Hitotsubashi University
Institute of Innovation Research

The determinants of parallel invention:
Measuring the role of information sharing and personal
interaction between inventors

Byeongwoo Kang
Rudi Bekkers

IIR Working paper WP#22-06

2022年11月

一橋大学イノベーション研究センター
東京都国立市中2-1
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The determinants of parallel invention: Measuring the role ofinformation sharing and personal interaction between inventors[Draft, November 1st, 2022]
Byeongwoo Kang* and Rudi Bekkers**
byeongwoo.kang@iir.hit-u.ac.jp
Institute of Innovation Research, Hitotsubashi University, Japan*r.n.a.bekkers@tue.nl
School of Innovation Sciences, Eindhoven University of Technology, The Netherlands**

Historical accounts describe numerous cases of parallel invention. Nowadays, with over half a million
inventions yearly that apply for patent protection at the USPTO alone, it is likely that there are a lotof parallel inventions among these. Yet, the mechanisms behind creating similar knowledge remain
unstudied. From both a theoretical and practical perspective, it is an interesting question to what
degree parallel inventions take place truly independent of each other, or whether they are the resultof the exchange of knowledge and ideas between inventors. In our empirical study, we use theunique setting of technical standardization, where it is possible to systematically observe knowledge
sharing as well as knowledge exchanges between inventors in detail. This study presents two novel
analyses, one focussing on the determinants of similar inventions (using an AI-based approach) and
one on the determinants of identical inventions (exploiting data from the patent granting
procedure). In both analyses, we find positive and significant effects for knowledge sharing as well as
for inventor interaction as determinants. The latter effect is the strongest: if meet in person anddiscuss their ideas, the likelihood of similar inventions increases up to a factor of approximately five,
to up to 2.3 percentage points. Empirically confirming the theoretical work of Amabile (1983, 1988)
on knowledge creation at the individual level and that of Nonaka (1994, 2006) on knowledge
creation at the organizational level, we reflect on the implications of our findings for companies
wishing to increase their inventive efforts.
Keywords:
Creativity; Idea twin; Knowledge creation; Patent similarity; Similar knowledge
JEL classification:
O31, D82
Disclaimer / funding
This work was supported by JSPS KAKENHI Grant Numbers 19K13768 and 20KK0296.
Acknowledgements
We would like to thank Atsushi Ohyama for providing constructive comments.

1

Introduction

The development of new ideas, knowledge or inventions is highly valued in society. If it is only amatter of time for a certain idea to come up (as suggested by Zeitgeist theories; see below), then it
will also not come as a surprise that the same idea can come up more than once, completely
independent of each other, either at about the same time or with some time interval. The
phenomenon of parallel invention, also known as multiple discovery or simultaneous invention, hasreceived considerable attention in the literature (see Simonton, 1979). Historical cases include thediscovery of calculus by Newton but also by Leibniz (Khamara, 2006; Bardi, 2009), the theory of
evolution of species, by Charles Darwin but also by Alfred Russel Wallace (Randy, 2008). Other
examples are technical inventions such as the crossbow, claimed to be invented in both Europe andChina (Foley et al., 1985) and the invention of the telephone by Alexander Graham Bell but also by
Elisha Gray. But identical ideas might also not be totally independent of each other. Their likelihood
may increase with similar training, with being exposed to the same set of knowledge or the samechallenge to be solved, or with interaction and communication between prospective inventors. Or
ideas may even have been copied. In fact, several of the above examples of independent discovery
were or are contested, such as the telephone (see Brooks, 1975, who contests independent parallel
invention of the telephone, as well as Shulman, 2009; Evenson, 2000, and Baker, 2000).
In current days, the incentives and expectations for researchers and engineers to come up with new
ideas are high. At the USPTO alone, more than half a million inventions are filed for patent
protection yearly (USPTO, 2020). This likely includes a large number of parallel inventions. At thesame time, our understanding of knowledge creation and the potentially resulting parallel inventions
is limited. Why do inventors come up with the same idea? What role do background/training,
concurrent knowledge and personal interaction between inventors play in the invention process?
This paper aims to contribute to such an understanding by an empirical study and uses a specificcontext allowing us to collect data that can disentangle several dimensions important to understandparallel invention. More specifically, we address the following research question: Assuming similar
training and background, to what degree can parallel invention be explained by (1) inventors being
exposure to the same (common) set of information on technical challenges/goals to be achieved or(2) the presence of personal interaction between inventors?
Our empirical data comes from the context of technical standard-setting, and, more specifically fromthe development of standards for mobile telecommunications in the 3GPP consortium. We collected
data in this specific context because it offers a unique opportunity to observe the moments at which
engineers get access to the same set of knowledge (including that of new challenges ahead that2

