Hitotsubashi University
Institute of Innovation Research

Effects of early patent disclosureon knowledge dissemination:evidence from the pre-grant publication system
introduced in the United States
Yoshimi Okada
Sadao Nagaoka

IIR Working Paper WP#15-12

Jul. 2015

Institute of Innovation Research
Hitotsubashi University
Tokyo, Japan
http://www.iir.hit-u.ac.jp

Effects of early patent disclosure on knowledge dissemination: evidence from thepre-grant publication system introduced in the United States
July 2015
Yoshimi Okada
Institute of Innovation Research, Hitotsubashi University
Sadao Nagaoka
Tokyo Keizai University
Abstract
In order to assess the disclosure function of the patent system, this study
examined the impact of the pre-grant publication system introduced in the
United States in 2000. Unlike earlier studies, the applicant (inventor)
non-self-citations (excluding examiner citations) were used to track knowledge
flow. The causal effects of disclosure were identified by examining the changes inbehavior before and after this legal change. The introduction of the pre-grant
publication system was found to accelerate the initiation of knowledge diffusion
significantly across all technology areas, except for Chemical field. The effect was
the strongest in the Computers & Communications field, which had the longest
publication lag before the reform. In addition, the initial slope of the diffusion
curve rose while the long-term level of citation flow declined in the Computers &Communications field. In contrast, both of them rose in the Electrical &Electronic field. These results suggest the possibility that early disclosure not
only stimulated complementary inventions but also helped inventors recognize
early the duplications and then helped the reductions of duplicative R&D and/or
applications, in a field with a long publication lag. In addition, we found that theexaminer citation curve begins significantly earlier and more sharply comparedto the applicant citation curve, which shows that examiner citation is a wrong
measure of knowledge flow.
Keywords: Disclosure, knowledge flow, patent, pre-grant publication
JEL: O33, O31, O38

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1. Introduction and Research Objective
The patent system plays two roles or functions in promoting innovation. The first is toprotect inventions from imitation for a certain period of time and thereby to promote
investment in research and development (R&D) and commercialization. The second role
is to disclose useful technological information to the public, facilitate the diffusion oftechnological knowledge, and avoid duplicated R&D activities. This study analyzes thesecond role, or more specifically, the effects of an 18-month pre-grant publication systemon the dissemination of the technological knowledge disclosed in the patent application
documents. The second function is fundamental to the patent system’s contribution to
the efficiency of R&D investments.
In order to examine the second role of the patent system, we analyzed the impactsof the introduction of an 18-month pre-grant publication system in the U.S. Most
industrialized countries have included the 18-month pre-grant publication system intheir patent system. Under the 18-month pre-grant publication system, technologicalknowledge pertaining to pending patent applications is made open to the public 18
months after the effective filing date of the patent application. We define “effective filingdate” as the earliest priority date for the applications claiming priority and as the filingdate for the applications claiming no preceding priority. The U.S. introduced the
18-month pre-grant publication system by enacting the American Inventor’s Protection
Act (AIPA) in 1999. Pending applications filed on or after November 29, 2000 are madeopen to the public 18 months after their effective filling date, with one exception. The
applicants of the patent applications for inventions that are filed only in the U.S. andnot in foreign countries can choose to keep their inventions secret before the patent
grant. Henceforward, we call the patent applications for inventions that are filed only in
the U.S. and not in foreign countries as “purely domestic applications.” However,crucially for our research, Graham & Hedge (2012, 2015) revealed that over 80% of
purely domestic patent applications opted for 18-month pre-grant disclosure rather
than keeping their inventions secret until the patent was granted.
The empirical investigation of the effects of the pre-grant publication system on
the diffusion of technological knowledge is important for two reasons: the acceleration ofknowledge diffusion is not the inevitable outcome, and the introduction of pre-grant
publication provides a significant opportunity for analyzing how disclosure affects theinvention process. Pre-grant publication does not affect knowledge flow if the knowledge
diffusion starts with the applications: the inventor may disclose details about theinventions in research workshops, trade shows, or through publications once his/herpatent application is completed (or even before the application, during the grace period).

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Moreover, it does not affect knowledge flow if the subsequent inventors rely primarilyon granted patents as their knowledge sources, either because the patents are granted
swiftly enough for the inventors’ decisions or because the information disclosed in the
granted patents are regarded to be more trustworthy. Thus, whether pre-grant
publication accelerates knowledge flow is an empirical issue.
While Johnson & Popp (2001) showed that the (total) citation flow begins with the
publication date (or equivalently, the grant date), their study has a fundamental
problem because of the inclusion of examiner citations as the measures of knowledge
flow. Examiner citations make us observe exactly such correlations, even without theknowledge diffusion effect. Examiner citations begin with the publications because
examiners can cite only published documents; simultaneously, they are required toundertake an ex-post search of all documents that are public at the priority date of theexamined applications but about which the inventors may not know at the time of theirinventions (section 5). Additionally, their results are based on the variations in grant lag,which may make a causal interpretation more difficult. Our study aims to identify theknowledge diffusion effects of disclosure based on the changes in the applicants’non-self-citations in response to the new disclosure rule under the AIPA. We use purely
domestic applications for our study since these publications are the first and only
disclosure of the inventions to the public; thus, their pre-grant publications constitutenew disclosure under the AIPA.
Additionally, this study examines the effects of the pre-grant publication systemon the pattern of knowledge diffusion, in order to explore the relative importance of thetwo potential roles of disclosure: reducing duplications in R&D and/or patenting andaccelerating the increase of knowledge stock for complementary inventions. In thisregard, it is important to recognize that there are two types of knowledge (citation)flows: those to the follow-up inventions based on the focal inventions and those to the
independently made duplicative inventions, and that they have significantly different
citation flows from each other. The latter citation flows emerge in the period
immediately following the publication of the focal cited patent since the US patent law
requires the inventors to disclose not only the prior arts they know when they invented
but also those they came to know by the time of examination of their applications.
Moreover, the duplicative inventions will decline over time as the inventors will avoid
the duplicative R&D and patent applications with respect to the disclosed inventions.
Thus, if early disclosure results in an immediate increase of the slope of the diffusion
curve but in a reduction in the long-term level of citation flow from the focal patent, it
would be consistent with the existence of duplicative R&D and/or patent applications

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and with their subsequent reductions caused by early disclosure. On the other hand, ifearly disclosure results in an increase of both the long-term level of citation flow as well
as of its initial slope, it would be consistent with the accelerated increase of knowledge
stock for complementary inventions. We expect the duplication reduction effect of early
disclosure would be stronger in fields where the disclosure was late prior to the legal
change. To the best of our knowledge, such effects have not been examined by theexisting literature. We find that the long-term level of citation flow declined
significantly only in the Computers & Communications field, where the initial slope
increased highly significantly. The publications lag before the reform was very long(around 48 months) in this field.
The rest of this paper is organized as follows. Section 2 presents a brief review ofthe extant literature. Section 3 describes the institutional background and presents the
hypotheses. Section 4 explains the data construction and presents the econometricmodel. Section 5 presents the estimation results and discussions, and section 6
concludes the paper with directions for future research.
2. Literature Review
Graham & Hedge (2012, 2015) found that the applicants of purely domestic patentapplications often opted for 18-month pre-grant disclosure rather than keeping theirinventions secret before the patent grants (by not opting for 18-month pre-grant
publications). This finding is important for our study because the U.S. reform in 2000
could have had a genuine impact on the acceleration of disclosure, even though purely
domestic applications are exempt from compulsory pre-grant publications. Further,using (i) the time lag between application and grant, (ii) the number of claims in apatent, (iii) patent renewal rates, and (iv) the number of later granted U.S. patents thatcite the focal patent as prior art, Graham & Hedge (2012, 2015) found that small
applicants chose disclosures over secrecy for important inventions.
Building on the model developed by Caballero & Jaffe (1993), Johnson & Popp
(2001) investigated patent citation data between 1976 and 1999 and showed that the
diffusion process begins with the publication (or the grant) of the patent (not with theapplication). They concluded that introducing an 18-month pre-grant publication
system would facilitate the diffusion of information by reducing the delay between filingand publication. However, they did not differentiate examiner citations and applicant(inventor) citations, or self-citations and citations by others, which is the fundamental
problem of their study. Examiner citations and self-citations do not represent knowledge
flow to the inventors through patent disclosures. If we use the entire citation data in thep. 4

