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Paper to be presented at DRUID19Copenhagen Business School, Copenhagen, Denmark

June 19-21, 2019

Scientific knowledge – a missing link to understand firm innovation in a high technologyindustry

Chandrika RatheeIE university

[email protected]

Marco S. GiarratanaIE Business School

[email protected]

Konstantina ValogianniIE Business School

Information [email protected]

AbstractScientific knowledge is crucial for innovation, especially so in a science-based industry such aspharmaceuticals and semiconductors in which the majority of the innovations depend on somescientific breakthrough. High technology firms in these industries often face the paradox to invest inbasic scientific research and also focus on capturing the returns from their R&D investments. In thisstudy, we explore the extent of overlap and differences between scientific and technologicalknowledge. We take granted patents as the repository of technological knowledge and publishedscientific articles as the repository of scientific knowledge and use novel text-based big data analyticsto explore prominent scientific and technological themes in the innovation space of EES (electricenergy storage) devices for electric vehicles. Our results suggest thematic differences that can beattributed to the value-appropriation challenges associated with the scientific knowledge that firmsface. This scientific research necessity and appropriability paradox make firms to disclose scientificknowledge in their claimed patents partially. On the other hand, in scientific publications, theresearchers are incentivized to disclose the knowledge gains in full detail. We further use text similaritymeasures to identify scientific knowledge themes in the patented innovations to depict an alternatepicture of firm innovation focus. We find that the top innovating firms appear to belong to differentclusters after considering the distribution of scientific knowledge themes in their innovation portfolios.Insights from our findings provide technology scholars a so-far missing view of the innovationdirections pursued by the firms.

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Mapping Patents and Scientific Articles by Text Mining: The Case of Electric Energy Storage Technology

Submission for the DRUID society conference 2019

Revised draft June 2019

ABSTRACT

Using text analysis, we construct and compare themes of the technology landscape in the

context of electric energy storage (EES) devices for electric vehicles. First, we use topic

modeling to construct thematic maps coming from 7734 patents granted to companies

(Industrial Research) and 675 pure academic scientific articles (Basic Research) between the

years 2001 and 2017. We find that modular innovation explains better Basic Research

directions, while architectural innovation is a good framework for interpreting Industrial

Research. Then, we use kNN classification algorithm to map patents with Basic Research

themes and construct an alternate view of firm patent portfolio. With this new methodology,

strategic performance directions hidden in patents text emerge more clearly, and companies

are reshuffled into different competitive clusters. Finally, we validate our results with

interviews with field experts.

Keywords: matching, patent, technological similarity, text mining, scientific articles, battery, Electric vehicles, Big Data

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INTRODUCTION

Basic research knowledge influences industrial research trajectories of for-profit

companies because they absorb basic research knowledge from, or outsource to, public

institutions (Henard & McFadyen, 2005; McMillan, Narin, & Deeds, 2000; Popp, 2017;

Rosenberg, 1990). There is extensive literature that highlights how R&D investments and

outcomes are the results of a complex process, involving formal and informal feedbacks

between companies and public research institutions (Cassiman & Veugelers, 2006; Feldman,

Feller, Bercovitz, & Burton, 2002; Freeman, 1982; Henard & McFadyen, 2005; Murray,

2002; Rosenberg, 1974, 2010)). Basic research is a source of valuable knowledge to firms

(Gittelman & Kogut, 2003; Popp, 2017): it facilitates firm problem solving and inventive

search being both a building block of firm absorptive capacity (Cohen & Levinthal, 1990;

Henard & McFadyen, 2005; Martinez-Senra, Quintás, Sartal, & Vázquez, 2015; Cockburn &

Henderson, 1998; Fabrizio, 2009), and a cognitive anchor for industrial researchers to

identify re-combinatory opportunities and new trajectories (Bikard, 2018; Fleming &

Sorenson, 2004; Gavetti & Levinthal, 2000; Vincenti, 1990). Collaborations between

industry and basic research are common in several industries (Mansfield & Lee, 1996), and

sometimes R&D laboratories of companies are composed by former academics who still

maintain a strong link with their previous profession (Giarratana, Mariani, & Weller, 2018).

However, given the division of labor that usually exists between basic and industrial

research (Arora, Fosfuri, & Gambardella, 2001), the two domains are characterized by

different communities of references, with diverse rules, behaviors, and aims. While industrial

inventors use R&D, property rights, and innovation to create competitive advantage and

generate profits for their companies (Liebeskind, 1996; Teece, 1986), academic inventors

usually respond to openness, autonomy, publication pressure, legitimacy, and reputation

(Gambardella, Panico, & Valentini, 2015; Merton, 1973). This means that the aims, the

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language, and the research trajectories of the two communities could be different and even

diverge sometimes (Dosi, 1982; Rosenberg, 2010).

Granted, literature still seeks to understand how basic research could help to better

interpret the map of industrial research trajectories coming from patents. The use of patent

information in order to place companies inside (or not) similar research trajectories enjoys

recently popular currency as a tool to understand R&D competition (Arts, Cassiman, &

Gomez, 2017; Kaplan & Vakili, 2015). This article seeks to identify whether basic and

industrial research generate different research trajectories inside a particular field, and to

what extent they are interrelated. How much do the research trajectories in basic and

industrial R&D differ? How could basic research trajectories be useful to interpret the

industrial ones?

We choose electric energy storage (EES) devices targeted for electric vehicles as our

empirical context. On one hand, given the rising importance of the electric vehicle demand,

in which energy storage is a fundamental technological factor, companies are now heavily

investing resources in R&D to secure an edge over competitors1. On the other, applied

technological advancement in EES devices are strongly dependent on the scientific

discoveries and trajectories in the fields of Electrochemistry and Materials Science (Pollet,

Staffell, & Shang, 2012; Rahman, Wang, & Wen, 2014; Yoo, Markevich, Salitra, Sharon, &

Aurbach, 2014).

From an empirical standpoint, we collected all USPTO patents (2001-2017) granted

to firms and all scientific publications in top journals by academic authors (2001- 2017)

related to ESS, to proxy industrial and basic research knowledge and languages. Then, we

apply topic modeling (Blei, 2012), and specifically Latent Dirichlet Allocation (LDA) (Blei, 1According to one report Ford Motor Co’s plan to double its electrified vehicle spending in batteries and electric cars where total investment by global automakers can exceed $90 billion mark. https://www.reuters.com/article/us-autoshow-detroit-electric/global-carmakers-to-invest-at-least-90-billion-in-electric-vehicles-idUSKBN1F42NW [Date Accessed: 29-Apr-2019]

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Ng, & Jordan, 2003), a machine learning algorithm to identify key themes in basic and

industrial research reflected in these two text corpora. After comparing the differences in

research trajectories between basic and industrial research, we introduce the k-Nearest

Neighbor (kNN) machine learning algorithm (Cover & Hart, 1967) to re-map industrial

research trajectories derived from patents with the taxonomy coming from scientific articles.

The combination of these two machine-learning algorithms allows us to compare the

differences among these two maps, when we track patent portfolios proximity of the top 20

firms for patent activities in EES.

Our approach is similar to Haans (2019) who shows how firms’ competitive

positioning could be interpreted by text mining techniques, in his case applied to firm web

site. Particularly, this article extends the conversation on basic versus industrial research by

highlighting the differences between the two in terms of research trajectories (McNie et al.

