Rawpatentdata

PATSTAT 2019b MySQL upload scripts

2019-11-06T04:09:00.000-08:00

At this link are available my new scripts for MySQL to upload PATSTAT 2019b data.

Only change in this data ediction is the removal of table TLS906_PERSON that is fully integrated in TLS206.
My scripts have been updated to remove MyIsam as default engine and to make them fully compatible with MySQL 8.0.

PATSTAT & Patentsview: complements or substitutes?

2019-10-07T00:39:00.003-07:00

Match of PATSTAT data (2019 spring) and PatentsView (jan 2019) is discussed here, with focus on how this match can help to enrich PATSTAT data with information not contained in USPTO patents (and the other way round).

PATSTAT & Patentsview: complements or substitutes? from Gianluca Tarasconi

Patents applicants: how to create the full time series

2019-09-30T06:19:00.004-07:00

I share a presentation I made @ EPo & KUL summer school in Vienna last september!
hope it's useful to someone else.

Patents change applicants data within time;
Main reason for change are ownership change, name/address change, M&A …
Applicant’s names contained in TLS206 is the ‘last available’ data;
PATSTAT Global + EP Register make available several sources to build a chain of names
and a timeline for patents contained;

Patents applicants: how to create the full time series from Gianluca Tarasconi

Patents citations from Pubmed Scientific publications

2019-07-18T06:25:00.003-07:00

A recently released dataset from Marx, Matt; Aaron Fuegi contains citations from USPTO patents granted 1947-2018 to articles captured by the MS Academic Graph (ID) from 1800-2018.

Files, tab-separated, are available at link : https://zenodo.org/record/3338601

The main file, pcs.tsv, contains the resolved citations matching patent number, MAG ID, the original citation from the patent, an indicator for whether the citation was supplied by the applicant, examiner, or unknown, and a confidence score (1-10) indicating how likely this match is correct.

There is also a PubMed-specific match in pcs-pubmed.tsv.

Authors also made availabel source code for generating the patent citations to science in pcs.tsv is available at https://github.com/mattmarx/reliance_on_science. Source code for generating jif.zip and jcif.zip (Journal Impact Factor and Journal Commercial Impact Factor) is at https://github.com/mattmarx/jcif.

Scripts and programs are mainly for stata & linux (DO files and sh scripts).

PATSTAT - patentsview concordance update 2019

2019-07-08T07:31:00.004-07:00

PatentsView is a platform built on data derived from USPTO bulk data files.

This dataset complements perfectly PATSTAT since the former has an native disambiguation of inventors and applicants and a geocoding system applied to inventors and applicants, while the latter links US data to other offices, allowing to calculate knowledge spillovers, family data etc.

At this link is possible to download a table of concordance between patent_id (Patentsview main key) and appln_id (from PATSTAT).

Overlapping of the two datasets is not perfect since Patentsview contains only granted patents after 1975, where PATSTAT has also application (ungranted) and timeframe covers also pre-1975 data.
On the other hand PATSTAT misses design patents before 2001, plants before 2001 and 'statutory invention registration' type of patent.

Data are from PATSTAT spring 2019 and Patentsview march 2019, thus also 2019 data in PW are partially missing in PATSTAT.

Patent familiarity calc scripts

2019-06-06T01:51:00.000-07:00

I made available on Github a set of MySQL and python scripts to create familiarity indicator by IPC class, on NBER patents dataset.

https://github.com/gtarasconi/NBER-familiarity-indicators

Inventor’s familiarity with components of the invention measured by the (a) recent and (b) frequent usage of focal patent’s classes across all US patents. Thus, we calculate a measure of familiarity for each separate class of a focal patent. Therefore, the more recently and frequently a class has been used, its individual measure will be higher.

Based on Fleming 2001
https://funginstitute.berkeley.edu/wp-content/uploads/2012/10/Recombinant-Uncertainty-in-Technological-Search.pdf

PATSTAT 2019a analysis of changes

2019-05-22T23:14:00.001-07:00

As Stated in EPO documents new version of PATSTAT comes with 2 mayor changes:

Person names in the original language (PERSON_NAME_ORIG_LG)
Field added in tables TLS206, 226, 906. This creates an inflation of records and lack of retrocompatibility with old personwise data.

