Parthiban Srinivasan (VINGYANI, India) When new technologies become easier to use, they transform industries. That's what's happening with artificial intelligence (AI) and big data. Machine learning is often described as a type of AI where computers learn to do something without being programmed to do it. Deep learning, a subset of machine learning, is proving to work especially well on classification. Big breakthroughs happen when what is suddenly possible meets what is desperately needed. For years, patent analysts have been searching and reviewing terabytes of information, not only patents but also non-patent information. Not only to find prior art but also to identify patents of interest, rate their quality, assess the potential value of patent clusters, and identify potential business partners or infringers. With the rapid increase in the number of patent documents worldwide, demand for their automatic clustering/categorization has grown significantly. Many information science researchers have started to experiment with machine learning tools, but the adoption in the patent information space has been sporadic. In this talk, we aim to review the prevailing machine learning techniques and present several sample implementations by various research groups. We will also discuss how data science compares with machine learning, deep learning, AI, statistics and applied mathematics.

1. Artificial Intelligence, Machine Learning, and
Deep Neural Networks:
What Does All of This Have to Do With Patent
Analytics?
Srinivasan Parthiban
II-PIC Conference 2017
Bangalore, India
November 02-03, 2017

2. AI for Everyone
Across all Industries
Transportation Healthcare
AlphaGoZero Sophia-First Humanoid

3. “I know it’s there, I just can’t find it” –
A findability Problem
Thematic Database Patent Search Reports
Patent Analysis, Claim Chart
Maps, Licensing in/out
opportunities
During Patenting After PatentingBefore Patenting

4. Big breakthroughs
happen when what is
suddenly possible
meets what is
desperately needed
Thomas L. Friedman

5. April 2017
January 2017
May 2017
The Beginning of the AI Revolution
in Patent Analytics

6. Trend of AI patents granted, 2000 to 2016
(number of items)
Number of AI patents granted by country Number of AI patents granted by technology
USPTO: United States Patent and Trademark Office; SIPO: State Intellectual Property Office of The People's Republic of China;
JPO: Japan Patent Office; PCT: Patent Cooperation Treaty; EPO: European Patent Office

7. AI Patent Applications
26% increase
3% decrease
U.S 15,317
China 8,410
Europe
Japan 2,071
South Korea
India
2,134
2,934
12,147
2005 -09 2010 -14
186% increase

8. The Rise of AI in PubMed
PubMed Search (17/10/2017): artificial intelligence/
machine learning/ deep learning (titles and abstracts only)
0
500
1000
1500
2000
2500
3000
3500
1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Year
Count
2942
2017

9. Top 4 (out of 8) Trends from PIUG 2017
• Deep learning and neural networking is the next big thing. In the patent
space, it is being used in many ways including improved translations,
classification, and search.
• Not all semantic engines are the same. Ask yourself: are they statistical
models, artificial intelligence, neural networks, document signatures, deep
learning, or a combination? How are they trained/tuned to assist with retrieval
of patent data?
• The big question is how do we as humans fit into patent analysis with the
newer technologies available? Currently, the best practice appears to be a mix
of machine learning and guidance from a human.
• Not all translations are equal. There are old versions that were direct word-
for-word translations, newer sentence based, and neural networks. Questions
around how translation affects the terminology used to describe new
inventions still remains.
By: Devin Salmon, Patent Analyst, IP.com

10. The Next 4 Trends from PIUG 2017
• Clean data is king: every tool ends up having essentially the same data from
all the patent offices. How they extract it, clean it up, and what additional
features are added are going to be the keys to searching and using newer
analytics tools.
• Determining the “correct” assignee is still a major problem. Furthermore,
how do we define “correct.”
• Typical visualizations can be broken into two groups: those used for
explanation and those used for exploration. The future lies in the creation
of visualization tools that can be used to make decisions.
• Tools for classification, subject grouping, and tagging have room for
improvement and future versions should likely capitalize on the use of
machines either to augment or replace a human.
By: Devin Salmon, Patent Analyst, IP.com

11. Thomson Data Analyzer
Biz-Int solutions (smart-charts)
Smart Search
Analytic Tools
DUNE
Derwent Innovation

12. Artificial
Intelligence
Machine
Learning
Deep
Learning
Cognitive Science

13. Supervised Learning
Supervised
learning
Global Innovation Index
Patent Activity
Supervised
learning
what we
know
what we
want to know
Transforms One Dataset into Another
Raising stars
Thanks
Stephan Adams, Magister
PIUG 2017 Workshop
Raising stars

14. Unsupervised Learning
Unsupervised
learning
List of
datapoints
List of cluster
labels
Groups your data
K-means clustering Hierarchical Clustering

15. +1 -2 -5
+1 +3 +5
Experiment with Different Moves
Receive a Score for Each Move
Interacting with the Environment
Use Math to Represent the Goal of Walking
Reinforcement Learning

16. averbis approach in a Nutshell
Define Categories1
Provide Examples & Train2
Let the System Categorize
Documents
3
Review Results4
Active
Learning
GO
Automatic Patent Categorization

17. Define Categories

18. Provide Examples

19. Train the System

20. Categorize Patents

21. Active Learning

22. Active Learning

23. Advanced Technology Fields Chosen
as Examples for Active Monitoring

24. Neurons and the Brain
neuron cell body
synapse
axon
nucleus
dendrites of
next neuron
axon
tips
neuron cell body
nucleus
synapse
dendrites
axon of
previous
neuron

25. Design Patterns for Recurrent Neural Network
Image
captioning
Sentiment
analysis
Machine
translation
Classify image
frame by frame
Selling coconut
and oil lamps on
the street
How are you
Am fine
Yegitheera
Channagithini
II-PIC Conference
2017 in Bangaluru
is absolutely
a great event
Image
classification
Cat
one to one one to many many to one many to many many to many

26. Patent Translation And Machine Learning
Next Step:
Neural Networks
For
Patent Translation
Thanks
Nigel Clarke, European Patent Office
PIUG 2017 Workshop

27. Research on Patent Document
Classification Based on Deep Learning
Patent Document –
Preprocessing
Feature Learning
With AutoEncoder
Classificiation using
SoftMax Regression
Bing Xia, Baoan LI* and Xueqiang Lv
Advances in Intelligent Systems Research, volume 133 (AIIE2016)

29. Expansion levels
Anti-seed
Level 2
Level 1
Codes
Seed
Citation
Patent Universe
Automated Patent Landscaping
Aaron Abood and Dave Feltenberger
Google, Inc
LTDCA-2016 proceedings

30. What Society Thinks I do What My Friends Think I Do
What other Computer
Engineers think I do What I actually do
30

31. parthi@vingyani.com
Thank You
Chennai, Tamil Nadu, India
www.vingyani.com