2016). Our first stylized fact is that basic research in EES is more similar to a modular

innovation process targeted to advance EES performances in particular components;

industrial research is more embedded in an architectural innovation process focused on

changing the basic design of a product with particular attention to adaptation problems of

different components (Henderson & Clark, 1990). Interestingly enough, it is the basic

research, and not the industrial, that seems more interested in solving clear product

performance objectives derived by components advances. There is scarce attention by

companies in patent texts in terms of performance issues. This could be the results of a

strategic bias due to how firms decide to reveal information in patent documents (Arora et al.,

2001; Henkel, Schöberl, & Alexy, 2014).

However, by re-classifying patents with a basic research taxonomy derived from

scientific articles, we construct an alternate view which highlights the performance focus of

firms, and hence provides a different perspective on R&D competition in terms of firm

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positions in a technology space. In this respect, we introduce a novel text-based method for

using themes and trajectories in scientific publication texts to categorize and decode

knowledge embedded in the patent texts. Text analytics have been successfully used to

measure the similarities between patents (Arts et al., 2017) and to identify the patents with

technological breakthroughs (Kaplan & Vakili, 2015; Verhoeven, Bakker, & Veugelers,

2016). In this study, we combine two main methods – topic modeling (LDA) (Blei, 2012) and

kNN text classification algorithm (Deng, Zhu, Cheng, Zong, & Zhang, 2016; Zhou, Li, &

Xia, 2009). This methodology allows us to extract and compare different competitive

technology maps and identify if firms fall together or not in two alternate views. With this

new methodology, performance issues hidden in patents text could emerge more clearly and

companies are reshuffled into different competitive clusters.

THEORY

Basic and Industrial Research

There is heterogeneity in the conceptualization and operationalization of the key

concepts such as innovation, technology and invention (Garcia & Calantone, 2002). Science

and technology are differentially associated with basic research and applied research

respectively, but innovation, invention and technology are often confused by different

labeling approaches (Bercovitz & Feldman, 2007; Huenteler, Ossenbrink, Schmidt, &

Hoffmann, 2016; Popp, 2017; Verhoeven et al., 2016). In this paper, we take a bird-eye view

approach by referring to industrial research as the outcome of R&D investments of for-profit

companies that is visible and protected in granted patents. Basic research is defined as the

research that appears published in top scientific journals of references produced by public

institutions. Thus, basic research, in our view, is near to the definition of invention as an

outcome of research activities that forms the basis of applied research. Applied research is

more near to industrial R&D, which in turn involves product development, manufacturing,

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marketing, distribution, and after-sales service (Garcia & Calantone, 2002; Makri, Hitt, &

Lane, 2010).

The process of basic and industrial research could be then quite diverse, given that

they are carried out by different organizations and communities (Arora & Gambardella,

1994) like competitive companies and public institutions. The firms operating in high

technology industries often, need to focus on capturing the returns from their R&D

investments (Liebeskind, 1996; Teece, 1986) in which basic research is necessary, but not

sufficient (Fleming & Sorenson, 2004; McMillan, Narin, & Deeds, 2000; Popp, 2017).

Industrial inventors are, usually, driven by company incentives to create for-profit research

outcomes both in terms of new products or processes or new protectable ideas (Gambardella

& Panico, 2008). The quest for appropriability leads firms to acquire IP protection for their

research outcomes in order to appropriate the full value (Arora & Ceccagnoli, 2006).

In contrast, the Public research is often conducted under an open-access approach, in

which the scientific community’s main research drivers are legitimacy, reputation, social

impact, and scientific publication peer-pressure (Giarratana et al., 2018; Merton, 1973). In a

nutshell, basic research, being more abstract in nature, requires a collaborative atmosphere in

which researchers from different institutions exchange and advance scientific knowledge

(Katz & Martin, 1997); industrial research is competitive in nature, and it is used by firms to

try to outperform or block competitors. Basic research is more lead by curiosity, social

needs, and intellectual freedom; industrial research is directed to profit aims, product or

process problem-solving, and benchmarking with competitors (Dosi, 1982; Jha & Lampel,

2014; McEvily & Chakravarthy, 2002). Thus, it is not surprising, then, that research

trajectories could sometimes diverge in the two cases (Rosenberg, 2010), since they belong to

paradigmatically different communities of research (Kuhn, 1970; Merton, 1973).

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As a direct corollary, the two communities tend to make their outcomes visible in

different outlets: scientific articles and patents, respectively. Scientific articles are aimed to

flag priority in discovery and to share knowledge and foster novel science contributions

(Godin, 1996). A scientific article is peer-reviewed by members of the same scientific

community, and it needs to conform to the standard language and to explicitly state the

arguments and the novelty of results. In contrast, patents are filed to define the boundaries of

the intellectual property and are particularly tailored to protect and clarify claims on novel

processes and products(Lanjouw & Schankerman, 2001). The patent examiner is usually a

public officer who reviews patent grating possibilities according to the actual laws, legal

rules, and state of knowledge as visible in active patents(Gans, Hsu, & Stern, 2008)

.Companies are then clearly concerned about what information to disclose in patents, and

what information to keep secret (Arora & Ceccagnoli, 2006; Henkel et al., 2014). In this

respect, the language used in the two types of documents differs not only by its logical

construct, but also in light of the main type of review process.

However, the role of learning and search over a trajectory is fundamental (Katila &

Ahuja, 2002). Any research advancement is the outcome of a search process and

recombination process (Arthur, 2007; Fleming & Sorenson, 2004). Basic and industrial

research are often interdependent, and firms formally and informally source from basic

research (Cassiman & Veugelers, 2006; Roberts & Mizouchi, 1988; Schilling & Phelps,

2007; (Bercovitz & Feldman, 2007; Meyer-Krahmer & Schmoch, 1998). Moreover, scholars

have recognized the science-technology link and the strong similarities between procedures

of basic and industrial research (Dosi, 1982). Evidence suggests that science serves as a map

for industrial research especially in highly coupled science-based industries (Fleming &

Sorenson, 2004) where sometimes “it is hard to draw a line between basic and applied

scientific research” (Nelson, 1959, p. 300). Recent research on the scientific article citations

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in patents has proven the importance of basic research to reach a patentable applied research

outcomes(Arora, Belenzon, & Patacconi, 2015; Branstetter & Ogura, 2005; Gittelman &

Kogut, 2003). However, there is a key point to fully understand the information present in

patents. While the basic research ideas could support industrial research, they could remain

primarily hidden and unarticulated in patents, even if they were fundamental in the discovery

process (Dosi, 1982, p. 153), also maybe for strategic reasons.

EES devices for electric vehicles: A high-technology industry

“In periods of technical change, when uncertainty is high, heuristics break down. As a

result, different firms’ interpretations are likely to diverge widely, and tight alignments

between cognition, capabilities, and incentives within firms likely break apart”(Kaplan, 2008,

p. 673), and most of the competitive efforts is directed to accrue first mover advantages

thanks to R&D investments (Lim, 2004). Superior Battery technology has fueled the Electric

Vehicle revolution which has disrupted the automobile industry(Dinger et al., 2010).

