The effect of patent acquisition on subsequent patenting activity

World Patent Information 59 (2019) 101933

Contents lists available at ScienceDirect

World Patent Information journal homepage: http://www.elsevier.com/locate/worpatin

The effect of patent acquisition on subsequent patenting activity So Young Kim a, Hyuck Jai Lee b, * a b

Future Technology Analysis Center, Korea Institute of Science and Technology Information (KISTI), Seoul, South Korea Future Technology Analysis Center, Korea Institute of Science and Technology Information (KISTI), 66 Hoegi-ro, Dondaemun-gu, Seoul, 02456, Republic of Korea

A R T I C L E I N F O

A B S T R A C T

Keywords: Patent buyer Patent acquisition Subsequent patenting activity Applicant type Technology field

The importance of the patent market has increased in the high technology market due to open innovation and social efficiency. As most high-tech firms cannot rely on their own repository of technologies, they need to employ external technologies. These can be acquired through mergers and acquisitions (M&A) or from the patent markets, which is often referred to as open innovation. Because of insufficient comprehensive data and the complexity and contingency provision of the contract, there have been only a few empirical studies on patent transactions. This study seeks to understand the patenting activities of patent buyers after acquisition. By merging patent bibliographies and patent assignment information, the original dataset was prepared to inves­ tigate the patent acquisition activities of the patentees who were granted patents from 2007 to 2011 and their patent application activities from 2012 to 2016. The factors affecting patent production and acquisition and the relationship between patent acquisition and subsequent production were examined. The results show that the patent acquisition activity depends on the applicant type and technology field, and affects the future patent production of the applicant. Thus, observing a competitor’s patent acquisition activities gives insights into the future direction of its technology development.

1. Introduction The patent market has rapidly grown in recent years [1–3]. A study estimates the increase of licensing revenues at approximately $100 billion worldwide between 1950 and 2003 [4]. The global patent rev­ enue is reported to have increased from $15 billion in 1990 to $100 billion in 2000 [5]. Several reports suggested the growth of the patent market in other aspects, such as the secondary patent market (from less than $1 billion in 2013 to $12.5 billion in20171) and the licensing market (from $135 billion $ in 2005 to $300 billion in20142). Part of the reason for the growth of the patent market is the recog­ nition of patents as a valuable source of income rather than a simple legal right protection for inventions [6,7]. The income from the patents, often represented as private and social gains by reallocation of the patent rights to other firms, is generated by patent transactions, which takes place through different forms such as licensing agreement, sales, or transfer. Another reason is that most of the high-tech industries nowa­ days operate in extremely complex technological environments. Firms

cannot rely upon the repository of technologies developed by them­ selves, and hence they have to go beyond their boundaries in pursuit of external sources of technologies [8], which is often referred to as open innovation [9]. External technologies can be acquired by buying from the patent markets [10,11] as well as through co-operation and alliance [12,13], mergers and acquisition (M&A). M&A allows not only to obtain tangible resources such as expert [14] but also to absorb technological knowledge and capabilities of the target company. Therefore, M&A for technical purpose has a substantial effect on subsequent internal inno­ vation activities of the acquirer [15,16]. According to the previous study the reasons and driving forces for patent transactions are reported [17]. The former is categorized into six groups: Strategic, monetary, managerial and firm-specific, patent-­ specific, transaction cost, and exogenous, and the latter is found to be most relevant to “the demand side” and “strategic behavior” with 40 companies in several industries. It is also found that the patents included in a firm’s core business are not likely to be sold, whereas the ones that are unrelated to the firm’s core business, with a short residual life and

* Corresponding author. E-mail addresses: [email protected] (S.Y. Kim), [email protected] (H.J. Lee). 1 K. Richardson, E. Oliver, M. Costa, B. Hinman, The 2017 brokered patent market – the fightback begins, www.IAM-media.com. 87 (2018) 8–19., https://www. richardsonoliver.com/wp-content/uploads/2018/01/The-Brokered-Patent-Market-The-Fightback-Begins-Back-IAM87-Richardson-Oliver-Costa.pdf. 2 WTO publications, Tables about charges for the use of intellectual property, International Trade Statistics 2007 and 2015, https://www.wto.org/english/res_e/stat is_e/its_e.htm. https://doi.org/10.1016/j.wpi.2019.101933 Received 3 April 2019; Received in revised form 16 October 2019; Accepted 29 October 2019 0172-2190/© 2019 The Authors. Published by Elsevier Ltd. This is an (http://creativecommons.org/licenses/by-nc-nd/4.0/).

open

access

article

under

the

CC

BY-NC-ND

license

S.Y. Kim and H.J. Lee

World Patent Information 59 (2019) 101933

minor awareness of suitable licensees are likely candidates for sale. The creation of monetary benefits through the enforcement of patent rights is the core of the business model of “non-practicing entities (NPE)". They do not produce patented products or services, but they have a significant impact on the patent market and affect industry innovation in terms of facilitating the integration and movement of knowledge [6]. Considering patents as valuable assets, which can be traded, the price of them is a significant factor to maximize commercial gains. Hence there have been studies, though very limited, on examining the rela­ tionship between the age of patent and price. According to a recent study [7] the relationship between the patent age and selling price was examined using some 500 US patent families which were sold in US market (patent auction) in different technology fields. The authors categorized patents into single invention and portfolio lots, each of which consists of several different cases, and performed change point analysis to demonstrate the change of patent price with age. They re­ ported that the price of patent sold after the change point (3700 days from grant) was higher especially for single invention lots for computers and communications fields. The mean price of patent sold after the change point was 496 thousand dollars and was much higher than the mean price of younger patents (180 thousand dollars). Their findings could have implications on the bundling strategy for technology transfer manager or patent holder’s technology. Patent transactions are important in high-technology markets for developing the market structure, which consists of large and small firms. The latter are, in general, specialized in new technologies but are short of mass production and marketing capacities as compared with the former. By transferring the technologies of small firms to their larger counterparts, social gains are realized efficiently [18]. Patent holding companies, in other perspective, create patent pools for standard essential patents (SEP) and allow cross-licensing transactions to pursue innovation more efficiently [13]. The representative companies that have recognized the commercial value of patents and have been active with patent transactions are IBM and Texas Instruments [17]. A limited number of empirical studies on patent transactions have been reported due to the scarcity of comprehensive data and the complexity and contingency provision of the contract. Studies based on patent auctions [7,19–24] and patent reassignments [25–29] are the representative ones. The study on patent transactions is in the early stages and most of the studies are therefore empirical, with a limited range of data and a variety of research interests. Some of the selected research interests are described below. Serrano [25] contributes the use of the data on the transfer and renewal of patent ownership to the empirical literature on patent transactions. Change of patent ownership, more specifically the depen­ dence of the rates of patent transfer and renewal on the types of pat­ entees and technology fields, is analyzed in this study using the legally required records in the assignment text provided by the US Patent and Trademark Office (USPTO). The relative importance of patent transfer and renewal with respect to the firm size for each technology field is another main interest of this study. The obvious finding is the difference in the relative importance of small and large patentees in some tech­ nology fields. For example, small patentees show considerably (3.0 times) larger sales rate than larger ones in the computer and telecom­ munication field, while this ratio is only 1.37 in the chemistry. Figueroa and Serrano [26] find that small firms are more engaged in patent licensing than larger ones, but this trend cannot always be applied to patent trade. They examined the determinants of small and large firms’ decisions on patent sale and acquisition using the combined dataset of patent assignment, patent renewal fee, and patent application and grant data. Their findings address several important issues: Small firms sell and disproportionately acquire more patents than large firms, especially those originally registered to other small firms. The employ­ ment of patent citation as a potential proxy of patent transaction is notable, in an extension of the usage of patent citation as the proxy of