analysis, we are bound to observe strong but spurious correlations between the
introduction of pre-grant publications and the acceleration of citation flows, even when
there are no such effects of pre-grant publications, for the following reasons. First, theexaminer citation flows associated with a particular patent application begin with its
earliest (pre-grant, if it exists) publication because examiners can cite only publisheddocuments, and they are required to search ex-post (at the time of patent examination,
not at the time of patent application) all the documents that are public at the priority
date of the examined applications.1 Therefore, examiners cite all published applications
that are relevant to the examination of the focal patent application, including thoseabout which the inventor of the focal application may have no knowledge at all before
his/her application. Because of this process, the examiner citation flows inevitably begin
with the first publication of the patent application. Moreover, these examiner citations
account for about half of the citations. While it has been well-known since the earliest
years of exploiting the patent citations in economic analysis that citations are often
added by those other than inventors, it is not a random noise2 for the purpose ofanalyzing the effect of pre-grant publications on knowledge diffusion. Rather, it is thecause of a systematic bias (in section 5, we show that examiner citations begin much
more sharply with the publication than inventor citations do).
The second problem of Johnson & Popp’s (2001) study is that their results related
to the effect of publication on knowledge flow are based on the variations in grant lags;
this may make a causal interpretation difficult because of the endogeneity of grant lags.
The citation flows may begin slowly for a patent with a long grant lag not because thepublication is delayed but because such a patent does not have much knowledge source
for further diffusion. Therefore, our study examines the changes in knowledge flowaccompanying the introduction of the pre-grant publication system. We compare theapplicant (inventor) citations, excluding self-citations, before and after the legal change.
Hegde & Luo (2013) investigated the effects of pre-grant publications on patent
licenses in biomedical technology to examine how the disclosure of a patent application
facilitates the licensing, separately from the effect of the grant. 3 They found that
post-AIPA patent applications are licensed significantly sooner than pre-AIPA patent
See Alcacer & Gittelman (2004, 2006) and Alcacer, Gittelman, & Sampat (2009) for
detailed explanations on how examiner and applicant citations are generated and forgeneral discussions about their implications in the analysis of knowledge flow and patentvalue.
2 See Jaffe, Trajtenberg & Henderson (1993) and especially Jaffe, Fogarty & Banks (1998)
for the “noise” of patent citations as a measure of knowledge flow.
3 Gans, Hsu & Stern (2008) discussed the importance of the certification of inventors’
property rights through patent grants for the licensing contract.
1

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applications controlling for the date of grants, especially those that do not haveequivalent foreign applications. More specifically, they found the following: i) the
likelihood of licensing in the window between 18-month publication and patent
allowance more than doubles for post-AIPA patent applications; ii) post-AIPA patentapplications are about 18 percentage points less likely to wait until allowance to belicensed; iii) the overall effects of AIPA are stronger for U.S. patent applications that do
not have equivalent foreign applications. Thus, they concluded that pre-grant patentpublications appear to facilitate transactions in the market for ideas and significantly
accelerate the commercialization of inventions. While this prior study convincingly
showed that disclosure facilitates licensing, it did not show the effect of early disclosureon knowledge flow with regard to subsequent inventions.
The extant studies have not analyzed the relative importance of the two potential
roles of disclosure: avoiding duplications in R&D and/or patenting and accelerating theincrease of knowledge stock for complementary inventions. In this context, it isimportant to note the difference between “fewer duplications” and “more obsolescence,”although we observe a decline in follow-up inventions in both cases. The obsolescence ofthe focal invention occurs because of the arrival of inventions competing with the focal
invention, while the duplications of the focal invention are reduced by its early
disclosure, quite independently from the arrival of competing inventions.
3. Institutional Background and Hypotheses on Knowledge Flow
(1) Changes caused by introduction of 18-month pre-grant publication system
The introduction of the 18-month pre-grant publication system affects knowledge
dissemination through two different types of changes: the acceleration of the timing of
publication and the expansion of the coverage of patent applications to be made public.
Figure 1 shows the change in the timing of publication before and after the patentlaw reform. Before the AIPA came into force, patent applications remained secret until
the patent issue date. In other words, the publication and the patent grant were
simultaneous events. After the AIPA came into force, pending patent applications are
published 18 months after the filing date, which is usually earlier than the grant date.
We define “publication lag” as “the difference between the filing date and the firstpublication date” and “grant lag” as “the difference between the filing date and the
grant date.” The publication lag is reduced by the introduction of the pre-grant
publication system, which is expected to accelerate and enhance the diffusion ofknowledge flow from the patent applications.
(Figure 1)

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In Figure 1, we considered only U.S. domestic applications. However, in manycases, applicants file patent applications for the same invention in foreign countries.
Figure 2 illustrates this case. If a U.S. applicant files for a patent in a foreign country,
the corresponding foreign equivalent patent application is published 18 months after its
priority date. Thus, the contents of the original U.S. patent application are effectively
made open to the public through the pre-grant publication of the foreign equivalent
patent application(s), although the publication language might not be English, whichmight pose a language barrier for U.S. nationals. Even before the AIPA law reform,
knowledge diffusion could begin through the pre-grant publications of the foreign
equivalent patent applications. In contrast, the contents of a purely domestic patentapplication that is filed only in the U.S. are made open to the public only through the
publication made by the United States Patent and Trademark Office (USPTO). Since
the effect of pre-grant publications made by foreign governments is not due to the AIPA
law change, we focus on “purely domestic applications” for our analysis.
(Figure 2)
Table 1 presents the statistics on publication and grant lags for the purely
domestic applications that claimed no priority based on earlier applications4 and thatwere eventually granted patents, which are the sample of our econometric analysis. The
mean publication lag (A), the mean grant lag (B), the difference (B - A), the standard
deviation of the publication lag, and the number of granted publications are indicatedby technological fields for each 12-month period 3 years before and after the law reform.
The PATSTAT (2013 October edition) is used as our database. The average reduction in
publication lag for the applications filed between the 1-year period up to December 2000
and the 1-year period up to November 2001 amounted to around 20 months in theComputers & Communications field (the largest among the six technology fields
considered in this study); the average reduction was several months in other fields (5months for the Mechanical field and 7.6 months for the Drugs & Medical field). 5Since
Applications claiming priority based on provisional applications and secondary
applications such as continuation applications, divisional applications, andcontinuation-in-part applications are not included in this analysis. Henceforward, we callsuch applications “normal applications.”
5 We can estimate the ratio of opting for pre-grant publication by calculating “thenumber of granted patents of which pre-grant publications were made” divided by “thenumber of granted patents.” According to the data of Figure 4, the ratio for the patents
filed in 2001 in each technological fields are as follows: Chemical, 61%; Computers &Communications, 67%; Drugs & Medical, 63%; Electrical & Electronic, 55%; Mechanical,
61%; and Others, 56%. The ratio is largest in Computers & Communications field, andsecond largest in Drugs & Medical field. According to table 1, mean grant lag in patents
filed between December 2000 and November 2001 is largest in Computers &4p. 7

the applications filed before November 29, 2000 could opt for pre-grant disclosures only
by taking special procedures, they were exceptions. Thus, there are only very small
differences between the publication lags and the grant lags before the AIPA law reform.
(Table 1)
Figure 3 indicates the change in the coverage of the patent applications to bedisclosed, which was caused by the introduction of the pre-grant publication system.
Before the AIPA reform, applications that were rejected and were eventually deemed to
be abandoned were not published. After the AIPA reform, the pending applications thatare eventually rejected and deemed to be abandoned are published as well. Since thesepublications are also potential sources of knowledge flows, we can expect an increase in
the knowledge diffusion flow through the expansion in the coverage of the patentapplications subject to disclosure.
(Figure 3)
Figure 4 presents the number and the composition of publications of purely
domestic normal applications over the filing years from 1990 to 2011. Publications areclassified into three categories: i) patent publications without pre-grant publications(indicated in blue); ii) patent publications for which pre-grant publications were
published (indicated in red); and iii) pre-grant publications that are not yet granted
patents (indicated in green). Because the third category (green) includes abandoned
applications as well as pending applications, its share becomes larger in subsequent
filing years. According to Carley, Hegde & Marco (2013), the influence of pending
applications is negligible for patent applications that were filed before 2002. They
reported that the grant rate including requests for continued examination (RCE) after
the final rejection (the application serial number did not change by this procedure) is
about 70% in 2001, which is consistent with our data (the ratio of red and green is about
2:1).
(Figure 4)(2) Applicant (inventor) citation data to measure knowledge flow
We use applicant (inventor) citation data to measure knowledge flow. When apatent application is filed to the USPTO, each individual responsible for the application(the applicant, the inventors, and the agent) has a duty to disclose to the USPTO all
information known to that individual to be material to its patentability. In order tofulfill this duty, applicants usually submit the Information Disclosure Statement (IDS).