EES for electric vehicles is a high technology industry(Cano et al., 2018) , with strong

links with basic research since advances are heavily dependent on the scientific

breakthroughs in the fields of Electrochemistry and Materials Science (Pollet et al., 2012;

Yoo et al., 2014). EES technology is hailed as one of the most crucial in today’s world with

the potential to change the world for better (Dell & Rand, 2001). The interest is well justified

given the global drive to shift to sustainable energy sources and the potential of improved

electric energy storage technology to enable the shift. The research in EES technology has

enjoyed a substantial share of government funding, and is vigorously pursued at various

government research centers and universities2. The support is not only limited to research

funding but also to the implementation of public policy favorable to the adoption of improved

2 In 2018 the US Department of Energy published a report highlighting the importance of technology and the department’s research efforts. https://www.energy.gov/technologytransitions/downloads/august-2018-spotlight-solving-challenges-energy-storage [Date Accessed:25-Jan-2019]

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technology to boost consumer demand. The recent rise in adoption of electric vehicles, which

are mainly relying on EES instead of the traditional internal combustion engines, is a case in

view. Electric vehicles have become an increasingly important “niche” in the transportation

sector3 4. Thus, EES technology is high in priority for both public research agencies

(government research agencies and universities) and private firms active in the industry.

The power generation in Electric vehicles are based on two main technological paradigms:

fuel-cells that generate electricity from hydrogen, and an EES device comprising of batteries

(Cano et al., 2018). These configurations differ substantially in underlying scientific

principles to justify being called as distinct paradigms. To identify comparable trajectories,

we need to focus on one paradigm at a time. In this study, we choose EES i.e. battery

paradigm as it is the one dominating the electric vehicles market so far5.

Research trajectories usually evolve reflecting the efforts of researchers to tackle

multi-dimensional performance tradeoffs. These tradeoffs form the basis of evaluation

criteria and critical challenges faced by the industry and the researchers. Apart from the

economic criteria of cost and size, some of the key research challenges for EES are life-span,

safety, performance across temperature ranges, charging time, and state of charge

behavior(Dinger et al., 2010; Rauh, Franke, & Krems, 2015). These performance concerns

are linked with improving on one or more technical parameters such as energy density, power

density, specific power, specific energy, Internal Resistance, Cycle Life and electrochemical

3 According to International Energy Agency (IEA), the number of electric cars on the road could reach a 220 million mark in 2030 given the policy initiatives and institutional ambitions to meet climate goals and other sustainability targets. https://webstore.iea.org/global-ev-outlook-2018 [Date Accessed:15-Jan-2019] 4 Under Horizon 2020 program, European Commission launched €10 million EIC Horizon Prize for innovative batteries for electric vehicles. https://ec.europa.eu/info/news/commission-launches-eu10-million-eic-horizon-prize-innovative-batteries-electric-vehicles-2018-feb-23_en[Date Accessed-20-Jan-2019] 5As per the IEA Global Electric Vehicle (EV) Outlook 2018 - in 2017, the global FCEV car stock surpassed 7 200 units- which was significantly less than BEVs and PHEVs (Advanced Fuel Cells TCP, 2018). Plus, most of the FCEVs are located in few locations for instance – California, United States, with more than 3 500 FCEV cars, accounted for almost half of the global FCEV fleet. https://webstore.iea.org/global-ev-outlook-2018 [Date Accessed:15-Jan-2019]

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impedance(Linden & Reddy, 2002). These parameters are linked with the proprietary

electrochemistry and overall design of an EES. It represents the generic space in which firms

compete with R&D investments. An EES or battery pack comprises of various components

and is specifically designed to meet the application requirements. For instance, Samsung SDI,

a leading battery manufacturer, develops customized high-performance battery packs to meet

application needs of global car manufacturers. Appendix A provides an illustration of the

generic design of an EES device revealing main components and design hierarchy6.

METHODS

Data

Scholars have relied on firm patents as a repository of technological knowledge representing

the outcome of industrial research (Aharonson & Schilling, 2016; Kim, Lee, & Kwak, 2017;

Martinelli, 2012). Scientific articles are usually seen as a repository of scientific knowledge

generated out of basic research efforts (Mina, Ramlogan, Tampubolon, & Metcalfe, 2007;

Murray, 2002). In this study, we collect two document sets: patents granted by the USPTO

(United States Patent and Trademark Office); and scientific articles published in the high

impact journals.

First, we collect the whole corpus of patent documents granted in the technological

field of EES devices for electric vehicles by the USPTO starting from the year 2001 until

2017. We select all the patents granted under the CPC (Cooperative Patent Classification)

class “H01M” that have “electric vehicle” or “electric car” anywhere in their text 7. We

6 Currently it is available on demand and will be published with this study as an online Appendix. 7Patents are classified based on CPC (Cooperative Patent Classification) scheme. The scheme is jointly developed and managed by USPTO(United States Patent and Trademark Office) and EPO (European Patent Office).The higher number of CPC classes and subclasses represent the technological complexity of patented innovation. We choose the CPC subclass “H01M” because it is designated for classifying the innovations in the technological field of electric energy storage devices and related innovations. As per official definition, CPC class “H01M” corresponds to “processes or means, e.g., batteries, for the direct conversion of chemical into electrical energy” https://www.uspto.gov/web/patents/classification/cpc/html/defH01M.html [Date Accessed:15-Jan-2019]

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collect 13,450 distinct patents granted to only private for-profit firms between year 2001 and

2017.

Second, we select all the scientific articles with “electric vehicle” appearing anywhere

in the text and published from year 2001 to year 2017, classified by Thomson Reuters Web of

Science Core Collection and Journal Citation Reports in the category “electrochemistry”. We

focus on “electrochemistry” category journals to ensure that we include only the highly

relevant scientific research undertaken by a common scientific community (Godin, 1996;

Ranga, Debackere, & Tunzelmann, 2003)8. We include only the articles, dropping reviews,

editorials, or news items. We also dropped out any research article whose author affiliation is

a private firm. Our final sample consists of 1,257 articles published in the top journals and

documenting the basic research undertaken at top public institutions (universities or research

institutions). The articles are published between year 2001 and 2017 both inclusive.

Appendix B provides the detailed descriptive of the patent dataset and Scientific articles

dataset. It also provides the comparative picture of yearly trend of these document sets9.

In general, we observe steady increasing trend in articles and patents with the biggest boost

between year 2012 and 2014, where the numbers show more than 200 percent rise. This trend

coincides with the rise of electric vehicles and massive increase in introduction of new

models in the electric vehicle market10.

The USPTO patent documents are uniformly structured with the innovation

information contained under the four main headings – “Title”, “Abstract”, “Claims”, and 8 The electrochemistry category is the best match for EES devices. InCites Journal Citation Reports dataset defines the electrochemistry category as “Electrochemistry covers resources that deal with the chemical changes produced by electricity and the generation of electricity by chemical reactions. Applications include dry cells, lead plate, storage batteries, electroplating, electrodeposition (electrolysis), purification of copper, production of aluminum, fuel cells, and corrosion of metals.” 9 At this stage, both the document sets contain artifacts related to Fuel cell paradigm as well, which we will identify and remove by using topic modeling. That reduced sample will reflect the EES technology or Battery paradigm which is the subject of this study. And, we will report the descriptives for that sample latter in results. 10 Both in Europe and United States, the electric cars saw a major boost between 2012 and 2014 with massive increase in new models being tested. https://setis.ec.europa.eu/sites/default/files/reports/Electric_vehicles_in_the_EU.pdf http://fortune.com/2015/01/08/electric-vehicle-sales-2014/

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“Description”. Every patent document has an identifying title, an abstract which provides the

brief snapshot of the innovation, a description section which identifies the application space

of the innovation, and a claims section which enlists the proprietary innovation claims

secured by the patent. “Claims” is one of the most scrutinized part by examiners of patents as

it enlists the legal/enforceable claims. In a nutshell, claims and description sections contain

the information crucial to identify the innovation11. Given the importance of contextual

information for our purpose, we include the entire information available in patents.

