Finding trends in large scale document sets using KMX Patent Analytics. Incl. use case example of eBook patent landscape (incl. Apple iPad, Samsung Galaxy and Amazon Kindle)
1. Treparel Dr. Anton Heijs
Delftechpark 26 CEO/CTO
anton@treparel.com
2628 XH Delft
The Netherlands
September 24, 2012
www.treparel.com
2. Analysing large patent portfolios: Nothing
remains uncovered
Agenda
• Introduction Treparel &
KMX Patent Analytics
• How to deal with Big Data
in patents?
• Landscape analysis of
large patent sets
• Use Cases: SWOT analysis
Fig 1: patent landscape of ebook technologies
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3. About Treparel
• Treparel is an innovative technology solution provider of
– Big Data Text Analytics and Visualization technology
– Patent Analytics solutions
• KMX is an integrated data analysis toolset which provides
– Fast and accurate insights in large unstructured document sets
to allow companies to make better informed decisions.
• KMX software platform
– Strong focus on R&D with university ecosystem
– Over 30 man years of software-development
– Used by knowledge driven organizations in technology, chemical,
and life sciences
• Based in Delft, The Netherlands since 2006.
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5. The importance of analytics in IP
Research Discovery Development Market Life Cycle
Launch Management
Market, Legal and Competitive Analysis
Patent Analysis
Research Analysis
$
Investment
Decreasing return Increasing Return
years
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6. Economic changes in the last decades
• Globalization :
– More companies, increased competition, lower margins
– Innovation for shorter product life cycles
– Manufacturing has become a commodity by outsourcing to
low wage countries
• The cost of R&D increased but competition leads to price
erosion
• Return on R&D investment drives importance for value
creation from IP
• Competitive edge of companies shifts from
production-based to knowledge-based
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7. The Intellectual Economy
Past : Selling products finances R&D
Research , Development , Marketing , Life Cycle
Discovery Manufacturing Sales Management
Revenues
Return on investments
Today: value creation and revenue generation from IP (to finance R&D)
Research , Development , Marketing , Life Cycle
Discovery Manufacturing Sales Management
Innovations
IP Licensing
Revenues Revenues
Return on investments
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7
8. Adapting to economic developments
The New Reality Value from IP portfolio
• IP strategy over time • Protect market share
– Increase the value of the IP portfolio • Save cost for royalties via cross
– Maximize the ROI from R&D licensing
• License management • Generate income from royalties
– analyze the impact of economic developments • Benefit from License Out from
and its effect for the IP strategy joint ventures or spin-offs
Drivers
• Number of Patent filings is growing
• Growing need to drive revenues from
licensing to fund R&D investment
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9. Trends and implications for the future
• Economies depend stronger on each other
• Innovation is a driver of economic growth
• Globalization generates many patents from China
and South Korea
• The number and complexity of patent filings is
growing
Growing need for fast and accurate analysis of
large sets of patents
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10. Getting more insight using less experts
Big Data Paradox:
• Limited (human) resources available for in-depth analysis
• Growing need for data driven decisions
Growing Data, Faster Insights: Big Data Analytics
Velocity Volume
Variety Complexity
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11. The big data paradox ; more data but less
knowledge ‘Information Gap’
10
Available data
8 Information Gap
6 Data driven decisions
4
2 Available experts for supporting decision making
0
1990 1995 2000 2005 2010 2015 2020
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12. Information democracy:
Information Creators and Consumers
• Creators
– Defines & prepopulate Analysis Pipelines and test it on
the data
– Deploys these pipeline using Cloud computing
• Required computing capacity can scale up with the business /
analytical needs
• Consumers
– Pre defined analytical reports
– Sharing feedback and input to the results to optimize
analysis
Sharing information empowers collaboration
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13. The traditional IP search and analysis
Request
Results
Database Tools
Data Information Creator Request Information Information Consumer
The traditional approach:
• Each search/analysis request is focussed on a specific question from one user
• When the number of request increases this requires more human searchers
• When the searches involve analysis of more patents this requires more time
• Very specific searches can not be automated
• Analysis of large documents sets can be automated – which is an opportunity to
analyse more and become more competitive
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14. Information democracy:
Proactive analysis to search and analysis
Patent Analyst
Database
Research
Database Running
Business
Analytics User
Marketing Pipeline
Database
Data Information Creators Push Information Information Consumers
Liberate the Information Search, facilitate the discovery process:
• The knowledge Creator:
• defines the analysis pipeline and test it on the data
• deploys the analyses using cloud computing resources
• Direct access for information consumers for in depth analyses
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15. Liberate more information using new
technologies
• Enable a small group of experts with tools to set-up IP
analysis pipelines
– Extend the search on request approach with a pro-
active analysis approach on large document sets
– Use analysis pipelines to auto generate visualizations
in a browser
– Invest in new technology coming from Big Data
Analytics
• Give a large group of users access to the internal
webpages providing them with rich statistical
information and interactive visualizations
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16. Combining proactive analysis with traditional
IP search and discovery
Personal Request
Tools
Tailor made Results
Database
Patent Business
Database User
Running Exchange
Information
Research
Database
Analytics
Pipeline Analyst
Marketing
Database
Data Information Creators Information Information Consumers
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17. Performing small to large scale SWOT
analysis
SWOT analysis example
Patent
Database Queries • What are most important
patents?