need to be solved) as well as if and when those engineers come together in the same space anddiscuss. The specific 3GPP setting is not only suitable in that it allows us to allow (separate)
observation for these two types of events, but also because of its detailed paper trail on these
events, such as full meeting details, including which participants were actually present. Furthermore,
our empirical data uses patents as the result of knowledge development – and to measure parallel
inventions. In the context of 3GPP, the strong incentives for companies to obtain Standard Essential
Patents (SEPs) means that almost all knowledge developed is appropriate via patenting. Following
two alternative ways to measure the occurrence of parallel invention – one relying on an AI-based
approach, the other on assessments done by patent examiners, our study empirically concludes thatwhen individuals and teams are exposed to identical knowledge input, they are likely to develop
new similar knowledge.
Our study contributes to management and economics literature that focuses on knowledge
development. While the phenomenon of parallel invention and the underlying theory has alreadyreceived considerable attention, the current literature lacks empirical testing. This paper, to the bestof our knowledge, is the first attempt at a systematic, quantitative study of parallel inventions. Ourfindings strengthen existing theories on knowledge development and creativity. Furthermore, ourfindings imply that exposure to new information plays an important role in creating new knowledge.
To encourage innovation, both on the individual and the organisational level, one organisations may
create an environment that promotes exposure to new information.
The remainder of this paper is structured as follows. Section 2 reviews the existing literature anddevelops hypotheses. Section 3 explains why the context of standardisation presents unique
opportunities to empirically investigate parallel inventions. Section 4 introduces our empiricalmodel, and Section 5 provides information on our dataset and main variables. The next two sections
present our main analyses, first for similar inventions (Section 6) and then for identical inventions(Section 7). Finally, Section 8 concludes, discusses research implications, and points out thelimitations of this study.
2

Literature review and hypothesis development

In this section, we review the literature on knowledge creation and the underlying processes, paying
special attention to studies that model knowledge creation. Aiming to empirically test thepredictions in that literature, we then develop working hypotheses.

3

2.1

Literature review: Modelling knowledge creation

Theories on knowledge creation have been developed both at an individual level and at anorganizational level. For knowledge creation at an individual level, we take as a starting point thehighly cited work by Teresa M. Amabile (1983, 1988), which was discussed and extended bysubsequent studies (Farr & Ford, 1990; Kim, 2017; Groenewoudt et al., 2019). For knowledge
creation at an organizational level, we start from the work of Ikujiro Nonaka (1994), which has been
followed by many high-impact studies (e.g., Nonaka & Konno, 1998 and Nonaka et al., 2006).
The knowledge creation models in both these strains of literature contain similar elements. In thecore, knowledge creation is modelled by the simple equation 𝑦𝑦 = 𝑓𝑓(𝑥𝑥), where new knowledge (y) isa function of information input (x). Information input includes scientific knowledge as well as non-scientific knowledge such as mere information, recognized problems, communications, and senses.
They stimulate an individual’s brain to create new knowledge.
The knowledge creation function is formed by many factors. An individual’s function depends onformal and informal training, personal experiences, and personality characteristics (Amabile, 1983;
1988). The function defines ways to perceive information input. Based on the function, an individual
analyzes information input, decomposes it into elements, and finds responses. Each individual forms
a different function of knowledge creation. Since each individual is not free from his/her uniquesocial, cultural, and historical contexts (Vigotsky, 1986; Nonaka and Takeuchi, 1995; Jehn et al.,1999), knowledge creation function is bound by those personal contexts
However, when one deals with technical knowledge creation or with expertise creation, knowledge
acquired from education and training is arguably much more important than personal contexts (Farr& Ford, 1990; Oldham & Cummings, 1996; Hammond et al., 2011). After all, to create new wirelesscommunication technologies, one must have acquired knowledge in electrical engineering,
electronics engineering, information processing, etc. In the same manner, to create new medical
knowledge, one must have acquired knowledge in biology, chemistry, anatomy, physiology, etc. One
decomposes and analyses information input based on the knowledge creation function formed
through formal education. If information input involves technical challenges, then new technicalsolutions are created. If information input is scientific curiosity, then new scientific theories are
created.
2.2

Hypothesis development

Considering the above literature, for our study, we will assume that individuals who have received
education and training in the same field are likely to have very similar information processing
4mechanism. Accordingly, when they are exposed to an identical information input, there is alikelihood that they create the same knowledge. For our study, we will focus specifically on the fieldof engineering and on the role of information exchanges in the knowledge development process,assuming that the background knowledge of the involved individuals (i.e., education and training)
will be similar. We will also focus on knowledge development on the organisational level, and more
specifically on the firm level, and study not between engineers within the same firm (who often
work together on a single invention). Below, we presume a setting where there are meetings taking
place between companies, where participants (firm representatives) are subject to knowledge
exposure, and where also personal communication between participants can take place.
Considering the theories discussed above, we formulate the following hypothesis concerningknowledge exposure:
H1: If firms are exposed at a given meeting to the same set of knowledge about challenges andgoals, the likelihood increases that after that meeting, these firms will bring forth more parallel
inventions.
Second, we focus on the role of personal interaction between individuals of different firms. Buildingon Nonaka & Konno (1998), we expect that when engineers discuss and interact in the same space,sharing context and tacit knowledge, the likelihood increases that they create similar knowledge
and, thus, bring forward parallel inventions. Because of intensive knowledge exchange between
engineers, we assume that when members are present in the same space, more similar knowledge iscreated than when members have only shared knowledge input without being in the same space.
Since this is about personal interaction, we consider the individuals (as participants of meetings, butalso as inventors). We formulate the following hypothesis:
H2: If individuals of different firms are personally participating at those given meetings, thelikelihood increases that after that meeting, these individuals will bring forth more parallel
inventions.
It is important to note that the second hypothesis is complementary to the first; the situation in thesecond hypothesis already implies presence at the same meetings, (and thus implies common
exposure to knowledge). We further note that, out of these meetings, in the idle time, personal
interaction may take place between individuals of the same company, but we leave that out of thescope of our analysis (and do not consider parallel inventions by individuals working for the samefirm).
5