Scientific articles are less uniformly structured as the patents due to absence of any

legal requirements. However, it bears a common structured that defines the research problem,

justifying the methods, and reporting the findings (Argamon, Koppel, Fine, & Shimoni, 2003;

Perneger & Hudelson, 2004). All articles have “Title”, “Abstract” and assigned keywords

that provide the summary of the research article and are freely available to the scientific

community. These sections together serve the purpose of delineating the paper's purpose and

central theme. The main body text of a scientific article contains details of the prior theory,

data sample, methods used, major findings and supporting justifications for the findings. The

structure of the main body of text varies across articles and journals. Hence, for scientific

published articles, we find the “Abstract”, “Title” and “keywords” as the most appropriate

and uniform formats which can be seamlessly aggregated across articles for a combined

analysis.

Methods

We first introduce Latent Dirichlet Allocation (LDA), an unsupervised topic modeling

algorithm, to identify key topics in basic and industrial research reflected in two text corpora:

scientific articles and patents document sets. LDA is one of the most robust topic modeling

algorithms, when it comes to uncovering topics discussed in text (Lu, Mei, & Zhai, 2011;

11 https://www.uspto.gov/patents-getting-started/general-information-concerning-patents#heading-17 [Date accessed :15-Feb-2019]

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Kaplan and Vakili, 2015). It is shown to have performed well in diverse text mining

environments and is one of the most preferred text mining algorithms (Hannigan et al., 2019;

Liu, Li, Liu, & Ponraj, 2011). In many ways, LDA is even superior to manual coding as is

capable of efficiently processing a massive collection of documents providing a unbiased and

replicable classification of topics (Huang, Lehavy, Zang, & Zheng, 2018).

In a second step, we use kNN algorithm. A kNN is a similarity-based machine

learning algorithm that uses pre-classified documents to categorize new documents based on

a chosen similarity measure (Altman, 1992). It is a powerful yet not computationally complex

algorithm for text categorization (Guo, Wang, Bell, Bi, & Greer, 2006; Zhang & Zhou,

2005). The algorithm categorizes a new document “D” (represented as a vector of word

weights) by calculating the similarity of the document “D” with respect to the documents in

the training set (documents whose class is known) and by finding the prominent class in the

“k” nearest neighbors of the document based on calculated similarity values (Guo et al.,

2006). In sum, first, from scientific articles and patents we extract prominent themes

identified as topic by the LDA model in the two domains. Second, the kNN algorithm is

employed to classify patents based on text similarity between themes of pre-classified

scientific articles. By doing so, we are able to create a new map to cluster patents using

knowledge derived from the scientific literature that could not be identified by the LDA

applied singularly to each document corpus.

Topic Model and Latent Dirichlet Allocation (LDA)

The topic modeling method LDA is a statistical model commonly used to identify

underlying semantic themes or “Topics” in text documents (Blei, 2012; Blei, Ng, & Jordan,

2003). It is a powerful data-driven approach for discovering the latent(hidden) thematic

structure in vast archives of text (Hastie, Tibshirani, & Friedman, 2009; Hofmann, 2001).

LDA assumes documents as probability distribution over latent topics and topics as

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probability distribution over words. The algorithm posits that each topic is associated with

unique words distribution. Each document is considered a mixture of a small number of

underlying topics and each word in the corpus is assumed to be associated with these topics.

Taking only the text contained in documents as input, the model generates automatic

summaries of prominent topics in terms of a discrete probability distribution over words for

each topic. It, then, provides a list of identified topics for the document corpus, and a

probability vector for each document suggesting a relative representation of identified topics

in it. Appendix C provides the illustration of above-explained intuition behind LDA using an

example of a patent document from our sample.

One crucial decision, while implementing LDA, is the selection of the number of

topics. Although LDA is an unsupervised machine learning model, it requires the number of

topics to be provided by the researcher. While each distinct word in itself can represent a

topic, the general purpose is to simplify and yet, identify the most significant number of

distinct and meaningful topics. Hence, it is recommended to manually analyze and constrain

the number of topics as per the required parsimony by the researcher (Blei & Lafferty, 2007).

Additionally, we have to be mindful of the hierarchal structure of the knowledge base for

extracting the interpretable valid semantic themes(Anoop V.S, Prem Sankar C, Asharaf S, &

Alessandro, 2015). As described in the theory section, an energy system for electric vehicles

comprises of two main technological paradigms: Fuel-cells, and an EES device comprising of

batteries. Hence we expect the documents to differentially address these paradigms.

To extract the trajectories in EES i.e. Battery paradigm, we need to ensure that we

exclude all documents primarily addressing Fuel cell paradigm. Otherwise the

trajectories might be contaminated. Hence, we perform a two-stage LDA. In stage-

one LDA, we run a 2-topic solution to identify the battery paradigm documents, and in

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stage-two LDA, we perform a 20-topic solution to identify the trajectories within the

battery paradigm12.

Our aim is not to identify novel themes (or breakthrough innovation) instead it is to

identify the relatively stable and recurrent themes reflecting the prominent research

trajectories. While the breakthrough innovations might range in hundreds as was done in

case of Kaplan & Vakili (2015), The prominent trajectories have to be lot lesser. Hence, we

look for a small number of distinct and uniform topics representing the most prominent

knowledge themes which can be compared across technological and scientific text corpus.

Our search for meaningful topics was guided by the identification of a distinct and

meaningful themes. For ensuring distinctness, we observe the inter-topic term overlap. 20

topics make sense with clear identifiable themes (addressing distinct performance or

technical dimensions) and optimal inter-topic term overlap13. Hence, we selected to explore

the 20 distinct trajectories.

Appendix D covers the detailed illustration of the Two-stage LDA and subsequent

steps to extract distinct and meaningful topics and subsequent naming of topics or Topic

labeling method used for theme identification of the generated topics.

kNN classification- Remapping of industrial research trajectories with scientific article

themes

The similarity between two documents is defined according to a distance metric between the

text-token vectors of the documents. The kNN algorithm steps are as follows: 1) it calculates

the Euclidean distance between the patent and each of the pre-classified scientific articles.

12 LDA identifies the prominent topics in the documents set. In stage one- the two topic LDA solution identifies the two prominent paradigms in EES for electric vehicles i.e. Battery and Fuel cell. In stage-2 of our approach, we retain the documents identified as belonging to Battery paradigm for further analysis and explore the key trajectories (Topics) within by running a 20 topic LDA model. 13 For scientific articles, the 20 topic solution have maximum term overlap of about 1 percent only(and average overlap is only 0.43 percent). This was calculated taking top 1000 terms of each identified topic. In contrast, for a 100-topic solution, we are not able to identify the clear topics with a huge inter-topic term overlap (maximum overlap of 33 percent and average overlap of 15 percent).

