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A set of ORM-style clients for publicly available intellectual property data

Project description

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Summary

A powerful library for accessing intellectual property, featuring:

  • 🍰 Ease of use: All sources use a simple unified API inspired by Django-ORM.
  • 🐼 Pandas Integration: Results are easily castable to Pandas Dataframes and Series.
  • 🚀 Performance: Fetched data is cached using the excellent requests-cache library for super-fast queries.

Docs, including a fulsome Getting Started and User Guide are available on Read the Docs. The Examples folder includes examples of using patent_client for many common IP tasks

Coverage

  • Free software: Apache Software License 2.0

Installation

pip install patent_client

If you only want access to USPTO resources, you're done! However, additional setup is necessary to access EPO Inpadoc and EPO Register resources. See the Docs.

Quick Start

To use the project:

# Import the model classes you need
>>> from patent_client import Inpadoc, Assignment, USApplication, Patent

# Fetch US Patents with the word "tennis" in their title issued in 2010
>>> pats = Patent.objects.filter(title="tennis", issue_date="2010-01-01->2010-12-31")
>>> len(pats) > 10
True

# Look at the first one
>>> pats[0].publication
Patent(publication_number=7841958, publication_date=2010-11-30, title=Modular table tennis game)

# Fetch US Applications
>>> app = USApplication.objects.get('15710770')
>>> app.patent_title
'Camera Assembly with Concave-Shaped Front Face'

# Fetch from USPTO Assignments
>>> assignments = Assignment.objects.filter(assignee='Google')
>>> len(assignments) > 23000
True
>>> assignment = Assignment.objects.get('47086-788')
>>> assignment.conveyance_text
'ASSIGNMENT OF ASSIGNORS INTEREST'

# Fetch from INPADOC
>>> pub = Inpadoc.objects.get('EP3082535A1')
>>> pub.biblio.title
'AUTOMATIC FLUID DISPENSER'

Documentation

Docs, including a fulsome Getting Started are available on Read the Docs.

Development

To run the all tests run:

pytest

A developer guide is provided in the Documentation. Pull requests welcome!

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