patent value [30]. Patents with high citation count (received) have a higher chance of being sold to large firms. Small firms disproportion­ ately cite other small firms’ patents. The patents with a higher propor­ tion of citations made by small firms have a higher chance of acquisition by a small firm. A study by Galasso, Schankerman, and Serrano [27] focuses on the effect of patent trade on litigation. Other focus of their study is the importance of the commercialization and enforcement gains from patent trade. The data for the study is a combination of US patents by individual inventors registered during 1983–2000 period, trade, and litigation in­ formation. Some of their findings address that the reduced litigation risk of the individually owned patents indicates that the enforcement gains are more important than market gains, and that the patents with a high probability of reassignment are expected to return large gains from patent transactions. The other finding is that characteristics such as the portfolio size of the buyer and the fit of the patent of interest to the buyer’s patent portfolio in terms of technology are related to the impact of the patent trade. Drivas and Economidou [28] examined the geographic effect on knowledge flows via two different channels, market and non-market, where knowledge flows via patent transactions and patent citation, respectively. They used the reassignment data of US-issued patents to examine the knowledge transfer based on whether the patent trans­ action is affected across the states and sectors in the US. According to their results, the geography in terms of distance and contingency re­ stricts patent transaction and hence the knowledge flows much more than in patent citation. Patent citations can be employed to capture actual knowledge flow from one party to another, other than trade of goods or inventors’ mobility. In other words, firms that bought the patent could also previously cite it as a token of interest in the knowl­ edge covered by it. Caviggioli [29] examined the strategies behind the patent acquisi­ tions via two different channels (patent market and M&A). The target of their study were ten firms sampled in three industries (i.e., automotive suppliers, semiconductors, and computer networks), which were the holding ones with respect to the number of patent transactions in each industry. According to their study, acquired patents are more complex than internally developed ones, and of higher technological merit, closer to basic research, and hence more technologically focused. Patents ac­ quired via patent market cover less complex technologies than ones acquired via M&A, according to characteristics such as the lower count of backward citations, claims, and inventors. Although the number is limited, previous studies have suggested ways to utilize patent transaction data from various perspectives, such as to investigate the characteristics of the technology market [29], to track the flow of knowledge between organizations [28], and to analyze the management of patent portfolio such as patent renewal, sales, and liti­ gation preparation [27,31]. A few studies compared external technolo­ gies acquired through patent transactions with internal technologies for a limited number of companies [18,29]. The current study aims to contribute to the existing literature by associating the external knowl­ edge acquired through the patent transaction directly with the internal innovation from the perspective of the patentee organization, using comprehensive data instead of a case study. In other words, this study explores the patent activities of patentees after the acquisition of external patents. The patent bibliographies and the patent assignment information were merged to prepare the original data set to investigate the patent acquisition activity of patentees from 2007 to 2011 and patent application activities from 2007 to 2016. The results show that patent acquisition depends on the type of patentee and the technology field of the patent. The effect of patent acquisition on the quantity and quality of subsequent patent production varies by the type of patentee and the technology field. This paper is organized as follows. Section 2 introduces the data construction process. Section 3 describes the patent acquisition activ­ ities of patentees with the descriptive statistics on explanatory variables. 2

S.Y. Kim and H.J. Lee

World Patent Information 59 (2019) 101933

Section 4 analyzes the relationship between patent acquisition and subsequent patent production. Section 5 discusses the findings and presents the conclusions.

security agreement); merger; change of name; government (interest agreement); employer assignment; security (agreement); other (license, joint research agreement, etc.); assignment; missing. A complete list of conveyance types and corresponding matching keywords are given in Appendix A. These reasons are listed in the order of the following classification process. A set of conveyance texts is classified based on a keyword search corresponding to a specific reason. For example, the keywords such as “correc*, conver*, amed*, etc.” are searched, and the matched documents are classified to the first conveyance type, “correction”. Remaining documents are treated with the second set of keywords for the second conveyance type, “release”. This process is repeated with the remaining pairs of conveyance text and keyword sets. The conveyance texts which do not match any of the keywords are classified as “missing”. In the next step, administrative events among the various types of reasons, such as change of patentee’s name or address, security interest, and correction, need to be screened to find the “effective” patent right transfer. In this process, identifying “employer assignment (patent right transfer within an organization from inventing employee to employer organization)” is especially important. According to the approach of Marco et al. the employer assignment is identified when a record meets the following conditions: “a) the earliest transaction recorded for the property (rf_id with earliest execution date); and b) the property was transferred alone (i.e., no other properties were listed in the PTO-1595 cover sheet); and c) the execution date is prior to the patent application disposal (grant or abandonment) date (or December 31, 2014 for pending applications); and d) keyword searching identifies the conveyance text as an assignment.” USPTO provides the descriptions and analysis of the patent assign­ ments that is used in this study as a reference material for mapping the conveyance text to the reasons of patent right transfer.⁸ Effective patent right transfer is generally recorded as “assignment” or “merger” besides “employer assignment”. Within these two types of patent right transfer, however, the size of the patent bundle needs to be considered to identify the patent trade for technological purposes. Though “merger” might not represent technology transfers in general [25], M&A of small firms very often occur for acquisition of technology. The patents traded in large bundles might not represent technological transfer as is in the case of a merger between two large firms. For these reasons, all transactions involving more than ten patents are excluded [33]. The result of data processing is summarized in Fig. 1. A total of 930,111 utility patents (P1) are granted in Phase 1, which are filed by 96,995 applicants (N1) after the name standardization. A total of 378,020 patents (T1) are acquired by 45,217 buyers (N4) after the name standardization in Phase 1. The name-matching process between the applicants of the granted patents (N1) and the buyers of the patent as­ signments (N4) in Phase 1 gives 20,193 unique names (N2) existing in both of the databases. These are those who have both the patent acquisition and production experiences: acquiring 312,272 patents (T2) from outside the firm either through merger or change in ownership and producing 692,864 patents (P2). Only 19,504 applicants (N3) out of N2 acquire 108,487 patents (T3) in small acquisition and produce 689,414 patents (P3). Some of the applicants in Phase 1 continue producing their own patents in Phase 2 and extend their patent portfolios, while others stop or reduce the patenting activities. Among the applicants in Phase 1, 29,941 applicants (30.9%) produced new patents in Phase 2, and 12,942 applicants (13.3%) produced more patents in Phase 2 than in Phase 1. The data excluded from the target dataset are due to the following reasons. The rest of the applicants (N1–N2), other than the identified

2. Data construction This study aims to understand the patenting activities of patent buyers³, and thus requires two types of data: patent bibliographies and patent assignment information. Patent bibliogra­ phies such as applicant names and International Patent Classifications (IPCs) are extracted from PATSTAT⁴ provided by the European Patent Office (EPO). The applicant names are standardized by a simple unification of upper/lower-case letters and elimination of spaces and punctuations (e.g. commas and periods). Number of utility patents (granted) are measured for each applicant. Citation count is measured for each patent to be used as a measure of patent quality. The information on patent acquisition is retrieved from the Patent Assign­ ment Text by USPTO, which includes the elements such as a unique identifier for each assignment (reel and frame number), the names of seller (assignor) and buyer (assignee), the date it was recorded at USPTO, the date the parties signed on the private agreement, associated patent numbers, conveyance text, and other information associated with the patent right transfer. The conveyance text, which is the indicator of the reason of patent transaction, is standardized following the process mentioned more in detail afterwards. The names of buyers are matched with the applicant names of the granted patents to examine the patent production activities of the applicant/buyer depending on the experi­ ence of patent acquisition from outside the firm. This study focuses only on the utility patents granted in the US. The granted patent provides exclusive rights to the inventor and the appli­ cant. Therefore, it is appropriate to analyze the granted patents in order to properly distinguish whether patents with exclusive rights are pro­ duced from inside or from outside. However, it takes considerable time⁵ from the patent filing to being granted. Knowledge secured by internal production or by inflow from outside should be regarded as affecting the production of patents filed after­ wards⁶. However, since it is difficult to measure the impact of each patent separately, it is appropriate to set the period to measure the impact. To examine the subsequent patenting activities, the overall timeframe (2007–2016) is divided into two, with a fiveyear⁷ period for each. The first (2007–2011) and second (2012–2016) time frames are referred to as Phase 1 and 2, respectively. Various reasons for patent right transfer are recorded in the “conveyance text” field of Patent Assignment database. As the convey­ ance text is in a free-text format rather than a set of pre-defined texts, it is difficult to identify the reason for each patent right transfer. The conveyance text needs to be mapped into a pre-defined set of reasons for ease of further analysis. Marco et al. proposed an approach for this task in their USPTO working paper [32], which is used in this study after minor modifica­ tions and a few additional treatments. The conveyance texts are mapped by keyword search and pattern matching into the following ten reasons of patent right transfer: correction (to a prior record); release (of a

3 In this study, ‘buyer’ means a patent acquirer who has obtained patent ownership after the patent application. 4 The version of PATSTAT used in this study is “2017 Autumn”, which may have incomplete coverage of data in 2015–2017 period. 5 According to USPTO statistics, ‘patent average total pendency’ was 32.4 months in 2012, and 26.6 months in 2016. 6 That is, patents introduced in 2007 may affect patents granted after appli­ cation at any time, and patents introduced in 2011 may affect patents applied after 2011, no matter how early. 7 About 11.7% of the data from the registered patents during the two-phase period exceeds five years from application to registration.

8 https://www.uspto.gov/learning-and-resources/electronic-data-product s/patent-assignment-dataset.

