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ICIC 2017: How to effectively monitor Technological Developments in IP

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Jochen Spuck (Swiss Federal Institute of Intellectual Property, Switzerland)
Kornel Marko (Averbis, Germany)
Modern, cutting-edge developments are not reflected in current patent classification systems, which tend to catalogue established technologies. Identifying patent portfolios in such emerging fields proves a challenging job for patent and technology experts.

Going beyond the mere identification of new IP, additional value may be added using a regional geographic weighting combined with consolidated portfolio owner information.

Effective monitoring of the technological field is achieved by training active-learning search engines to hunt for highly relevant patent documents, thus keeping IP portfolios for emerging technologies up to date. The system we have developed permits extremely accurate updates with drastically reduced noise and with low workload which have proven to be invaluable in a world of drastically increasing data blur.

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ICIC 2017: How to effectively monitor Technological Developments in IP

  1. 1. Analyzing and monitoring technological developments in the changing patent landscape Sample study: Factory 4.0 Contents • ip-search Technology Fields: Definitions and analysis • Example studies of Factory 4.0: Technology aspects in the patent landscape • Using supervised learning to better collect, analyse and keep track of future technologies ICIC, 23.10.2017, by ip-search
  2. 2. 2 Patent landscape analysis: What for? DEFINITIONS •Topic •Package ANALYSIS •Basic •Advanced •Professional RESULT •Report •Charts •Key figures OUR PATENT LANDSCAPE ANALYSIS YOUR BUSINESS DECISION  BUSINESS STRATEGY  PATENT STRATEGY  MARKET STRATEGY  PRODUCT STRATEGY  EXPERTISE  MITIGATE YOUR RISK Smart solutions for your competitive advantage Acquire fundamental information to make your business decision with reduced risk ICIC, 23.10.2017, by ip-search
  3. 3. Technology fields today: Patent informations from and for a business perspective Consolidated analysis, human expertised full text evaluation and data visualisation by 3. Analysis • Visualisation • Identification of trends, targets and opportunities • Benchmarking • Developing decision ground Supervised learning categorisation by: 4. Active Monitoring • Updating the selection with high efficiency using machine learning and text mining • Easy assessing and evaluating of the data • Re-assessing, and re- adjusting the learned target ICIC, 23.10.2017, by ip-search Examples out of 60 cutting edge technology fields and up to 200 geographic regions, elaborated by: • Smart House • Process Automation • Fintec • Autonomous Driving • Li-Batteries • 3D Printing • Wearables • .. 1. Define, collect and categorise the patent data Added value information, such as owner information or quality indices delivered by: Added value information, such as geographic segmentation or technology field inputs by: • Patent owner consolidation • Owner type identification (University, Company) • Legal situation (only active patent families) • Quality indexing over the full database • Geographical indication 2. Adding business-relevant Parameters, normalize factors
  4. 4. The Patent Asset IndexTM Methodology by PatentSight: Visualize and Compare Patent Quality on all worldwide active patent families 20% patents worldwide with highest rating 50% patents worldwide with low rating 30% patents worldwide with medium rating ICIC, 23.10.2017, by ip-search
  5. 5. Source: Roland&Berger Industry 4.0: from global buzz to reality Definition of the industrial application of Industry 4.0: «Factory 4.0» But what about «Factory 4.0» in the Patent Landscape?? USE CASE: Factory 4.0 and the patent landscape ICIC, 23.10.2017, by ip-search