• Who owns them?
• What is growth of
patents by:
• Technology?
• Owner?
• Country?
• Year?
5000 patents 1000 patents 500 patents
Business Overview
User and details
Ranking Filtering
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18. Auto reporting & analysis for multiple users
• Reporting of aggregated
results:
– Pie & bar charts
• Providing overview of the
subject:
– landscape visualization
• Enabling rich interaction
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Page 18
19. Use Case1: SWOT analysis of Ebooks
• Perform proactive SWOT analysis of ebooks market
Amazon Kindle – Apple – Samsung/Google and other players
• Who owns what?
• What can we learn from competitive technology landscape?
• Why?
• Determine a company/technology position and opportunities
• We do this in KMX by:
1. Query to get patents on electronic paper technology
2. Landscape analysis
3. Classification/Ranking
4. Filter and select subset
5. Iterate step 1-to-5
Treparel KMX – All rights reserved 2012 Fig 2: Overview landscape visualization of 4257 patens 19
19
20. Analysis of ebook technology
Fig 3: Overview landscape visualization of 4257 patents
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21. Use document classification to rank the
patents
Purple = most important patents
Red = least relevant patents
Fig 4: Ranked patents using a classifier for ebook technology (In purple the selection of relevant
patents for deeper analysis)
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22. Drill deeper in the data to learn more
Fig 5: Landscape visualization going from 4257 to 1049 to 369 patents
After removing the irrelevant patents we use filtering to
determine:
• Who are the important players (assignees, inventors)?
• Where are the important patents filed (countries)?
• What is the trend over time (growth of patents over the years)?
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23. Define the relevant set of patents to identify
your strengths & opportunities
Purple = most important patents
Red = least relevant patents
Fig 6: Landscape visualization of 369 most important patents in ebook technology
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24. The role of language:
Clustering of Patents in Chinese text
Fig 7: Patent landscape visualization using the chinese or englisch text
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25. Use Case 2: SWOT Technical & Biological patents
• Perform SWOT analysis in a converging market: analyze claims with mixed
technologies
• Who owns what?
• How does the technology landscape looks like?
• Why ?
• Determine a company/technology position and opportunities
• We do this in KMX by:
1. Query to get patents on mechanical/electronic/optics mixed with
biological technology
2. Landscape analysis
3. Classification/Ranking
4. Filter and select subset
5. Iterate step 1-to-5
Treparel KMX – All rights reserved 2012 Fig 8: Landscape visualization of 10920 patents 25
26. Use Case: SWOT analysis : patents covering
multiple technology areas (engineering & biology)
• Patents become more
complex to analyse
• Examples:
• More detailed claims
• Mixed technologies in
the claims
• Obtaining an landscape
overview is then key
• Analysis from the users
perspective is essential
(classification/ranking)
Fig 9: Landscape visualization of 10920 patents
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27. Patents from total set with biological focus
• Using the text from the
title/abstract and claims
• landscape analysis
provides overview
• Using document
classification to
determine sub clusters
• Using classification and
ranking to determine
the most relevant
documents from the
users perspective
Fig 10: Landscape visualization of 134 patens
28. Key takeaways
Global economy demands value generation from an IP strategy
Big Data Paradox:
• Limited (human) resources available for in-depth analysis
• Growing need for data driven decisions
The information creators (patent searchers) focus on
• Providing proactive information on generic analysis tasks
• Perform specific analysis for single user request
The information consumers (patent council)
• Get knowledge from automated analysis with
interactive capabilities
• Obtain SWOT analysis knowledge of competitors
• Built and optimize patent strategies
Treparel KMX – All rights reserved 2012 www.treparel.com 28