3

S.Y. Kim and H.J. Lee

World Patent Information 59 (2019) 101933

Fig. 1. Target dataset derivation process.

buyers, are not-matched buyers or not buyers. The rest of the patents (P1–P2), other than those granted to the identified buyers, are granted to the applicants in one of the following categories: the patents without non-inventor applicants, those granted to the applicants without any patent acquisition, those whose applicants cannot be matched with buyers due to typos or incorrect company suffixes in their names. Remaining patents (T1-T2), other than those acquired by the identified buyers, are acquired by the buyers who cannot be linked to the appli­ cants due to similar reasons as granted patents, such as typo, abbrevi­ ation, wrong company suffixes in name, address included name or nonpracticing entities (NPEs) which have no experience of patent production. Improved methods could possibly identify more buyers from the overall applicants (N1) than currently identified ones (N2). However, the proportion of the unidentified buyers occupying the granted patents and acquired patents are much lower than already identified ones. The ac­ tivities of these unidentified buyers are considered low for both patent production and patent acquisition. This implies that currently available data would be sufficient for exploratory studies to understand the gen­ eral behavior of applicants and buyers. The main focus of this study is the activity of the buyers that are involved in patent transactions for technological purposes. This sets the target of the analysis on the buyers who have experienced small acquisition including up to ten patents (N3) and patents granted (P3) to and acquired (T3) by them. The World Intellectual Property Organization (WIPO) provides a concordance table between International Patent Classification (IPC) and 35 technology fields [34]. This table was developed to provide a method for the analysis of country structures and international comparison in technology level. This study also examines the technology field depen­ dence of the patenting behavior of patent buyers. To give technological classifications to the patents produced or acquired, the WIPO table was used. IPC codes assigned to the patent, either granted to or acquired by the applicants of interest, are extracted from PATSTAT database, and the

corresponding technology fields are assigned according to the concor­ dance table. As there is no distinction in the order of appearance of the IPCs and it is difficult to assign exact proportion to each of the IPCs, all IPCs are treated in equal weight and counted according to the “whole counts” method given in the article [35]. Due to the multiple IPCs assigned to a patent, more than one technology field could be assigned to each patent. According to a previous study, however, the effect is limited [34]. 3. Characteristics of applicants and patent acquisition 3.1. Variables on patenting activities of applicants The main research focus of this study is the patenting activities of patent buyers after the transaction, particularly the patent buyer that acquired patents in Phase 1 (2007–2011) in small scale. The patenting activities of these buyers in terms of quantity and quality in Phase 2 (2012–2016) are analyzed against other applicants. Factors that could affect the activities such as the applicant’s size and technological field are considered as well. The variables used for the analysis of patenting activities are listed in Table 1 with descriptions and summary statistics. All the variables are measured for each applicant or buyer. GR_INC, the variable to measure the quantitative increase of patent production, is defined as the ratio of the number of patents granted to an applicant in Phase 2 to the sum of the numbers of patents granted in Phases 1 and 2. GR_INC has the value between 0 and 1. GR_INC, which is larger than 0.5, indicates that the patent production of an applicant increases in Phase 2 from Phase 1. The mean value of GR_INC is 0.159, which is very low because many ap­ plicants in Phase 1 do not produce any patents in Phase 2. Q_INC, the variable to measure the qualitative enhancement of the patents of an applicant, is defined as the ratio of the number of citations per patent (CPP) in Phase 2 to the sum of the numbers of citations per patent in Phases 1 and 2. Similar to GR_INC, Q_INC, which is larger than 0.5, indicates that the CPP increases in Phase 2 compared to Phase 1. 4

S.Y. Kim and H.J. Lee

World Patent Information 59 (2019) 101933

Table 1 Variables: descriptions and summary statistics. Variable

Definition

Obs.

Mean

Std.dev

Min.

Max.

GR_INC G1 G2 Q_INC CPP1 CPP2 ACQ_RATIO A1 ACQ_QRAT

Increase of the number of patents granted: G2/(G1þG2) The number of patent granted per applicant in Phase 1 The number of patent granted per applicant in Phase 2 Increase of the quality of patents granted: CPP2/(CPP1þCPP2) The number of citation per patent for each applicant in Phase 1 The number of citation per patent for each applicant in Phase 2 Ratio of patents from outside to total patent obtained in Phase 1: A1/(G1þA1) The number of patent acquired from transaction by small size acquisition per applicant in Phase 1 Ratio of the CPP of acquired patents to CPP of total patent obtained for each applicant in Phase 1: A1_CPP/(CPP1þA1_CPP) The number of citation per patent of acquired patent from outside for each applicant in Phase 1 Applicant type dummies (9 types) Technology field dummies (35 fields)

96,995 96,995 96,995 24,617 96,995 24,617 96,995 96,995 19,504

0.159 9.218 11.437 0.359 0.788 0.784 0.084 1.240 0.438

0.265 154.513 232.606 0.340 1.513 1.564 0.199 11.702 0.407

0 1 0 0 0 0 0 0 0

0.999 24,058 36,501 1 89 134 0.985 1478 1

19,504

3.196

10.372

0

521

A1_CPP ATYPE TECHF

When measuring this variable, however, the aging effect of citation needs to be considered; the number of citations of a patent increases with time. Therefore, the citation count is measured only for those received within three years after patent registration. The calculation period of CPP 2 is limited to the first two years of Phase 2. The absence of registered patents in the 2012–2013 period makes it impossible to measure the CPP value, and thus the observation decreases to 24,617. When both CPP1 and CPP2 are zero, Q_INC is set to zero. ACQ_RATIO is to measure the portion of acquired patents in the total obtained patents and is defined as the ratio of patents acquired from the transaction to the total patent obtained either by application or trans­ action in Phase 1. ACQ_RATIO, which is larger than 0.5, indicates that the portion of acquired patents is larger than that of the granted ones. The current mean value of 0.084 is equivalent to the case that an applicant registers hundred patents and acquires 9.17 patents in Phase 1. Another variable related to patent acquisition is ACQ_QRAT, which is to measure the portion of the acquired patents in terms of quality of patents (citations received). ACQ_QRAT is defined as the ratio of CPP of acquired patent from transaction to the sum of the CPPs of granted and acquired patents. Citation count is measured within three years of acquisition and registration, as in the case of Q_INC. The absence of acquired patents decreases the observation to 19,504. When the citation counts of both the acquired and granted patents are zero, ACQ_QRAT is set to zero.

ANOVA test results (bottom of Table 2) indicate the portion of the significantly different pairs of applicant types out of all available ones when comparing two applicant types. This is to examine the patenting activities with respect to the applicant types in terms of quantity and quality. The experience of patent acquisition depends on the type of appli­ cants. The percentage of buyers (the applicants that have acquired patents) in the large, medium, and small Company groups is 99.0, 79.6, and 19.0%, respectively. The rates of buyers in the University and Hospital groups are 28.7 and 27.6%, respectively. In the case of the Government and non-profit organization group, this rate is 17.1%. The rate is less than 10% for the other applicant types. The mean of the number of patents acquired from transaction per applicant (A1) is 1.240 for all applicants. This means that on average, every applicant acquired 1.24 patents from transaction in Phase 1. The applicants in large the Company group, on the contrary, acquired 109 patents, while those in the medium Company and University groups acquired 13 and 2 patents, respectively. Applicants in the remaining groups acquired less than one patent. When A1 is normalized with the amount of granted patents, the types of applicants have different ACQ_RATIO values. The mean value of ACQ_RATIO is 0.084, which means that an applicant acquires 9.17 patents when 100 patents are granted. In the case of the applicants in medium and small Company and University groups these values are 11.9, 9.65 and 9.89, respectively, which are significantly different from those of the applicants in the groups such as Government and Non-profit organizations (6.50). Applicants in large and medium Company groups show significant difference from others in qualitative enhancement of patent acquisition (ACQ_QRAT). Those in small Company show some difference as well. Patent production (G1 and G2) shows significant differences be­ tween the types large and medium Company, while the increase of patent (GR_INC) shows significant differences between the groups except for three pairs: small Company and Gov./Non-profit, small Company and Multiple, University and Hospital. The mean value of GR_INC is lower than 0.5 despite the increase in the average of patent production from Phase 1 to 2. This is attributed to the fact that only a fraction of the applicants produced patents in Phase 1 continues pro­ ducing patents. Increase in patent quality (Q_INC) differs for the types of applicant. According to the result shown in Table 2 the behavior of patents production and acquisition depends on the types of applicant (see post hoc test results), and so the applicant type needs to be considered when examining the relationship between patent production and acquisition.

3.2. Descriptions of the types of applicants EPO provides PATSTAT Standardized Name (PSN) and PSN Sector, which were created by the Catholic University of Leuven, Belgium (ECOOM), as a EUROSTAT project for patentee sector allocation [36]. PSN Sector categorizes patentees (applicants) into several groups such as company, government, non-profit organization, university, hospital, multiple assignment among them, individual, or unknown (which cannot be determined).⁹ PSN Sector is used to categorize the types of applicants. Following the criterion of Serrano [25], the group “Company” is divided into three subgroups: small, medium, and large. Companies are categorized on the number of patents that a company is granted every year: equal to or less than five patents for small, equal to or less than hundred for medium, and more than hundred for large. The applicant types used in this study, including these sub­ groups, consist of nine groups. The variables shown in Table 1 are measured with respect to the types of applicants and given in Table 2. Means and standard deviations, and the result of variance analysis (ANOVA) are given as well. Post-hoc

3.3. Descriptions of the technology fields As mentioned in section 2, all the patents of interest are classified into the 35 technology fields according to the concordance table be­ tween IPCs and the fields to examine the field dependence of buyer’s

9 Pasimeni [39] reported a SQL-based method to reduce the uncertainty in PSN sector, which, together with other methods given in references of the article, is left for the application in further study.