3Standardisation as a context to understand parallel inventions

Given our hypothesis, the major challenge in collecting data is to be able to observe, in a systematicmanner, (1) when they are exposed to a common set of information, e.g., on technicalchallenges/goals, (2) personal interaction between prospective inventors, and (3) the relevant
inventions they bring forth, which may or may not be the result of that knowledge exchange. We
carry out our data collection in the specific context of technical standardisation at 3GPP, acollaboration between the major regional standard-setting organisations in the field of
telecommunications. The standardisation process is a knowledge creation process (Abdelkafi et al.,2021), and, in 3GPP, engineers invent new technologies to provide faster, more reliablecommunications technologies, catering for an increasingly wide range of applications and usages.
With over 5.3 billion people across the globe that use mobile devices based on 3GPP standards (suchas the 3G W-CDMA and 4G LTE standards), and another 15 billion Internet-of-Things (IoT) devices
using these standards (GSMA, 2022:4), the 3GPP standards are among the most successful technicalstandards ever. Yet, we did not select 3GPP for the success of its standards but because of its
standard development process and its associated publicly available paper trail, which makes it
uniquely suitable to test our hypothesis on knowledge development. Below, we briefly sketch why
this 3GPP context is uniquely suitable to answer our research questions; for a more in-depth
discussion of 3GPP itself, we refer to Bekkers, 2001 and Kang & Bekkers, 2015.
The first aspect that makes 3GPP attractive for our case is that it carries out technicalstandardization. Employing the principles Open Standardization as laid out by the World Trade
Organization (WTO, 2000), this means, among other things, that there is a transparent process inwhich technical development takes place, which is extensively documented. Consequently, we canobserve knowledge exchange in a very detailed way, whereas normally, knowledge exchange thattakes place within and across companies is not publicly available or otherwise observable in asystematic manner. The 3GPP context allows us to observe both knowledge sharing and personal
inventor interaction:-

Knowledge sharing takes place via the documents and minutes that are available right after
a 3GPP meeting to all participants. This way, the members (at the firm level) obtain thesame information about the new functionalities and goals to be achieved, and can startworking on this,6-Interaction takes place via intensive discussions at 3GPP meetings (Kang & Motohashi,2015), and the publicly available lists of attendants allow us to observe the individuals (andtheir firms) who engaged in that meeting. For our analysis, we will assume that personal
interaction between individuals of firms takes place during these two-monthly meetings (seealso Section 8, where we discuss possible limitations of this assumption).

The second aspect that makes 3GPP attractive for our case is that almost all inventions made in thiscontext are patented and can be well tracked to 3GPP work. In the field of mobile
telecommunications, there is a race to obtain patents that would be required to implement thestandard developed by 3GPP (Bekkers et al., 2002; Leiponen, 2008; Bekkers et al., 2011; Berger etal., 2012). Such patents, known as Standard Essential Patents (SEPs), are well understood to be very
attractive, as they can be licensed out, and can secure a strong bargaining position when negotiating
cross-licenses to both SEPs and non-SEPs held by other patent owners (Kang & Bekkers, 2015).
Tracking patented inventions to 3GPP is possible because all participants are subject to policies thatoblige them to disclose whether they believe to own a patent that is or may become essential. These
declarations are made publicly available, creating a valuable data source for our study (see Section 5,
below, for details). And since patent data also includes inventor names, we can link this data to thelist of attendants at 3GPP meetings. Another reason why almost all technical progress is patented inthis context is that the process of standards setting implies that companies need to share theirknowledge (disclose) in order to get it into the standard, making trade secrets worthless.
Finally, a third aspect is that the training and background of the 3GPP participants that engage ininventions and list as inventors on patents can be regarded as similar (which was one element of ourresearch question). Most of the standardisation engineers have academic backgrounds in electricalengineering, communication engineering, electronic engineering, and information sciences. Students
in those fields learn subjects such as information theory, communication theory and systems, signal
processing, coding theory and applications, network designs, etc. Therefore, standardisationengineers have a common knowledge pool from academic training.
In the belief that 3GPP, for the reasons listed above, is an appropriate empirical l setting for our
research, Figure 1 presents a stylized depiction of the way we test our hypothesis in the 3GPP
context.