“RELEVANT_CLAIM” attribute in the TLS215_CITN_CATEG table
A new attribute “RELEVANT_CLAIM” has been added to the TLS215_CITN_CATEG table. This attribute contains a single number referring to the claim to which the citation refers to.

About first change you can find below, for offices with more than 100.000 person_ids, how are they most affected, where the last column shows inflation rate

APPLN_AUTH	Count_person_id_2018b	Count_person_id_2019a	ratio
AR	150688	182005	121%
AT	1142798	1232406	108%
AU	1870980	2008555	107%
BE	196598	204115	104%
BR	1223470	1315337	108%
CA	2524565	2675965	106%
CH	650912	664626	102%
CN	6192040	9956730	161%
CS	166131	178125	107%
CZ	130967	233777	179%
DD	226783	234141	103%
DE	5095800	5258606	103%
DK	624763	687156	110%
EP	6624815	6625117	100.005%
ES	1291041	1389533	108%
FI	365453	384041	105%
FR	1471641	1588271	108%
GB	1757273	1797304	102%
GR	134008	173190	129%
HK	300604	426287	142%
HU	234866	303144	129%
IL	128159	167805	131%
IN	190763	213962	112%
IT	661777	719438	109%
JP	2802730	3787058	135%
KR	1906273	3031637	159%
MX	524748	620385	118%
MY	121991	158129	130%
NL	144408	155950	108%
NO	370993	444431	120%
NZ	261084	329085	126%
PL	370207	492642	133%
PT	218054	249693	115%
RO	131598	152849	116%
RU	979629	1831707	187%
SE	406973	420885	103%
SG	277368	363149	131%
SU	1536615	1718307	112%
TW	1004135	1143685	114%
UA	164487	339271	206%
US	12270415	12630422	103%
WO	4119901	4756241	115%
ZA	511180	621550	122%

About the latter, number of records in TLS215 are more than doubled since for each category (now categories can be multiple as AD, AX ...) one line for relevant claim is created.

PATSTAT 2019a MySQL upload scripts

2019-05-19T00:00:00.000-07:00

At this link are available my new scripts for MySQL to upload PATSTAT 2019a data.

New features in this ediction:

Person names in the original language (PERSON_NAME_ORIG_LG)
Field added in tables TLS206, 226, 906. This creates an inflation of records and lack of retrocompatibility with old personwise data.

“RELEVANT_CLAIM” attribute in the TLS215_CITN_CATEG table
A new attribute “RELEVANT_CLAIM” has been added to the TLS215_CITN_CATEG table. This attribute contains a single number referring to the claim to which the citation refers to.

webscraping: download of ANVUR list of journals in Python

2019-05-08T07:21:00.000-07:00

Recently I had the task of creating a dataset of scientific journal classified by ANVUR (italian agency for research rating);
Unfortunately the lists are splitted by research area and available only in PDF at URL

http://www.anvur.it/attivita/classificazione-delle-riviste/classificazione-delle-riviste-ai-fini-dellabilitazione-scientifica-nazionale/elenchi-di-riviste-scientifiche-e-di-classe-a/

In order to make my life easier I created a Python 3 script that downloads all PDFs and via Tabula library, transforms PDF tables into CSVs.

I put the script below (note URL is hardcoded, for future uses change it)

Still CSVs need some work due to multiline titles.
To make life easie I made 2 xls files with A journals and all journals, for Areas 11 12 13 and 14.

XLS files can be downloaded here.