5

S.Y. Kim and H.J. Lee

Table 2 Means and ANOVA results of variables by applicant type.

6

Group

ATYPE

Number of applicants

Number of buyers (%)

G1

G2

GR_INC

CPP1

CPP2

Q_INC

A1

ACQ_RATIO

A1_CPP

ACQ_QRAT

1

Company Large

207

205 (99.0) 2101 (79.6)

3

Small

80,344

University

3012

5

2258

387 (17.1)

6

Government/Nonprofit Hospital

185

51 (27.6)

7

Multiple

569

57 (10.0)

9.568 (45.320) 8.804 (66.606) 4.627 (10.133) 1.830 (2.860)

8

Individual

6470

522 (8.1)

1.253 (1.153)

0.124 (2.346)

9

Unknown

1312

86 (6.6)

1.233 (1.135)

0.331 (1.991)

Total

96,995

19,504 (20.1)

9.218 (154.513) 5915

11.437 (232.606) 4536

0.755 (0.391) 0.746 (0.640) 0.810 (1.678) 0.534 (0.987) 0.473 (0.650) 0.877 (1.845) 0.874 (3.007) 0.543 (0.951) 0.714 (1.449) 0.784 (1.564) 6.695

108.995 (161.602) 12.659 (38.291) 0.671 (3.188)

4

15,231 (19.0) 864 (28.7)

0.822 (0.328) 0.792 (0.525) 0.815 (1.558) 0.632 (1.232) 0.525 (1.241) 0.512 (0.922) 0.562 (1.068) 0.674 (1.472) 0.658 (1.530) 0.788 (1.513) 23.82

4.357 (4.415) 3.742 (7.291) 3.336 (11.208) 1.842 (10.233) 0.463 (1.002) 1.365 (3.309) 2.368 (4.691) 1.158 (3.593) 2.073 (5.986) 3.196 (10.373) 9.068

0.712 (0.237)

2638

0.500 (0.206) 0.426 (0.251) 0.158 (0.264) 0.276 (0.320) 0.168 (0.271) 0.270 (0.328) 0.117 (0.241) 0.024 (0.108) 0.055 (0.171) 0.159 (0.265) 734.4

0.064 (0.078)

Medium

2602.908 (4192.397) 119.380 (222.242) 2.196 (15.982) 16.415 (79.635) 11.729 (112.395) 6.330 (16.501) 1.330 (5.744)

0.467 (0.104)

2

1897.937 (2716.065) 89.038 (86.038) 2.564 (3.415)

0.000 1-all; 2-all;

0.000 1-all; 2-all;

0.000 Except: 3–5,7; 4–6,

0.000 2-4,5; 3–4,5,7,8; 58

0.000 2-4; 3–4,5; 48

Analysis of Variance F-value P-value Post-hoc test (Scheffe, p < 0.05)

0.348 (0.354) 0.356 (0.358)

0.106 (0.129) 0.088 (0.206)

0.420 (0.387)

2.192 (10.906) 0.993 (5.496)

0.090 (0.175) 0.061 (0.166)

0.287 (0.368)

0.946 (2.629)

0.088 (0.171)

0.277 (0.336)

0.290 (1.442)

0.042 (0.139)

0.363 (0.346)

0.143 (0.684)

0.043 (0.150)

0.256 (0.360)

0.174 (0.980)

0.038 (0.149)

0.359 (0.340)

0.084 (0.199)

32.3

1.240 (11.702) 3218

0.000 1-3,4,5,7, 8,9; 2–3,4, 5,7,8; 4–8, 9; 6-9

0.000 1-all; 2-all; 3–4; 4–5,7,8,9;

0.000 2-3,5,7,8, 9; 3–5,7, 8,9; 4–5, 7,8,9

59.54

0.000 1-4; 2–4,8; 3–4,8

0.551 (0.352) 0.436 (0.413) 0.336 (0.388) 0.259 (0.367) 0.296 (0.380) 0.363 (0.424) 0.286 (0.362) 0.379 (0.431) 0.438 (0.408) 61.2 0.000 1-all; 2-all; 3–4,5,6,8

World Patent Information 59 (2019) 101933

Note: Standard deviations are reported in parentheses in each column except the number of buyers.

0.452 (0.174)

S.Y. Kim and H.J. Lee

World Patent Information 59 (2019) 101933

patenting behavior. The variables shown in Table 1 are measured with respect to the technology fields and given in Table 3. The contribution of patent acquisition depends on the technology fields. As shown in Fig. 2, the top three technology fields with the highest rate of buyers are Pharmaceuticals (26.4%), Biotechnology (25.2%), and Organic fine chemistry (24.7%). Technology fields such as Other consumer goods (12.6%), Furniture, Games (12.8%), and Ma­ chine tools (12.8%) have the lowest rate of buyers. The contribution of acquired patents in terms of quantity (ACQ_­ RATIO) is high in the Pharmaceuticals (0.101), Biotechnology (0.099), and Medical technology (0.095), while that in terms of quality (ACQ_QRAT) is high in Digital communication (0.099), Medical tech­ nology (0.089), and Pharmaceuticals (0.088). Fig. 3 compares the quantitative contribution (ACQ_RATIO) and qualitative contribution (ACQ_QRAT) of acquired patent to an applicant’s patent portfolio. The increase in patent production (GR_INC) is high in Microstructural and nano-technology (0.327), Pharmaceuticals (0.201), and Macromolecular chemistry and polymers (0.200), while that in patent quality (Q_INC) is high in Micro-structural and nano-technology (0.123), Computer technology (0.107), and Digital communication (0.107). In total, 595 pairs of technology fields are available to compare 35 technology fields, and the post-hoc test results using Scheffe’s procedure show that a considerable number of pairs are significantly different in terms of various patenting activities. The ratio of technology pairs with significantly different values with respect to ACQ_RATIO, ACQ_QRAT, GR_INC, and Q_INC are 31.1, 22.0, 23.2, and 8.7%, respectively. This implies that the technology field needs to be considered when examining the patenting behaviors of patent buyers.

and a 56.6% increase in CPP values. Companies, on the contrary, have a small change in Q_INC. The analysis so far shows that the patent acquisition from outside has a positive relationship with the increases in patent production and patent quality in some of the applicant types. Another research interest of this study is to determine how much the patent acquisition affects the increases in patent production and quality by applicant. It would be reasonable to develop a model with the increases in patent production and quality as dependent variables. However, this would require other independent dataset such as the financial information including sales, personnel, and R&D cost, which are out of the scope of this study. Instead, as the simplest possible form, a model is developed in this study only with the variables related to patent such as normalized quantity and quality of acquired patent, applicant type, and dummy for the technology field to get a rough estimation of the characteristics of the variables. As the first step, the effect of volume of patent acquisition on the increase in patent production within the group of applicants that acquire patent (buyers) in Phase 1 and continue patent production in Phase 2. Models (1) to (4) in Table 5 fall into this category. Model (1) is the simplest one including only the standardized vari­ able (ACQ_RATIO) and applicant type (ATYPE) dummies. Model (2) is the addition of the interaction between those two variables to model (1). Model (3) is the addition of technology field (TECHF) dummies to model (2). Model (4) is the addition of the interactions between ACQ_RATIO and TECHF dummies. In all of the models, according to the result, ACQ_RATIO has a positive and significant effect on GR_INC, which is consistent with the previous analysis with technology fields. It was found in model (4) that the effect of ACQ_RATIO decreases in the case of large Company and Government and Non-profit organizations. Models (5) to (8) examine the effect of the quantity and quality of acquired patents on the increase in patent quality within the patent buyer group that continues patent production in Phase 2, especially within 2012–2013. Model (5) is the simplest one including only the standardized quantity and quality (ACQ-RATIO and ACQ_QRAT, respectively) and applicant type. Model (6) is the addition of the inter­ action between ACQ_QRAT and ATYPE to model (5). Models (7) and (8) control the technology field. Model (7) is the addition of TECHF to model (5), while model (8) is the addition of the interactions between ACQ_QRAT, ATYPE, and TECHF. According to the result ACQ_QRAT has positive and significant effect on Q_INC in all of the four models. When TECHF is not controlled, ACQ_RATIO has a negative effect on the quality of the patents in Phase 2. However, when TECHF is controlled, its effect is positive. The TECHFs, among the TECHF dummies, that have significant negative effect in models (7) and (8) are IT methods for management (7), Analysis of biological materials (11), Macromolecular chemistry, polymers (17), Food chemistry (18), and Environmental technology (24). The change of the effect from negative to positive is possibly caused by controlling these fields. TECHFs such as Materials, metallurgy (20), Micro-structural and nanotechnology (22), Handling (25), Machine tools (26), and Engines, pumps, turbines (27) are found to increase the effect of ACQ_QRAT.

