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II-PIC 2017: Artificial Intelligence, Machine Learning, And Deep Neural Networks: What Does All Of This Have To Do With Patent Analytics?


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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.

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II-PIC 2017: Artificial Intelligence, Machine Learning, And Deep Neural Networks: What Does All Of This Have To Do With Patent Analytics?

  1. 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. 2. AI for Everyone Across all Industries Transportation Healthcare AlphaGoZero Sophia-First Humanoid
  3. 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. 4. Big breakthroughs happen when what is suddenly possible meets what is desperately needed Thomas L. Friedman
  5. 5. April 2017 January 2017 May 2017 The Beginning of the AI Revolution in Patent Analytics
  6. 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. 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. 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. 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,
  10. 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,
  11. 11. Thomson Data Analyzer Biz-Int solutions (smart-charts) Smart Search Analytic Tools DUNE Derwent Innovation
  12. 12. Artificial Intelligence Machine Learning Deep Learning Cognitive Science
  13. 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. 14. Unsupervised Learning Unsupervised learning List of datapoints List of cluster labels Groups your data K-means clustering Hierarchical Clustering
  15. 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. 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. 17. Define Categories
  18. 18. Provide Examples
  19. 19. Train the System
  20. 20. Categorize Patents
  21. 21. Active Learning
  22. 22. Active Learning
  23. 23. Advanced Technology Fields Chosen as Examples for Active Monitoring
  24. 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. 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. 26. Patent Translation And Machine Learning Next Step: Neural Networks For Patent Translation Thanks Nigel Clarke, European Patent Office PIUG 2017 Workshop
  27. 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)
  28. 28. 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
  29. 29. What Society Thinks I do What My Friends Think I Do What other Computer Engineers think I do What I actually do 30
  30. 30. Thank You Chennai, Tamil Nadu, India