Communications, 51 months and second largest in Drugs & Medical, 31 months. This is
why the publication lag is largest in Computers & Communications and second largest
in Drugs & Medical.

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We believe that the IDS citation data effectively reflect what applicants (inventors)
actually know. Thus, applicant citation is a good measure of knowledge flow for thefollowing legal reasons.
There are two legal effects of submitting the IDS. First, the risk of
unenforceability in future litigation is diminished (Cotropia, Lemley & Sampat, 2013).
If an applicant deliberately has not disclosed a prior art that is material to patentability,
an accused infringer can raise an issue of “inequitable conduct” that renders the patent
unenforceable. By submitting the IDS, the applicant can diminish this risk. The second
effect is to allow the applicant to acquire the strengthened presumption of validity(Juneau & MacAlpine, 2000; Buchanan, 2006; Allison & Lemley, 1998; KSRInternational, Co. v. Teleflex, Inc., 2007). Presumption of validity against the group ofprior arts that were considered by the patent examiner is stronger compared to thatagainst the prior arts that were not considered by the patent examiner. Because of these
legal effects,6 there is a strong incentive and a legal mechanism (i.e., the IDS) for theapplicants to submit all the prior art documents that they are aware of to the USPTO.
(3) Availability of U.S. applicant citation data in PATSTATWe used the PATSTAT database (2013 October edition) provided by the European
Patent Office for the citation data of U.S. patents that originated in the reference dataof U.S. patents. There are two origins of the reference citations associated with U.S.
granted patents: “Notice of References Cited” (form PTO-892) and the IDS. The former
is prepared by the patent examiner during the patent examination process, and thelatter is provided by the patent applicant. Since 2001, the references listed by thepatent examiner in a “Notice of References Cited” are indicated with an asterisk in the“References Cited” section on the front page of a patent document. Regarding the“Notice of References Cited,” the Manual of Patent Examination Procedure (MPEP
section 2001) states that “the examiner does not list references which were previously
cited by the applicant (and initialed by an examiner) on an Information DisclosureStatement.” Therefore, the overlap between the references cited in the “Notice ofReferences Cited” (i.e., examiner citation) and the references disclosed in the IDS is
expected to be very small. Citation origin data are available only where the publication
year of the citing patents is in and after 2001.
We investigated randomly chosen 50 sample patents issued between 2005 and2009 by examining the associated image file wrapper using the public PAIR system.
6 In addition to these two effects, there are strong incentives for the patent attorney to fulfill
the duty. If a patent attorney deliberately ignores the duty, he/she might be in danger of
disqualification.

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Figure 5 illustrates the results. The overlap between PTO-892 cited documents and IDS
disclosed documents is very small, and 94% of the IDS disclosed documents are reflectedin the applicant citation data, as indicated in the “References Cited” section of U.S.
patent documents. By using the patent citation data in the PATSTAT database, we caneffectively capture the references that are actually disclosed by the applicant in the IDS.
In contrast, if we use the entire citation data, 42% of the cited documents are cited byonly the patent examiner, and the noise is quite large.
(Figure 5)(4) Comparing applicant citation flows and examiner citation flows
Figure 6 presents the citation-lag dependence of probability of both applicant
citation and examiner citation in the Computers & Communications and Electrical &Electronic fields (We explain construction of these data in detail later in section 4.4.).
The shape of the probability of citation vs. citation-lag curve is quite different between
the examiner and applicant citations. The examiner citation curve begins significantly
earlier and more sharply than the applicant citation curve in both the Computer &Communications and Electrical & Electronic fields. The peak of the examiner citation
curve is around the 18-month citation lag, and it comes much earlier than that of theapplicant citation curve. If we use total citations as the measure of knowledge flow, we
would get superfluous results about knowledge flow. Thus, it is essential to use
applicant citation data to measure knowledge flow. We will discuss this later in detail in
section 5.2.
(Figure 6)(5) Hypotheses on the effects of early disclosure on knowledge flow
In analyzing the effects of early disclosure, we consider two types of knowledge
flows. First, disclosure generates follow-up inventions, until the focal invention becomesobsolete stochastically. For simplicity, we assume no obsolescence of the focal patent in
the rest of this section (we do take this into account in our empirical analysis). Second,
disclosure can affect the patentability and the patent scope of the duplicative inventions
that are concurrently made by other firms. In this case, disclosure does not generate
new inventions but affects the scope of the patents of independently made inventions. In
both cases, the citations are made with reference to the focal patent; thus, the citation
flows reflects these two types of knowledge flows.
There is a major difference in the pattern of the diffusion curve following the focal
invention of these two types of knowledge flows. We expect that the first type of

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knowledge flow starts gradually from the level of zero given the time necessary for therecognition and invention, increases over time, and subsequently levels off (unless there
is obsolescence), as assumed by Caballero & Jaffe (1993) and Johnson & Popp (2001).
On the other hand, the second type of knowledge flow increases rapidly initially, since
the citing inventions are independently made and not endogenously developed based on
the focal invention, however, it subsequently declines toward zero since the firms avoid
duplicative inventions.
We assume that the combined knowledge flow can be approximated by thefollowing logistic function in equation (1), where T represents the time, t is the date of
the patent application of the focal invention, andgives the lag between theapplication date and the inflection point7:(1); ,In equation (1), the parameter for the ceiling level of citation flow (measures thelong-term level of the inventions generated by the focal patent; it is affected only by the
first type of knowledge flow. The parameter (flow increases for a small

indicates how rapidly the knowledge(the slope of the diffusion curve); both types of knowledge

diffusions affect this parameter.
Next, we consider the effects of early disclosure. The direct effect would be an
acceleration of the initiation of diffusion from the date of the patent application of the
focal patent (t), if the pre-grant publication results in early disclosure. We can measure
this by the early arrival of the inflection point (smaller ).
In addition, early disclosure would expand the total knowledge stock based on
the focal patent that is available for an inventor for a new invention at each point oftime, since the inventions based on the focal patent are also disclosed early. Thus, whenthese disclosed inventions are all complementary as knowledge stock for creating new
inventions, early disclosure will accelerate the growth of the knowledge stock available
for new inventions. This effect will result in both the increase in the long-term level ofcitation flow (α) as well as the increase of the initial slope parameter (in this model .

Moreover, when the duplicative R&D and patent applications are important, early
disclosure can help an inventor recognize early the focal invention and avoid duplicative
R&D investments and/or patent applications in the long run. The early disclosure will

7

This would be the case if the second type of knowledge spillover is relatively small.

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force the inventors pursuing similar R&D projects to cite the focal patent disclosed
earlier when applying for patent protections for their inventions. If an inventor applies
for a patent for a duplicative invention without knowing about the focal patent, theinventor will not (cannot) cite the focal patent at the time of patent application. On theother hand, if the focal patent is already published and the inventor of a duplicative
invention recognizes it, the focal patent has to be cited. Thus, the early disclosure of the
focal patent will result in more citation flows via these duplicative inventions
immediately after its pre-grant publication, resulting in a significant increase of theinitial slope parameter ( . At the same time, the number of granted patents citing thedisclosed invention will decline in the long run because of the early disclosure. That is,there will be a reduction in the long-term level of citation flow ( ) in this model.
The preceding discussions are summarized in the following three hypotheses. The
first hypothesis to be tested in our study is related to the timing of the initiation ofknowledge diffusion, counted from the date of the patent application of the focal patent.
It is essentially the same hypothesis as tested by Johnson & Popp (2001). However, we
aim at providing evidence allowing a more causal interpretation by using actual data
based on applicant non-self-citations that are generated after the legal change.