4. Effects of patent acquisition on patent production In the previous sections, the applicant type and technology field dependences of patent production and acquisition were examined. In this section, the relationship between patent acquisition and production (registration) is examined. The change of an applicant’s patenting activity over time in terms of quantity and quality can be examined by the variables GR_INC and Q_INC, respectively. Table 4 shows these two variables as a function of the applicant type and the experience of patent acquisition. The mean value of GR_INC increases in all applicant types except for large Company, Hospital and Multiple when the applicants acquire patents. The increase is significant with the applicant types such as medium and small Companies, Government and Non-profit organiza­ tion, and University. In the case of medium Company with patent acquisition, GR_INC is approximately 19.9% larger than without, which is a 37.5% increase in terms of patent count in Phase 2. This means that if a medium-sized company that produces 5–100 patents a year has ac­ quired a patent in Phase 1, they will produce 37.5% more patents in Phase 2 than a company that has not. For all applicants with patent acquisition, the mean value of GR_INC is 2.8% larger than without, which is equivalent to 5.8% larger in patent production in Phase 2. The mean value of Q_INC shows significant differences in large, medium, and small Companies, Government and Non-profit organiza­ tions, and University depending on the experience of patent acquisition. However, the differences in Q_INC are less distinct than those of GR_INC, which can be found at the lower boxplots of Fig. 4, where the wide distribution of Q_INC from the 1st to 3rd quartile, almost close to the half of the whole range (0–1), supports this behavior. In spite of the wide distribution of Q_INC, the mean for all applicants is 17.1% larger with patent acquisition than without. This is a 28.2% difference when con­ verted to CPP value. The applicant group with the largest change in Q_INC with patent acquisition is Government and Non-profit organiza­ tions, where the difference is a 35.9% increase. University shows a large difference in Q_INC, which is a 33.5% increase with patent acquisition. The increase of Q_INC with these applicant groups are equivalent to 61.9

5. Conclusion and discussion In this paper, we investigated the patenting activities of applicants after patent acquisition. The applicant type and technology field de­ pendences of patent production and acquisition were examined and the relationship between patent acquisition and production was also examined in terms of patent quantity and quality. We linked the patent assignment data with the patent bibliography data by collecting infor­ mation on the patent acquiring activities of the applicants who were granted patents from 2007 to 2011 and their patent application activ­ ities from 2012 to 2016. The results of the investigation for the applicant type and technology field dependences of patent production and acquisition show that large 7

S.Y. Kim and H.J. Lee

World Patent Information 59 (2019) 101933

Table 3 Means and ANOVA results of variables by technology field. NR

TECHF

Applicants

Buyers (%)

G1

G2

GR_INC

CPP1

CPP2

Q_INC

A1

ACQ_RATIO

ACQ_QRAT

1

Electrical machinery, apparatus, energy Audio-visual technology Telecommunications

13,512

Digital communication Basic communication processes Computer technology

6853

5138

8

IT methods for management Semiconductors

9

Optics

6937

10

Measurement

14,570

11

4990

12

Analysis of biological materials Control

13

Medical technology

11,316

14

Organic fine chemistry

8662

15

Biotechnology

8749

16

Pharmaceuticals

9955

17

Macromolecular chemistry, polymers Food chemistry

4101

Basic materials chemistry Materials, metallurgy

7342

Surface technology, coating Micro-structural and nano-technology Chemical engineering

8063

5070

25

Environmental technology Handling

26

Machine tools

7826

27

5175

31

Engines, pumps, turbines Textile and paper machines Other special machines Thermal processes and apparatus Mechanical elements

32

Transport

8005

33

Furniture, games

7408

34

Other consumer goods

6407

Civil engineering

7550

2144 (15.9) 1722 (18.2) 1828 (20.5) 1523 (22.2) 681 (17.4) 3826 (20.5) 1049 (20.4) 1214 (19.6) 1195 (17.2) 2574 (17.7) 1090 (21.8) 1377 (16.6) 2457 (21.7) 2136 (24.7) 2206 (25.2) 2629 (26.4) 812 (19.8) 459 (17.1) 1464 (19.9) 818 (16.6) 1468 (18.2) 112 (15.5) 1608 (16.7) 793 (15.6) 1096 (13.0) 999 (12.8) 763 (14.7) 655 (15.4) 1578 (16.3) 508 (14.6) 1040 (13.1) 1103 (13.8) 948 (12.8) 809 (12.6) 1040 (13.8) 18,364 (18.9)

6.566 (42.212) 10.520 (110.416) 9.693 (79.689) 10.816 (95.180) 9.242 (52.849) 10.540 (169.111) 3.853 (24.081) 14.003 (115.638) 10.522 (91.880) 5.694 (34.180) 3.322 (10.214) 4.318 (22.169) 4.504 (28.112) 6.002 (25.158) 4.803 (23.724) 6.466 (30.082) 5.010 (18.983) 3.588 (31.160) 3.943 (15.177) 3.552 (10.040) 4.417 (20.659) 3.119 (6.564) 3.452 (11.560) 3.066 (11.504) 3.034 (12.017) 3.094 (11.394) 5.466 (38.843) 6.534 (69.887) 2.986 (9.884) 2.949 (11.843) 3.581 (16.356) 4.952 (33.513) 2.928 (15.252) 2.852 (15.299) 2.887 (21.132) 5.831 (63.327) 16.889

7.168 (57.717) 11.737 (138.935) 10.488 (115.153) 18.207 (198.685) 8.295 (52.827) 14.216 (250.673) 4.512 (43.682) 15.606 (150.042) 9.619 (105.780) 5.515 (40.701) 2.544 (10.320) 4.411 (30.039) 5.679 (43.129) 6.057 (32.237) 4.742 (33.994) 6.794 (36.805) 5.891 (31.059) 3.818 (56.234) 4.370 (26.536) 3.484 (14.650) 4.633 (26.285) 8.702 (27.856) 3.200 (16.790) 2.881 (19.684) 2.660 (17.123) 3.270 (19.292) 6.230 (61.739) 5.516 (73.884) 2.866 (15.189) 2.706 (17.335) 4.068 (30.687) 6.281 (66.311) 2.704 (25.366) 2.244 (19.763) 2.744 (32.495) 6.486 (92.341) 14.884

0.170 (0.272) 0.176 (0.281) 0.162 (0.267) 0.194 (0.297) 0.173 (0.271) 0.182 (0.283) 0.171 (0.275) 0.185 (0.281) 0.165 (0.265) 0.170 (0.271) 0.174 (0.268) 0.167 (0.272) 0.188 (0.287) 0.199 (0.288) 0.188 (0.280) 0.201 (0.296) 0.200 (0.290) 0.150 (0.256) 0.186 (0.282) 0.188 (0.281) 0.191 (0.281) 0.327 (0.377) 0.164 (0.269) 0.151 (0.260) 0.159 (0.265) 0.179 (0.279) 0.167 (0.269) 0.162 (0.262) 0.167 (0.270) 0.160 (0.265) 0.177 (0.279) 0.151 (0.265) 0.130 (0.244) 0.137 (0.249) 0.149 (0.262) 0.173 (0.276) 34.910

0.853 (1.419) 1.021 (1.763) 1.188 (1.863) 1.517 (2.236) 0.974 (1.459) 1.402 (2.601) 1.648 (2.595) 0.836 (1.465) 0.679 (1.131) 0.771 (1.419) 0.439 (0.978) 1.150 (1.910) 0.710 (1.231) 0.424 (1.176) 0.424 (1.147) 0.425 (1.096) 0.409 (0.898) 0.364 (0.975) 0.468 (1.146) 0.448 (0.824) 0.475 (0.922) 0.816 (1.311) 0.525 (1.010) 0.609 (1.044) 0.590 (1.066) 0.504 (0.862) 0.757 (1.348) 0.482 (1.039) 0.554 (1.136) 0.586 (1.193) 0.604 (1.013) 0.825 (1.364) 0.801 (1.331) 0.644 (1.219) 0.773 (1.230) 0.769 (1.511) 384.976

0.214 (0.785) 0.264 (0.889) 0.280 (1.046) 0.334 (1.162) 0.243 (0.729) 0.335 (1.199) 0.262 (1.048) 0.224 (0.787) 0.180 (0.619) 0.200 (0.727) 0.106 (0.447) 0.280 (1.056) 0.189 (0.654) 0.142 (0.525) 0.134 (0.551) 0.145 (0.546) 0.113 (0.415) 0.082 (0.518) 0.127 (0.485) 0.117 (0.504) 0.132 (0.476) 0.234 (0.720) 0.124 (0.482) 0.124 (0.464) 0.156 (0.589) 0.150 (0.499) 0.165 (0.597) 0.124 (0.503) 0.142 (0.549) 0.132 (0.487) 0.174 (0.568) 0.222 (0.781) 0.183 (0.711) 0.144 (0.593) 0.191 (0.692) 0.192 (0.744) 63.731

