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Measuring the Innovation Impact of an Organization using Patents Citations

This notebook shows how to use the Dimensions Analytics API to analyze the innovation impact of an organization.

There are two ways to achieve this. The first approach counts direct links between patents and the GRID organization we are interested in (i.e., it counts how many patents’ inventors are affiliated to the GRID organization). This method is faster to implement and can be easily achieved via a single patents API query:

> search patents where assignees in ["grid.89170.37"] return patents limit 500
Returned Patents: 192
---
[1] METHODS AND SYSTEMS FOR OBJECT IDENTIFICATION AND FOR AUTHENTICATION (id: https://app.dimensions.ai/details/patent/WO-2007149621-A3 )
[2] ADENOVIRAL VECTOR-BASED MALARIA VACCINES (id: https://app.dimensions.ai/details/patent/EP-1929021-A2 )
[3] MULTIPLE BAND SHORT WAVE INFRARED MOSAIC ARRAY FILTER (id: https://app.dimensions.ai/details/patent/WO-2016040755-A1 )
[4] ARMOR PLATE (id: https://app.dimensions.ai/details/patent/WO-2011142867-A3 )
..etc..

The second method counts indirect links via publications, hence it permits to gain some insight into the indirect innovation impact of a research organization.

This is the method we’ll be focusing on, in this tutorial. The goal therefore is to extract and inspect patents that cite publications from the research organization in question.

Since the various content-types included in the Dimensions database are deeply interlinked, the Dimensions APIs allow to perform this analysis via a few simple steps:

  • We start from a GRID identifier (representing a research organization in Dimensions)

  • We use the publications API to extract all publications where at least one author is/as affiliated to the GRID organization, for a selected time-period

  • We then use the patents API to discover patents that include citations to any of those publications

  • Finally, we analyse the patents data to highlight trends e.g. about countries, inventors etc..

[1]:

import datetime
print("==\nCHANGELOG\nThis notebook was last run on %s\n==" % datetime.date.today().strftime('%b %d, %Y'))

==
CHANGELOG
This notebook was last run on Jan 25, 2022
==

Prerequisites

This notebook assumes you have installed the Dimcli library and are familiar with the ‘Getting Started’ tutorial.

[2]:

!pip install dimcli plotly tqdm -U --quiet

import dimcli
from dimcli.utils import *

import os, sys, time, json
from tqdm.notebook import tqdm as progressbar

import pandas as pd

import plotly.express as px
from plotly.offline import plot
if not 'google.colab' in sys.modules:
  # make js dependecies local / needed by html exports
  from plotly.offline import init_notebook_mode
  init_notebook_mode(connected=True)

print("==\nLogging in..")
# https://digital-science.github.io/dimcli/getting-started.html#authentication
ENDPOINT = "https://app.dimensions.ai"
if 'google.colab' in sys.modules:
  import getpass
  KEY = getpass.getpass(prompt='API Key: ')
  dimcli.login(key=KEY, endpoint=ENDPOINT)
else:
  KEY = ""
  dimcli.login(key=KEY, endpoint=ENDPOINT)
dsl = dimcli.Dsl()

Searching config file credentials for 'https://app.dimensions.ai' endpoint..

==
Logging in..
Dimcli - Dimensions API Client (v0.9.6)
Connected to: <https://app.dimensions.ai/api/dsl> - DSL v2.0
Method: dsl.ini file

1. Choosing a GRID Research Organization

For the purpose of this exercise, we are going to use grid.4305.2 (The Open University, UK). Feel free to change the parameters below as you want, eg by choosing another GRID organization.

[3]:


GRIDID = "grid.10837.3d" #@param {type:"string"}

#@markdown The start/end year of publications used to extract patents
YEAR_START = 2005 #@param {type: "slider", min: 1950, max: 2020}
YEAR_END = 2015 #@param {type: "slider", min: 1950, max: 2020}

if YEAR_END < YEAR_START:
  YEAR_END = YEAR_START

from IPython.core.display import display, HTML
display(HTML('---<br /><a href="{}">Open in Dimensions &#x29c9;</a>'.format(dimensions_url(GRIDID))))

#@markdown ---
---
Open in Dimensions ⧉

2. Extracting Publications Data

By looking at the Dimensions API data model, we can see that Patents and Publications are connected by a property called publication_ids, which goes from Patents to Publications. This property represents the publications citations found in patents.