Hypothesis 1 on earlier initiation of knowledge diffusion:
The introduction of the pre-grant publication system accelerated the initiation ofknowledge diffusion, especially in technological fields where the grant lag waslong.
The second hypothesis is related to the heterogeneous impact of early disclosureon the shape of knowledge curve, depending on the relative importance of citation flows
indicating cumulative inventions and those indicating the duplicative inventions

Hypothesis 2 on fewer duplications vs. larger knowledge stock
The effects of pre-grant publications on the shape of the diffusion curve can beheterogeneous, depending on the relative importance of the two citation flows. Ifearly disclosure results in an increase of the initial slope of the diffusion curve
but in a reduction of the long-term level of citation flow from the focal patent, it
would be consistent with the existence of duplicative R&D and/or patentapplications and with their subsequent reductions by early disclosure. On theother hand, if early disclosure results in an increase of both the long-term levelp. 12

of citation flow as well as of its initial slope, it would be consistent with theaccelerated increase of knowledge stock for complementary inventions.
Additionally, the pre-grant publication system would have expanded the disclosureof those inventions that are not granted patents.

Hypothesis 3 on knowledge flow effect of more disclosures
The pre-grant publication system increased the sources of knowledge flows since
the pending patent applications that were eventually abandoned and notgranted patents were also published under this system.

４. Data and Econometric Model(1) Constructing data for non-self-citations by applicants
In order to construct the applicant citation data excluding self-citations, we first
constructed a citing-cited matrix based on the U.S. patent bibliographic data for both
citing and cited patent documents using the PATSTAT database (2013 October edition).
This covers the effective filing date, patent classification, and citation origins (whether
the references are provided by the applicants or the examiners).
In order to construct samples that are comparable with regard to the average
quality of the inventions before and after the introduction of the pre-grant publication
system, we selected the publications of granted patents and the pre-grant publications
that were eventually granted patents as our dataset of cited patent documents. Cited
patent applications were limited to purely domestic patent applications that were filed
only in the U.S. and not in foreign countries. We think that the introduction of pre-grant
publication does not significantly affect the choice of the applicants for purely domestic
patent applications. After the legal reform, applicants of purely domestic applications
can still choose keeping their inventions secret before the patent grant. Therefore,
introduction of pre-grant publication system does not work as a deterrent for theapplicants that opt for secrecy before grant. Our cited patent data do include thepatents of which applicants opted for pre-grant secrecy. Moreover, the introduction of
pre-grant publication system does not affect the behavior of the applicants who opt forfiling overseas. Such applicants need to apply for foreign applications irrespective of theintroduction of the pre-grant publications.
Note that the applicants are not restricted to U.S. residents. The data included
applications filed by foreign applicants seeking U.S. patents only (and no foreignp. 13

patents) for an invention. Secondary applications or progenitor applications (such ascontinuation

applications,divisional

applications,andcontinuation-in-partapplications) were removed from the dataset of both citing and cited applications. One
reason for this decision was to measure accurately the timing of when the invention wasdisclosed to the public. For example, the knowledge diffusion might have begun with the
publication of the preceding original parent patent application rather than with that of
the focal secondary or progenitor patent applications, if the original application was
published and contained the same information as that of the focal patent application.
By removing the secondary (progenitor) applications from our dataset of cited patentdocuments, we did not have to consider this type of knowledge diffusion. The second
reason was to reduce ex-post citations. Since the IDS obligation continues until thepatent issue date, the IDS includes not only the applicants’ knowledge at the filing date(ex-ante knowledge) but also the knowledge that the applicants or their agent acquired
after the filing date (ex-post knowledge). If we include secondary (progenitor)
applications in the dataset of citing applications, the extent of ex-post knowledge
contamination would increase significantly. For example, applicants of secondary(progenitor) applications usually include in the IDS those references that were provided
by the patent examiner during the original parent application process. In order tomeasure the timing of knowledge flow accurately, we dropped those applications thatinvolved priority based on provisional application(s) from the dataset of cited patentdocuments. Therefore, the effective filing date of the cited patent is the same as thefiling date.
In order to distinguish non-self-citation from self-citation in the citation matrix
data, we used the patent assignee data prepared under the NBER Patent Data Project(https://sites.google.com/site/patentdataproject/).

The

data

providethepatentassignee-identified data for U.S. Patents that were published between 1976 and 2006.
We prepared the concordance table between the patent assignee identification numbersof PATSTAT and the patent assignee identification numbers prepared under the NBER
Patent Data Project using the granted patent numbers of single patent owners as theconnecting keys. Using the data in this concordance table, the PATSTAT applicant ID
data in the citation matrix is transformed to the NBER assignee ID data. We restrictedcited publications to those publications whose assignees were identified by the NBER
assignee ID data. Henceforward, we call these applications “applications by the
NBER-identified assignees.” We classified a citation as a self-citation if the NBER
assignee ID was the same for the cited and the citing patent document. For jointly-held
patents, the citations were classified as self-citation if at least one owner was common

p. 14

in the cited and the citing patents. Otherwise, we classified the citations as
non-self-citation. Finally, self-citations were removed from the citation matrix.
Table 2 provides the descriptive statistics on the publication and grant lags forthe granted purely domestic normal applications by the NBER-identified assignees. A
comparison of Table 2 and Table 1 suggests that there is no major sample selection bias
in terms of publication lag and grant lag because of our focus on the patents of
NBER-identified assignees.
(Table 2)
We divided the applications into six technological categories following Hall, Jaffe,& Trajtenberg (2001): “Chemical,” “Computers & Communications,” “Drugs & Medical,”“Electrical & Electronic,” “Mechanical,” and “Others.” The data included several
classification codes that were not listed in Hall, Jaffe, & Trajtenberg (2001). In thosecases, we classified them into an appropriate technological category by referring to thecategory and subcategory assignment list prepared by the NBER patent project(https://sites.google.com/site/patentdataproject/Home/downloads/patn-data-description).
The data pertaining to the United States Patent Classification is missing inpublications that were published in and after September 2012 in the PATSTAT
database (2013 October edition). This is another cause of truncations of the citation
data, in addition to the pending patent examination status of potentially citing patentapplications.
(2) Constructing data of Citation probability among cohorts
We divided the citation matrix data (section 4.2) into cohorts (Cco,i,t,I,T) according
to citation origin (co), cited patent’s technological field (i), cited patent’s effective filingtime measured by calendar month (t), citing patent’s technological field (I), and citing
patent’s effective filing time measured by calendar month (T). Additionally, wemeasured the size of potentially citing cohorts by the number of granted patents (MI,T)
and the size of potentially cited patent document cohorts by the number of purely
domestic normal patent applications by the NBER-identified assignees (Ni,t). Note that

MI,T is not restricted to the publication of purely domestic applications. MI,T includes thepatents that were filed internationally and the patents claiming priority based onprovisional applications.
Next, we obtained the probability of citation between each pair of cohorts (thecited patent's filing time t and the citing patent's filing time T) for each technical field(i=I): Pco,i,t,I,T, using the following formula:p. 15

,, , ,,, , ,
,

∗

(3)

,

(3) Econometric model
To analyze the dynamics of the patent citation flows, we used the model inEquation (4), which is similar to what Johnson & Popp (2001) used. The dependent
variable is the probability of citations between a pair of cohorts:,, , ,In Equation (4),