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The Euclidean distance between a patent and an article is calculated based on the difference

between their text token frequency vectors p⃗ and a⃗ respectively. The token frequency

vectors p⃗ and a⃗ were generated by applying the bag-of-words model on the combined

corpus of the patents and the text of the articles. “n” represents the number of unique tokens

(unigrams and bigrams); 2) it then sorts the Euclidean distances in increasing order to select

the 21 nearest neighbors of the patent based on distance values; 3) among these 21 neighbors,

it counts the number of data points to each topic representing a unique scientific knowledge

theme; 4) finally, it assigns the patent to the most prominent topic out of the 21 topics14.

Appendix E illustrates this approach for reclassification of patents based on text

similarity with Scientific articles taking examples from our dataset.

RESULTS

Basic research view:Topics in scientific articles

We run the two-stage LDA described before on our sample of 1,257 distinct scientific

articles. In the first stage LDA, we identify that 675 out of 1257 articles belong to EES or

battery paradigm15. Table 1 provides the key descriptive statistics of the published articles in

our sample.

------------------------------ Insert Table 1 about here ------------------------------

The articles are collaborative works between on average five authors who are involved with a

publication and some articles are coauthored by over fifteen authors. Hundred percent of the

articles involve funding from one or more external public agencies. Some of the projects are

14 As a robustness test, we also classified based on 20 and 21 nearest neighbors and we found the comparable results. The classification did not change much for k =20. The confusion matrix results suggest the Accuracy: 0.8606 with 95% CI : (0.8527, 0.8683) and Kappa : 0.8426 between two classifications . 15 In stage one LDA solution-we identify two topics as battery paradigm and fuel cell. And we categorize a document as battery paradigm if it has above 60 percent probability of being generated form battery paradigm Topic.

17

being funded by as high as nine public organization. On average, an article belongs to three

distinct Web of Science categories.

In the second stage LDA, performed on identified EES articles, we generate 20

Topics representing unique and identifiable prominent Basic research trajectories. Table 2

provides the thematic map of the 20 identified Topics (Trajectories) and their relative

prominence as measured in terms of number of articles.


The revealed trajectories lead to an important observation. We find that the Basic

research is prominently concerned with the performance aspects and evaluation of EES

devices. The key performance criteria for electric vehicle batteries are energy density, safety,

lifespan and aging, charging time, specific power, and cost16 17. The generated basic research

topics reflect these concerns prominently. For instance, Topic 1 and 9 (Refer Table 2)

concerns state of health (SOH) estimation and monitoring for the EES devices. SOH is a

measure of general condition of a battery, and of its ability to deliver the specified

performance compared with a new battery. It indicates battery aging. Aging of lithium-ion

cells is an inevitable phenomenon limiting the lifetime in electric vehicle application and

there are various estimation approaches to measure SOH (Lin, Tang, & Wang, 2015).

Another important metric is State of charge (SOC) which is equivalent of a fuel

gauge for the battery pack. State of energy (SOE) estimates the residual energy of the battery.

Accurate SOC/SOE estimation is one of the main tasks of battery management systems,

which will help improve the system performance and reliability, and SOE and SOC together

16 Dinger, A., Martin, R., Mosquet, X., Rabl, M., Rizoulis, D., Russo, M., & Sticher, G. (2010). Batteries for Electric Cars: Challenges, Opportunities, and the Outlook to 2020, 18. 17 Standards for the performance and durability assessment of electric vehicle batteries, a technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. https://publications.europa.eu/en/publication-detail/-/publication/64b72ca2-d02a-11e8-9424-01aa75ed71a1[Date accessed: 15-Jan-2019]

18

address ‘range anxiety’ (i.e. concerns about not being able to do long journeys) which is the

common factor preventing consumers from purchasing fully-electric vehicles. Topics 10 and

14 capture these performance themes. Another major concern with electric vehicles is safety.

One of the threats to battery safety is thermal run-away, which leads to heat built up in the

cells of the cars' massive lithium ion battery systems and ultimate incidences of electric

vehicle catching fire. Cell temperature monitoring and Pack Cooling system addresses this

concern. Topic 3 and 8 capture these themes. Ultimately, battery degradation due to

mechanical or electrochemical stress is crucial for estimating the life of the EES device.

These are captured in Topics 2, 7 and 19. A few themes also capture the state of art high

performance battery technologies such as Lithium polymer, Lithium–Sulfur, and sodium ion

batteries (Zhu, Zou, Cheng, Gu, & Lu, 2018). The Topics 13, 15, 18, and 20 reflect these

alternate chemistries.

This focus of basic research on performance evaluation appears somehow

counterintuitive. The basic research is generally assumed to be distant from the performance

concerns given its abstract and theoretical questions. However, in our case it seems that

addressing performance issues confer an advantage in publication hazards (Etzkowitz &

Leydesdorff, 2000; Gibbons, 1994; Hessels & van Lente, 2008). On this line, Gibbons (1994)

marks that basic research goes under a shift with increasing orientation towards goals and the

production of relevant knowledge (Hessels & van Lente, 2008) that are key to practical

applications, and transdisciplinary research. Public and academic inventors are assumed to

be more prone to final application’s quality criteria.

19

Industrial research view: Topics in patents

We run the two-stage LDA on 13,450 distinct electric vehicle patents. In stage one,

we identify that 7,734 patents articles belong to EES or battery paradigm18. Table 3 provides

the key descriptive statistics of the identified EES/ Battery patents.


Single firm patents are most prominent with only a few being the work of

collaboration between four firms at max. The descriptive statistics also suggest that the EES

is a fairly complex field with many interrelated areas as is reflected in number of CPC

(Cooperative Patent Classification) subgroups and number of claims assigned to patents. On

average, the patents are assigned eleven CPC (Cooperative Patent Classification) subgroups,

with some of the patents having as high as 154 distinct classifications. The complexity is also

reflected in the number of claims as a patent has fourteen claims on average with some

patents having over 140 distinct claims.

Figure 1 provides the comparative yearly trend of the patents and scientific articles

identified as EES technology artifacts over the 17 years.

------------------------------ Insert Figure 1 about here ------------------------------

In the second stage LDA, we generate 20 Topics representing unique and identifiable

prominent Industrial research trajectories. Table 4 provides the thematic map of the 20

identified Topics (Trajectories) as revealed by the two-stage LDA approach applied to the

patent dataset.

18 In stage one LDA solution-we identify two topics as battery paradigm and fuel cell. And we categorize a document as battery paradigm if it has above 60 percent probability of being generated form battery paradigm Topic.

20


In contrast to Basic research topics, the industrial research topics do not reveal

explicit performance focus. Instead, we observe that the industrial research trajectories can be

divided in terms of their architectural and modular change focus. The architectural vs.

modular innovation is a well-trodden framework in the innovation literature (Clark, 1985;

Henderson & Clark, 1990). Any research advancement requires two types of knowledge-

component knowledge and architectural knowledge. A component is defined as a physically

distinct portion core design concept of the product that performs a well-defined function.

Architectural change involves “change in the way the components of a product are linked

together, while leaving the core components (and thus the basic knowledge underlying the

components) untouched” (Henderson & Clark, 1990, p. 10). Modular (architectural)

innovation involves only changes in the core component keeping the architecture (modules)

the same.