0.094 (0.234) 0.101 (0.241) 0.099 (0.234) 0.107 (0.236) 0.105 (0.240) 0.107 (0.242) 0.079 (0.211) 0.105 (0.242) 0.094 (0.235) 0.093 (0.236) 0.072 (0.220) 0.095 (0.236) 0.094 (0.238) 0.097 (0.249) 0.086 (0.238) 0.092 (0.243) 0.089 (0.240) 0.055 (0.195) 0.082 (0.232) 0.080 (0.233) 0.093 (0.244) 0.123 (0.279) 0.077 (0.224) 0.068 (0.210) 0.084 (0.233) 0.094 (0.249) 0.088 (0.233) 0.079 (0.232) 0.079 (0.229) 0.070 (0.215) 0.096 (0.247) 0.091 (0.234) 0.075 (0.221) 0.070 (0.215) 0.082 (0.227) 0.090 (0.235) 17.439

0.596 (3.415) 0.925 (7.754) 1.145 (9.098) 1.483 (11.038) 0.760 (5.009) 1.211 (12.228) 0.705 (3.381) 1.018 (6.972) 0.732 (4.142) 0.602 (3.105) 0.683 (3.149) 0.524 (2.856) 1.038 (6.386) 1.054 (4.590) 1.065 (4.393) 1.200 (5.153) 0.704 (3.953) 0.504 (2.053) 0.538 (2.026) 0.400 (1.399) 0.478 (1.995) 0.330 (1.139) 0.471 (2.128) 0.366 (1.482) 0.336 (1.843) 0.287 (1.251) 0.454 (3.423) 0.447 (3.067) 0.400 (1.591) 0.342 (2.511) 0.327 (1.726) 0.467 (3.336) 0.382 (3.125) 0.277 (1.249) 0.394 (2.327) 0.700 (5.379) 29.958

0.057 (0.160) 0.069 (0.177) 0.079 (0.190) 0.090 (0.203) 0.060 (0.161) 0.084 (0.197) 0.094 (0.209) 0.065 (0.167) 0.062 (0.169) 0.065 (0.169) 0.085 (0.185) 0.068 (0.174) 0.095 (0.207) 0.094 (0.196) 0.099 (0.202) 0.101 (0.204) 0.071 (0.175) 0.074 (0.183) 0.079 (0.182) 0.064 (0.169) 0.069 (0.173) 0.066 (0.172) 0.066 (0.171) 0.065 (0.173) 0.054 (0.160) 0.051 (0.154) 0.056 (0.162) 0.060 (0.164) 0.067 (0.173) 0.062 (0.168) 0.052 (0.155) 0.053 (0.158) 0.054 (0.159) 0.055 (0.161) 0.060 (0.170) 0.071 (0.179) 56.142

0.065 (0.218) 0.077 (0.235) 0.087 (0.247) 0.099 (0.261) 0.075 (0.229) 0.081 (0.234) 0.077 (0.231) 0.083 (0.245) 0.075 (0.236) 0.071 (0.226) 0.059 (0.211) 0.067 (0.219) 0.089 (0.258) 0.077 (0.240) 0.076 (0.239) 0.088 (0.256) 0.077 (0.245) 0.054 (0.206) 0.064 (0.222) 0.060 (0.216) 0.059 (0.212) 0.042 (0.184) 0.060 (0.213) 0.054 (0.200) 0.048 (0.191) 0.043 (0.182) 0.055 (0.201) 0.058 (0.211) 0.059 (0.214) 0.050 (0.194) 0.050 (0.194) 0.056 (0.202) 0.046 (0.183) 0.044 (0.184) 0.056 (0.203) 0.068 (0.223) 32.648

2 3 4 5 6 7

18 19 20 21 22 23 24

28 29 30

35 b

Total

9465 8,937

3917 18,709

6188

8287

2685

4936

722 9626

8435

4260 9707 3490 7934

96,914

(continued on next page)

8

S.Y. Kim and H.J. Lee

World Patent Information 59 (2019) 101933

Table 3 (continued ) NR

TECHF

Analysis of Variance F-value P-value Different pairs ratioa (%)

Applicants

Buyers (%)

G1

G2

GR_INC

CPP1

CPP2

Q_INC

A1

ACQ_RATIO

ACQ_QRAT

0.000 10.756

0.000 9.580

0.000 23.193

0.000 60.336

0.000 32.437

0.000 8.739

0.000 20.504

0.000 31.092

0.000 22.017

a ‘Different pairs ratio’ indicates the ratio of pairs with significantly different mean values to all possible pairs as the result of the post-hoc test. Standard deviations are reported in parentheses in each column except the number of buyers. b The total numbers of applicants and buyers are less than those shown in Section 2. This is attributed to the fact that some patents in PATSTAT do not have IPC information and that the applicants/buyers who possess/buy only these patents are excluded from the aggregation of this table.

Fig. 2. The ratio of buyers by technology fields in Phase 1 (2007–2011).

and medium companies and universities acquire more patents that other types of applicants. However, the normalized number of acquired pat­ ents on the number of granted patents is the highest in medium com­ panies, the next in small company, and the lowest in large company. The large volume of patent acquisition by universities may seem to contra­ dict knowledge production and dissemination as a function of univer­ sities in general. Some of these phenomena were found to be due to the acquisitions of ownership portion from outside inventors other than employees. Universities have acquired more patents from hospitals, research institutes, or other universities than companies. The reasons why universities acquire patents and their effects may be the subject of future research. The normalized quality of acquired patents on the quality of granted patent measured by the number of citations is the highest in the large Company group, followed by the medium and the small. The normalized quality of patents acquired by the large Company is higher than that of others for one of two reasons: relatively high quality of patent acquired by the large company or the relatively low quality of patents granted to the larger company. The results show that the main reason is higher

quality of patents acquired by larger company through the CPP1 and A1_CPP columns of Table 2. In the technology market, the capital and influence of large companies can bring the technology protected by highly cited patent to large companies. Alternatively, the characteristics of the specific technology field in which large companies are involved can also bring the highly cited patent to them. It is interesting to investigate the relationship between the size of company and the cita­ tion of acquired patents. The results of examining technology field dependences of patent production and acquisition show the great difference in the ratio of patent buyers and normalized quantity and quality of the acquired patents by technology. Therefore, it is important to consider the tech­ nology field in analyzing patent acquisition activity. It is interesting that the percentage of patent buyers is very high (21.7–26.4%) in the Biomedical field including medical technology, Biotechnology, and Pharmaceuticals, the average number of patents acquired per applicant is high, and the normalized quantity and quality of acquired patents are also high. Cammarano et al. [37] suggested that the technology market in the Biomedical field is relatively efficient, and that patents are often 9

S.Y. Kim and H.J. Lee

World Patent Information 59 (2019) 101933

Fig. 3. Quantity (ACQ_INC) vs. Quality (ACQ_QRAT) of Acquired patent Note: Each number represents the technology field shown in Fig. 2 and Table 3. Table 4 Patenting activity comparisons by applicant type and patent acquisition. Applicant type

Acquisition

ALL

N Y N Y N Y N Y N Y N Y N Y N Y N Y N Y

Company large Company Medium Company Small Gov./Non-profit Hospital Individual Multiple University Unknown

GR_INC

Q_INC

obs.

mean (std. dev.)

p-value (Welch’s t-test)

obs.

mean (std. dev.)

p-value (Welch’s t-test)

18,717 11,224 2 205 494 1977 16,530 7905 455 269 41 41 278 53 90 31 712 722 115 21

0.511 (0.202) 0.525 (0.221) 0.563 (0.191) 0.500 (0.206) 0.392 (0.225) 0.470 (0.232) 0.512 (0.200) 0.532 (0.219) 0.511 (0.204) 0.548 (0.207) 0.610 (0.202) 0.609 (0.172) 0.458 (0.156) 0.476 (0.182) 0.555 (0.182) 0.537 (0.190) 0.565 (0.201) 0.597 (0.190) 0.516 (0.184) 0.593 (0.157)

0.000

14,559 10,058 2 205 486 1958 12,799 6874 318 238 32 35 239 46 67 30 522 657 94 15

0.336 0.393 0.387 0.468 0.424 0.460 0.336 0.370 0.309 0.420 0.419 0.421 0.279 0.330 0.238 0.363 0.306 0.409 0.242 0.346

0.000

0.719 0.000 0.000 0.021 0.993 0.507 0.643 0.002 0.052

used to solve well-defined and detailed problems. In the group of fields including Telecommunications, Digital communication, and Computer technology, the percentage of patent buyers is high (20.5–22.2%) and the average number of patents acquired per applicant is high. The normalized quantity and quality of acquired patents are higher than the average, but they are lower in terms of quantity and higher in terms of quality than in the Biomedical fields. This could imply that the com­ panies in Telecommunications, Digital communication, and Computer

(0.361) (0.305) (0.016) (0.104) (0.208) (0.164) (0.363) (0.334) (0.384) (0.309) (0.449) (0.327) (0.364) (0.389) (0.348) (0.295) (0.388) (0.302) (0.349) (0.419)

0.029 0.000 0.000 0.000 0.976 0.412 0.072 0.000 0.375

technology fields focus on acquiring a few advanced technologies. It would be interesting to investigate the impact of subsequent patent acquisitions on the applicant’s internal capabilities and R&D activities in these two areas in more detail with specific cases, and practical implications. We also investigated the relationship between patent acquisition and production in terms of patent quantity and quality. The results show that the patent buyers produced more patents in most applicant types. In 10