∗

∗

∗

(4)

represents the total number of patent documents that are

published between the cited patent's filing time in month t and the citing patent's filingtime in month T in technological category i. While calculating, only the firstpublication for each application is counted in order to eliminate duplication in the countof the publications that originated from the same invention. Patent publications
categorized as “B2” (which were published after the pre-grant publications) were notcounted. Publications for secondary (progenitor) applications (such as continuation
applications, divisional applications, and continuation-in-part applications) were notcounted because all or at least the large part of the contents of these applications would
have been disclosed in the preceding parent applications.
The model consists of two factors: i) a diffusion factor with the ceiling level
constant (α), and ii) an obsolescence or decay factor (new inventions make older patents
obsolete). We modified Johnson & Popp's (2001) model in the following four aspects. (1)While Johnson & Popp (2001) used the difference in publication timing between citing
and cited patents as citation lag, we used the difference in effective filing date between
citing and cited patent documents as citation lag. This is because we want to measure
the effects of early publications on the initiation of knowledge diffusion counted fromthe date of application. (2) While Johnson & Popp (2001) used the number of patents inall technological fields as a determinant of the decay factor, we used the number ofpatent documents in the field, which is the same as that of the cited patent documents.
We believe this approach makes more sense, given the heterogeneity of the pace oftechnological progress across technology fields. (3) We focused on the probability ofcitations where the citing patent document and cited patent document belong to thesame technological category. (4) While Johnson & Popp (2001) used (1-exp(- *citation_lag)) as the diffusion model, we used the logistic function ( α /(1+exp(- *citation_lag))), following the standard used in the diffusion literature. In our regression
analysis, the former function performed much worse than the latter did. The main
reason for the differential performance of these two models seems to be the fact thatJohnson & Popp (2001) used the total citations, more than 40% of which are examinerp. 16

citations, and they used the difference in publication timing between citing and cited
patents as the citation lag. We believe that our model is better given the more
appropriate choice of citation data for measuring knowledge flows.
In order to investigate the changes in knowledge diffusion flow caused by thelegal revision, we used a dummy variable representing the date of the introduction of
the pre-grant publication system (rev), which is set to 0 if the filing date of the cited
patent is between December 1999 and November 2000 and is set to 1 if the filing date of
the cited patent is between December 2000 and November 2001. We used three models
is a ceiling level of citation probability in the(Equations (5) to (7)). Coefficientlogistic function of the information diffusion factor. Coefficient

stands for the slope

parameter in the logistic function of the information diffusion factor. Coefficient
represents the location of the inflection point of the logistic function, with the horizontal
axis representing the citation lag (T-t), where the value rises most steeply. Coefficient
stands for the decay parameter of the obsolescence factor in the focal technological field.
Coefficientmeasures the change in the level of the ceiling of the citation probability

caused by the law revision. Coefficientmeasures the acceleration in the diffusioncaused by the law revision. Coefficientmeasures the increase of the slope of the

diffusion curve caused by the law revision. Model 1 (Equation 5) is the most generalbase model for hypothesis testing, and it includes

,

, and

. Model 2 (Equation 6)

and Model 3 (Equation 7) are simplified models. Model 2 includes only
includes only

, and Model 3.

Model 1 (the general base model for hypothesis testing)
∗

,, ,,

∗

∗

∗

∗

∗

∗

(5)

Model 2 (the model accommodating only the inflection point change)
,, ,,

∗

∗

∗

∗

∗

(6)

Model 3 (the model accommodating only the ceiling level change)
,, ,,

∗

∗

∗

∗

∗

(7)

Based on the estimation results from Model 1, we can test the first two
hypotheses. Hypothesis 1 implies that the pre-grant publication system causes theinflection point to arrive earlier (the estimated value ofis negative). Hypothesis 2

implies that the introduction of the pre-grant publication system makes
significantly positive and the ceiling

negative if the initial duplications aresubstantive and the pre-grant publication has a significant effect of reducing suchp. 17

duplications. It also states that the introduction of the pre-grant publication system
makes bothandpositive if the pre-grant publication enhances complementary

inventions.
Models 2 and 3 concentrate the total effects into one parameter, which provide asummary measure of the effects of pre-grant publication on knowledge diffusion. Model
2 provides the effective overall acceleration of the initiation of the knowledge diffusion,
while Model 3 provides a summary of the change in the overall level of citation
probability.
(4) Estimation Method
We implemented regression analysis of the applicant citation flow by nonlinear
regression using Model 1 to Model 3 (Section 4.4). We used the data with the filing dateof the cited patent documents before and after 1 year from the law revision, i.e., from
December 1999 to November 2001. In order to reduce the influence of the truncation of
citing patents, regression was performed in the range of 0 < citation lag <= 96 (month).
To control for heteroskedasticity, we used analytical weights (,∗, ) with reporting

heteroskedasticity robust standard error. Table 3 shows the descriptive statistics of thedata used for the nonlinear regression.
(Table 3)
5. Results and Discussion(1) Effects on knowledge diffusion curves
Table 4 presents a summary of the results for the six technology fields.

Focusing

on the field of Computers & Communications, where the publication lag was the longest
before the introduction of pre-grant publication, Figure 6 shows the probability of actual
citation and the fitted value over time using Model 1, comparing the citation
probabilities for the cited patent documents with filing date between December 1999 toNovember 2000 and those with filing date between December 2000 to November 2001.
(Table 4)
In the Computers & Communications field, the legal change caused the inflection
point to arrive significantly earlier (the estimated value ofis -16.0 months)

according to Model 1. Thus, the results show a significant acceleration of knowledge flow,which is consistent with Hypothesis 1. This result is consistent with the significant
decline (around 20 months) of the publication lag (the difference between the
publication date and the effective filing date) because of the reform in this field.

p. 18

Simultaneously, the long-term level of citation probability measured bydeclinedsignificantly (around 15%), while the slope of the diffusion curve measured by

wasenhanced significantly (by over 34%). These results are highly consistent with thepossibility of an early finding of the existence of the close prior patent applications dueto their early publications and the reduction of duplicative inventions in the long run.
As stated earlier, the Computers & Communications field had the longest publication
lag prior to the legal reform. Most observers would agree that it is a highly competitivetechnology field. The long publication lag in a highly competitive field might have madeduplicative R&D activities and patent applications serious. These results support the
duplication reduction effect of early disclosure in this field, as stated in Hypothesis 2.
As for the result from the simplified Model 2 that includes only

, the legal

change caused the inflection point to arrive significantly earlier overall (the estimatedvalue ofis -8.87 months), which is actually smaller than the estimate obtained from

Model 1. According to Model 3, which includes onlyaround 19% relative to, a significant positive value of

was estimated. This means that the level of citation

probability increased by 19% overall because of the law revision (note, however, that asubstantial part of this increase is likely to reflect the citation flows indicating
duplicative R&D and/or patenting in this field), even though the long-term level ofcitation flow declined.
(Figure 7)
Further, we observe significantly negative values of(earlier start of diffusion)according to both Model 1 and Model 2 in all the other technological fields (except theChemical field). The estimated length of the earlier start of diffusion varies from 3
months (Mechanical) to 6 months (Drugs & Medical) in Model 1. It varies from 4 months(Mechanical) to 9 months (Drugs & Medical) in Model 2. Thus, Hypothesis 1 is
supported for four more fields: Electrical & Electronic, Drugs & Medical, Mechanical,
and Others, in addition to Computers & Communications.
In the Chemical field, according to Model 1, we observe negative value ofat the10 % significance level. However, in Model 2, we do not find statistically significanceeffect. Hypothesis 1 is marginally supported only in Model 1 and not in Model 2. There
are many zero value points (more than 300) in the citation probability data. This meansthat the citation pair data is too small for obtaining strong and robust statisticallysignificant results.
Moreover, we observe significantly positive coefficients forin Model 3 in thesefour fields. The estimated increase of the average long-term citation probability varies
from 9% (Others) to 25% (Drugs & Medical) among the four fields with significant

p. 19

results. In this model, the effect of pre-grant publication is strongest in Drugs &Medical among the four fields. The decline in the publication lag is the largest in thisfield, although it is much smaller than that in the Computers & Communications field.
According to Model 1, in the Drugs & Medical and Electrical & Electronic fields,
we observed significantly positive coefficients forcoefficients for, whileand significantly negativeis statistically insignificant for the Drugs&

Medicalfield but significantly positive for the Electrical & Electronic field. These results are
consistent with the greater number of complementary R&D projects in the two fields.
Compared to the Computers & Communications field, the effect of facilitatingcomplementary R&D is more dominant than the duplication reduction effect.
In summary, knowledge diffusion began earlier and was significantly facilitated by
the introduction of the pre-grant publication system. Except in the Chemical field, there
is significant evidence for the significant effects of pre-grant publications on theaccelerated initiation of diffusion (five out of six fields). In addition, the initial slope ofthe diffusion curve rose while the long-term level of citation flow declined in theComputers & Communications field, which had the longest publication lag before thereform. In contrast, both of them rose in the Electrical & Electronic fields. These results
suggest the possibility that early disclosure not only stimulated complementary
inventions but also helped inventors recognize early the duplications and then helped
their reductions, in a field with a long publication lag.
(2) Robustness checks
We did the following two robustness checks. First, we assessed the effect of ex-post
knowledge on citation flow. The applicant’s duty to candor (i.e., IDS obligation)
continues until the patent is issued. Strictly speaking, the IDS contains not only theapplicant’s knowledge before filing (ex-ante knowledge) but also what he/she gets toknow after filing the patent application (ex-post knowledge). Table 5 presents thenumber of citations (column A) where “publication lag > citation lag” and the number ofcitations (column B) where “publication lag <= citation lag.” The former case involves
ex-post knowledge because the applicants of citing applications cannot identify the cited
applications made by the other firms that are not yet published. The share of case A waslarger before the law revision compared to that after the revision (i.e., more ex-post
knowledge was incorporated in citations before the introduction of the pre-grant
publication system). This finding shows that the knowledge diffusion effects we found
are robust when considering the inclusion of ex-post knowledge in the IDS information,
which was larger before the legal change.