# python 35

# pdf downloader code extractor

from bs4 import BeautifulSoup
import requests
import time
import codecs
import PyPDF2
import os
from tabula import read_pdf
import pandas as pd

if __name__ == "__main__":

    istable = input('Pdf are tables?[N]') or 'N'
    dnl = input('download PDFs?[Y]') or 'Y'

    if dnl=="Y":
        archive_url = "http://www.anvur.it/attivita/classificazione-delle-riviste/classificazione-delle-riviste-ai-fini-dellabilitazione-scientifica-nazionale/elenchi-di-riviste-scientifiche-e-di-classe-a/"
        response = requests.get(archive_url)

        soup = BeautifulSoup(response.text, 'html.parser')

        pdf_links = [link['href'] for link in soup.find_all('a') if link['href'].endswith('pdf')]

        for link in pdf_links:

            if link[:4]!='http':
                link = archive_url + link

            '''iterate through all links in and download them one by one'''

            # obtain filename by splitting url and getting
            # last string
            file_name = link.split('/')[-1]

            print ("Downloading file:%s" % file_name)

            # create response object
            r = requests.get(link, stream=True)

            # download started
            with open(file_name, 'wb') as f:
                for chunk in r.iter_content(chunk_size=1024 * 1024):
                    if chunk:
                        f.write(chunk)

            print ("%s downloaded!\n" % file_name)

        print ("All file downloaded!")

    pdfDir = ""
    txtDir = ""

    if pdfDir == "": pdfDir = os.getcwd() + "\\" # if no pdfDir passed in
    if txtDir == "": txtDir = os.getcwd() + "\\" # if no txtDir passed in

    for pdf_to_read in os.listdir(pdfDir): # iterate through pdfs in pdf directory
        fileExtension = pdf_to_read.split(".")[-1] # -1 takes always last part
        if fileExtension == "pdf":
            pdfFilename = pdfDir + pdf_to_read
            textFilename = txtDir + pdf_to_read + ".txt"
            textFile = open(textFilename, "a") # make text file

            if istable == 'N':

                pdf = PyPDF2.PdfFileReader(open(pdfFilename, "rb"))
                for page in pdf.pages:
                    textFile.write(page.extractText()) # write text to text file

            else:
                df= read_pdf(pdfFilename, pages="all")
                df.to_csv(textFilename)

            textFile.close()

PATSTAT data coverage

2019-05-06T06:20:00.001-07:00

When doing advanced statistical analysis, it is important to understand the coverage and content of the data you are working with. PATSTAT Global contains data coming from all over the world. Quality, timeliness and completeness vary a great deal depending on patent office.

EPO compiled a Tableau dashboard that maps the content and coverage of PATSTAT Global.

This chart shows the percentage of patent applications having a certain data element (e.g. CPC classification) by patent authority and application year in PATSTAT Global.

https://public.tableau.com/profile/patstat.support#!/vizhome/CoverageofPATSTAT2018AutumnEdition/CoveragePATSTATGlobal

PATSTAT autumn 2018 MySQL upload scripts

2018-10-17T08:36:00.002-07:00

at this link is possible to download a batch of scripts for MySQL that will allow you to upload new PATSTAT edition autumn 2018.

This release has some improvements as:

* Table TLS201_APPLN and TLS211: attribute granted changed from 0/1 to Y/N.

* Table TLS212_CITATION: Euro-PCT applications did not have the citations from the international search report linked to the respective application (and publication). These are the so called A0 publications. To avoid this, EPO simply duplicated the citations from the international search report, and linked them to the respective EP publications.

* Table TLS803_LEGAL_EVENT_CODE: has been redesigned to match WIPO ST.27.

PATSTAT projects on github

2018-10-16T05:05:00.003-07:00

Refilling PATSTAT addresses

this project contains a docker container in Python and MySQL to refill persons where addresses is missin

https://github.com/cortext/patstat/tree/master/parsed%20addresses

Classify Legal Entities And Individuals From Patent Applicants

A batch of MySQL script to discriminate type of applicant

https://github.com/cortext/patstat/tree/master/applicants%20classification

Add official name of patent office

https://github.com/cortext/patstat/tree/master/nomenclatures/offices_classification

building descriptions for the International Patent Classification

An API embedded into a VM to get the full description of IPC codes

https://github.com/cortext/patstat/tree/master/nomenclatures/ipc_descriptions

PATSTAT loader

https://github.com/simonemainardi/load_patstat

psClean

Python library and associated code for preparing PATSTAT inventor-patent data for disambiguation with either the Torvik-Smallheiser or Open City Dedupe algorithms.