S.Y. Kim and H.J. Lee

World Patent Information 59 (2019) 101933

Fig. 4. Distribution of patenting activity changes in terms of quantity (a) and quality (b) by applicant type and patent acquisition.

particular, even after controlling the applicant types and technology fields, there is a significant positive correlation between the normalized quantity of patents acquired and the quantitative and qualitative improvement of the patent production. The results imply that companies that accept external knowledge and technology in the form of patents improve in terms of knowledge and technology that can be expressed in the form of patents. This is not the only reason for the acquisition of a patent. We tried to distinguish the acquisition of patents for technology and knowledge absorption as much as possible by analyzing only “small acquisitions.” Under the criterion of small acquisition, it can be said that the acquisition of patented technology and knowledge improves the applicant’s patent performance. However, the effect of the quantity of acquired patents on the quality improvement of patent production has been both negative and positive depending on the technology field control. This study contributes to the existing literature by exploring pat­ enting activities of patent buyers using comprehensive data not a case

study. The results show that the patent acquisition activity depends on the type of applicant and technology fields and the patent acquisition affects the future patent production of the applicant. Based on the results of this study, we can suggest that observing the competitor’s patent acquisition activities gives a hint about the future direction of compet­ itor’s technology development. However, this study has a few limitations. Since the patent acquisi­ tion is the result of self-selection, both the patent acquisition and the patent production may be the result of the invisible “technological capability”. Though patents can be used as the main source of infor­ mation for measuring the technological innovation, the result can be more enriched when combined with other information sources such as scientific journal articles, technology reports, and even with other sources, social media for example, which are not directly related to science and technology [38]. To minimize this aspect without additional data, the comparison of patent production performance (Table 4) was made only for applicants with patent activity in the second phase. In the 11

S.Y. Kim and H.J. Lee

World Patent Information 59 (2019) 101933

Table 5 Impact of patent acquisition on the increase in patent production in the quantitative and qualitative aspects. Variables

GR_INC

Q_INC

(1) ACQ_RATIO ACQ_QRAT

0.262 (0.009)

(2) ***

ATYPE dummies Company Medium Company large Gov./Nonprofit University

0.012 * (0.006) 0.059 *** (0.015) 0.044 *** (0.013) 0.099 *** (0.008) Hospital 0.107 ** (0.033) Multiple 0.010 (0.038) Individual 0.077 ** (0.029) Unknown 0.017 (0.046) Interaction: ACQ_RATIO * ATYPE Company Medium Company large Gov./Nonprofit University Hospital Multiple Individual Unknown

***

0.025 (0.008) 0.075 (0.020) 0.052 (0.022) 0.103 (0.016) 0.112 (0.065) 0.155 (0.075) 0.134 (0.071) 0.000 (0.140)

**

0.079 (0.038) 0.212 (0.190) 0.025 (0.056) 0.010 (0.045) 0.012 (0.185) 0.369 (0.163) 0.114 (0.131) 0.028 (0.227) 0.420 (0.005)

*

*** * *** . * .

(4)

0.330 (0.006)

***

0.049 (0.003) 0.086 (0.005) 0.080 (0.007) 0.109 (0.004) 0.070 (0.020) 0.005 (0.026) 0.054 (0.024) 0.004 (0.043)

*** *** *** *** ***

*

*

***

0.395 (0.006)

***

(5)

0.292 (0.026)

***

0.044 (0.006) 0.098 (0.007) 0.111 (0.012) 0.094 (0.009) 0.076 (0.039) 0.086 (0.061) 0.035 (0.065) 0.013 (0.175)

*** *** *** *** .

0.019 (0.015) 0.118 (0.031) 0.107 (0.031) 0.051 (0.022) 0.011 (0.120) 0.212 (0.126) 0.043 (0.119) 0.014 (0.284) 0.406 (0.010)

(6)

0.089 (0.014) 0.085 (0.008)

***

0.063 (0.008) 0.052 (0.020) 0.042 (0.019) 0.041 (0.012) 0.101 (0.052) 0.024 (0.053) 0.086 (0.048) 0.048 (0.088)

***

***

* * *** .

.

*** *** *

.

***

0.361 (0.007)

***

(7)

0.091 (0.014) 0.094 (0.009)

***

0.094 (0.013) 0.112 (0.062) 0.066 (0.029) 0.025 (0.017) 0.015 (0.075) 0.056 (0.079) 0.116 (0.073) 0.210 (0.175)

***

0.058 (0.020) 0.088 (0.083) 0.055 (0.051) 0.047 (0.034) 0.218 (0.137) 0.174 (0.128) 0.069 (0.124) 0.231 (0.218) 0.358 (0.007)

**

***

. *

***

(8)

0.114 (0.009) 0.168 (0.005)

***

0.052 (0.005) 0.080 (0.007) 0.039 (0.011) 0.055 (0.007) 0.082 (0.031) 0.039 (0.042) 0.071 (0.043) 0.343 (0.107)

***

0.279 (0.010)

***

*** *** *** **

**

***

0.092 (0.009) 0.253 (0.024)

***

0.129 (0.007) 0.192 (0.011) 0.073 (0.015) 0.082 (0.009) 0.050 (0.040) 0.044 (0.062) 0.061 (0.059) 0.189 (0.271)

***

0.177 (0.012) 0.242 (0.018) 0.080 (0.029) 0.052 (0.019) 0.128 (0.083) 0.015 (0.103) 0.002 (0.114) 0.233 (0.404) 0.249 (0.014)

***

(Intercept)

0.424 (0.005)

TECHF dummies Interaction: Main * TECHF Observations adjusted R2 F-statistic TECHF( ) (p < 0.05)

No

No

Yes

Yes

No

No

Yes

Yes

No

No

No

Yes

No

No

No

Yes

11,224 0.083 114.000

11,224 0.083 61.040

27,975 0.112 82.670 3, 5, 9, 10, 11, 15, 18, 28 13, 22, 31, 32

27,975 0.115 43.610 3, 5, 9, 10, 11, 14, 15, 16, 28 31, 32

9226 0.032 31.760

9226 0.034 18.760

21,549 0.076 41.020 7, 11, 13, 15, 16, 17, 18, 24

21,549 0.090 25.890 7, 11, 17, 18, 19, 20, 24, 27

31, 32

35

TECHF(þ) (p < 0.05) INT.TECHF ( ) (p < 0.05) INT.TECHF (þ) (p < 0.05)

***

(3)

0.269 (0.010)

–

35

3, 9, 10, 11, 14, 15, 16, 25, 28

20, 22, 25, 26, 27

***

*** *** ***

*** ** **

***

Note: Models are estimated using ordinary least squares. Standard errors are in parentheses. TECHF( ) indicates that the estimate of technology field dummy is negative at the 5% level of significance. In the interaction variable involving TECHF, main variable indicates ACQ_RATIO on the model 1–4, ACQ_QRAT on the model 5–8.

future, financial data and related variables for applicants can be used with a more detailed model to control the applicant’s technological capability. On the other hand, uncertain applicants who are excluded from the sample because of their unstandardized names are likely not to have well-established processes for patent production and acquisition.

Therefore, it is possible that descriptive statistics such as the number of granted patents and acquired patents were overestimated than including the uncertain applicants. It is also possible that the effect of the patent acquisition will be overestimated. Future studies need to not only expand the research topic but also reduce the number of applicants 12

S.Y. Kim and H.J. Lee

World Patent Information 59 (2019) 101933

excluded from the analysis to improve the applicant name matching rules, or to identify all the applicants manually by narrowing the field of analysis.

Funding This work was supported by Korea Institute of Science and Tech­ nology Information through Grant No. K-18-L13-C02-S01 and K-19-L03C03-S01.

Appendix

A. List of conveyance types and corresponding matching keywords Order

Conveyance type

Matching keywords

1 2 3 4 5 6

correc*, conver*, amend*, supplement*, re-record*, rerecord*, error*, “add “, added, wrong, duplicat* releas*, terminat*, foreclos*, surrender*, reassign*, discharge* merge*, amalgamation*, incorporat*, absorption Include (change, name) Not include (ownership, percent, respective) confirmator*, government* employ*

7 8

Correction Release Merger Change of name Government Employer assignment*) Security Other

9 10

Assignment**) Missing

Secur*, mortgage*, lien*, asset*, purchase*, collateral, acqui*, financ*, bankrupt*, dividend, guarant*, option Licen*, research, decree, letter*, appoint*, court*, order*, judg*, judicial, settle*, certifie*, administration, county*, superior, contractor, sublicense Assignment*, bill, transfer*, contribution, ownership*, reassignment, quit*, interest*, sale*, will –

*Includes the part of ‘Assignment’ types that are re-classified as employer assignments by the Marco [32]’s rules. **Excludes the transfers that are re-classified as ‘employer assignment’ types.