p. 20

(Table 5)
As another robustness check on the effect related to the acceleration of diffusion,
we directly assessed which of the pre-grant publication date and the grant date are
more linked with the timing of citations (knowledge flow) in the post-reform era byrunning a simple ordinary least squares (OLS) regression at the individual patent level.
Our hypothesis is that the pre-grant publication date, rather than the grant date, is theprimary determinant of the earliest citation date for such patent applications. For thisestimation, we prepared the following citation data. First, we limited the citation data
to those for which the filing date of the cited application is between December 2000 andNovember 2001. Subsequently, we limited the citations to those for which pre-grant
publications were made for the cited applications. In order to eliminate ex-post
knowledge, citations that did not satisfy the condition “publication lag <= citation lag”
were dropped from our data. Further, we limited the sample to the citing patents that
had the earliest effective filing date for each cited patent documents.
The model for the linear regression is as follows: the dependent variable is

Citing_eff_F_T, which is the effective filing time of the citing patent. There are twomain independent variables: t_pub is the pre-grant publication time of the cited patentdocument, and t-grant is the granted time of the cited patent document (all measured incalendar month). We used Di (technological field dummies, I = 1,2,3,4,5) as the control
variables.

Citing_eff_F_T =+* t_pub +* t_grant + Di

In order to reduce the effects of truncations, citation lag was limited to the condition
citation_lag <= 96. In this model, t_grant is likely to be endogenous, given that theapplicant may be willing to spend more time for those inventions that need more time
for commercialization, while the knowledge flow may occur only late for suchapplications. However, such endogeneity tends to work against finding support for the
hypothesis.
Table 6 presents the summary of the linear regression results. The coefficient oft_pub is statistically significant and is close to 1. The coefficient of t_grant is almost zeroand is less than 1/100 of that of t_pub; it is not statistically significant. This result
implies that if the publication is delayed by 1 month, the effective filing date of the firstciting patent will be delayed by 1 month, while the grant time of the cited patentdocuments has no influence. One important reason for the finding that the coefficient oft_pub is smaller than 1 could be the effect of truncations of the citing patents. We
limited the citation lag to citation_lag <= 96; however, the truncation of the citing
patents is more serious for the cited patents that are filed later. For those cases, there isp. 21

a significant possibility that we would not yet observe the potentially citing patents thathave the earliest effective filling date but are still under examination and have not been
granted a patent.
(Table 6)(3) Comparing applicant citation flows and examiner citation flows
Figure 8 presents the citation lag dependence of the cumulated citations forapplicant citation and examiner citation. The examiner citation curve rises right after
the origin (where citation lag = 0), while the applicant citation curve slowly rises after
the origin and rises steeply after more than 20 months (where many publications aremade). More than 20% of the total examiner citations made within the citation lag of 96
months are made within the 18-month citation lag, while the corresponding ratio of theapplicant citations is less than 10%.8 This difference seems to reflect the difference inthe duties of the patent examiners and those of the applicants as well as the differencein the timing when the citations are made. Applicants are primarily obliged to submit
what they know at the time of patent application; they are not obliged to search for all
the relevant documents disclosed over time that are available at the priority date of the
focal patent, while patent examiners have to do so until they make the final decision
related to granting the patent. These facts indicate if we use total citations as themeasure of knowledge flow, we would get superfluous results about knowledge flow.
Thus, it is essential to use applicant citation data to measure knowledge flow.
(Figure 8)(4) Citation flow volume expansion
Pending patent applications that were eventually abandoned and not granted patents
began to be published following the introduction of the pre-grant publication system. We
wanted to distinguish self-citations and non-self-citations made for these new
publications. However, in many cases, assignee information was lacking for theapplications that were not granted and for which only pre-grant publications were made.
Figure 9 evaluates the contribution of these new publications to the knowledge flow.
The cited patent documents were divided into two groups: (group 1) patent documents ofthe patent applications that were eventually granted patents (indicated in lavender);(group 2) pre-grant publications for which patents were not granted yet (indicated in

8 Appendix 1 presents the total citations made within the citation lag of 18 months incolumn A, the total forward citations made within the citation lag of 96 months in column B,
and the ratio of A to B in column C for both applicant citations and examiner citations.

p. 22

green). Figure 9 shows the number of total applicant forward citations made within the
96-month citation lag by a group of cited patent documents.9 The ratio of citations madeby group 2 to total citations varies according to the technological field. It is highest (over20%) in the field of Computers & Communications and lowest (less than 10%) in theElectrical & Electronic field. Although this number includes self-citations, the third
hypothesis was strongly supported.
(Figure 9)
6. Conclusions and Further Research
In this study, we investigated the effects of the introduction of the pre-grant publication
system on the diffusion of knowledge. Johnson & Popp (2001) showed that the (total)
citation flow begins with publications and suggested that earlier publications would
accelerate knowledge flow. However, their study has a fundamental problem ofincluding examiner citations as the measure of knowledge flow, since examiner citations
make us observe exactly such correlations, even without any knowledge diffusion effect.
In fact, we found that the examiner citation curve begins significantly earlier and more
sharply than the applicant citation curve: the shapes of the two curves differ
significantly, reflecting the differences in the duties of patent examiners and applicants.
This finding supports the need to use applicant non-self-citations as the measure ofknowledge flow. In addition, their study used the variation in the grant lag as the sourceof identification, which could make a causal interpretation difficult. This study usedapplicant (inventor) non-self-citations (excluding examiner citations) to track
knowledge flow and identified the causal effect of disclosure by examining the changes
in behaviors before and after the legal reform.
Further, this study examined the effects of the pre-grant publication system on
the pattern of knowledge diffusion in order to explore the relative importance of the twopotential effects of disclosure—avoiding duplications in R&D and/or patenting andaccelerating the increase of knowledge stock for further inventions—. If duplicative
inventions are important, early disclosure of the focal patent increases the initial slopeof citation since it makes an inventor with a duplicative invention recognize the patentapplications more at the time of its patent application. In addition, if it helps reducing
duplicative R&D and/or patenting in the long run, the initial increase will be associatedwith the decline of the long-run level of citation flows from the focal patent. In contrast,
if early discloser promotes the rapid expansion of knowledge stock for complementary
9 The citation lag dependence of the number of total applicant citations by the group of cited
patent documents is shown in Appendix 2.

p. 23

inventions, it increases both the initial slope as well as the long-term level of citation
flow. The duplication reduction effect of early disclosure is expected to be stronger in thefields where the disclosure was late before the legal change. This issue has not been
explored in the extant literature.
We tested three hypotheses in this study:(1) The introduction of the pre-grant publication system accelerated theknowledge diffusion, especially in technological fields where the grant lag
was long.
(2) The effects of pre-grant publications on the diffusion curve can beheterogeneous, depending on the relative importance of the two citation
flows: those for duplicative inventions and those for complementary
inventions.
(3) The pre-grant publication system increases the sources of knowledge flows
since the pending patent applications that were eventually abandoned andnot granted patents were also published under this system.
The results of the nonlinear regression analysis show that knowledge diffusion
began significantly earlier following the introduction of the pre-grant publication
system. Except in the Chemical field, there is highly significant evidence for the effects
of pre-grant publication on the acceleration of knowledge diffusion (five out of six fields).
The effect was the strongest in the Computers & Communications field (where thepublication lag was the longest before the reform), and it was the second strongest in
the Drugs and Medical field.
In addition, the initial slope of the diffusion curve rose while the long-term level ofcitation flow declined in the Computers & Communications field, which had the longest
publication lag before the reform. In contrast, both of them rose in the Electrical &Electronic field. These results suggest the possibility that early disclosure not only
stimulated complementary inventions but also helped inventors recognize early the
duplications and then helped the reductions of either duplicative R&D themselves
and/or of duplicative applications, in a field with a long publication lag.
Additionally, the publications of the pending patent applications that were
eventually abandoned and not granted patents significantly contributed to the
expansion of the knowledge flow.
Since AIPA reform introduced a pre-grant publication system that allows theapplicants of purely domestic applications to forgo pre-grant publication, it did notconstrain the applicants who opt for secrecy before a grant. As to the publishedapplications before the grant, our study convincingly show that the knowledge diffusionp. 24