https://github.com/markhuberty/psClean

fuzzygeo

fuzzygeo provides a fuzzy geocoding routine for geocoding at the named entity (city or similar) level
https://github.com/markhuberty/fuzzygeo

psClassify
a simple supervised learning algorithm to classify PATSTAT records into two categories:

person names
not person names

https://github.com/mkln/psClassify

Google dataset search

2018-09-21T01:16:00.000-07:00

Recently Google launced a new service aiming to index local, public and national data repositories: Google Dataset Search.

Dataset Search lets you find datasets wherever they’re hosted, whether it’s a publisher's site, a digital library, or an author's personal web page.

Google also developed guidelines for dataset providers to describe their data in a way that search engines can better understand the content of their pages.

The approach is based on an open standard for describing this information (schema.org) and anybody who publishes data can describe their dataset this way.

The engine also links, where possible, the dataset to Google Scholar articles using them.

Full story @ link
https://www.blog.google/products/search/making-it-easier-discover-datasets/

How to build indicators from PATSTAT step by step

2018-09-19T01:55:00.001-07:00

A presentation I prepared about how to build a patents based indicator (Patents orìiginality) step by step for EPO PATSTAT avoiding most common pitfalls, included commented SQL code
hope you will find it useful

Patstat indicators step by step from Gianluca Tarasconi

MySQL upload scripts for EEE-PPAT 2018a

2018-09-13T01:20:00.004-07:00

EEE-PPAT table is an extension of the PERSON TABLE (TLS206) produced by ECOOM (Catholic University of Leuven) and Eurostat. The extension concerns sector allocation and name harmonization of applicants.

2018a version contains 56611335 records, and it has improvements for standard names and sector, compared to PATSTAT ediction.

It can be required at no cost by contacting technoinfo@ecoom.be

MySql script I created for uploading the table can be downloaded here

Online regular expression validators

2018-08-30T23:22:00.000-07:00

a quick note on two useful on line tools allowing test and validate regular expressions.

First one is

https://regex101.com

that allows tests in multiple dialects (php, javascript, python...) and gives a verbose but useful explanation of the steps.

Last but not least it allows to share links to tests prepared like this one:

https://regex101.com/r/9y9n85/20

also useful is:
https://regexper.com/

that comes with the comforting subtitle
You thought you only had two problems…
and allows crreating diagrams about how a given regexp works

example:

https://regexper.com/#Results%20%3D%20%28%5Cd%2B%29

Linked Open EP data

2018-06-18T06:24:00.002-07:00

at URL
https://www.epo.org/searching-for-patents/data/linked-open-data.html
is now available an linked open data version of EP patent data.

Linked Open EP data is a data product provided by the EPO. It contains EP publications with their
bibliographic and family information.
It also contains some basic information on non-EP patents, which are related to EP patents, e. g. because they are a priority of an EP document or they are in the same familyas an EP application.

The product also comes with a simple application programming interface (API), allowing you to consult reference data, explore that data and try out ideas on a small scale.
A SPARQL interface enables you to analyse the data.

Linked open EP data uses Uniform Resource Identifiers (URIs) to identify patent applications, publications and other resources present in patent data. This allows data in one dataset to be linked to data in another dataset. Given its URI, data about a resource can be retrieved in a variety of formats over the web. For occasional use there is a simple data browser, an API and a query interface. For heavier use, bulk data is available for download.

Each application has a unique identifier which looks like a URL and has this structure:

https://data.epo.org/linked-data/doc/application/cc/nnnnnnnn

where
cc stands for the Office code.
nnnnnnnn stands for the application number.

Applications disappearing across PATSTAT edictions

2018-06-08T03:12:00.001-07:00

Interesting fact, especially if you have to run periodical reports: new edictions in PATSTAT not only add new application but a small number of existign applications disappear across edictions.

