PATSTAT autumn 2018 MySQL upload scripts

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

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