Hence, we need to 1. query for all publications with authors affiliated to our GRID ID 2. query for patents citing these publications

[4]:

# Get full list of publications linked to this organization for the selected time frame

q = f"""search publications
        where research_orgs.id="{GRIDID}"
        and year in [{YEAR_START}:{YEAR_END}]
        return publications[id+doi+title+type+journal+year+research_orgs+researchers+category_for+times_cited]"""
print("===\n", q, "\n===")
pubs_json = dsl.query_iterative(q, limit=1000)
pubs = pubs_json.as_dataframe()

Starting iteration with limit=1000 skip=0 ...

===
 search publications
        where research_orgs.id="grid.10837.3d"
        and year in [2005:2015]
        return publications[id+doi+title+type+journal+year+research_orgs+researchers+category_for+times_cited]
===

0-1000 / 9751 (3.62s)
1000-2000 / 9751 (6.01s)
2000-3000 / 9751 (3.82s)
3000-4000 / 9751 (4.28s)
4000-5000 / 9751 (3.25s)
5000-6000 / 9751 (3.18s)
6000-7000 / 9751 (2.81s)
7000-8000 / 9751 (3.08s)
8000-9000 / 9751 (3.12s)
9000-9751 / 9751 (2.13s)
===
Records extracted: 9751

[5]:

pubs.head()

[5]:
category_for doi id research_orgs researchers times_cited title type year journal.id journal.title
0 [{'id': '2201', 'name': '01 Mathematical Scien... 10.1007/978-3-319-25684-9_27 pub.1043112221 [{'acronym': 'OU', 'city_name': 'Milton Keynes... [{'first_name': 'Mike', 'id': 'ur.012151256767... 1 Sense-it: A Smartphone Toolkit for Citizen Inq... chapter 2015 NaN NaN
1 [{'id': '3616', 'name': '2004 Linguistics'}, {... 10.1007/978-3-319-25684-9_15 pub.1012221339 [{'city_name': 'Coventry', 'country_name': 'Un... [{'first_name': 'Koula', 'id': 'ur.01351150775... 2 Designs for Heritage Language Learning: A Phot... chapter 2015 NaN NaN
2 [{'id': '2921', 'name': '0912 Materials Engine... 10.1007/978-3-319-18215-5_16 pub.1026169656 [{'acronym': 'OU', 'city_name': 'Milton Keynes... [{'first_name': 'David', 'id': 'ur.01333472307... 0 The Pugwash UK 2050 High Renewables Pathway chapter 2015 NaN NaN
3 [{'id': '2201', 'name': '01 Mathematical Scien... 10.1112/blms/bdv086 pub.1000803237 [{'acronym': 'OU', 'city_name': 'Milton Keynes... [{'first_name': 'Marston D E', 'id': 'ur.01175... 3 Chiral maps of given hyperbolic type article 2015 jour.1137079 Bulletin of the London Mathematical Society
4 [{'id': '3416', 'name': '1605 Policy and Admin... 10.1111/aman.12440 pub.1002172908 [{'acronym': 'OU', 'city_name': 'Milton Keynes... [{'first_name': 'Sarah', 'id': 'ur.0743477115.... 0 Not Trying: Infertility, Childlessness, and Am... article 2015 jour.1055099 American Anthropologist

Quick look at publications statistics

[6]:

px.histogram(pubs,
             x="year",
             color="type",
             barmode="group",
             title=f"Publication distribution by year - {GRIDID}")

What are the main subject areas?

We can use the Field of Research categories information in publications to obtain a breakdown of the publications by subject areas.

This can be achieved by ‘exploding’ the category_for data into a separate table, since there can be more than one category per publication. The new categories table also retains some basic info about the publications it relates to eg journal, title, publication id etc.. so to make it easier to analyse the data.

[7]:

pubs_categories = pubs.explode('category_for')
pubs_categories.dropna(subset=["category_for"], inplace=True)

def for_nice_name(for_dict):
    "transforms a category JSON into a nice looking title"
    if type(for_dict) == dict:
        name = for_dict['name']
        return ''.join([i for i in name if not i.isdigit()])
    else:
        return ""

# new col for nice name
pubs_categories["category_for_name"] = pubs_categories['category_for'].apply(lambda x: for_nice_name(x))
# new col for tot-pubs count
pubs_categories['count_pubs'] = pubs_categories.groupby("category_for_name")['id'].transform('count')

Let’s view the top categories using a pie chart.

