Finding trends in large scale document sets

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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)

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Finding trends in large scale document sets

  1. 1. Treparel Dr. Anton Heijs Delftechpark 26 CEO/CTO anton@treparel.com 2628 XH DelftThe Netherlands September 24, 2012www.treparel.com
  2. 2. Analysing large patent portfolios: Nothing remains uncoveredAgenda• 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 technologiesTreparel KMX – All rights reserved 2012 www.treparel.com 2
  3. 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.Treparel KMX – All rights reserved 2012 www.treparel.com 3
  4. 4. IP landscape trend example: 1970-2007Alstom GEDong Feng Siemens 4
  5. 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 yearsTreparel KMX – All rights reserved 2012 www.treparel.com 5
  6. 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-basedTreparel KMX – All rights reserved 2012 www.treparel.com 6
  7. 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 investmentsTreparel KMX – All rights reserved 2012 www.treparel.com 7
  8. 8. Adapting to economic developmentsThe 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-offsDrivers• Number of Patent filings is growing• Growing need to drive revenues from licensing to fund R&D investmentTreparel KMX – All rights reserved 2012 www.treparel.com 8
  9. 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 patentsTreparel KMX – All rights reserved 2012 www.treparel.com 9
  10. 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 VolumeVariety Complexity Treparel KMX – All rights reserved 2012 www.treparel.com 10
  11. 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 2020Treparel KMX – All rights reserved 2012 www.treparel.com 11
  12. 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 collaborationTreparel KMX – All rights reserved 2012 www.treparel.com 12
  13. 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 competitiveTreparel KMX – All rights reserved 2012 www.treparel.com 13
  14. 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 analysesTreparel KMX – All rights reserved 2012 www.treparel.com 14
  15. 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 visualizationsTreparel KMX – All rights reserved 2012 www.treparel.com 15
  16. 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 ConsumersTreparel KMX – All rights reserved 2012 www.treparel.com 16
  17. 17. Performing small to large scale SWOT analysis SWOT analysis example PatentDatabase 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 Treparel KMX – All rights reserved 2012 www.treparel.com 17
  18. 18. Auto reporting & analysis for multiple users • Reporting of aggregated results: – Pie & bar charts • Providing overview of the subject: – landscape visualization • Enabling rich interaction Treparel KMX – All rights reserved 2012 www.treparel.com 18Page 18
  19. 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-5Treparel KMX – All rights reserved 2012 Fig 2: Overview landscape visualization of 4257 patens 19 19
  20. 20. Analysis of ebook technology Fig 3: Overview landscape visualization of 4257 patentsTreparel KMX – All rights reserved 2012 www.treparel.com 20
  21. 21. Use document classification to rank the patentsPurple = most important patentsRed = least relevant patents Fig 4: Ranked patents using a classifier for ebook technology (In purple the selection of relevant patents for deeper analysis) Treparel KMX – All rights reserved 2012 www.treparel.com 21
  22. 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)?Treparel KMX – All rights reserved 2012 www.treparel.com 22
  23. 23. Define the relevant set of patents to identify your strengths & opportunitiesPurple = most important patentsRed = least relevant patents Fig 6: Landscape visualization of 369 most important patents in ebook technology Treparel KMX – All rights reserved 2012 23
  24. 24. The role of language: Clustering of Patents in Chinese text Fig 7: Patent landscape visualization using the chinese or englisch textTreparel KMX – All rights reserved 2012 www.treparel.com 24
  25. 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-5Treparel KMX – All rights reserved 2012 Fig 8: Landscape visualization of 10920 patents 25
  26. 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 patentsTreparel KMX – All rights reserved 2012 www.treparel.com 26
  27. 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. 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 strategiesTreparel KMX – All rights reserved 2012 www.treparel.com 28

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