from them accelerated after the legal change, especially in those fields where the grantlag was large. Furthermore, there are significant citation flows to the non-granted
patent applications. Thus, the introduction of the pre-grant publication under the AIPA
reform seems to have had positive effects on innovation.
There are a number of other issues that could be examined in future research.
Since the applicant’s duty to candor continues until the patent is issued, strictly
speaking, the IDS should contain not only the applicant’s knowledge before filing but
also what he/she gets to know after filing the patent application. All the ex-post
knowledge has not been eliminated from our applicant citation data. It would beinteresting to identify exactly how ex-post knowledge is included in the applicant
citation data by examining the IDS documents. Another aspect that requires a separate
investigation is the effect of the introduction of the pre-grant publication system oninternal knowledge sharing (measured by self-citation) in a firm. If the applicant is anindividual person or a small enterprise, the knowledge flow within the entity would not
be influenced much by the introduction of the pre-grant publication system. However, inthe case of a large company, the pre-grant publication system might promote theknowledge diffusion within the organization. Finally, the pre-grant publication system
may have helped the examination process of the patent offices by facilitating thesharing of more accurate information on the patent search results across examiners.
Future research could explore these additional knowledge flow effects of the pre-grant
publication system.
Acknowledgment
This project was supported by the JSPS Grant-in-Aid for Scientific Research
JSPS(B:26285055). We would like to thank JSPS for its granting aid. We would like tothank Professor Mark Schankerman, Professor Stuart Graham and Professor Tetsuo
Wada for giving comments to the earlier versions of this paper. We also would like tothank research assistant Junko Morikawa for her help in file wrapper investigation.

p. 25

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Figure 1 Change in timing of publication before and after the AIPA reform

Figure 2 Cases where patent applications are filed in foreign counties for the same
inventionp. 28

Figure 3 Change in application coverage before and after the AIPA reformp. 29

Figure 4 Number and composition of publications of purely domestic normal
applications over the filing years from 1990 to 2011
A

Number of publications(Chemistry)

Ratio of publication kinds(Chemistry)

4,000

100%

3,500

90%
80%

3,000

70%

2,500

60%

2,000

50%

1,500

40%
30%

1,000

Granted without PGPub

Granted without PGPub

2010

PGPub(Granted)

2010

PGPub(Granted)

2008

PGPub(Not Granted)

2008

PGPub(Not Granted)

2006

2004

2002

2000

1998

1996

1994

1990

2010

2008

2006

2004

2002

2000

1998

1996

1994

0%
1992

0
1990

10%
1992

20%

500

B

Ratio of publication
kinds(Computers &Communications)

Number of publications(Computers &Communications)
25,000

2006

2004

2002

2000

1998

2011

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

0

1996

5,000

1994

10,0001992

15,000

1990

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

20,000

PGPub(Not Granted)

PGPub(Not Granted)

PGPub(Granted)

PGPub(Granted)

Granted without PGPub

Granted without PGPub

p. 30

C

Number of publications(Drugs & Medical)

Ratio of publication kinds(Drugs & Medical)

4,000

100%

3,500

90%
80%

3,000

70%

2,500

60%

2,000

50%

1,500

40%
30%

1,000

PGPub(Not Granted)

PGPub(Not Granted)

PGPub(Granted)

PGPub(Granted)

Granted without PGPub

Granted without PGPub

2010

2008

2006

2004

2002

2000

1998

1996

1994

1990

2010

2008

2006

2004

2002

2000

1998

1996

1994

0%
1992

0
1990

10%
1992

20%

500

D

Number of publications(Electrical & Electronic)

Ratio of publication kinds(Electrical & Electronic)

12,000100%
90%

10,000

80%
70%

8,000

60%
6,000

50%
40%

4,000

30%
20%

2,000

10%

PGPub(Not Granted)

PGPub(Not Granted)

PGPub(Granted)

PGPub(Granted)

Granted without PGPub

Granted without PGPub

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

0%
1990

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

0

p. 31

E

Number of publications(Mechanical)

Ratio of publication kinds(Mechanical)

9,000

100%

8,000

90%

7,000

80%
70%

6,000

60%

5,000

50%

4,000

40%

3,000

PGPub(Not Granted)

PGPub(Not Granted)

PGPub(Granted)

PGPub(Granted)

Granted without PGPub

Granted without PGPub

2010

2008

2006

2004

2002

2000

1998

1996

1994

1990

2010

2008

2006

2004

2002

2000

1998

1996

0%
1994

0
1992

10%
1990

20%

1,000

1992

30%

2,000

F

Ratio of publication kinds(Others)

Number of publications(Others)100%

14,00090%

12,00080%

10,000

70%
60%

8,000

50%
6,000

40%

4,000

30%
20%

2,000

10%

PGPub(Not Granted)

PGPub(Not Granted)

PGPub(Granted)

PGPub(Granted)

Granted without PGPub

Granted without PGPub

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

1990

0%

0

p. 32

Figure 5 Citation origin and overlap of U.S. patent referencesp. 33

Figure 6 Probability of applicant citation and examiner citation
A Computers & Communications

Probability of non-self citation(Computers & Communications, 00Dec.-01Nov.)
Applicant.000015.000015.00e-06
0

Probability of citation.00002

Examiner0

10 20 30 40 50 60 70 80 90 100 0

10 20 30 40 50 60 70 80 90 100

Citation lag (month)

B Electrical & Electronic

Probability of non-self citation(Electrical & Electronic, 00Dec.-01Nov.)

5.00e-06.00001.000015

Applicant0

Probability of citation.00002

Examiner0

10 20 30 40 50 60 70 80 90 100 0

10 20 30 40 50 60 70 80 90 100

Citation lag (month)p. 34

Figure 7 Probability of non-self-citation in Computers & Communications before andafter law revision
A Actual data only

Probability of applicant non-self citation(Computers & Communications)

5.00e-06.00001.000015

2000Dec.-2001Nov.

0

Probability of citation.00002

1999Dec.-2000Nov.

0

10 20 30 40 50 60 70 80 90 100

010 20 30 40 50 60 70 80 90 100

Citation lag (month)

B

Actual data plotted with estimated value (red) by regression in model 3

n
o
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p. 35

Figure 8 Total number of applicant citations and examiner citations vs. citation lag
A

s
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&
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n
o
i
t
a
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r
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p
A

p. 36

Figure 9 Total applicant forward citations made in 96-month citation lag by group ofcited patent documents

Total citations made

5,000
0

0

2,000 4,000 6,000
0Drugs & Medical

99Dec.-00Nov. 00Dec.-01Nov.

Electrical & Electronic

Mechanical

Others

10,00005,000
099Dec.-00Nov. 00Dec.-01Nov.

20,00099Dec.-00Nov. 00Dec.-01Nov.

10,00099Dec.-00Nov. 00Dec.-01Nov.

0

10,000 20,000 30,000

Computers & Communications
20,000 40,000 60,000

Chemical

10,000(Citation lag<= 96)

99Dec.-00Nov. 00Dec.-01Nov.

Cited: Evetually granted

99Dec.-00Nov. 00Dec.-01Nov.

Cited: Not grantedp. 37

Table 1 Statistics for granted purely domestic applications claiming no priority based on earlier applications
term
97Dec. - 98Nov.

98Dec. - 99Nov.

99Dec. - 00Nov.

00Dec. - 01Nov.

00Dec. - 01Nov.

02Dec. - 03Nov.