While the majority of topics (i.e. 13 out of 20 ) reflect themes concerning system

architecture or design modifications (i.e. Topics 2,3,4,5,6,8,12,13,15,16,18,19 and 20). 7

Topics (i.e. Topics- 1,7,9,10,11,14, and 17) reflect the modular changes in cell

electrochemistry and electrochemical composition. It is worth noting that industrial research

is dominated by trajectories focused on architectural changes. For Instance, Topics 3, 5 and

16 – concern with design of the battery pack assembly and modules. These topics relate to

various configurations of components within a battery pack and hence can be considered as

primarily architectural. Topic 4 and 13 concern with the bus bar design and cell connectors

which concerns with the various configurations of how batteries and cells are connected

within the battery pack. Topic 2 and 20 capture the battery cooling and thermal management

configurations. Topic 12 and 19 reflect the configurations of battery insulation and safety

21

device designs. In contrast, Topics 7,9,10 and 17- reflect the changes in composition of core

components of an EES system and hence capture primarily modular inventions.

Hence, it seems that the thematic differences between industrial and basic research can be

interpreted in terms of architectural /modular approach and their differential performance

focus.

In order to have further evidence on this intuition, we propose to differentiate between

architectural and modular innovations based on the component term concentration index for

the text. High concentration (i.e., high focus on just a few components) suggests modular

nature of innovation and low concentration (distributed focus on components) suggests

architectural nature of the innovation. In so doing, we calculate the component terms

concentration indices for whole set of articles and patents. Figure 2 shows the comparative

plots for articles and patents based on component concentration (or HHI) values19 .


We observe two interesting patterns. Scientific articles on average have higher HHI

value, i.e. high component focus in article text (mean HHI is 0.7638 and median HHI is

0.9339) than patents (mean HHI is 0.4927, median HHI is 0.4230). This indicates the

prominence of modular innovations in scientific articles. Density distributions of

concentration measure (HHI) add even more confirmation: They show how patents are more

biased towards architectural themes, while articles towards modular ones, even if both text

corporate have a mix of the two (Kernel density curves in Figure 2).

We also investigate the relative performance focus of patents and articles. Figure 3

presents the comparative analysis of patents and articles in terms their performance focus. 19 We perform the analysis based on the following distinct components – Binder, separator, electrolyte, insulator, connector, battery charging system , anode(or negative electrode), cathode(or positive electrode), control system( or battery monitoring system), battery pack , cell module , cooling system( or thermal management system)

22

The results indicate that, on average, a scientific article has about 3 times the proportion of

performance specific terms than that in a patent text suggesting higher performance focus in

scientific articles than patents20.


From this evidence, we confirm that industrial research trajectories are characterized

more by architectural changes in EES which reflects generic focus of firm’s innovation on

product and process innovation as a source of competitive advantage (Haneda & Ito, 2018;

Rothwell, 1992; Utterback & Abernathy, 1975) . Modular changes instead imply component

focus and changes in the core design components which need sound basic research insights to

increase major functions performed by the product(Miracle, 2005; Whitesides & Crabtree,

2007).

Surprisingly, although industrial research is aimed at solving specific problems and

enhance firm competitive advantage, the performance issue is not evident in the patent texts.

Performance could represent the more sensible part of an industrial research outcome that

firms prefer to make less evident in a patent document (Arora & Merges, 2004).

Mapping industrial research with a basic research lens

In this paragraph, we reclassify patents based on similarities with scientific articles.

The intuitive idea is to see if new industrial research trajectories emerge when we interpret

them with basic research maps (i.e., maps derived from patent texts with maps derived from

article texts). Using the kNN algorithm, we classified 7,734 battery paradigm patents on the

20 sub-topics identified by scientific articles, through calculating the text similarities among

20 We perform the analysis based on the following conservative set of performance terms –safety, specific power, specific energy, peak load, capacity, high power, impedance, high energy, state of health, temperature range, charging performance, state of charge, thermal management, range, charging performance, charging rate, battery monitoring, efficiency, life span, cost, and cycle life.

23

patents and scientific articles21. We classified the patents based on 21 most similar articles

(the results are robust for k=20 as well). We then contrast this view with the one of the 20

subtopics identified in the 7,734 battery paradigm patents.

To provide an easy-to-read visualization, we select the top 20 firms based on the

number of patents granted. These top 20 firms cumulatively own about 65% of the total

patents granted in the battery space22. We scrutinize the patent portfolios of these firms to

identify whether their position and reciprocal proximity changes with the pure patent

classification method, and the method that mixes patent and article mapping.

We can clearly identify five distinct clusters with both criteria. Figure 4 presents the

comparative map of distribution of firms in clusters based on distribution of industrial

research themes in their patent portfolios, and based on the themes reclassified with basic

research map.

------------------------------------ Insert Figure 4 about here

------------------------------------

Since EV industry is in the nascent stage, the firms are still figuring out profitable

industry positions. For instance, while Tesla seems to be clear about their roles as EV

automakers, German Automaker Volkswagen has announced to venture downstream and

enter the battery cell manufacturing23. Given the uncertainty, we cautiously evaluated the

firm’s primary role based on the business details provided in their 10-k statement (webpage,

where 10k was not available)) of the firms for year 2016 and classified firms in four types 21 About 13.6% (12119 unique tokens) terms are common between 7734 patents and 675 scientific articles, and based on these terms the text similarity was calculated for reclassification of patents based on scientific topics. 22 The top 20 firms in decreasing order of patents are : Samsung ( 840 ), Toyota ( 754 ), LG Corp ( 707 ), Panasonic Corp ( 554 ), Robert Bosch GmbH ( 451 ), Renault Nissan Mitsubishi Alliance ( 378 ), General Motors Corporation ( 270 ), Hitachi, Ltd. ( 195 ), Toshiba ( 166 ), FORD MOTOR CO. ( 125 ), Honda ( 123 ), Johnson Controls inc ( 113 ), Sony Corp ( 110 ), GS Yuasa Corporation ( 94 ), Yazaki Corporation ( 91 ), Tesla ( 84 ), Sumitomo group ( 81 ), Hyundai Motor Company ( 72 ), Renesas Electronics Corporation ( 69 ), and Semiconductor Energy Laboratory Co., Ltd. ( 48 ). 23 https://www.vwvortex.com/news/volkswagen-announces-1billion-investment-european-battery-production-northvolt-ab/

24

based on their primary line of business: Electric vehicle or Automobile manufacturers (EV),

battery pack manufacturers (BS), battery management or control system manufacturers (CS),

and automotive parts suppliers/manufacturers (AS). Our results (Figure 4) show how

companies do not group by business activity, even with in the same role, firms seem to

structure their technological portfolios differently. Given that we found that industrial

research themes are primarily architectural and basic scientific research themes are

component/performance related, the results suggest that firms with the similar architectural

design might have different performance mix in the modular components. It was interesting

to find the unique positioning of Tesla in both the clusters. To further understand these

thematic differences, we explore the differences between clusters in terms of thematic mix of

their portfolio (i.e., averaged values for firms within the cluster). Figure 5 presents the

thematic mix of clustered patent portfolios in terms industrial research themes and Figure 6

presents the thematic mix of clustered patent portfolios using the basic scientific maps.

--------------------------------------- Insert Figure 5 & 6 about here

---------------------------------------

We find that while other major EV manufacturers practice a distributed portfolio (in

both views), Tesla is highly focused in its research portfolio. Table 5 provides the descriptive

snapshot of the clusters using basic research maps, and Table 6 provides the descriptive

snapshot of the industrial research view clusters using only patents.

