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Rassenfosse - IProduct database of patent products pairs

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Parallel sessions 1 - 20 September 2016

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Rassenfosse - IProduct database of patent products pairs

  1. 1. IPRoduct: A database of patent-product pairs OECD Blue Sky III Forum Ghent, Belgium, September 19–21, 2016 Gaétan de Rassenfosse Assistant Professor in Innovation and IP Policy Ecole polytechnique fédérale de Lausanne (EPFL), Switzerland @gderasse I acknowledge financial support from the U.S. NSF (SMA - 1645264)
  2. 2. We are not able to trace innovations to the marketplace  Innovations are intangibles and, hence, difficult to study.  Scholars have focused on patents (and scientific publications and trademarks) as “output” of the innovation process.  But these are (i) abstract manifestations of science; (ii) intermediate innovation output; (iii) not tied to market outcome.  As a result, innovation studies have largely failed to study the “real impact” of innovation. Exceptions exist, such as studies on drug patents or case studies of particular technologies. IPRoduct: A database of patent-product pairs 2
  3. 3. I want to observe innovations at the point at which they reach consumers
  4. 4. The holy grail of innovation research  I propose to build a table that links IPR data to product data.  Physical patent marking means that it is difficult to collect such information on a large scale.  But companies can now use virtual patent marking. IPRoduct: A database of patent-product pairs  Allows them to collect damages for patent infringement with respect to infringing activity that occurs before the infringer is put on actual notice of the infringement.
  5. 5. The idea is simple: collect information available on line 5
  6. 6. But implementation is quite challenging  Several technical issues for collecting and structuring the data: – Format ranging from straight HTML files listing all products and the associated patents to non-OCR PDF files – Information buried in the deep web (dynamic pages and forms) – Product lines rather than actual products – etc.  But great technology is available. We are developing a software to do the job. We use the Scala programming language, developed by EPFL prof. Martin Odersky and used by tech. companies such as LinkedIn, Foursquare for their big data applications. IPRoduct: A database of patent-product pairs 6
  7. 7. We are currently developing the crawler and the parser IPRoduct: A database of patent-product pairs 7 2016/09 2017/09 Phase I (own funds) Phase II (NSF + own funds) Phase III (looking for sponsor-s) Objectives  Assessing feasibility  Software development  Small-scale database  Scoping Objectives  Scaling up  UKIPO, EPO data  Trademark data  Price data  etc. Objectives  Manual collection  Early assessment Public release of code and data Data and code sharing policy will depend on sponsors
  8. 8. Overview of the Phase I database  About 30 companies associated with 1,000 products and 3,000 U.S. patent documents.  In fields as diverse as cosmetics, food and beverages, computer software, telecommunications, medical devices, consumer goods, pharmaceuticals and building material. IPRoduct: A database of patent-product pairs 8 A: “Human Necessities” B: “Perform. Operations, Transporting” C: “Chemistry, Metallurgy” D: “Textiles, Paper” E: “Fixed Constructions” F: “Mech. Eng., Light., Heat., Weapons” G: “Physics” H: “Electricity”
  9. 9. The Project in Brief Stylized Facts Policy Relevance
  10. 10. 0 50 100 150 200 250 300 350 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Numberofproducts Number of patents 3 Median number of patents per product (average of 7.74) Number of patents per product  323 products are associated with only 1 U.S. patent.  Can serve as a proxy for R&D investments, complexity of technology, evidence of patent thickets, etc.
  11. 11. 0 200 400 600 800 1000 1200 1400 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Numberofpatents Number of products 2 Median number of products per patent (average of 3.43) Number of products per patent  1320 patents are associated with just one product.  Can be used to measure product similarity, patent importance, etc.
  12. 12. 0% 2% 4% 6% 8% 10% 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Average age (in years) 13 Mean age of patents associated with a product, in years Proportion of products by average patent age  About 1% of products correspond to patents filed on average less than 3 years ago.  May be indicative of the speed of technological obsolescence.
  13. 13. 9 Mean number of countries for which patent protection was sought Proportion of patents with a family member in the jurisdiction 0% 20% 40% 60% 80% 100% USPTO EPO JPO Triadic  45% of inventions are protected at the USPTO, the EPO and the JPO.  Witnesses the high economic value of patents associated with a product.
  14. 14. 9 Mean number of countries for which patent protection was sought
  15. 15. 8 Number of years between the oldest and the most recent patent in a product Maximum time length between two patents linked to the same product  About 40% of products have patents filed in a time interval of maximum 2,000 days (5.5 years).  Indicative of how long a company has been developing the product, and may inform about follow-on innovation strategies.
  16. 16. 22% Proportion of families linked to a product 11 5 7 4 2 20 43 15 300 122 74 56 44 169 191 750 2151 407 57 1382 267 446 38 392 3199 978 7569 3216 690 37077 19886 0% 10% 20% 30% 40% 50% 60% 70% 80% Familieslinkedtoaproduct Proportion of families linked to a product  Company #1 has 11 families, and 73% of these families are linked to a product.  Families not linked to a product may signal failed commerci- alization attempts, FTO patents, etc.
  17. 17. The Project in Brief Stylized Facts Policy Relevance
  18. 18. Budget justification is an important policy aspect  The IPRoduct database will provide a concrete manifestation of S&I and the role of government funding, in a systematic manner.  Can also be used to identify case studies, in order to increase public engagement with science. IPRoduct: A database of patent-product pairs 18 Patents Papers Grants Procurement Universities PROs
  19. 19. But policy relevance goes beyond budget justification  Provides a better understanding of how IP impacts the economy.  Opens many research questions in many different disciplines (e.g., economics, management, law, marketing, science of science policy). For instance, it may provide new insights on the private returns to patenting or the value of patents.  Novel industry-level S&T indicators – Time to market: average patent age – Patent density: average number of patents per product  Also directly serves one purpose of the America Invents Act, namely to increase transparency of the patent system. IPRoduct: A database of patent-product pairs 19
  20. 20. Thank you! Gaétan de Rassenfosse Ecole polytechnique fédérale de Lausanne Email: gaetan.derassenfosse@epfl.ch Web: http://www.gder.info Twitter: @gderasse

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