Below query counts patents disappearing. Results are listed in the table below for offices/appln_kind with more than 1000 disappearing applications

APPLN_AUTH	APPLN_KIND	EARLIEST_PUBLN_year	Count_APPLN_ID
'AU'	'A'	9999	1142
'AU'	'D'	1968	1067
'AU'	'D'	1969	1351
'AU'	'D'	1970	1458
'AU'	'D'	1971	1491
'AU'	'D'	1972	1389
'AU'	'D'	1973	7322
'AU'	'D'	1974	7474
'AU'	'D'	1975	4478
'AU'	'D'	1976	2554
'AU'	'D'	1977	4680
'AU'	'D'	1978	7522
'AU'	'D'	1979	11628
'AU'	'D'	1980	9765
'AU'	'D'	1981	7680
'AU'	'D'	1982	7652
'AU'	'D'	1983	7804
'AU'	'D'	1984	7995
'AU'	'D'	1985	8747
'AU'	'D'	1986	9701
'AU'	'D'	1987	9809
'AU'	'D'	1988	10245
'AU'	'D'	1989	6807
'AU'	'D'	1990	11410
'AU'	'D'	1991	10272
'AU'	'D'	1992	8128
'AU'	'D'	1993	5961
'AU'	'D'	1994	7735
'AU'	'D'	1995	9861
'AU'	'D'	1996	12173
'AU'	'D'	1997	11543
'AU'	'D'	1998	15028
'AU'	'D'	1999	15274
'AU'	'D'	2000	15162
'AU'	'D'	2001	11687
'AU'	'D'	2002	3623
'KR'	'A'	2005	1956
'KR'	'A'	2006	2954

Some offices show a systematic decrease of number of applications

office	app kind	earl pub date	n apps 2018a	n apps 2016b	Colonna1
'IT'	'A'	2010	9040	9257	-2%
'IT'	'A'	2011	9411	9681	-3%
'IT'	'A'	2012	8913	9152	-3%
'IT'	'A'	2013	8858	9180	-4%
'IT'	'A'	2014	8234	8494	-3%
'IT'	'A'	2015	3833	3865	-1%

SELECT
a.APPLN_AUTH,
a.APPLN_KIND,
a.EARLIEST_PUBLN_year,
Count(DISTINCT a.APPLN_ID) AS Count_APPLN_ID
FROM
patstat2016b.tls201_appln a
LEFT JOIN patstat.tls201_appln b ON b.APPLN_ID = a.APPLN_ID
WHERE
b.APPLN_ID IS NULL
GROUP BY
a.APPLN_AUTH,
a.APPLN_KIND,
a.EARLIEST_PUBLN_year,
b.APPLN_ID

creating a Raspberry PI VM

2018-05-02T13:09:00.005-07:00

Little more than a note for myself:
a good video about installing a Raspberry PI desktop VM using VMware:

https://carpediemsystems.co.uk/2017/10/16/raspberry-pi-desktop-virtual-machine/

Bytheway if I'll ever do a Raspberry fisical PC I'll absolutely use this cover:

https://www.etsy.com/it/listing/537575621/commodore-64-raspberry-pi-zero-case?ref=shop_home_active_5

PATSTAT 2018a vs GDPR

2018-05-02T10:56:00.001-07:00

Due to the incumbing GDPR start date, and the high number of natural persons that PATSTAT includes, it is a non trivial question whether this new ediction has a reduced number of addresses for person_ids in TLS206.
I made some comparison vs 2017b version and apparently changes are minimal; below a table with the ratio of missing address and missing country, between 2017b and 2018a.