[8]:

categories = pubs_categories.drop_duplicates(subset="category_for_name").sort_values("count_pubs", ascending=False)[['category_for_name', 'count_pubs']]

px.pie(categories[:20],
       names="category_for_name", # the dimension for the slices
       values="count_pubs",  # the metric
       color_discrete_sequence=px.colors.sequential.Bluyl,
       title=f"Top FOR categories")

3. Extracting Patents linked to Publications

In this section we extract all patents linked to the publications dataset previously created. The steps are the following:

  • we loop over the publication IDs and create patents queries, via the referencing publication_ids field of patents

  • we collate all patens data, remove duplicates from patents and save the results

  • finally, we count patents per publication and enrich the original publication dataset with these numbers

[9]:

#
# the main patents query
#
q = """search patents
        where publication_ids in {}
      return patents[basics+publication_ids+category_for]"""

BATCHSIZE = 400
VERBOSE = False # set to True to see patents extraction logs


#
# loop through all pub IDs in chunks and query patents
#
print("===\nExtracting patents data ...")
patents_json = []
pubsids = pubs['id']

for chunk in progressbar(list(chunks_of(list(pubsids), 400))):
    data = dsl.query_iterative(q.format(json.dumps(chunk)), verbose=VERBOSE)
    patents_json += data.patents
    time.sleep(1)


patents = pd.DataFrame().from_dict(patents_json)
patents.drop_duplicates(subset='id', inplace=True)
print("Patents found: ", len(patents))

===
Extracting patents data ...

Patents found:  210

Let’s preview the data

[10]:

patents.head(5)

[10]:
assignee_names assignees category_for filing_status id inventor_names publication_date publication_ids times_cited title year granted_year
0 [UNIV OSAKA, NAKANO KENJI] [{'city_name': 'Osaka', 'country_name': 'Japan... [{'id': '2581', 'name': '0601 Biochemistry and... Application WO-2018139679-A1 [NAKANO KENJI, CUI LIN, OBIKA SATOSHI, YAMAMOT... 2018-08-02 [pub.1050805130] 2 NUCLEIC ACID DRUG CAPABLE OF INHIBITING INVASI... 2018 NaN
1 [UNIV LIVERPOOL, UNIV HOSPITALS BRISTOL NHS FO... [{'city_name': 'Bristol', 'country_name': 'Uni... [{'id': '3142', 'name': '1112 Oncology and Car... Application WO-2019224542-A1 [PROBERT CHRIS, BOND ASHLEY, GREENWOOD ROSEMARY] 2019-11-28 [pub.1042614068, pub.1047923881, pub.102126644... 1 BIOMARKERS FOR COLORECTAL CANCER 2019 NaN
2 [GENEURO SA, US HEALTH] NaN [{'id': '3114', 'name': '1108 Medical Microbio... Application WO-2018136775-A1 [PERRON HERVÉ, MEDINA JULIE, NATH AVINDRA, STE... 2018-07-26 [pub.1001213892, pub.1028293805, pub.102276888... 0 ANTI-HERV-K ENVELOPE ANTIBODY AND USES THEREOF 2018 NaN
3 [GENEURO SA, US HEALTH] NaN [{'id': '3114', 'name': '1108 Medical Microbio... Application WO-2018136774-A1 [PERRON HERVÉ, MEDINA JULIE, NATH AVINDRA, STE... 2018-07-26 [pub.1001213892, pub.1028293805, pub.102276888... 0 ANTI-HERV-K ENVELOPE ANTIBODY AND USES THEREOF 2018 NaN
4 [Geneuro SA] NaN [{'id': '3114', 'name': '1108 Medical Microbio... Application EP-3351265-A1 [PERRON HERVÉ, MEDINA JULIE] 2018-07-25 [pub.1020608346, pub.1022768885, pub.106434678... 1 ANTI-HERV-K ENVELOPE ANTIBODY AND USES THEREOF 2017 NaN

Enriching publications with patents citations metrics

Each patent record contains all the publication_ids it cites, so we can take this metric so to enrich the original publications dataset we created above.

