1. Patent Analysis
22/05/2014

2. Linear model of innovation
basic research
applied research
Development
production or operations
Market

3. Stokes’s diagram
Low use Known use New Use

4. History
• Patent, the adjective, means "open," and patent, the noun, is the customary
abbreviation of "open letter”
• “patent," a literal translation of the Latin “litterae patentes“
• 1474 by the Republic of Venice
• In the 16th century, patents were widely used by German prince
• US Congress passed the first patent law in 1790 and amended it in 1793
• Austria in 1794
• PCT – Patent Cooperation treaty 1970 148 countries are contracting parties to this
treaty.
India
• The Patents Act 1970
• Signed by India, effective December 7, 1998
• The Patents (Amendment) Act 1999
• The Patents (Amendment) Act 2005

5. Why patent analysis
– Almost 1,000,000 new patent documents published every year over the
last 10 years
– Source of technological information
Patent information is important for
– large companies
– small and medium enterprises
– scientists and researchers
– inventors
– investors
– patent attorneys, patent searchers

6. Types of patent searches
• Patentability search
• Freedom to operate (FTO) search
• Prior Art Search
• Watching search
• Patent Infringement

7. Patent Infringement
• Famous cases
– Microsoft v. Motorola
– Apple Inc. v. Samsung Electronics Co., Ltd.
– Novartis AG v. Union of India (2007)

8. Patent Bibliometrics
• National productivity
• Inventor productivity
• Referencing cycles
• Citation impact
• Within country preference
• Forecasting emerging technologies
Technological Convergence
• patent is cited in subsequent patents
• patent classification
Science Technology Linkage / Industry academia linkages
• Non patent references
Knowledge spillover
Reverse Technology Transfer

9. Steps in Patent Analysis
• Establish a clear target and purpose for your analysis.
• Choose a data collection plan
• Choose the appropriate tools.
• Data Collection
• Fill the gaps in the data.
• Analyze the data.
• Disseminate findings to decision maker.
• Customize an in-house database.
• Keep abreast of emerging services and databases.

10. Patent databases
• US Patent and Trademark Office (USPTO)
• Espace - European Patent Office (EPO)
• WIPO database – includes links to national patent offices
• Google patent
• Free Patents Online
• Delphion, Dialog, GlobalPat, Latipat, Patent Lens, Publicus

11. Google Patent search

12. USPTO Advanced Search

13. Example of bibliographic data as presented on the front page of a US
patent specification

14. European Patent Office (Espacenet)
www.pathservicez.com 14

15. Example of bibliographic data as presented on the front page of a
European patent specification filed through the PCT

16. Example of bibliographic data as presented on the front page of a
PCT patent specification

17. WIPO Patentscope

19. Example of bibliographic data as presented in the Official Journal
of the Indian Patent Office

20. Understanding information in a patent
• bibliographic data
• technical information
• legal information

21. Bibliographic data
• Application number
• Patent number
• Document kind codes
• Filing date
• Priority data
• Publication number
• Publication date
• Date of patent/Date of publication of the grant of the patent
• International Patent Classification
• Applicant/Proprietor/Assignee
• Inventor(s)
• Representative/Agent
• Designated States
• Divisional application
• References cited
• Title
• Abstract

22. Patent Classification System
• Section A – Human Necessities
• Section B – Performing Operations; Transporting
• Section C – Chemistry; Metallurgy
• Section D – Textiles; Paper
• Section E – Fixed Constructions
• Section F – Mechanical Engineering; Lighting; Heating; Weapons; Blasting
• Section G – Physics
• Section H – Electricity

23. IPC - Structure
• The Classification represents the whole body of knowledge which may be regarded as
proper to the field of patents for invention, divided into eight sections. Sections are the
highest level of hierarchy of the Classification.
• Each section is subdivided into classes. Classes are the second hierarchical level of the
Classification.
• Each class comprises one or more subclasses. Subclasses are the third hierarchical level
of the Classification.
• Each subclass is broken down into subdivisions referred to as "groups," which are either
main groups (i.e., the fourth hierarchical level of the Classification) or subgroups (i.e.,
lower hierarchical levels dependent upon the main group level of the Classification).

24. Kind Codes
• As patent documents change in their content between
publication and grant, patent authorities classify the
different versions using kind codes. Kind codes will
follow the application or granted patent number e.g. EP
0996622 (A1) or EP 0996622 (B1). Although there is
some harmonization in the document kind codes used
by the different patent authorities, they can vary
• http://www.patentlens.net/daisy/Telomerase/2943/version

25. Readings
• Breitzman, A. F., & Mogee, M. E. (2002). The many applications of patent analysis. Journal of Information Science, 28(3), 187–205.
• Clarkson, G., & DeKorte, D. (2006). The problem of patent thickets in convergent technologies. Annals of New York Academy of
Science, 1093, 180-200.
• Guan, J., & Chen, Z. (2012). Patent collaboration and international knowledge flow. Information Processing and Management, 48(1), 170-
181.
• Karki, M. M. S. (1997). Patent Citation Analysis: A Policy Analysis Tool World Patent Information, 19(4), 269-272.
• Karvonen, M., & Ka¨ssi, T. (2011). Patent analysis for analysing technological convergence. Foresight, 13(5), 34-50.
• Karvonen, M., & Kässi, T. (2013). Patent citations as a tool for analysing the early stages of convergence. Technological Forecasting &
Social Change, 80, 1094–1107.
• Karvonen, M., & Kässi, T. (2013). Patent citations as a tool for analysing the early stages of convergence. Technological Forecasting and
Social Change, 80(6), 1094–1107.
• Kim, M.-S., & Kim, C. (2012). On A Patent Analysis Method for Technological Convergence. Procedia - Social and Behavioral Sciences,
40(657 – 663).
• Leydesdorff, L., & Bornmann, L. (2012). Mapping (USPTO) patent data using overlays to Google Maps. Journal of the American
Society for Information Science and Technology, 63(7), 1442–1458.
• Meyer, M. (2000). Does science push technology? Patents citing scientific literature. Research Policy, 29(3), 409–434.
• Narin, F. (1994). Patent bibliometrics. Scientometrics, 30(1), 147-155.
• Narin, F., Hamilton, K. S., & Olivastro, D. (1997). The increasing linkage between U.S. technology and public science. Research
Policy, 26, 317-330.
• Nelson, A. J. (2009). Measuring knowledge spillovers:What patents, licenses and publications reveal about innovation diffusion.
Research Policy, 38(6), 994–1005.
• Nelson, R. R. (2001). Observations on the Post-Bayh-Dole Rise of Patenting at American Universities. Journal of Technology Transfer,
26(1-2), 13-19.
• Sung, K., Kong, H.-K., & Kim, T. (2013). Convergence indicator: the case of cloud computing. The Journal of Supercomputing, 65(1),
27-37.
• Wang, X., Zhang, X., & Xu, S. (2011). Patent co-citation networks of Fortune 500 companies. Scientometrics, 88(3), 761-770.

26. Two cases
• Geographic Localization of Innovation
• Accessing competency of a Developing country
in high technology : Case study based on Indian
Patenting activity in the Biotechnology Sector