The full file can be downloaded from here.

	comparison
receiving office	no addr	no cy
AM	1	1
AP	0.999897	1
AR	0.98578	1.000087
AT	0.999115	0.999986
AU	1.000121	1
BA	1	1
BE	0.997123	0.996925
BG	0.999715	0.998334
BR	0.999992	1.000001
BY	1	1
CA	0.985612	1
CH	0.999506	0.998478
CL	0.879132	0.937244
CN	1.018181	1
CO	1	1
CR	0.926902	1.000045
CS	1.00001	1
CU	0.982067	1.026533
CY	0.983142	1.000003
CZ	0.988476	1
DD	1.000011	1.000001
DE	0.99377	1
DK	0.985258	0.984897
DO	0.970156	1.000007
DZ	1	1
EA	1.420315	1.000002
EC	1	1
EE	0.996433	1.074769
EG	1	1
EM	1	1
EP	0.98777	1.325289
ES	1.012263	1
FI	0.996808	1.004037
FR	0.996585	1
GA	1	1
GB	0.998379	1.000232
GC	1	1
GE	1.019985	1.000007
GR	0.998001	0.999271
GT	1.009547	1.000005
HK	1.001682	1
HN	1	1
HR	0.949633	0.948773
HU	0.97796	1.000315
ID	1	1
IE	0.999699	0.999191
IL	1.000888	0.998899
IN	0.999968	1.000006
IS	0.99893	0.997454
IT	1.000013	1
JO	1	1
JP	1.000014	1
KE	1	1
KG	1	1
KR	1.069759	1
KZ	1	1
LT	0.862678	1.000022
LU	1.094461	1.000001
LV	1	1
MA	1.007003	1.000003
MC	0.999452	1.000001
MD	0.981279	1.000003
ME	1	1
MN	1	1
MO	1	1
MT	0.951049	1.00009
MW	1	1
MX	1.019108	1
MY	0.900944	1.000002
NI	1	1
NL	0.995279	0.995683
NO	0.995618	1
NZ	1.000104	1.000002
OA	0.999788	1
PA	1	1
PE	0.969967	1.000002
PH	0.964729	1.01545
PL	0.963278	1.00499
PT	0.971913	1
RO	0.994103	0.990226
RS	0.908287	1.000014
RU	0.971084	1.001344
SA	1.126561	1.000011
SE	1.000564	1
SG	0.960119	0.959793
SI	0.948312	1.000001
SK	0.990072	1
SM	1	1
SU	0.996975	1
SV	0.972366	1.000019
TH	1	1
TJ	1	1
TN	0.67446	0.67446
TR	1.000336	1
TT	1	1
TW	0.951257	1
UA	0.997054	1
US	0.981089	0.981932
UY	0.963563	1.000003
UZ	1	1
VN	1	1
WO	0.970849	1.002561
YU	0.999976	1
ZA	0.986705	1.000029
ZM	1	1
ZW	1	1

PATSTAT spring 2018 MySQL upload scripts

2018-04-20T08:21:00.002-07:00

at this link is possible to download a batch of scripts for MySQL that will allow you to upload new PATSTAT edition spring 2018.

This release has some improvements as:

* Table TLS201_APPLN: a new attribute name “RECEIVING_OFFICE” has been added. This means now PCT applications have "WO" as application authority, and the previous value is shifted to the new field.

* Table TLS212_CITATION: Euro-PCT applications did not have the citations from the international search report linked to the respective application (and publication). These are the so called A0 publications. To avoid this, EPO simply duplicated the citations from the international search report, and linked them to the respective EP publications.

* Table TLS231_INPADOC_LEGLA_STATUS: a new attribute EVENT_ID has been added to serve as a primary key to this table.

Welcome back REGPAT (+ MySQL upload scripts)

2018-04-18T05:25:00.001-07:00

After a couple of years OECD released a new version of REGPAT, the PATSTAT plug and play extension that provides for EP and PCT person_ids an efficient NUTSIFICATION.
This edition provides regional data for 45 countries, exceeding thus the classing EU28 by adding AU, CA, ZA, US etc.
NUTS version is 2013, only London NUTS remained to version 2010.

At this link are available my MySQL scripts for uploading the tables.

