

Figure 19: weak signals for which incentives to patenting should be considered

Figure 20: numbers of first patents filed by patent jurisdiction extended to another patent jurisdiction

Figure 21: Proportion of non-extended patents and extended patents by patent jurisdiction

Figure 22: weak signals with a fragmented research ecosystem.

Figure 23: weak signals with research ecosystems that could be impacted by Brexit.

Figure 24: percentage of organisations for main territories per number of documents produced

Figure 25: percentage of organisations for the main territories per number of documents produced.

Figure 26: Organisations most frequently present in the top 10 organisations engaged in R&D in the 256 weak signals, grouped by topics.

Figure 27: percentage of organisations from the main territories per weak signal topic.

Figure 28: weak signals in Energy

Figure 29: Summary of the recommendations for the weak signals in Energy

Figure 30: weak signals in ICT

Figure 31: Summary of the recommendations for the weak signals in ICT

Figure 32: weak signals in Health

Figure 33: Summary of the recommendations for the weak signals in Health.

Figure 34: weak signals in Biology

Figure 35: Summary of the recommendations for the weak signals in Biology

Figure 36: weak signals in Viruses

Figure 37: Summary of the recommendations for the weak signals in Viruses.

Figure 38: weak signals in Materials

Figure 39: Summary of the recommendations for the weak signals in Materials

Figure 40: weak signals in Physics and Engineering

Figure 41: Summary of recommendations for weak signals in Physics & Engineering

Figure 42: weak signals in Social Sciences

Figure 43: weak signals in Environment

Figure 44: Summary of the recommendations for the weak signals in Environment.

Figure 45: Distribution of weak signals per number of years for which documents were retrieved

Figure 46: List of persistent weak signals.

Figure 47: Information contained in the analysis of the weak signals.