7

Figure 1. Exposure and exchange of knowledge in 3GPP

4

Empirical framework

To answer our research question (Section 1) and test the hypothesis developed above, we built adataset that includes information to create two independent variables related to the two aspects ofour research question and includes patent data that allows for observing parallel inventions.
Eventually, we test our hypotheses according to the following regression:

Yab = 𝛽𝛽0 + 𝛽𝛽1 CEXab + 𝛽𝛽2 IIAab + 𝛼𝛼T + 𝛿𝛿1Pa + 𝛿𝛿2Pb + uab(1)Here, Yab represents a parallel invention involving the pair of patents a and b (see Sections 5.2 and
5.3 for details). The first independent variable, CEXab (common exposure), is a dummy variableequaling 1 when the inventors of patents a and b were both exposed to the same, common set of

information, which, in line with the previous paragraph, we can observe as both patents being filed
after the same 3GPP meeting at which this information was made available to thecompanies/inventors. The second independent variable, IIAab (inventor interaction), is a dummy
variable equaling 1 when there has been personal interaction between inventors of patents a and b,
which, in line with the previous paragraph, we can observe as the inventor(s) of both these patents
being physically present at the same 3GPP meeting. It is important to note that we consider thissecond independent variable as an additional effect to the first – if there is interaction between
inventors at a meeting, then there must have been a meeting so the first independent variable mustbe true as well. The regression also controls for time trends, by including year controls T. Moreover,
we include patent-level variables control (Pa and Pb) for patent-specific characteristics otherwise8

unobserved, such as the patent value proxied as the normalized 7-year forward citations scoreexcluding self-citations.
More details on the outcome variables, independent variables of interest, and controls are provided
in the next section.
5

Dataset and main variables

5.1

Dataset construction

Our dataset combines three main types of data sources: meeting data from 3GPP, data on patents
declared as potential essential from ETSI, and detailed patent data from the PATSTAT database as
well as from additional OECD and USPTO patent datasets. This section provides further informationon the creation and properties of our dataset.
Section 3 already introduced 3GPP. In this consortium, most participants are firms, although also
other types of entities (e.g., public research labs and government bodies) can participate as long as
they are members of one of the collaboration regional standard-setting 3GPP is made of. 1 Attracting
thousands of engineers working on different topics within mobile communications, 3GPP formed
Technical Specification Groups (TSG), which each may be composed of multiple Working Groups(WG), in which the technical work is carried out. In this paper, we focus data collection on TSG RAN
WG1, an important group where fundamental radio technologies on the so-called physical layer are
defined. This working group organises frequent meetings – almost bi-monthly. Our 3GPP data builds
upon the data collection presented in [reference removed for peer reviewing]. It covers all
77 meetings (on average 4.5 days long), held between its establishment in January 1999 up to
February 2010, at quite regular spacing in time of approximately 2 months, and we collected andcleaned information of 939 individual participants at these meetings, affiliated with 53 different
firms.
As explained in Section 3, all 3GPP participants are subject to policies that oblige them to disclose
whether they believe to own a patent that is or may become essential. This obligation is not within
3GPP itself but exists within the partnering organisations via which they need to participate. The
rationale and specifics of such policies have been the topic of many studies; for further details werefer to Contreras, 2013 and Ohana & Biddle, 2018. While there are several partneringorganisations, Europe-based ETSI has by far the largest and most detailed database of such

1

For convenience, this paper simply refers to ‘firms’.

9disclosures (Baron & Pohlmann, 2018; Bekkers et al., 2020; Jones et al., 2021). Considering thatacross all the disclosed patent families, the US is the jurisdiction with the highest coverage, we
extracted from this database and cleaned up all related 14,510 patent applications applied at the
USPTO, 2 covering the time period of our 77 3GPP meetings
For each of these 14,510 disclosed standard essential patents, detailed patent data (patent families,
inventors assignees, citations, etc.) was obtained from the EPO PATSTAT patent database (Autumn
2020 edition). We complemented this data with patent quality indicator data by the OECD
(Squicciarini et al., 2013), as well as USPTO Patent Prosecution Research Data set (Lu et. Al., 2017)(for more details on the latter dataset, see at Section 5.2). Also, for the 939 individual participants at
3GPP and the 53 different firms they are associated with, we identified their USPTO patentapplications in PATSTAT.
5.2

Outcome variable: similar inventions

Most common approaches to identifying similar inventions are based on text analysis. Conventionalmodels measure text similarity are based on keyword frequency and co-occurrence measures (Artset al, 2018, 2021), but lack the ability to understand contexts and meanings. Recently, AI models
were developed to address this shortcoming, such as the Bidirectional Encoder Representations
from Transformers (BERT) developed by Google (Devlin et al., 2019). Such systems deal with context
and meaning by using multidimensional vectors, a famous example being “King – Man + Woman =Queen” (Baldwin, 2015). Reimers and Gurevych (2019) applied the bi-encoder design to sentence
similarity tasks and developed a Sentence-BERT (SBERT) model that lessens heavy calculations while
maintaining the accuracy of BERT.
For our study, we employed an improved Sentence-BERT model specialized for patent documents,
developed by researchers from Aalborg University called PatentSBERTa (Bekamiri et al, 2021). Thismodel takes into account the specific language and structure used in patent documents. Using
PatentSBERTa, we calculated the similarity of patents using the patent claim text that is available in
the USPTO Pre-Grant Data set in USPTO’s PatentsView 3. Since this claim data is only available for