  6. 6. Patent Landscape of Industrial application of Industry 4.0: «Factory 4.0» Technology Fields of interest: Total 1.109 Mio active patent families (Status 12/2016) AverageQuality CompetitiveImpact™ Forward Citation count (weighed and normalized) Technology Relevance Technology Fields Basic Process Automation Sensors Digital Communication Ceramics Technology Fields Advanced Additive Manufacturing Advanced Manufacturing Blockchain/Bitcoin Predictive Maintenance Artifical Intelligence 3D Printing IoT Smart House IoT Smart City Autonomous Driving Robotics Nanomaterials Carbon&Graphene Factory 4.0 Patents: From basic to advanced technology, combination and integration is key Question: What is the quality level of patents in the technological area of Factory 4.0 ICIC, 23.10.2017, by ip-search 3D-Printing to advanced technologies Autonomous Driving IoT Smart House / IoT Smart City Add.Manufact Adv.Manufact Robotic Art.Intelligence Carbon&Graphene Nanomaterial Bitcoin and further to combined and integrated technologies Robotic&ArtIntelligence Sensors&ArtIntelligence Sensors&Robotic DigiCom&Robotic DigiCom&Sensor Increasing quality due to forward citations: From basic technologies Digital Communication Sensors CeramicsProcess Automation Low quality patents Top 10% Worldclass patents
  7. 7. Digital Revolution (esp. in the Industry 4.0 Factory area): Its not about digital or digital communication, but about the clever combination of technologies. Even further, the more and the better technologies are combined, the higher the patent quality and the higher the technological maturity Problem for patent analysts: How to collect, analyse and keep track of the patent collections of these all technologies, combinations, changes and developments??? Factory 4.0: Assumption and Conclusion ICIC, 23.10.2017, by ip-search
  8. 8. Worlds Average Patent Landscape of Industrial application of Industry 4.0: «Factory 4.0» Technology Fields of interest: Total 1.109 Mio active patent families (Status 12/2016) AverageQuality CompetitiveImpact™ Forward Citation count (weighed and normalized) Technology Relevance Technology Fields Basic Process Automation Sensors Digital Communication Ceramics Technology Fields Advanced Additive Manufacturing Advanced Manufacturing Blockchain/Bitcoin Predictive Maintenance Artifical Intelligence 3D Printing IoT Smart House IoT Smart City Autonomous Driving Robotics Nanomaterials Carbon&Graphene and further to cross sector overlapping technologies Robotic&ArtIntelligence Sensors&ArtIntelligence Sensors&Robotic DigiCom&Robotic DigiCom&Sensor Increasing quality due to forward citations: From basic technologies Digital Communication Sensors CeramicsProcess Automation Factory 4.0 In-Depth Analysis: Demo Case 3D-Printing 3D-Printing to advanced technologies Autonomous Driving IoT Smart House / IoT Smart City Add.Manufact Adv.Manufact Robotic Art.Intelligence Carbon&Graphene Nanomaterial Bitcoin Next step: In-Depth Analysis of 3D-Printing as a demo case: ICIC, 23.10.2017, by ip-search
  9. 9. Demo Case 3D-Printing: Categorization of technology 3D Printing Technologies: • SLS/SLM Selective Laser Sintering (Powder/Laser Sintering or Melting) • SLA/DLP Stereolithography (Photocuring of Photopolymers in Liquids) • FDM Fused Deposition Modeling (Meltable Polymers, Extrusion Nozzles) • Powder/Binder-InkJet or Builder/Liquid Solidifying • Specialty Technoloy or Application: Bioprinting or Tissue/Organs etc. Collected but not specifically classified: Generic 3D Patents, 3D but Technology-unspecific materials, Digital and Software related patents, End- products (& some noise) Goal: Define relevant technologies and identify related patents ICIC, 23.10.2017, by ip-search
  10. 10. 12 Demo Case 3D-Printing: Activities First patents Early stages First success & scepticism Boom phase Question: How active are players and since when 3D printing technologies: • SLS Selective Laser Sintering • SLA Stereolithographie & DLP • FDM Fused Deposition Modeling • Powder-Binder Printing • Bioprinting ICIC, 23.10.2017, by ip-search
  11. 11. Demo Case 3D-Printing: Trends in Technologies SLS SLA FDM Powder-Binder Bioprinting Question: What kind of trends can be seen Boom, new technology or just a divers application? High productivity New successful? variants (DLP,...) Widespread use ICIC, 23.10.2017, by ip-search Proper defined technology field?
  12. 12. Demo Case 3D-Printing: Inventive countries* SLS SLA FDM Powder-Binder Bioprinting Question: Where are players active * Calculation is based on inventor addresses Technological fields: ICIC, 23.10.2017, by ip-search