[11]:

def count_patents_per_pub(pubid):
  global patents
  return len(patents[patents['publication_ids'].str.contains(pubid)])

# turn lists into strings to ensure compatibility with CSV loaded data
# see also: https://stackoverflow.com/questions/23111990/pandas-dataframe-stored-list-as-string-how-to-convert-back-to-list
patents['publication_ids'] = patents['publication_ids'].apply(lambda x: ','.join(map(str, x)))

progressbar.pandas()
pubs['patents'] = pubs['id'].progress_apply(lambda x: count_patents_per_pub(x))

Now the patents column gives us the top publications by number of citing patents

[12]:

pubs.sort_values("patents", ascending=False).head()

[12]:
category_for doi id research_orgs researchers times_cited title type year journal.id journal.title patents
9287 [{'id': '3120', 'name': '1109 Neurosciences'},... 10.1096/fj.04-3458fje pub.1002742563 [{'city_name': 'Paris', 'country_name': 'Franc... [{'first_name': 'Babette Barbash', 'id': 'ur.0... 972 Blood‐brain barrier‐specific properties of a h... article 2005 jour.1017429 The FASEB Journal 24
3284 [{'id': '3120', 'name': '1109 Neurosciences'},... 10.1096/fj.11-201384 pub.1033473743 [{'acronym': 'CU', 'city_name': 'Ithaca', 'cou... [{'first_name': 'Tengfei', 'id': 'ur.010276261... 116 Cell‐penetrating anti‐GFAP VHH and correspondi... article 2012 jour.1017429 The FASEB Journal 16
3612 [{'id': '2581', 'name': '0601 Biochemistry and... 10.1111/j.1474-9726.2012.00795.x pub.1006294106 [{'city_name': 'Newcastle upon Tyne', 'country... [{'first_name': 'Glyn', 'id': 'ur.01064645067.... 380 A senescent cell bystander effect: senescence-... article 2012 jour.1030078 Aging Cell 15
5821 [{'id': '2203', 'name': '03 Chemical Sciences'... 10.1021/bc900397s pub.1055155906 [{'city_name': 'Coventry', 'country_name': 'Un... [{'first_name': 'Chiara', 'id': 'ur.0112461512... 41 Modification of Thiol Functionalized Aptamers ... article 2009 jour.1100499 Bioconjugate Chemistry 9
9259 [{'id': '2209', 'name': '09 Engineering'}, {'i... 10.1089/ten.2005.11.1611 pub.1059313359 [{'acronym': 'UCL', 'city_name': 'London', 'co... [{'first_name': 'James Benjamin', 'id': 'ur.07... 114 Neural Tissue Engineering: A Self-Organizing C... article 2005 jour.1398624 Tissue Engineering 8

4. Patents Data Analysis

Now that we have extracted all the data we need, let’s start exploring them by building a few visualizations.

How many patents per year?

[13]:

px.histogram(patents, x="year",
             color="filing_status",
             barmode="group",
             title=f"Patents referencing publications from {GRIDID} - by year")

Who is filing the patents?

This can be done by looking at the field assigness of patent. Since the field contains nested information, first we need to extract it into its own table (similarly to what we’ve done above with publications categories).

[14]:

# ensure the key exists in all rows (even if empty)
from dimcli.shortcuts import normalize_key
normalize_key('assignees', patents_json)
# explode assigness into separate table
patents_assignees = pd.json_normalize(patents_json,
                                   record_path=['assignees'],
                                   meta=['id', 'year', 'title'],
                                   meta_prefix="patent_",
                                errors="ignore")
top_assignees = patents_assignees.groupby(['name', 'country_name'],
                                          as_index=False).count().sort_values(by="patent_id", ascending=False)
# preview the data: ps the patent_id column is the COUNT of patents
top_assignees[['name', 'country_name', 'patent_id']].head()

WARNING: the `shortcuts` module is deprecated. Use instead ``from dimcli.utils import *``

[14]:
name country_name patent_id
24 French Institute of Health and Medical Research France 18
25 French National Centre for Scientific Research France 17
58 Pasteur Institute of Lille France 13
88 University of Paris France 7
98 Wisconsin Alumni Research Foundation United States 7 
[15]:

px.bar(top_assignees,
       x="name", y="patent_id",
       hover_name="name", color="country_name",
       height=900,
       title=f"Top Assignees for patents referencing publications from {GRIDID}")

[16]:

px.scatter(patents_assignees,
           x="name", y="country_name",
           color="patent_year", hover_name="name",
           height = 1000,
           hover_data=["id", "patent_id"],  marginal_y="histogram",
           title=f"Assignees for patents referencing publications from {GRIDID} - Yearly breakdown")

What are the publications most frequenlty referenced in patents?