They consist of 11,260 patent applications applied for directly at the USPTO and 3,250 US patent applications
that were not applied for directly at the USPTO but via the WO/PCT route. The 3,250 WO/PCT-route US patentapplications don’t match directly with the USPTO Patent Prosecution Research Data, patent claim data, or theOECD Patent Quality database. But, they can be linked via their patent family member in the USPTO with the
USPTO Patent Prosecution Research Data, patent claim data, and the OECD Patent Quality database.
3
https://patentsview.org/download/pg-data-download-dictionary210

patents filed in 2005 or later, our dataset for this analysis covers 11,787 US patent applications. 4 The
11,787 patent applications result in a total of 69.5 million patent pairs. 5
5.3

Outcome variable: identical inventions

For our second outcome variable, we go one step further than the mere similarity between
inventions, and want to determine the occurrence of identical inventions. To do so, we exploit thefact that patent examiners, when preparing search reports for new patent applications, specificallydistinguish earlier patents that challenge the novelty or inventive step of the patent under
examination (Czarnitzki et al, 2011). The presence of such blocking citation means that the twopatents in question have a high degree of similarity (and, in fact, means that the patent under
examination cannot be granted as applied for, although the application could remove subject matterto obtain a patent with a smaller scope). At the USPTO, a patent (or claim thereof) is rejected on thebasis of a lack of novelty under 35 U.S.C. § 102; this is similar to what is called an “X” categoryreference in WO/PCT international search report, or at the EPO. Furthermore, at the USPTO, apatent is rejected on obviousness (i.e., lack of inventive step) under 35 U.S.C. § 103; this is similar towhat is called a “Y” category reference in WO/PCT international search report or at the EPO (USPTO,
2019: p. L-19; EPO, 2020: §9.2.1). For any given patent, information on whether a particular citation
challenged novelty and/or inventive step of an applied patent can be found in the related Office
Action documents in the public PAIR database of the USPTO (see USPTO, 2010). Until recently, it
would take a lot of manual work to collect such unstructured, in-text information for larger sets ofpatents. In 2017, staff of the USPTO Office of the Chief Economist created a novel dataset on office
reject actions and their causes, called the USPTO Patent Prosecution Research Data (PPRD) dataset(Lu et. Al., 2017), and in our study, we use this dataset to observe blocking citations 6. This dataset isgenerated using machine-learning techniques and covers all relevant mailed office actions in the
period between 2008 and mid-2017. Note that this effectively restricts our analysis to the time
period from 2008 (start availability of PPRD data) to February 2010 (the last 3GPP TSG RAN WG1part of our dataset). While this is a short period, the large number of patents in our dataset and thehigh frequency of 3GPP meetings leave us with ample data to carry out our analysis.

Note that our 11,787 US patent applications include continuing applications data which are filed in or after
2005, have a priority dates ae before 2005. Continuation patents are often used by firms participating in thestandardization as a strategy to obtain patents that are essential to technical standards (Omachi, 2004). Ourcase is not an exception.
5
The total numbers of pairs for n data points equals n×(n−1)/2 pairs, so for our 11,787 patents we haveapprox. 69.5 million patent pairs.
6
Unlike for the EPO or the PCT, information on blocking citations for USPTO patents is not available in the
PATSTAT database, unless these patents came from the PCT route and have an international search report.
411

Since this analysis is not dependent on the availability of USPTO Pre-Grant Data set, we can include alonger time span and include 14,510 US patent applications in our dataset. Using the above method,
we identified 5347 applications with blocking relationships between them. These pairs form ouroutcome variable in the analysis in Section 7.

5.4

Patent-level controls

Our analyses include a number of patent controls that be confounding sources of variation. Firstly,
we control for time trends, by including the meeting number preceding the oldest patent of the pair(there are approx. 6 meetings per year, so this is more granular than years). Second, the control forthe time that lapsed until the second patent in the pair, again measured by the meeting numbers.
Moreover, we include patent-level variables control for patent-specific characteristics otherwise
unobserved, such as the patent value proxied as the normalized 7 year forward citations score, thesize of the patent family, and the number of claims. For each observation, we include these control
variables for both patents of the pair (Px and Py). The patent-level variables are all obtained from theOECD Patent Quality dataset (Squicciarini et al., 2013).
6

Main analysis: Similar inventions

In this section, we present our results investigating similar inventions using the AI-based similarity
measure as introduced in Section 5.2, above. Turning now to our analysis outcomes, we first look at
the descriptive characteristics of our dataset (Table 1) and correlations between all variables (Table
2). Unsurprisingly, the is quite a lot of correlation between the control variables concerning patentquality; after all, the different measured dimensions of patent quality are often strongly related(Gambardella et al, 2008).
Table 1: Descriptive statistics of the dataset for the ‘similar inventions’ analysis
Variable

Obs.