Statistics
A: mean Publication lag
B: mean Grant lag
B-A
sd pub lagn
A: mean Publication lag
B: mean Grant lag
B-A
sd pub lagn
A: mean Publication lag
B: mean Grant lag
B-A
sd pub lagn
A: mean Publication lag
B: mean Grant lag
B-A
sd pub lagn
A: mean Publication lag
B: mean Grant lag
B-A
sd pub lagn
A: mean Publication lag
B: mean Grant lag
B-A
sd pub lagnComputers &Drugs & Medical
Communications
24.72
36.03
25.04
24.80
36.53
25.12
0.08
0.50
0.08
9.80
15.01
11.94
3,298
11,124
2,670
24.53
42.32
25.48
24.78
42.76
25.81
0.25
0.45
0.33
10.55
18.32
12.61
3,059
11,990
2,542
26.12
48.54
29.20
26.34
49.01
29.50
0.22
0.47
0.30
12.00
22.03
14.09
2,823
12,981
2,433
19.75
28.76
21.64
27.37
50.97
31.08
7.63
22.21
9.44
7.40
20.33
9.78
2,684
13,608
2,246
20.29
28.14
22.04
30.90
50.45
37.33
10.61
22.31
15.29
9.04
19.98
11.59
2,533
13,167
1,998
20.65
27.63
22.85
33.80
51.17
43.10
13.15
23.54
20.25
9.43
19.39
12.50
2,217
12,826
1,715

ChemicalElectrical &Electronic
27.27
27.44
0.17
11.59
8,662
26.29
26.61
0.32
12.14
8,627
26.84
27.05
0.20
12.49
8,491
19.75
26.33
6.58
7.96
8,552
19.88
27.50
7.62
8.09
8,830
20.22
29.42
9.20
8.42
8,191

Mechanical
23.89
23.97
0.09
9.04
6,738
22.88
23.03
0.16
9.59
6,356
24.23
24.38
0.15
11.02
6,515
19.28
25.54
6.26
7.36
6,069
19.61
26.75
7.14
7.70
5,978
19.93
28.78
8.85
8.18
5,525

Others
22.93
23.01
0.08
9.86
9,802
23.08
23.26
0.18
11.26
9,485
25.03
25.15
0.12
12.55
9,121
19.49
26.36
6.87
8.99
7,969
20.03
27.94
7.91
9.53
8,012
20.47
30.79
10.32
9.82
7,446

p. 38

Table 2 Statistics for purely domestic normal applications by NBER-identified assignees that were granted patents
term
97Dec. - 98Nov.

98Dec. - 99Nov.

99Dec. - 00Nov.

00Dec. - 01Nov.

00Dec. - 01Nov.

02Dec. - 03Nov.

Statistics
A: mean Publication lag
B: mean Grant lag
B-A
sd pub lagn
A: mean Publication lag
B: mean Grant lag
B-A
sd pub lagn
A: mean Publication lag
B: mean Grant lag
B-A
sd pub lagn
A: mean Publication lag
B: mean Grant lag
B-A
sd pub lagn
A: mean Publication lag
B: mean Grant lag
B-A
sd pub lagn
A: mean Publication lag
B: mean Grant lag
B-A
sd pub lagnChemical
25.31
25.40
0.08
9.96
2,502
25.06
25.34
0.28
10.33
2,351
26.89
27.10
0.21
11.43
2,169
20.11
27.84
7.74
7.45
2,108
20.22
31.24
11.02
8.68
2,002
20.45
33.74
13.29
8.86
1,641

Computers &Drugs & Medical
Communications
36.24
25.56
36.74
25.68
0.50
0.12
14.42
11.71
10,349
1,766
42.57
26.26
43.02
26.63
0.44
0.37
17.43
12.43
11,118
1,662
47.96
30.35
48.39
30.69
0.43
0.34
20.41
14.12
11,838
1,615
28.27
22.31
49.77
32.26
21.51
9.95
19.18
9.76
12,423
1,572
27.42
22.01
48.57
37.81
21.14
15.80
18.68
11.14
11,657
1,295
26.74
22.86
49.10
42.61
22.36
19.75
18.16
11.68
10,915
1,053

Electrical &Electronic
27.77
27.94
0.18
11.56
7,511
26.68
27.03
0.35
12.14
7,514
27.17
27.39
0.22
12.38
7,363
19.95
26.60
6.65
8.13
7,525
19.91
27.54
7.63
7.93
7,750
20.15
29.31
9.16
7.90
7,116

Mechanical
24.97
25.05
0.07
9.48
4,134
24.01
24.23
0.22
10.09
3,980
25.64
25.82
0.18
11.31
4,123
19.82
27.01
7.18
7.67
3,861
19.74
27.85
8.11
7.51
3,701
19.59
29.29
9.70
7.19
3,424

Others
25.12
25.22
0.10
10.70
4,460
25.95
26.17
0.21
12.61
4,452
27.76
27.92
0.16
13.11
4,492
20.43
29.23
8.80
9.69
3,994
20.59
30.82
10.23
10.03
4,015
20.44
32.57
12.13
9.71
3,615

p. 39

Table 3 Descriptive statistics of data used for regression analysis

p. 40

Table 4A Summary of nonlinear regression results,, , , ,

1
2
1
2
1
2

p. 41

Table 4B Summary of nonlinear regression results,, , , ,

1
2
1
2
1
2

p. 42

Table 5 Number of citations made before publication of cited applications vs. number of citations made on and after publication of cited
applications

Technological fields
ChemicalChemicalComputers & Communications
Computers & Communications
Drugs & Medical
Drugs & Medical
Electrical & Electronic
Electrical & Electronic
Mechanical
Mechanical
OthersOthersFilling year month
1999Dec.-2000Nov.
2000Dec-2001Nov.
1999Dec.-2000Nov.
2000Dec-2001Nov.
1999Dec.-2000Nov.
2000Dec-2001Nov.
1999Dec.-2000Nov.
2000Dec-2001Nov.
1999Dec.-2000Nov.
2000Dec-2001Nov.
1999Dec.-2000Nov.
2000Dec-2001Nov.

A: Publication lag > citation lag
176
122
9,450
5,296
399
343
1,124
888
298
199
500
354

B: Publication lag <= citation lag share of A
2045
7.9%
1802
6.3%
21923
30.1%
28242
15.8%
3591
10.0%
4139
7.7%
11658
8.8%
12812
6.5%
3870
7.1%
3770
5.0%
4715
9.6%
4189
7.8%

p. 43

Table 6 Summary of linear regression results
SourceSS

Model132599.127

Residual
Total

1730878.97
1863478.1

df

MS
7

5388
5395

18942.7324
321.247025
345.408359

Citing_eff_F_T
t_pub: Cited First Publication time(month)

Coef.

Number of obs. =5396

F(7, 5388)

=

58.97

Prob > F=

0.0000R-squared=

0.0712

Adj R-squared=

0.0700

Root MSE=

17.923

Std. Err.

t

P>t[95% Conf.

Interval]

0.883

0.0539

16.36

0.000

0.777

0.988

-0.00747

0.0151

-0.50

0.621

-0.0371

0.0221

Computers & Communications

-7.70

1.09

-7.07

0.000

-9.83

-5.56

Drugs & Medical

-5.89

1.31

-4.51

0.000

-8.45

-3.33

Electrical & Electronic

-4.12

1.11

-3.71

0.000

-6.29

-1.94

Mechanical

-1.46

1.22

-1.20

0.231

-3.86

0.933

Others-0.486

1.20

-0.41

0.685

-2.83

1.86

_cons

28.60

1.62

17.67

0.000

25.43

31.78

t_grant: Cited Grant time(month)Technological field dummyp. 44Appendix 1 Applicant citations and examiner citations made in 18-month citation lag and 96-month citation lag

p. 45

Appendix 2 Citation lag dependence of the number of total applicant citations by groupof cited patent documents

Total citations made(Citation lag<= 96)

5000

0

40

60

80

100

0

20

60

80

100

Mechanical

20

40

60

80

100

20

40

60

80

100

80

100

Others

0

5000
0

0

0

0

10000

10000

10000 20000 30000

Electrical & Electronic

40

20000

20

0

6000
4000
2000
0

0

Drugs & Medical

10000

Computers & Communications
20000 40000 60000

Chemical

0

20

40

60

80

100

0

20

40

60

Citation lag(month)
99Nov.-00Dec.(Total)
00Nov.-01Dec.(Granted)

00Nov.-01Dec.(Total)
00Nov.-01Dec.(Not Granted)

p. 46