ii-pic 2017: artificial intelligence, machine learning, and deep neural networks: what does all of...
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Artificial Intelligence, Machine Learning, and Deep Neural Networks: What Does All of This Have to Do With Patent Analytics?
Srinivasan Parthiban
II-PIC Conference 2017Bangalore, India
November 02-03, 2017
AI for EveryoneAcross all Industries
Transportation Healthcare
AlphaGoZero Sophia-First Humanoid
“I know it’s there, I just can’t find it” –A findability Problem
Thematic Database Patent Search ReportsPatent Analysis, Claim Chart Maps, Licensing in/out opportunities
During Patenting After PatentingBefore Patenting
Big breakthroughs
happen when what is
suddenly possible
meets what is
desperately needed
Thomas L. Friedman
April 2017
January 2017
May 2017
The Beginning of the AI Revolution in Patent Analytics
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
AI Patent Applications
26% increase
3% decrease
U.S 15,317
China 8,410
EuropeJapan 2,071
South Korea
India
2,134
2,934
12,147
2005 -09 2010 -14
186% increase
The Rise of AI in PubMed
PubMed Search (17/10/2017): artificial intelligence/ machine learning/ deep learning (titles and abstracts only)
0
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3500
1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Year
Co
un
t
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2017
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
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
Thomson Data AnalyzerBiz-Int solutions (smart-charts)
Smart Search
Analytic Tools
DUNE
Derwent Innovation
Artificial Intelligence
Machine Learning
Deep Learning
Cognitive Science
Supervised Learning
Supervisedlearning
Global Innovation IndexPatent Activity
Supervisedlearning
what we know
what we want to know
Transforms One Dataset into Another
Raising stars
ThanksStephan Adams, MagisterPIUG 2017 Workshop
Raising stars
Unsupervised Learning
Unsupervisedlearning
List of datapoints
List of clusterlabels
Groups your data
K-means clustering Hierarchical Clustering
+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
averbis approach in a Nutshell
Define Categories1
Provide Examples & Train2
Let the System CategorizeDocuments
3
Review Results4
ActiveLearning
GO
Automatic Patent Categorization
Define Categories
Provide Examples
Train the System
Categorize Patents
Active Learning
Active Learning
Advanced Technology Fields Chosen as Examples for Active Monitoring
Neurons and the Brainneuron cell body
synapse
axon
nucleus
dendrites of next neuron
axontips
neuron cell body
nucleus
synapse
dendrites
axon ofpreviousneuron
Design Patterns for Recurrent Neural Network
Image captioning
Sentimentanalysis
Machinetranslation
Classify image frame by frame
Selling coconut and oil lamps on the street
How are youAm fine
YegitheeraChannagithini
II-PIC Conference2017 in Bangaluruis absolutely a great event
Image classification
Cat
one to one one to many many to one many to many many to many
Patent Translation And Machine Learning
Next Step: Neural Networks
ForPatent Translation
ThanksNigel Clarke, European Patent Office
PIUG 2017 Workshop
Research on Patent Document Classification Based on Deep Learning
Patent Document –Preprocessing
Feature LearningWith AutoEncoder
Classificiation usingSoftMax Regression
Bing Xia, Baoan LI* and Xueqiang LvAdvances in Intelligent Systems Research, volume 133 (AIIE2016)
Expansion levels
Anti-seed
Level 2
Level 1
Codes
Seed
Citation
Patent Universe
Automated Patent LandscapingAaron Abood and Dave Feltenberger
Google, IncLTDCA-2016 proceedings
What Society Thinks I do What My Friends Think I Do
What other Computer Engineers think I do What I actually do
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