Mean

Std. Dev.

Min.

Max.

PatentSBerta

63,598,603

0.605655

0.085727

0.075

1

Common _exposure (CEX)

63,598,603

0.023197

0.15053

01Inventor _interaction (IIA)

63,598,603

0.002542

0.050352

01

Meeting #63,598,603

40.97815

9.264258059.5

Meeting_gap (conf2 - conf1)

63,598,603

6.569517

12.0161-57

59.5

Patent a fwd_cits7

63,598,603

23.69602

46.90869

0

1283

Patent b fwd_cits7

63,598,603

24.47055

46.81946

0

1283

Patent a family_size

63,598,603

5.359195

5.059932

1

32

Patent b family_size

63,598,603

5.338993

5.185586

1

32

Patent a claims

63,598,603

12.61906

15.14831

1

231

Patent b claims

63,598,603

12.35937

13.7409

1

231

12

Table 2: Correlation table for the ‘similar inventions’ analysis
Variable

Correlation

PatentSBerta

1

Common _exposure (CEX)

0.00471

Inventor _interaction (IIA)

0.0136

0.32761

Meeting #0.0776

0.06920.03911

Meeting_gap (conf2 - conf1) -0.019-0.0843-0.0276-0.67961

Patent a fwd_cits7-0.0111-0.0005-0.18150.13250.02981

Patent b fwd_cits7

0.0097-0.00420.00140.0137-0.1415-0.0014

1

Patent a family_size

0.0193-0.0146-0.0073-0.20150.1416

0.3581-0.0022

1

Patent b family_size-0.00140.00690.00270.014-0.1408-0.0024

0.3228-0.0091

Patent a claims

0.05540.0026-0.0006-0.0359

0.005

0.2445-0.0018

0.376-0.0024

6.1

1

Results for similar inventions

As the outcome variable in this analysis, the PatentSBERTa similarity score, is a continuous variable,
we employ a fractional logit regression specification. The results are shown Table 3, which shows
marginal effects.
Column (1) is the regression model with only the first independent variable (common exposure, CEX)
present. The marginal effect is positive and significant: common exposure increases the likelihood ofa parallel invention by 0.03 percentage points when the independent variable changes from zero toone. Column (2) adds the time dimension controls, reduced the magnitude but still shows a positiveand significant effect of out independent variable. When we add all the patent quality control
variables (Column 3) the findings remain stable. Hence, we can accept Hypothesis 1: If firms are
exposed at a given meeting to the same set of knowledge about challenges and goals, the likelihood
increases that after that meeting, these firms bring forward similar inventions.
Columns (4-6) in Table 3 present the result for the second independent variable (inventor
interaction, IIA). Also here, the marginal effect is positive and significant, regardless of adding
controls in the same fashion as above. The marginal effect we observe now is much stronger as forcommon exposure, from 1.8 to 2.3 percentage points (so, the difference is 9 to 18 times). Hence, we
can accept also Hypothesis 2: If individuals of different firms are personally participating at thosegiven meetings, the likelihood increases that after that meeting, these individuals bring forward
similar inventions. Note that we do not perform an analysis with both CEX and IIA because the latteralready implies the first (i.e., if there is inventor interaction at meetings, then there also has beencommon exposure at these meetings).

13

Table 3: Fractional logit regressions (marginal effects). Dependent variable: PatentSBERTa patent
similarity score
Common _exposure (CEX)

(1)

(2)

(3)

0.0027[37.75]***0.001[13.64]***0.001[14.17]***

Inventor _interaction (IIA)

(4)

(5)

(6)

0.0234[117.08]***0.0184[91.78]***0.0178[88.92]***Meeting #0.0011[704.39]***0.0012[721.88]***0.0011[702.13]***0.0012[719.64]***

Meeting_gap

0.0004[370.22]***0.0004[344.23]***0.0004[370.10]***0.0004[344.03]***Patent a: fwd_cits7

0.0001[223.74]***0.0001[223.26]***

Patent a: family_size

0.0000[43.43]***Patent a: claims

0.0003[378.03]***0.0001[43.53]***0.0003[378.14]***

Patent b: fwd_cits7

0.0000[52.68]***

Patent b: family_size-0.0003[-129.27]***0.0000[52.50]***-0.0003[-129.37]***

Patent b: claims

0.0004[528.00]***0.0004[527.89]***63,598,603

N

763,598,603

63,598,603

63,598,603

63,598,603

63,598,603

Main analysis: Identical inventions

In this section, we present our results investigating identical inventions using the patent examiner XY blocking citations (introduced in Section 5.3 above) as the outcome variable. In terms of
independent variables and controls, the analysis is similar to that presented in Section 6, above. As
this dataset has a different number of total observations (see above), we again present thedescriptive characteristics of our dataset (Table 4) and correlations between all variables (Table 5).
In these tables, ‘Blocking_cit’ represents the outcome variable presented in this section, whereas‘Any-cit’ is the outcome variable used for the robustness test presented in Annex A.
Table 4: Descriptive statistics for the ‘identical inventions’ analysis
Variable

Obs

Mean

Std. Dev.