------------------------------ Insert Table 5, 6 about here

------------------------------ Also, Cluster 3 comprises of all major Electric vehicle manufacturers. While, it is

reassuring to see Yazaki Corporation-a automotive parts supplier with a focus on wire

harnesses, instruments and components such as connectors and terminals24- to fall in Cluster

24 https://www.yazaki-group.com/global/

25

3 which is dominated by Industrial research themes Battery Bus-bar design and Battery

mounting & supporting structure. It is interesting to see Yazaki Corporation differ from other

EV manufactures when looked at from Basic science view suggesting differing performance

focus.

To better interpret our findings, we presented our main results to industry engineers

with product R&D experience in this sector and asked for comments. We conducted five

semi structured interviews with engineers employed with different firms working in different

roles to gain a varied perspective. The following points resonated across interviews and

summarize the interpretation. In general, the thematic differences between industrial research

and basic research was not found surprising. The prominence of component focus in academy

research and Architectural / design focus in industrial research was attributed to differential

conceptualization of novelty for firms and academics and ease of capturing it. For instance,

on manager opined-

“...it’s difficult to patent (on few component enhancements) because you need really to find the novelty and for the novelty to be distinguished from the another… is in the combination…”

It is much more rewarding to hold on and claim novelty by patenting overall device

with components assembled together. While Academic research focus on individual cell

components and change compositions to enhance performance, yet these improvements

might not make much business sense until it can be mass manufactured at competitive cost.

Furthermore, patents are strategically crafted where firms have little incentive to share

specific "performance" evaluation results in patents. Also, specific application details are

avoided to enhance the scope of the patent and reduce imitability. One Manager illustrated by

giving this example:

“…For example ... if you have technology to put a heater in the seat of the car and you patent it saying for seat of the car … someone might use the same technology for another application for example … heat the roof, window etc... and you lose-out, hence when you have an idea . you might not know all the

26

applications …and hence you patent the general technology so… in future, you can develop more applications …”

Although the clusters could not be uniformly interpreted. The engineers highlighted

the nascent stage of industry suggesting that since there is lot of uncertainty about the firm

role and research directions – The clusters have limited interpretability as such. For instance,

one executive stated –

“Here we see mixed picture because its increasingly unfolding research…”

Overall, it was observed that two classifications when used together provide a better

view of firm’s technological trajectories. While the industrial view can suggest on firm’s

design and architectural preferences, the basic research view makes visible the firm’s

component and performance focus.

DISCUSSION AND CONCLUSION

This paper explores how basic research could help to better interpret the map of

industrial research trajectories primarily constructed based on patents. We employ the novel

machine learning algorithms to explore this question. First, we use topic modeling to

construct thematic map of the Industrial and basic research by extracting top twenty topics in

each. Then we use kNN classification algorithm to map patents with basic research themes

and construct an alternate view of firm research portfolio. As a result of Topic modeling- we

construct and compare the alternate views of the technology landscape in the industrial

context of electric energy storage (EES) devices for electric vehicles. We find differences

between Basic and industrial research in terms of their differential performance focus and

innovation type outcomes. Our first proposition is that basic research in EES is more similar

to a modular innovation process targeted to advance EES performances in particular

components; industrial research is more embedded in an architectural innovation process

focused on changing the basic design of a product with particular attention to adaptation

27

problems of different components. Also, basic research, and not the industrial, seems more

interested in solving clear product performance objectives focused on component level

improvements. Surprisingly, we find relatively scarce mention of performance issues or gains

in patents. This can be attributed to a strategic bias due to how firms decide to reveal

information in patent documents (Arora et al., 2001; Henkel et al., 2014).

We re-classify patents with a basic research taxonomy derived from scientific articles,

and construct an alternate view which highlights a complementary view of firm research

portfolios, and hence provides a different perspective on R&D competition in terms of firm

positions in a technology space. We introduce a novel text-based method for using themes

and trajectories in scientific publication texts to categorize and decode knowledge embedded

in the patent texts.

For practitioners, such as market analysts and firms, this study sends a reminder that

solely relying on patents as the window to firm research directions might present an

incomplete picture. We suggest an alternate way to proxy competitive similarities and

suggest how to make visible which was so far held hidden for secrecy reasons.

Like every research, our study suffers certain limitations discussed as below. We do not

analyze the temporal dimension of research trajectories. Although our dataset has time

dimension, we do not report evolution of topics over time. We treat the old and new

trajectories at par for our results. Studying the temporal variation in topics provides an

evolutionary perspective to the trajectories and might prove promising in exploring

technological evolution and tradeoffs. We are also limited in stating any performance

implications. As is, there is no measure of patent performance attached to these clusters. This

can be a promising natural extension of this study. Also, since we searched and selected our

initial sample based on “Electric vehicle” search, we might have selected out the generic cell

electrochemistry research undertaken by specialized cell component manufacturing firms

28

who might not mention electric vehicles in their patents. Our findings are grounded in and

hence are valid directly for our selected industrial context. We cannot claim for statistical

generalization to other industries. Instead we appeal for analytical generalization as we

believe that our main propositions might hold for other high technology industries. Our

limitation to one industry is a methodological constraint as the text analysis is sensitive to

data context.

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TABLES AND FIGURES TABLES

Table 1- Descriptive statistics for EES published articles. Statistic Number of

Articles Mean St. Dev. Min Max

Publication Year 675 2001 2017 Number of Authors per Article 675 4.82 2.22 1 18

Number of Funding organizations per Article 414 2.34 1.49 1.00 9.00

Number of Web of science Categories 675 2.96 1.29 1 5

Number of references 675 31.79 20.57 3 284 Number of Citations 675 42.38 62.87 0 776 Note: Top 5 Journals ranked by number of articles are - Journal of Power Sources (383), Journal of the Electrochemical Society (94), Electrochimica Acta (84), International Journal of Electrochemical Science (20), and Journal of Solid State Electrochemistry (17). Table 2- 20 Topic LDA solution for Scientific articles.

Note: Number in brackets refer to the Scientific articles classified to that topic. This provides a thematic map of Basic research in EES technology for Electric vehicles.

Scientific articles

EEStechnologyforElectricvehicles (675)•Topic 1 Battery SOH monitoring ( 48 )•Topic 2 Battery corrosion & Recycling ( 31 )•Topic 3 Battery Pack cooling ( 47 )•Topic 4 Low temperature performance ( 43 )•Topic 5 Nimh battery deterioration ( 32 )•Topic 6 Battery discharge performance ( 26 )•Topic 7 Mechanical degradation ( 29 )•Topic 8 Thermal monitoring ( 31 )•Topic 9 SOH estimation method ( 19 )•Topic 10 SOC estimation method ( 29 )•Topic 11 Low temperature performance ( 37 )•Topic 12 Leadacid battery ( 48 )•Topic 13 Lithium Sulfur battery ( 20 )•Topic 14 SOE estimation method ( 27 )•Topic 15 Li-ion Polymer battery ( 48 )•Topic 16 Capacity estimation method ( 41 )•Topic 17 Dynamic thermal behavior monitoring ( 37 )•Topic 18 High-power Sodium-ion battery ( 16 )•Topic 19 Capacity degradation ( 27 )•Topic 20 High-voltage graphite Li-ion battery ( 39 )

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Table 3- Descriptive statistics for EES patents.

