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II-SV 2017: How to effectively monitor Technological Developments in IP

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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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II-SV 2017: How to effectively monitor Technological Developments in IP

  1. 1. How to effectively analyse and monitor Technological Developments in IP Sample study: Industry 4.0, Factory 4.0 • Technology fields: Quantitative & qualitative, business related analysis. • Making use of technology field analysis to get the decisive competitive advantage by being faster and by having the right informations at hand . • Example studies of Industry 4.0 technology aspects • Actively monitoring technology fields, using the Averbis machine learning engine to keep ahead of time and ahead of the competition. SDV-II, 25.04.2017
  2. 2. Technology fields today: Patent Informations from and for a business perspective. Technology fields today: Patent Informations from and for a business perspective. • • Smart House • Process Automation • Fintec • Autonomous Driving • Li-Batteries • 3D Printing materials • Wearables • .. 1. Define, collect and categorise the patent data Examples out of 40 cutting edge technology fields and up to 200 geographic regions, elaborated together with Added value information, delivered by • Patent owner consolidation • Ownertyp identification (University, Company) • Legal situation (only active patent families) • Quality indexing over the full database • Geographical indication 2. Adding business-relevant Parameters, normalize factors SDV-II, 25.04.20172 3. Analysis • Visualisation • Identification of trends, targets and opportunities • Benchmarking • Developing decision ground 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
  3. 3. SDV-II, 25.04.20173 Source: Roland&Berger Industry 4.0: from global buzz to reality Definition of Industrial application of Industry 4.0: «Factory 4.0» But what about «Factory 4.0» in the Patent Landscape?? USE CASE: Industry 4.0 or Factory 4.0 and the patent landscape
  4. 4. Worlds Average SDV-II, 25.04.20174 Patent Landscape of Industrial application of Industry 4.0: «Factory 4.0» Technology Fields of interest: Total 1.109 Mio active patent families AverageQuality CompetitiveImpact™ Forward Citation count (weighed and normalized) Technology Relevance Technology Fields Process Automation Sensors Digital Communication Ceramics Additive Manufacturing Advanced Manufacturing Blockchain/Bitcoin Predictive Maintenance Artifical Intelligence 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 Bitcoin to advanced technologies Autonomous Driving IoT Smart House / IoT Smart City Add.Manufact Adv.Manufact Robotic Art.Intelligence Carbon&Graphene Nanomaterial Increasing quality due to forward citations: From basic technologies Digital Communication Sensors Ceramics Process Automation Factory 4.0 Patents: From basic to advanced technology
  5. 5. Factory 4.0 Patent Players in 4 Regions*: Which players and regions are prepared? (*only EP and some WO publications, based in inventors adress) 5 Factory 4.0 Basics: Sensors Add. Manuf. Nanomaterial Robotik Autonomous Adv. Manuf. MET Region Zürich /CH 1101 active MET Region Munich/DE 3202 activeMET Region Ile de France /1537 active MET Region SF/USA 4275 active SDV-II, 25.04.2017
  6. 6. Worlds Average SDV-II, 25.04.20176 Patent Landscape of Industrial application of Industry 4.0: «Factory 4.0» Target Technology fields for Active Monitoring AverageQuality CompetitiveImpact™ Forward Citation count (weighed and normalized) Technology Relevance Technology Fields Process Automation Sensors Digital Communication Ceramics Additive Manufacturing Advanced Manufacturing Blockchain/Bitcoin Predictive Maintenance Artifical Intelligence IoT Smart House IoT Smart City Autonomous Driving Robotics Nanomaterials Carbon&Graphene and further to cross sector technologies Robotic&ArtIntelligence Sensors&ArtIntelligence Sensors&Robotic DigiCom&Robotic DigiCom&Sensor Bitcoin to advanced technologies Autonomous Driving IoT Smart House / IoT Smart City Add.Manufact Adv.Manufact Robotic Art.Intelligence Carbon&Graphene Pred./Prev. Maintenance Nanomaterial Increasing quality due to forward citations: From basic technologies Digital Communication Sensors Ceramics Process Automation Factory 4.0 Patents: Keeping in touch with the development -> Active Monitoring
  7. 7. SDV-II, 25.04.20177 Advanced Technology fields choosen as examples for Active Monitoring: Advanced Manufacturing Additiv Manufacturing Predictive / Preventive Maintenance Bitcoin
  8. 8. SDV-II, 25.04.20178 Advanced Technology fields choosen as examples for Active Monitoring: Advanced Manufacturing Additiv Manufacturing Predictive / Preventive Maintenance Bitcoin Top15 Player for the 4 technologiesTop 10 Countries
  9. 9. SDV-II, 25.04.20179 The question is: How to actively keep technology fields up to date, without doing a search always and again?  Automatic patent categorization
  10. 10. • 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 Solution
  11. 11. Our Approach in a NutshellDefine Categories1 Provide Examples & Train2 Let the System Categorize Documents 3 Review Results4 Active Learning GO Our Approach in a Nutshell
  12. 12. Patent Classification Patent Categorization From Scratch
  13. 13. Patent Classification: Define Categories Define Categories
  14. 14. Patent Classification: Provide Examples Provide Examples
  15. 15. Train the System
  16. 16. Categorize Patents
  17. 17. Active Learning
  18. 18. Active Learning
  19. 19. • Split the (manually) labeled document collection into training set (90%) and into test set (10%) • Train the classifier on the 90% • Test the classifier on the 10% (already labeled) • Count true positives, false positives and false negatives • Repeat 10 times 19Cross-Evaluation
  20. 20. 20Evaluation: Collection n=3.621 Bitcoin; 30; 1% Advanced- Manufacturing; 909; 26% Predictive- Maintenance; 527; 15% Additive- Manufacturing; 64; 2% Rest; 2000; 56%
  21. 21. 21Evaluation: Cross-Validation (F)
  22. 22. 22Evaluation: Error Analysis (n=3.621) 0 42 44 4 12 44 44 1 0 BITCOIN (30) ADVANCED MANUFACTURING (909) PREDICTIVE MAINTENANCE (527) ADDITIVE MANUFACTURING (64) REST (2000) FALSE POSITIVES FALSE NEGATIVES
  23. 23. Monitor Technological Developments • … or use the Web-based API
  24. 24. 24Summary • Actively monitor technology field developments using automatic patent categorization • Based on machine learning by training on document collections provided by experts • Reliable even with few training examples • Technology fields are valuable patent collections with multiple use benefits (if they are collected by experts) • Combination of data, normalized, weighted and enriched with business information is the base for competitive advantage
  25. 25. 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 9 years Dr. Kornél Markó • Computer Scientist, Natural Language Processing • Co-Founder of Averbis GmbH, 2007 Questions? SDV-II, 25.04.2017

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