Min

Any-cit

12,902,460

0.000329

0.0181330Max
1

Blocking_cit

12,902,460

0.00010.01002201

Common _exposure (CEX)

12,902,460

0.048969

0.21580301Inventor _interaction (IIA)

12,902,460

0.007758

0.087737

01

Meeting #12,902,460

51.2706

3.07125747

59.5

Meeting_gap (conf2 - conf1)

12,902,460

2.241004

4.290131

-12.5

12.5

Patent a- fwd_cits7

12,902,460

18.05416

27.15476

0356- family_size

12,902,460

4.250456

5.450398027- claims

12,902,460

11.45191

15.18933

0141

Patent b- fwd_cits7

12,902,460

20.66979

29.05446

0356- family_size- claims

12,902,460

4.726902

4.983562

0

27

12,902,460

12.41338

13.69101

0

141

14

Table 5: Correlation table for the ‘identical inventions’ analysis
VariableAny-citBlocking_cit

Correlation
1
0.5525

1

Common_exposure (CEX)

0.00220.00151

Inventor_interaction (IIA)
Meeting #0.00540.003

0.38971-0.0027-0.000700.01871

Meeting_gap (conf2 - conf1) -0.0002
Patent a fwd_cits7
0.025

0.00020-0.0462-0.631410.014400.0085-0.0658

0.066810.01220.006

00.01710.0179-0.0403-0.00321

0.01160.0055-0.02310-0.16940.14590.4305-0.00421

0.01160.00370.039200.0113-0.2249-0.0062

0.3327-0.0131

0.01110.00610-0.0049-0.07630.05420.37520.003

0.42930.002910.00560.003900.00770.01050.0121

0.005

0.26810.00340.2792-0.0082

Patent b fwd_cits7
Patent a family_size
Patent b family_size
Patent a claims
Patent b claims

1

Turning now to our main analysis, shown in Table 6, where Column (1) is the regression model with
only the first independent variable (common exposure, CEX) present. The marginal effect is positiveand significant: common exposure increases the likelihood of a parallel invention by 0.01 percentage
points when the independent variable changes from zero to one. Column (2) adds the time
dimension controls, which do not impact the magnitude or significance. Adding all the patent quality
variables (Column 3), the findings remain stable. Also now, we can accept Hypothesis 1. Columns (46) in Table 6 present the result for the second independent variable (inventor interaction, IIA). Also
here, the marginal effect is positive and significant, regardless of adding controls in the same fashion
as above. The marginal effect we observe is twice as strong as for common exposure. Hence, we canagain accept also Hypothesis 2.

15

Table 6 Logistic regressions (marginal effects). Dependent variable: blocking citation between patentpair a and b
Common _exposure (CEX)(1)
0.0001[5.11]***(2)0.0001[5.04]***(3)0.0001[5.10]***

Inventor _interaction (IIA)

(4)

(5)

(6)

0.0002[9.46]***

0.0002[9.44]***

0.0001[9.46]***

Meeting #0.0000[-2.14]**-0.0000[-2.07]**-0.0000[-2.33]**-0.0000[-2.27]**

Meeting_gap-0.0000[-0.27]-0.0000[-0.03]-0.0000[-0.49]-0.0000[-0.29]Patent a: fwd_cits7

0.0000[26.25]***0.0000[26.12]***

Patent a: family_size

0.0000[4.72]***

0.0000[4.79]***

Patent a: claims

0.0000[9.67]***

0.0000[9.61]***

Patent b: fwd_cits7

0.0000[14.11]***0.0000[14.01]***Patent b: family_size

0.0000[6.57 ]***

0.0000[6.62]***

Patent b: claims

0.0000[8.43]***

0.0000[8.41]***

N

12,902,460

12,902,460

12,902,460

12,902,460

12,902,460

12,902,460

A separate robustness effects analysis, presented in Annex A, confirms our findings. In thisrobustness test, we observe the occurrence of parallel invention by observing any citation
relationship between the two patents in the pair (instead of only blocking patents). We find a similar
magnitude of effects for common exposure (CEX), and even stronger effects inventor interaction(IIA) shows the likelihood a bit larger or similar than CEX.
8

Discussion and conclusion

This study aims to explain how the occurrence of parallel invention can be understood by thepresence of common knowledge across prospective inventors (like knowledge of the task to beachieved) and by personal interaction taking place between prospective inventors. For the first time,
we use an empirical, quantitative approach to study this topic. We do so by using data of 3GPP RAN1
standardisation activities, where engineers compete to create new knowledge, and where sharedknowledge and personal knowledge interactions can be observed. We identify parallel inventions in
two ways: (1) similar inventions, where our measurements are based on an AI-based approach called
PatentSBERTa, and (2) identical inventions, where our measurements are based on the outcome ofpatent examinations, which contains information on patents that are rejected because exactly thatsame idea was already contained in another patent. Since the latter is a more stringent measure ofparallel inventions, we would expect less strong effects.
We find that:16

