1. The EP-INV-Patstat db and preliminary results
Andrea Maurino
DISCo - Dip. di Informatica, Sistematica e Comunicazione Università di Milano Bicocca viale Sarca 336/14, , Milano (Italy)

2. Index APE-INV project EP-INV-PatStat Feedback Web application
Preliminary results
Ongoing works
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3. A preliminary truth The world is dirty! and Real world data are dirty!
A mandatory and prelimnary task before to realize any analysis or statistic is
Clean your data
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4. Disambiguation of academic inventors: ESF-APE-INV
Project chair: Francesco Lissoni (uniBocconi)
Technical Manager: Andrea Maurino (uniMiB)
Project steps:
Reclassification of all patents by inventor (INV)
Matching between inventors and academic scientists (APE)
Results expected:
To produce a freely-available database of “Academic Patenting in Europe”
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5. EP-INV-PatStat PATSTAT_PUBL_NR PATSTAT_APPL_ID INVENTORS_INFO
DISAMBIGUATION
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6. Which is the part of PatStat interested by disambiguation?
Users should not consider these tables, SUBSTITUTIVE TABLES with disambiguated inventors and inventors information are provided by APE-INV project
Source: PatStat documentation

7. INVENTORS_INFO
INVENTORS_INFO table
CODINV2
NAME-SURNAME
COUNTRY / GCOUNTRY
STATE
REGION / GREGION
COUNTY / GCOUNTY
CITY / GCITY
STREET / GSTREET
ZIP / GZIP
LONGITUDE
LATITUDE
GACCURACY
Fields preceded by letter G are the result of Google-based standardization algorithm, all the other fields are cleaned PatStat addresses (eg. CITY and GCITY)
We report Google information only when GACCURACY is larger than or equal to 6 (i.e. Address is available at the level of Street).

8. From APE-INV to PatStat, PATSTAT_PUBL_NR and PATSTAT_APPL_ID
In order to connect DISAMBIGUATION and INVENTORS_INFO tables with PatStat dataset we include in the repository other two tables:
PATSTAT_PUBL_NR
allows to link each inventor (as identified by the CODINV2 code in the APE-INV dataset) to her granted patents (PUBLN_NR).
PATSTAT_APPL_ID
Allows to identify the APPLN_ID corresponding to each PUBLN_NR (NB In the specific case of EP patents there is a one-to-one correspondence between APPLN_ID and PUBLN_NR).
The table reports also the information of the PatStat edition the APPLN_ID refers to.
PATSTAT_PUBL_NR
CODINV2
PUBLN_NR
100 1
101 2
102 3
115 4
PATSTAT_APPL_ID
PUBLN_NR
APPLN_ID
PEDITION 1 5
42011 2 6 3 7 4 8

9. DISAMBIGUATION.txt DISAMBIGUATION table
CODINV2: is a stable key generated within the APE-INV project. It identifies uniquely any distinctive combination of inventor and address
CODINV: is a code associated to each CODINV2 after applying the disambiguation procedure. If two or more distinct CODINV2s are found to be the same person, they are assigned the same CODINV
CODINV
CODINV2 1
100
101 2
102 3
115
Dite qui che in futuro speriamo di passare da CODINV e CODINV2 codes (che sono nostri idiosincratici) a PERSON_ID, se PatStat riuscirà a crearne uno stabile. Dite anche per i brevetti successivi al 2000 abbiamo comunque una tavola di conversione CODINV2-PERSON_ID scaricabile dal sito web, all'indirizzo:

10. Feedback web application
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11. Why sharing data
Instead of looking for one golden algorithm, APE-INV proposes data dissemination and users’ feedback recording
2 kinds of users:
Take the data and run (dissemination only): they use the data in their studies a-critically. No benefit for the project, risky for them (data are disambiguated according to the state-of-the-art of dissemination techniques, but we can always do better..).
Critical users (dissemination+feedback): they use the data, usually sub-samples of the whole dataset, and have the possibility to increase the data quality:
Hand checked data and survey work on smaller samples
Algorithms fitting better sub-sample specificities (es. Country, firm, technological field)
Data sources external to PatStat helping the disambiguation effort
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12. How does data dissemination work?
Access with id and password
Choose the country(s) of inventors you need (eg. My research is on Italian inventors)
Get the EP-INV dataset and the CONTROVERSY.txt
Query results in txt format.

13. Some results
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14. Number of academic patents, 1996-2006

15. Ownership distribution of academic patents lower bound estimates

16. Ownership distribution of academic patents, upper bound estimates

17. Ongoing works
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18. Temporal Record linkage
“Panta rei” (Heraclitus) everything flows, everything is constantly changing.
Database may keep trace of these never ending changes
Examples
People change names
Xin Dong Xin Luna Dong
People change works
Havely moves from Univ. of Wa. to Google
Nations change
YUGOSLAVIA  Serbia-Montenegro Serbia Kosovo
Based on the paper P. Li, X.L.Dong, A.Maurino, D.Scrivistava, linking temporal data, VLDB 2011
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19. An example person_id person_name person_address appln_filing_date
110670
ABELE, MANLIO, G.
5 EAST 22ND STREET;NEW YORK, NY 10016
18/10/1990
06/04/1992
110671
5 EAST 22ND STREET, 205;NEW YORK, NY 10010
20/02/1991
110672
ABELE, Manlio, G.
5 East 22nd Street, 205,New York, NY 10010
110674
5 East 22nd Street,New York, NY 10016
12/04/1995
12/03/1996
110675
Abele, Manlio
250 East 54th St.,New York, NY 10022
19/03/2004

20. Experimental Evaluation
Effectiveness test:
Data set: patent data, 1871 records, 359 entities, in
Comparison: three existing algorithms, w./o. decayed similarity

21. Thanks! 疑问
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