AI-SDV 2021: Mazahir Bhagat - Mapping Canadian Patented Inventions

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The Canadian Intellectual Property Office’s (CIPO) IP Analytics Team uses Intellectual Property (IP) data to showcase the innovation undertaken by Canadian institutions and inventors for specific technology areas or industry sectors. Such analysis is published as reports on the Government of Canada’s website. An example of CIPO’s latest report is titled Processing Artificial Intelligence: Highlighting the Canadian Patent Landscape. Data visualizations are an important feature of these reports since it makes the data easily comprehensible and assists in identifying trends, patterns, and outliers within the data. One visualization commonly used in these reports is a patent landscape map which is essentially a heatmap used to highlight prominent word sequences found in the text fields of the patent dataset. At the moment, such maps are produced using a proprietary algorithm by Clarivate Analytics’ Derwent Innovation tool. In an effort to customize the visualizations to facilitate presenting the information from different perspectives, CIPO is currently working towards developing several in-house solutions for patentlandscape mapping using open-source and freeware tools. The objective of the presentation at the conference would be to present these in-house solutions and gather feedback from experts in this field. One of the in-house solutions involves a network analysis tool developed by researchers at the University of Leiden called VOSviewer. The tool extracts prominent word sequences from the title and abstract fields of the patent data and their location on the map is determined by VOSviewer’s in-built mathematical algorithm such that word sequences that frequently appear on the same patent documents are placed within close proximity on the map. In addition, the relevancy of each word sequence is measured using the Kullback-Leibler distance and only the most prominent word sequences are retained on the map. Lastly, these prominent word sequences are further grouped together if they are synonymous using a word embedding architecture called Word2Vec. Other in-house solutions that will be presented are currently in the early stages of development and use an interactive data visualization library in Python called plotly and a JavaScript library called D3.js. Developing such in-house solutions allows the team to have better control over the underlying methodology and gather more meaningful insights from IP data. These insights, in turn, highlight the importance of IP rights and assist in the delivery of CIPO’s key mandates to build IP awareness and advance innovation.

Internet

Mazahir Bhagat, Data Scientist,
Canadian Intellectual Property Office
October 4, 2021
Mapping Canadian
Patented Inventions
AI-SDV 2021
Building a prosperous and innovative Canada

2
OUTLINE
1. Who are we?
2. Data Visualizations
3. Patent Landscape Map
4. Methodology

PATENT LANDSCAPE MAP – USING DERWENT INNOVATION
7
• Produced using a proprietary algorithm by Clarivate Analytics’ Derwent Innovation tool
Limitations
• Reliance on a licensed tool.
• Lack of complete transparency in methodology.
• Unable to handle large datasets.

8
PATENT LANDSCAPE MAP – USING PLOTLY IN PYTHON

METHODOLOGY
10
1. Prominent Word
Sequence Identification
2. Word Sequence
Distance Generation
3. Synonymous Word
Sequence Groupings
4. Data Visualization

11
1. Prominent Word
Sequence Identification
2. Word Sequence Distance
Generation
3. Synonymous Word
Sequence Groupings
4. Data Visualization
Publication Number Title Abstract
AU200014826A
Computer-aided
configurator for
configuring a plant in
the basic materials
industry
The present invention relates to a method for the computer-aided
planning of a plant in the basic materials industry, in particular a
metallurgical plant and/or facility, with provision of component and/or
plant data in a data structure and input of user-specific data
WO2000019356A1
Neural network based
prototyping system
and method
Constructing and simulating artificial neural network (74) and
components thereof within a spreadsheet environment results in user
friendly neural networks which do not require algorithmic based
software in order to train or operate. Such neural network…..

12
ii. Determining Prominence
i. Part-of-speech tagging
1. Prominent Word
Sequence Identification
2. Word Sequence Distance
Generation
3. Synonymous Word
Sequence Groupings
4. Data Visualization
• Word sequence → one or more consecutive words such that
the last word is a noun and the words before it are either a
noun or an adjective (a.k.a. noun phrases).
• In Python, use nltk package to perform part-of-speech
tagging.
• In VOSViewer, prominence is determined by a relevancy
score.
• In Python, use tf-idf matrix
• term frequency → occurrence of word sequence in a
document
• inverse-document frequency → occurrence of word
sequence across all documents

13
1. Prominent Word
Sequence Identification
2. Word Sequence Distance
Generation
3. Synonymous Word
Sequence Groupings
4. Data Visualization
• Currently using VOSViewer’s in-built mathematical algorithm to determine the word sequences’ co-ordinates.
• Computes a co-occurrence matrix → two word sequences which frequently occur across multiple patents appear close to each other.

14
1. Prominent Word
Sequence Identification
2. Word Sequence Distance
Generation
3. Synonymous Word
Sequence Groupings
4. Data Visualization
Word2Vec Cosine Distance / Similarity
*Note: Each mapping is 300-dimensional and not 2-dimensional.
• The more synonymous two-word sequences are,
the smaller their cosine distance

label x y weight<Occurrences>
analago 1 2 3
cell culture medium 2 1 5
benzene derivative 3 2 2
… … … …
… … … … …
0 0 0 0 …
0 3 0 2 …
0 0 5 0 …
0 0 0 0 …
x
y
Data Matrix
1. Prominent Word
Sequence Identification
2. Word Sequence Distance
Generation
3. Synonymous Word
Sequence Groupings
4. Data Visualization
Vector
… … … … …
z z z z …
z 3 z 2 …
z z 5 z …
0 z z z …
x
y Range:
0 < z < 5
x
y
Convoluted Data Matrix
15

ALTERNATE SOLUTION: CIRCLE PACK LAYOUT IN D3.JS
16
Highlights
• Word sequences can be assigned to a sub-category
• Word sequences in each sub-category naturally differentiates the sub-category from other sub-categories

Thank you
Mazahir Bhagat
Data Scientist
Data and Analytics Team
Canadian Intellectual Property Office
mazahir.bhagat@ised-isde.gc.ca

AI-SDV 2021: Mazahir Bhagat - Mapping Canadian Patented Inventions

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