Collect a Company’s Patent Porfolio

This notebook will attempt to amass, for any given company, their entire worldwide patent porfolio.*

U.S. Portfolio

Collecting their U.S. portfolio will proceed in a few steps:

1. Collect all applications ever assigned to the company

   1. Those applications naming the company as an applicant

   2. Those assigned to the company via an assignment

2. Prune all applications assigned out of the company, as recorded in USPTO assignment records

*NOTE: I say “attempt” because the notebook relies on the USPTO system, and the International Patent Document (INPADOC) service. Not all foreign jurisdictions participate in INPADOC, and not all INPADOC member states are as good at contributing to the system as we would like. So coverage is good, but not perfect.

First, we do our standard imports, and grab the USApplication and Assignment objects from patent_client

import pandas as pd
from patent_client import USApplication, Assignment

company_name = 'Luminopia'

Step 1.a: Collect all applications naming the company as applicant

applicant_apps = USApplication.objects.filter(first_named_applicant=company_name).values_list('appl_id', flat=True).to_list()

Step 1.b: Collect all applications assigned to the company

# The assigned apps is either a single value, or a list of values if more than one property was assigned
assigned_apps = Assignment.objects.filter(assignee=company_name).explode('properties').values_list('appl_id', flat=True).to_list()

And now we combine the two

all_apps = set(list(applicant_apps + assigned_apps))

print(f'Total U.S. Applications Ever Owned by {company_name}: {len(all_apps)}')

Total U.S. Applications Ever Owned by Luminopia: 4

Step 2: Collect all applications ever assigned out of the company

assigned_out_apps = set(Assignment.objects.filter(assignor=company_name).to_pandas()
                   .query('conveyance_text == "ASSIGNMENT OF ASSIGNORS INTEREST"')
                   .properties.explode().apply(lambda x: x.appl_id).to_list())
print(f'U.S. Applications assigned out of {company_name}: {len(assigned_out_apps)}')

U.S. Applications assigned out of Luminopia: 1

Step 3: Subtract one set from the other

owned_apps = list(set(all_apps) - set(assigned_out_apps))
len(owned_apps)

4

owned_apps

['16420557', 'PCT/US17/27629', None, '62323284']

Step 4: Generate status report

import pandas as pd
from patent_client.parser import parse

us_df = USApplication.objects.filter(appl_id=owned_apps).values(
    'appl_id',
    'app_filing_date',
    'patent_number',
    'patent_issue_date',
    'patent_title',
    'inventors.0.name',
    'applicants.0.name',
    'app_status',
    'app_status_date',
    'expiration.initial_term',
    'expiration.pta_or_pte',
    'expiration.extended_term',
    'expiration.terminal_disclaimer_filed',
).to_pandas()

us_df

	appl_id	app_filing_date	patent_number	patent_issue_date	patent_title	inventors.0.name	applicants.0.name	app_status	app_status_date	expiration.initial_term	expiration.pta_or_pte	expiration.extended_term	expiration.terminal_disclaimer_filed
0	16420557	2019-05-23	None	None	Visual disorder treatment	Travers; Dean	Luminopia, Inc.;	Final Rejection Mailed	2022-07-12	None	None	None	None
1	PCT/US17/27629	2017-04-14	None	None	METHODS AND HEAD-MOUNTED APPARATUS FOR VISUAL ...	WENDLAND; ALEXANDER	LUMINOPIA, INC.;	RO PROCESSING COMPLETED-PLACED IN STORAGE	2017-04-20	None	None	None	None
2	62323284	2016-04-15	None	None	Treatment of Visual Disorders and Conditions w...	Travers; Dean	Luminopia, Inc.;	Provisional Application Expired	2017-04-16	None	None	None	None

Foreign Patent Portfolio

Now we will fetch the company’s wordwide portfolio, as it appears in the Inpadoc system maintained by the EPO

from patent_client import Inpadoc

foreign = Inpadoc.objects.filter(applicant=company_name)

And now, a status report:

foreign_df = foreign.values(
    'country',
    appl_id='biblio.application_reference_epodoc.number',
    app_filing_date='biblio.application_reference_epodoc.date',
    pub_number='biblio.publication_reference_epodoc.number',
    pub_date='biblio.publication_reference_epodoc.date',
    title='biblio.title',
    applicant='biblio.applicants_epodoc.0',
    first_named_inventor='biblio.inventors_epodoc.0').to_pandas()
foreign_df = foreign_df[foreign_df['country'] != 'US']
foreign_df

	country	appl_id	app_filing_date	pub_number	pub_date	title	applicant	first_named_inventor
0	WO	WO2017US27629	2017-04-14	WO2017181010	2017-10-19	METHODS AND HEAD-MOUNTED APPARATUS FOR VISUAL ...	LUMINOPIA INC [US]	TRAVERS DEAN [US]

And now we will take our results, and write to an excel file

writer = pd.ExcelWriter(f'/Users/parkerhancock/{company_name}_portfolio.xlsx')
us_df.to_excel(writer, sheet_name='US Portfolio')
foreign_df.to_excel(writer, sheet_name='Foreign Portfolio')
writer.save()

/var/folders/bt/s4hv2ltx71nf43jxmddn4s5h0000gn/T/ipykernel_11834/622620193.py:4: FutureWarning: save is not part of the public API, usage can give in unexpected results and will be removed in a future version
  writer.save()