Statistic Number of Patents

Mean St. Dev. Min Max

Patent Grant Year 7,734 2001 2017 Application Year 7,734 1993 2017 Patent processing time (in days) 7,734 1253.7 558.5 63 4470 Number of Assignee organizations per patent 7,734 1.1 0.3 1 4

Number of Inventors per patent 7,734 3.1 1.9 1 19 Number of CPC subgroups per patent 7,734 10.6 8.5 1 154

Number of CPC groups per patent 7,734 2.9 1.7 1 16 Number of Claims per patent 7,734 14.1 9.2 1 141 Note –Patents are classified based on CPC (Cooperative Patent Classification) scheme. The scheme is jointly developed and managed by USPTO (United States Patent and Trademark Office) and EPO (European Patent Office). The higher number of CPC classes and subclasses represent the technological complexity of patented innovation. Table 4- 20 Topic LDA solution for Patents document set.

Note: Number in brackets refer to the patents classified to that topic. This provides a thematic map of Industrial research in EES technology for Electric vehicles.

Patents

EEStechnologyforElectricvehicles (7734)•Topic 1 - Nonaqueous electrolyte ( 692 )•Topic 2 - Battery cooling system ( 459 )•Topic 3 - Battery module design ( 444 )•Topic 4 - Battery Busbar design ( 290 )•Topic 5 - Battery assembly-bipolar,flattype, monobloc ( 208 )•Topic 6 - Battery monitoring circuit design ( 562 )•Topic 7 - Li-ion Battery-Electrochemical composition ( 336 )•Topic 8 - Electrode/Cell assembly design ( 432 )•Topic 9 - Advanced Li-ion batteries- Electrode composition ( 519 )•Topic 10 - Advanced Li ion batteries- Electrolyte composition ( 377 )•Topic 11 - Alkaline battery ( 213 )•Topic 12 - Battery housing & Safety vent design ( 330 )•Topic 13 - Battery cell connector ( 278 )•Topic 14 - Solid state Li secondary battery ( 313 )•Topic 15 - Battery pack control system ( 309 )•Topic 16 - Battery mounting & supporting structure ( 345 )•Topic 17 - Battery separator composition ( 439 )•Topic 18 - Battery state & parameter estimation methods/devices ( 502 )•Topic 19 - Rechargeable battery Insulation & fuse circuit ( 497 )•Topic 20 - Heat transfer & Thermal management system ( 189 )

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Table 5- Basic Research view clusters: Descriptive for the identified five firm clusters where firms are clustered based on distribution of Basic scientific themes in their patent portfolios.

Cluster Theme concentration (HHI range 0-1)

Most Prominent themes Number of firms

Firms Average patent count per Firm

1 0.15 2-Battery corrosion & Recycling

6 Samsung; Toyota; Panasonic Corp; Robert Bosch GmbH; GS Yuasa Corporation; Sumitomo group

462.33

2 0.12 16-Capacity estimation method

5 LG Corp; Renault Nissan Mitsubishi Alliance; Hitachi, Ltd.; Sony Corp; Yazaki Corporation

296.20

3 0.20 3-Battery Pack cooling 5 General Motors Corporation; FORD MOTOR CO.; Honda; Johnson Controls inc; Hyundai Motor Company

140.60

4 0.23 12-Leadacid battery 3 Toshiba; Renesas Electronics Corporation; Semiconductor Energy Laboratory Co., Ltd.

94.33

5 0.51 16-Capacity estimation method

1 Tesla 84.00

Table 6- Industrial Research view clusters: Descriptive for the identified five firm clusters where firms are clustered based on distribution of Industrial themes in their patent portfolios.

Cluster Theme concentration (HHI range 0-1)

Most Prominent theme Number of firms

Firms Average patent count per Firm

1 0.25 19 - Rechargeable Battery Insulation & fuse circuit

2 Samsung; Robert Bosch GmbH

645.50

2 0.10 8 - Electrode/Cell assembly design

6 Toyota; Panasonic Corp; Renault Nissan Mitsubishi Alliance; Hitachi, Ltd.; GS Yuasa Corporation; Renesas Electronics Corporation

340.67

3 0.09 4 - Battery Bus-bar design 6 LG Corp; General Motors Corporation; FORD MOTOR CO.; Honda; Yazaki Corporation; Hyundai Motor Company

231.33

4 0.26 1 - Nonaqueous electrolyte 4 Toshiba; Sony Corp; Sumitomo group; Semiconductor Energy Laboratory Co., Ltd.

101.25

5 0.26 12 - Battery housing & Safety vent design

2 Johnson Controls inc; Tesla

98.50

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FIGURES

Figure 1- Yearly trends of published scientific articles(675) and granted patents(7734).

Note: The Patent shows declining trend from 2016 to 2017, this is because we have truncated patents for year 2017. We have patents granted till July, 2017("2017-07-25”) in our dataset. 1658 patents were granted from July 2017 till end of year 2017 for electric vehicles in category(H01M) which we do not have.

Figure 2- Illustration of comparative analysis of component focus of Basic research (articles) vs Industrial research(Patents).

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Figure 3- Comparative performance terms Density plots. It illustrates the comparative analysis of patents and articles in terms their performance focus.

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Figure 4: Comparative map of distribution of firms in clusters based on differential patent portfolio mix in terms of industrial research themes and basic research themes.

Note: The Firms can be identified as Electric vehicle manufacturers(EV), Battery system manufacturers(BS), Automotive parts supplier (AP) and Battery management or control system manufacturers(CS) based on their primary business activity or role in Electric Vehicles industry.

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Figure 5- The thematic mix in patent portfolio of 5 identified clusters based on distribution of Industrial research themes.

Note: The Industrial research themes are as follows 1 - Nonaqueous electrolyte ,2 - Battery cooling design ,3 - Battery module design ,4 - Battery Busbar design ,5-Battery assembly -bipolar,flattype, monobloc ,6 - Battery monitoring circuit design , 7 - Li-ion Battery-Electrochemical composition ,8 - Electrode/Cell assembly design ,9 - Advanced Li-ion batteries- Electrode composition , 10 - Advanced Li ion batteries- Electrolyte composition ,11 - Alkaline battery , 12 - Battery housing & Safety vent design , 13 - Battery cell connector , 14 - Solid state Li secondary battery , 15 - Battery pack control system ,16 - Battery mounting & supporting structure ,17 - Battery separator composition, 18 - Battery state & parameter estimation methods/devices , 19 - Rechargeable battery Insulation & fuse circuit , and 20 - Heat transfer & Thermal management system.

Figure 6: The thematic mix in patent portfolio of 5 identified clusters based on distribution of Basic research themes.

Note: The Basic research themes are as follows-1- Battery SOH monitoring,2-Battery corrosion & Recycling,3-Battery Pack cooling,4-Low temperature performance,5- Nimh battery deterioration,6-Battery discharge performance,7-Mechanical degradation, 8-Thermal monitoring, 9-SOH estimation method,10-SOC estimation method,11-Low temperature performance,12-Leadacid battery,13-Lithium Sulfur battery,14-SOE estimation method,15- Li-ion Polymer battery,16-Capacity estimation method,17-Dynamic thermal behavior monitoring,18- High-power Sodium-ion battery,19-Capacity degradation,20-High-voltage graphite Li-ion battery

abstract - druid › acc_papers › w4nf8gjz27... · themes and construct an alternate view of firm...

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