  13. 13. Demo Case 3D-Printing: Technology share of top 15 companies* SLS/SLM SLA/DLP FDM Powder-Binder Bioprinting Question: Who is strong in which technology Printer Manufacturer Materials manufacturer ICIC, 23.10.2017, by ip-search * Analysis of the entire field, calculation based on Patent Asset Index ™
  14. 14. 0,0 2,0 4,0 6,0 8,0 10,0 12,0 14,0 16,0 18,0 20,0 0 50 100 150 200 250 Stratasys 3D Systems BASF Electro Optical Systems United Technologies Evonik HP Inc. Carbon3D voxeljet Renishaw ExOne MarkForged Bego Organovo SLM Solutions Group 16 Note: Increasing translucency of bubbles means they mark an earlier point in time. The development over time is shown for Reporting Date 2011 to 2017. Bubble area = Total Strength Patent Asset Index™ Sorted by PAI AverageQuality CompetitiveImpact™ Quantity Portfolio Size Demo Case 3D-Printing: Performance of Top Players Question: How do top players develop over time Development over the years 2000-2017 ICIC, 23.10.2017, by ip-search
  15. 15. Conclusion Part 1: • Collecting patents “classically” in combination with business relevant information leads to valuable insights, …BUT. • Badly defined, changing or dynamic technology fields make a classical approach difficult and rather impossible to maintain and monitor regularly. • To increase precision and to monitor such fields, we need other tools in addition to what we have. But while all kinds of semantic or automatic tools were introduced since years, only very few seem to work properly towards a real benefit in the patent environment.  Supervised learning engines, properly integrated and made for patents can be a game changer 17 ICIC, 23.10.2017, by ip-search
  16. 16. • is a machine-learning based document classification software • automatically classifies documents into customer-specific categories • continuously learns from and imitates the behavior of IP professionals Our approach and partner: ICIC, 23.10.2017, by ip-search and averbis
  17. 17. Define Categories1 Provide Examples & Train2 Let the System Categorize Documents 3 Review Results4 Active Learning GO Procedure in a Nutshell ICIC, 23.10.2017, by ip-search and averbis
  18. 18. Step 1: Define Categories ICIC, 23.10.2017, by ip-search and averbis
  19. 19. Step 2: Provide Examples as Training Material ICIC, 23.10.2017, by ip-search and averbis
  20. 20. Step 3: Train the System ICIC, 23.10.2017, by ip-search and averbis
  21. 21. Step 4: Categorize Patents ICIC, 23.10.2017, by ip-search and averbis
  22. 22. Step 5: Active Learning by Re-Training ICIC, 23.10.2017, by ip-search and averbis
  23. 23. 25 Step 6: Reviewing Results and further, now corrected and fine-tuned analysis Averbis Internally: Statistical Analysis Externally with ip-search: Fulltext analysis or advanced analysis in Patentsight or export to various other tool ICIC, 23.10.2017, by ip-search and averbis
  24. 24. Define Categories1 Provide Examples & Train2 Let the System Categorize Documents 3 Reviewed or Unreviewed Results 4 Regular export to customer New Prototype: Active Monitoring Feeding in new patents regularly via API etc. ICIC, 23.10.2017, by ip-search and averbis
  25. 25. 27 Summary and Conclusions • Actively monitoring technology field using supervised machine learning might be the key to collect and maintain (patent) data collections, proper, flexibel and up to date. • Machine learning and (re-)training in the patent world is becoming much easier, more reliable and flexible, even with few training examples, therefore quick to adapt and refocus. • Technology fields are valuable patent collections with multiple usage benefits (if they are collected properly). • Combination of data, normalized, weighted and enriched with business information is the base for competitive advantage analysis. • For a most comprehensive patent analysis many factors are to be considered and experts should be consulted. ICIC, 23.10.2017, by ip-search and averbis
  26. 26. Dr. Jochen Spuck • Chemist, Expert in polymer chemistry • Head of Product Development at ip- search • Patent Professional at the Swiss Federal Institute of Intellectual Property since 10 years Dr. Kornél Markó • Computer Scientist, Natural Language Processing • Co-Founder of Averbis GmbH, 2007 Questions? ICIC, 23.10.2017, by ip-search and averbis

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