[17]:

pubs_cited = pubs.query("patents > 0 ").sort_values('patents', ascending=False).copy()
pubs_cited.head()

[17]:
category_for doi id research_orgs researchers times_cited title type year journal.id journal.title patents
9287 [{'id': '3120', 'name': '1109 Neurosciences'},... 10.1096/fj.04-3458fje pub.1002742563 [{'city_name': 'Paris', 'country_name': 'Franc... [{'first_name': 'Babette Barbash', 'id': 'ur.0... 972 Blood‐brain barrier‐specific properties of a h... article 2005 jour.1017429 The FASEB Journal 24
3284 [{'id': '3120', 'name': '1109 Neurosciences'},... 10.1096/fj.11-201384 pub.1033473743 [{'acronym': 'CU', 'city_name': 'Ithaca', 'cou... [{'first_name': 'Tengfei', 'id': 'ur.010276261... 116 Cell‐penetrating anti‐GFAP VHH and correspondi... article 2012 jour.1017429 The FASEB Journal 16
3612 [{'id': '2581', 'name': '0601 Biochemistry and... 10.1111/j.1474-9726.2012.00795.x pub.1006294106 [{'city_name': 'Newcastle upon Tyne', 'country... [{'first_name': 'Glyn', 'id': 'ur.01064645067.... 380 A senescent cell bystander effect: senescence-... article 2012 jour.1030078 Aging Cell 15
5821 [{'id': '2203', 'name': '03 Chemical Sciences'... 10.1021/bc900397s pub.1055155906 [{'city_name': 'Coventry', 'country_name': 'Un... [{'first_name': 'Chiara', 'id': 'ur.0112461512... 41 Modification of Thiol Functionalized Aptamers ... article 2009 jour.1100499 Bioconjugate Chemistry 9
9259 [{'id': '2209', 'name': '09 Engineering'}, {'i... 10.1089/ten.2005.11.1611 pub.1059313359 [{'acronym': 'UCL', 'city_name': 'London', 'co... [{'first_name': 'James Benjamin', 'id': 'ur.07... 114 Neural Tissue Engineering: A Self-Organizing C... article 2005 jour.1398624 Tissue Engineering 8 
[18]:

px.bar(pubs_cited[:1000],
       color="type",
       x="year", y="patents",
       hover_name="title",  hover_data=["journal.title"],
       title=f"Top Publications from {GRIDID} mentioned in patents, by year of publication")

What are the main subject areas of referenced publications?

[19]:

THRESHOLD_PUBS = 1000
citedids = list(pubs_cited[:THRESHOLD_PUBS]['id'])
pubs_categories_cited = pubs_categories[pubs_categories['id'].isin(citedids)]

[20]:

px.scatter(pubs_categories_cited, x="year", y="category_for_name", color="type",
           hover_name="title",
           hover_data=["doi", "year", "journal.title"],
           height=800,
           marginal_x="histogram", marginal_y="histogram",
           title=f"Top {THRESHOLD_PUBS} {GRIDID} publications cited by patents - by subject area")

Is there a correlation between publication citations and patents citations?

Note: if the points on a scatterplot graph produce a lower-left-to-upper-right pattern (see below), that is indicative of a positive correlation between the two variables. This pattern means that when the score of one observation is high, we expect the score of the other observation to be high as well, and vice versa.

[21]:

px.scatter(pubs, x="patents", y="times_cited",
           title=f"Patents citations VS Publication citations")

Where to go from here

In this Dimensions Analytics API tutorial we have seen how, starting from a GRID organization, it is possible to extract a) publications from authors associated to this organization, b) patents citing those publications (from any organization).

This only scratches the surface of the possible applications of publication-patents linkage data, but hopefully it’ll give you a few basic tools to get started building your own application. Here are some ideas for customizing this notebook:

  • Change the GRID ID to another one you are more familiar with

  • Use a different way to select publications: e.g. not using an organizations segment, but a category, a country, a funder or a combination of the many Publication filters available

  • Do a more in-depth analysis of the patents inventors: do they have other publications linking to the publications they cite in patents? Are there patterns of collaborations with the institutions they cite?


Note

The Dimensions Analytics API allows to carry out sophisticated research data analytics tasks like the ones described on this website. Check out also the associated Github repository for examples, the source code of these tutorials and much more.

../../_images/badge-dimensions-api.svg