Along with REGPAT also are available, by filling the form at web page:
https://survey.oecd.org/Survey.aspx?s=94638c7a1e694786a72d54c620e08ec8

the following datasets from OECD

OECD Patent Quality Indicators Database (series of indicators capturing the technological and economic value of EPO and USPTO patents);
OECD Triadic Patent Families Database (set of patents jointly filed at EPO, JPO and USPTO);
OECD Citations Database (references to patent and non-patent literature cited in EPO, USPTO or PCT patents);
OECD HAN database (harmonised patent applicants’ names).

Patentsview PATSTAT concordance

2018-04-05T07:05:00.000-07:00

The PatentsView initiative was established in 2012 and is a platform built on a newly developed database that longitudinally links inventors, their organizations, locations, and overall patenting activity. The platform uses data derived from USPTO bulk data files.

This dataset complements perfectly PATSTAT since the former has an native disambiguation of inventors and applicants and a geocoding system applied to inventors and applicants, while the latter links US data to other offices, allowing to calculate knowledge spillovers, family data etc.

At this link is possible to download a table of concordance between patent_id (Patentsview main key) and appln_id (from PATSTAT) and some basic statistics.
[update the link now contains the link to march2019 version.

Overlapping of the two datasets is not perfect since Patentsview contains only granted patents after 1975, where PATSTAT has also application (ungranted) and timeframe covers also pre-1975 data.
On the other hand PATSTAT misses design patents before 2001, plants before 2001 and 'statutory invention registration' type of patent.

Data are from PATSTAT october 2017 and Patentsview dec 2017, thus also 2017 data in PW are partially missing in PATSTAT.

Gender dictionaries for PATSTAT

2018-02-25T23:49:00.000-08:00

Gender studies has become more and more an important issue in innovation studies.
This means to be able to tag inventors with gender, that is not a trivial task.

Two recently released tools might be helpful in this task.

First one: recently UKIPO released Gender profiles in worldwide patenting: An analysis of female inventorship where it's possible to download not only the research paper but also a name-gender dataset and a gender inference table; they are directly linked to PATSTAT via Person_id, based on autumn 2016 version.

Last but not least PCT released Identifying the gender of PCT inventors; the paper analyzes the gender of inventors in international patent applications and compile a worldwide gender-name dictionary, which includes 6.2 million names for 182 different countries to disambiguate the gender of PCT inventors. Gender Name Dictionary, tables and data are available for download.

USPTO - 2018 Roundtable on Empirical Methods in IP

2018-01-10T00:01:00.003-08:00

2018 Roundtable on Empirical Methods in IP

May 4, 2018 - May 5, 2018

CFP: 2018 Roundtable on Empirical Methods in Intellectual Property

We are pleased to announce the fifth annual Roundtable on Empirical Methods in Intellectual Property. Northwestern Pritzker School of Law, Cardozo Law School, and the United States Patent & Trademark Office are negotiating an agreement to co-host the event. The roundtable will take place in Chicago at the Northwestern University Pritzker School of Law on May 4-5, 2018.

The roundtable is intended to give scholars engaging in empirical and experimental studies of IP a chance to receive feedback on their work at an early stage in their research. Accordingly, the roundtable will be limited to a small cohort of scholars discussing projects that are still in their developmental stages. Projects that will have substantially begun data collection by the time of the roundtable are inappropriate. Pilot data collection is, however, appropriate.

The roundtable will be organized around a modest number of projects. Each project presenter will be expected to circulate a description of the project of no more than 10 pages by April 13. Each project will be assigned to an expert commenter and will be allotted 45 minutes of discussion by the attendees.

We welcome applications from scholars in the social sciences and law. Travel and lodging support for presenters will be provided.

Applications are due by March 5, 2018. We will notify applicants by March 16, 2018.

To apply to present at the workshop, please email empirical-ip@law.northwestern.edu with the following information:

Name
Institutional Affiliation
Title of Project
Description of Project (<750 addressed="" and="" be="" empirical="" employed.="" including="" issues="" methods="" span="" the="" to="" words="">
Acknowledgement that the project will not have reached data collection by the time of the workshop.