References

[17] F. Caviggioli, E. Ughetto, The drivers of patent transactions: corporate views on the market for patents, R D Manag. 43 (2013) 318–332, https://doi.org/10.1111/ radm.12016. [18] J.S. Gans, D.H. Hsu, S. Stern, When does start-up innovation spur the gale of creative destruction? RAND J. Econ. 33 (2002) 571, https://doi.org/10.2307/ 3087475. [19] F. Caviggioli, E. Ughetto, Buyers in the patent auction market: opening the black box of patent acquisitions by non-practicing entities, Technol. Forecast. Soc. Chang. 104 (2016) 122–132, https://doi.org/10.1016/j.techfore.2015.11.031. [20] T. Fischer, J. Leidinger, Testing patent value indicators on directly observed patent value - an empirical analysis of Ocean Tomo patent auctions, Res. Policy 43 (2014) 519–529, https://doi.org/10.1016/j.respol.2013.07.013. [21] S. Sreekumaran Nair, M. Mathew, D. Nag, Dynamics between patent latent variables and patent price, Technovation 31 (2011) 648–654, https://doi.org/ 10.1016/j.technovation.2011.07.002. [22] C. Odasso, G. Scellato, E. Ughetto, Selling patents at auction: an empirical analysis of patent value, Ind. Corp. Chang. 24 (2015) 417–438, https://doi.org/10.1093/ icc/dtu015. [23] K.A. Sneed, D.K.N. Johnson, Selling ideas: the determinants of patent value in an auction environment, R D Manag. 39 (2009) 87–94, https://doi.org/10.1111/ j.1467-9310.2008.00540.x. [24] K.a. Piirainen, A.D. Andersen, P.D. Andersen, Foresight for Sectoral Development: Sectoral Development as a ‘Third Mission’ Activity at the Technical University of Denmark, vols. 1–27, Eur. IFA Acad. Semin, 2013. [25] C.J. Serrano, The dynamics of the transfer and renewal of patents, RAND J. Econ. 41 (2010) 686–708, https://doi.org/10.1111/j.1756-2171.2010.00117.x. [26] N. Figueroa, C. Serrano, Patent Trading Flows of Small and Large Firms, Cambridge, MA, 2013, https://doi.org/10.3386/w18982. [27] A. Galasso, M. Schankerman, C.J. Serrano, Trading and enforcing patent rights, RAND J. Econ. 44 (2013) 275–312, https://doi.org/10.1111/1756-2171.12020. [28] K. Drivas, C. Economidou, Is geographic nearness important for trading ideas? Evidence from the US, J. Technol. Transf. 40 (2015) 629–662, https://doi.org/ 10.1007/s10961-014-9360-0. [29] F. Caviggioli, A. De Marco, G. Scellato, E. Ughetto, Corporate strategies for technology acquisition: evidence from patent transactions, Manag. Decis. 55 (2017) 1163–1181, https://doi.org/10.1108/MD-04-2016-0220. [30] B. Hall, A. Jaffe, M. Trajtenberg, The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools, 2001, https://doi.org/10.3386/w8498. [31] C.J. Serrano, The dynamics of the transfer and renewal of patents, RAND J. Econ. 41 (2010) 686–708, https://doi.org/10.1111/j.1756-2171.2010.00117.x. [32] A.C. Marco, A.F. Myers, S. Graham, P.D. Agostino, K. Apple, The USPTO patent assignment dataset: descriptions and analysis, USPTO Econ. Work. Pap. 2015 (2015). [33] A. De Marco, G. Scellato, E. Ughetto, F. Caviggioli, Global markets for technology: evidence from patent transactions, Res. Policy 46 (2017) 1644–1654, https://doi. org/10.1016/j.respol.2017.07.015. [34] U. Schmoch, Concept of a technology classification for Country comparison. https ://www.wipo.int/edocs/mdocs/classifications/en/ipc_ce_41/ipc_ce_41_5-annex1. pdf, 2008.

[1] M. Benassi, A. Di Minin, Playing in Between : IP brokers in markets for technology, Manag. Sci. 21501 (2007) 38, https://doi.org/10.1121/1.2166709. [2] A. Arora, A. Gambardella, Ideas for rent: an overview of markets for technology, Ind. Corp. Chang. 19 (2010) 775–803, https://doi.org/10.1093/icc/dtq022. [3] G. Dushnitsky, T. Klueter, Is there an eBay for ideas? Insights from online knowledge marketplaces, Eur. Manag. Rev. 8 (2011) 17–32, https://doi.org/ 10.1111/j.1740-4762.2010.01002.x. [4] S. Athreye, J. Cantwell, Creating competition? Res. Policy 36 (2007) 209–226, https://doi.org/10.1016/j.respol.2006.11.002. [5] N. Kulatilaka, L. Lin, Impact of licensing on investment and financing of technology development, Manag. Sci. 52 (2006) 1824–1837, https://doi.org/10.1287/ mnsc.1060.0589. [6] J. Cheng, T. Lan, S.J. Liu, Patent market dynamics: in view of the business model of non-practicing entities, World Pat. Inf. 48 (2017) 61–76, https://doi.org/10.1016/ j.wpi.2017.01.003. [7] P. Vimalnath, A. Gurtoo, M. Mathew, The relationship between patent age and selling price across bundling strategies for United States patents, predominately for computer and communication technology, World Pat. Inf. 48 (2017) 1–11, https:// doi.org/10.1016/j.wpi.2016.12.001. [8] J. Hagedoorn, G. Duysters, External sources of innovative capabilities: the preferences for strategic alliances or mergers and acquisitions, J. Manag. Stud. 39 (2002) 167–188, https://doi.org/10.1111/1467-6486.00287. [9] H.W. Chesbrough, Open Innovation: the New Imperative for Creating and Profiting from Technology, Harvard business school press, 2003. [10] M.A. Hitt, R.E. Hoskisson, R.A. Johnson, D.D. Moesel, The market for corporate control and firm innovation, Acad. Manag. J. 39 (1996) 1084–1119, https://doi. org/10.2307/256993. [11] G. Ahuja, R. Katila, Technological acquisitions and the innovation performance of acquiring firms: a longitudinal study, Strateg. Manag. J. 22 (2001) 197–220, https://doi.org/10.1002/smj.157. [12] T.A. Mahnken, M.G. Moehrle, Multi-cross-industry innovation patents in the USA a combination of PATSTAT and Orbis search, World Pat. Inf. 55 (2018) 52–60, https://doi.org/10.1016/j.wpi.2018.10.003. [13] C.V. Trappey, A.J.C. Trappey, Y.H. Wang, Are patent trade wars impeding innovation and development? World Pat. Inf. 46 (2016) 64–72, https://doi.org/ 10.1016/j.wpi.2016.06.004. [14] A. Spithoven, P. Teirlinck, External R&D: exploring the functions and qualifications of R&D personnel, Int. J. Innov. Manag. 14 (2010) 967–987, https://doi.org/ 10.1142/S1363919610002969. [15] B. Cassiman, M.G. Colombo, P. Garrone, R. Veugelers, The impact of M&A on the R&D process: an empirical analysis of the role of technological- and marketrelatedness, Res. Policy 34 (2005) 195–220, https://doi.org/10.1016/j. respol.2005.01.002. [16] E.F.M. Wubben, M. Batterink, O. Omta, Getting post-M&A integration mechanisms tuned in to technological relatedness and innovation synergy realisation, Technol. Anal. Strateg. Manag. 28 (2016) 992–1007, https://doi.org/10.1080/ 09537325.2016.1181738.

13

S.Y. Kim and H.J. Lee

World Patent Information 59 (2019) 101933

[35] OECD, OECD Patent Statistics Manual (2009), https://doi.org/10.1787/ 9789264208612-zh. [36] B. Van Looy, M. du Plessis, T. Magerman, Data production methods for hamonized patent statistics: patentee sector allocation, SSRN Electron. J. (2006), https://doi. org/10.2139/ssrn.944464. [37] A. Cammarano, M. Caputo, E. Lamberti, F. Michelino, Open Innovation and Intellectual Property: a Knowledge-Based Approach, 2017, https://doi.org/ 10.1108/MD-03-2016-0203. [38] A. Comai, Beyond patent analytics: insights from a scientific and technological data mashup based on a case example, World Pat. Inf. 55 (2018) 61–77, https://doi.org/ 10.1016/j.wpi.2018.10.002. [39] F. Pasimeni, SQL query to increase data accuracy and completeness in PATSTAT, World Pat, OR Inf. 57 (2019) 1–7, https://doi.org/10.1016/j.wpi.2019.02.001.

So Young Kim is a researcher of the Korea Institute of Science and Technology Infor­ mation (KISTI). She received her Ph.D. from the Technology Economics, Management, and Policy Program at Seoul National University. Her area of interest includes open innova­ tion, market of technology, competitive intelligence, technology intelligence, sciento­ metrics and R&D policy. Hyuck Jai Lee is a researcher of the Korea Institute of Science and Technology Informa­ tion (KISTI). He received his Ph.D. from Sogang University, Seoul, Korea. His area of in­ terest includes research evaluation, data mining, competitive intelligence and scientometrics.

14