(1) A pair of patent applications have a higher likelihood of representing parallel inventions of they
were both filed after a standardisation meeting where information was shared on the tasks to beachieved. This finding implies that when independent individuals are given with the same
information input, they are more likely to create parallel inventions. For all the analyses we
performed, this effect is robust: for similar inventions, we estimate this effect to be 0.1 to
0.3 percentage points; for identical inventions, the effect is smaller (as to be expected) at 0.01
percentage points.
(2) On top of the above, a pair of patent applications has a higher likelihood of representing parallel
inventions if the inventors of both patents were personally present at the meeting in question. Thisfinding implies that when independent individuals are present in the same space and have theopportunity to communicate, they are likely to create parallel inventions. For all the analyses we
performed, this effect is robust: for similar inventions, we estimate this effect to be 1.8 to
2.3 percentage points; for identical inventions, the effect is smaller (as to be expected) at
0.02 percentage points. Overall, these effects are much stronger than those for information sharing,
stressing the importance of personal communication.
In short, we see a mechanism in which identical knowledge inputs result in similar new
knowledge outputs when independent knowledge creators are educated in similar fields and are
given an identical knowledge input. We argue that this mechanism is an unrevealed mechanism ofmultiple discoveries and inventions.
In terms of academic contribution, our work provides empirical the theoretical work of Amabile(1983, 1988) on knowledge creation at the individual level and that of Nonaka (1994, 2006) on
knowledge creation at the organizational level, but also echoes Schumpeter’s view of innovation as“new combinations of new or existing knowledge, resources, equipment and so on” (Schumpeter,
1934). In terms of managerial implication, our work demonstrates how exposure to new knowledge
and personal contact between inventors of different organisations can be used to increaseknowledge production, but also how it can lead to parallel invention (which might be undesired andwhere a given company at least will want to be the first to apply for patent protection, if applicable).
Being the first empirical study in this field, our study also has limitations. Firstly, our observations are
based on 3GPP’s standardization, where all inventors have engineering backgrounds, which ischaracterized by its science base. To understand more about the degree of generatability of ourfindings, the study would be replicated in other academic fields, such as humanities and social
sciences. However, there are some challenges in doing so, especially in funding a specific context
17

that allows to observe phenomena like shared information and communication. Knowledge creationin engineering is based purely on science. This is somewhat different from other academic fields,such as humanities and social sciences, where research may be biased by political correctness orideology to some extent. Secondly, our approach can’t see observe the possibility of mere theft ofideas (see Bekkers, Martinelli, and Tamagni, 2020). While such theft may exist, its occurrence isarguable much lower than the phenomenon we are investigating here – also because collaborationin 3GPP is a multistage process, continuing over decades now, and those willing to engage in idea
theft will need to consider the longer-term repercussions. Lastly, in the 3GPP standardisation
context in which we carried our study, we cannot observe whether in-between meetings, engineersof different companies came together and discussed their ideas. While competitors coming together
in such a context where their ideas (and patents) may compete may seem less likely, yet in 3GPP
there may be such collaborations when they prepare joint submissions for an upcoming meeting. In
any case, we consider our measured effects as a lower bound of the studied effects, which may behigher if we were able to observe such in-between meetings.
Annex A: Robustness check
In this robustness test, we perform the same regression in Table 6, but now the outcome variable isany citation between the patents in a pair (instead of a blocking citation). This is arguable a less strict
definition of similarity; after all, such citations could also indicate relevance (for assessing whether apatent meets the patentability criteria) rather than that the cited patent is factually the same
invention. Results (marginal effects) are shown in Table 7. Columns (1) to (3) show that for the firstvariable of interest, common exposure (CEX), we observe the same marginal effects as in the main
analysis (as presented in Table 6, above). Columns (4) to (6) show that for the second variable ofinterest, inventor interaction (IIA), the marginal effects are also positive and significant, as in themain analysis, but have a higher magnitude: the marginal effect for IIA is four to five times higher
than that for CEX.
Table 7 Logistic regressions (marginal effects). Dependent variable is any citation between patentpair x and yCommon _exposure (CEX)(1)
0.0001[7.77]***(2)0.0001[6.44]***(3)0.0001[6.84]***

Inventor _interaction (IIA)

(4)

(6)

(5)

0.0005[16.97]***0.0005[16.39]***0.0004[15.07]***

Meeting #-0.0000[-11.94]***-0.0000[-8.19]***-0.0000[-12.12]***-0.0000[-8.29]***Meeting_gap-0.0000[-7.29]***-0.0000[-2.50]**-0.0000[-7.38]***-0.0000[-2.52]**0.0000[46.06]***

Patent a: fwd_cits7

0.0000[46.29]***Patent a: family_size

0.0000[14.60]***0.0000[14.73]***18

Patent a: claims

0.0000[15.22]***0.0000[15.10]***Patent b: fwd_cits7

0.0000[27.09]***Patent b: family_size

0.0000[25.73]***0.0000[26.88]***0.0000[25.93]***Patent b: claims

0.0000[8.65]***

0.0000[8.52]***

N

12,902,460

12,902,460

12,902,460

12,902,460

19

12,902,460

12,902,460

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