1 Construction of Japanese Patent Database and Preliminary Findings on Patenting Activities in Japan Akira Goto and Kazuyuki Motohashi RCAST, University of Tokyo EPIP Conference, Milan, 24-25, February 2006

2 Outline 1. Introduction 2. Methodology and Basic Features of Japanese Patent Database 3. Comparing Japanese citation data with those of Europe and the U.S. Overview of innovation activities by Japanese Patent Database 4. Citation Analysis by Japanese Patent Database 5. Conclusion

3 1.Introduction  “ Nothing exists until it is measured ” Niels Bohr Difficulty in measuring knowledge despite the talk of “ knowledge society ” Patent as a source of information on technological knowledge Great contribution of NBER patent citation database to innovation research

4  Need for a patent database using JPO patents IIP PATENT DATABASE IIP: Institute of Intellectual Property (only for Japanese) An English version will be available at

5 2. Methodology and Basic Features of Japanese Patent Database  Original Data Source; JPO Seiri hyojunka Data (Literally translating, “ Arranged and Standardized Data ”, hereafter, JPO Patent Database)  It contains the information generated through acceptance of application to examination process by JPO.  About 50 boxes of half inch tape.

6  From this original data, we chose variables important for innovation research, using NBER database as benchmark.  It covers patents applied after  The number of patent applications included is 9,027,486.  It is made of the following five files.

7 ① Patent application files (9,027,486records)  Application number  Application date  Examination request date  Applicant number  Number of claims at patent application  Lead IPC code at patent application  Aggregated technology category

8 Aggregated Technology Category

9 ② Patent registration file (2,618,699records)  Application number  Registration number  Registration date  Rights expiration date  Rights holder number  Number of claims at patent registration  Lead IPC code at patent registration  Aggregated technology category

10 ③ Applicant file(626,708 records)  Applicant number  Applicant name  Applicant type (individual, corporation or government)  Country and prefecture code  JPO applicant code

11 ④ Rights holder file(204,622 records)  Rights holder number  Rights holder name

12 ⑤ Citation information file(5,318,225records )  Citing patent application number  Cited patent application number  Citation type

13 3.Overview of innovation in Japan by IIP patent Database  9,027,486 applications, 4,427,840 requests for examinations, 2,594,044 grants from 1964 through  Information on patent right termination is also available.  Data truncation problem

14 Patent application by technology

15 Patent application by applicant type

16 Patent registration by technology

17 Number of claims by technology

18 Patent Life Length by Applicant Type

19 Patent Life Length by Application Date

20 3. Citation in IIP patent Database  Two types of information on citation---both are given by patent examiner, not inventor (1)as prior art to reject application: subject to change according to examination practices (2)In Patent Gazett, reference to previous patent is made occasionally. Notable previous related patents. Only for patents granted, and after 1985.

21 Patent counts with citation made

22 4. Comparing JP Citation Data with those of Europe and US  JP Citation data: (1) prior art for a reject application by examiner and (2) those in Patent Gazett (again by examiner ’ s citation)  EP Citation data: minimum number of important reference documents  US Citation data: applicant ’ s citations (but over 40% of citations are made by examiner, Alacer and Gittleman, 2004), citation inflation?

23 Correction of patent data biases by OECD patent family data JP-1 EP-1 JP-2 JP-4 JP-5 US-1 JP-101 JP-102 JP-103 JP-104 Family A Family B Family AA Family BB EP-101

24 Data JP Citation: IIP Patent Database EP Citation: EP Citation Database US Citation: NBER Patent Database + B. Hall Extension OECD Patent Family: Data

25 Overlapping Citing Cited Pairs?

26 What about citation lags?

27 Correlation of citation count Backward citation count (citation made) Forward citation count (citation received)

28 5. Citation Analysis  Selecting granted patents from all citing and cited pairs to capture cumulative process of innovation  5,318,225 -> 1,602,130 for 2,618,699 granted patents  Comparing patent counts, generality and originality index across applicant ’ s type and technology field

29 Share of patents with citing made and citation received

30 Indicators for backward citation (citation made)

31 Indicators for forward citation (citation received)

32 Descriptive regressions of patent indicators (1)

33 Descriptive regressions of patent indicators (2)  High citing patent counts:  printing, machine tools, non-organic chemistry, drug, organic molecule, dyes, metallurgy, textile, optics, communication technology  High cited patent counts:  printing, paper, drug, organic molecule, display and information storage  High generality index:  separating and mixing, organic molecule, metallurgy

34 6. Conclusion  Construction of IIP Patent Database: filling a hole of patent database initiative in the triad  Open up opportunities for further research of on innovation and further data developments: for example IIP patent data base can be linked with JPO ’ s Survey on Intellectual Property Activities  Citation data analysis  Substantial differences: not only by patent system (JP-EP vs US), but also home biases  On the other hand, substantial cross co-relation for citation received information (indicator of important patent for cumulative innovation)  Preliminary citation analysis conducted

35 Future works  Data cleaning of applications name  Using Japanese character makes things better  Standardized name and JPO applicant coded after 1992  International coordination: match to Derwent codes? Using patent family information  Adding other variables such as inventor information, post grant opposition etc.  Linkage with firm level data such as financial report data