1. A multidimensional approach to visualising and analysing patent portfolios Edwin Horlings Global TechMining Conference, Leiden, 2 September 2014

2. 2 | A multidimensional approach to visualising and analysing patent portfolios Structure what is the problem I want to solve, for myself and the community building a data infrastructure around PATSTAT multidimensional: which dimensions outputs: for visualisation and analysis how it can be used: 3 use cases no quick and dirty substitute for sound empirical analysis

3. 3 | A multidimensional approach to visualising and analysing patent portfolios Problem Number of different applications of patent analysis measuring the globalisation of R&D of Dutch multinationals identifying emerging technologies for human materials transplants analysis patterns of academic patenting Need for custom queries normalisation: global rather than local properties and indicators of individual applications for statistical analysis visualisations

4. 4 | A multidimensional approach to visualising and analysing patent portfolios A data infrastructure for PATSTAT QUERY SET 1: Pre-construct aggregated information for all of PATSTAT per application, INPADOC family, and single priority family basic information on application and publication aggregate tables for citations between applications and families Why aggregate? time saving normalise globally rather than locally QUERY SET 2: for a specific set of patents collect information from aggregate tables, calculate local information (e.g. topics within the dataset), and produce output for statistical analysis and visualisation

5. 5 | A multidimensional approach to visualising and analysing patent portfolios Five dimensions for analysis DimensionExamples Timedate of application date of publications distance in time between application, publication, citation, and granting Citationforward and backward to patents and non-patent literature references Topicsclusters of highly similar patents as measured, for example, by cooccurrence of IPC codes or words Diversityvariety of topics distribution of applications among topics Qualityeconomic value technical impact nature of the invention

6. 6 | A multidimensional approach to visualising and analysing patent portfolios Indicators for patent quality IndicatorInterpretationReference sizelarger families are more valuableLerner (1994) scopebroad patents are more valuableLanjouw et al. (1998) backward citationspatents with more backward citations have higher value and are more incremental Trajtenberg M. (1990) Lanjouw and Schankerman, (2001) forward citations (within 5 years) technological importance and economic value of inventions Trajtenberg M. (1990) number and share of NPLRs distance to science, technical qualityCallaert et al. (2006) Branstetter (2005) claims and adjusted claims number of claims reflects expected patent value and technological breadth Tong and Davidson (1994) Squicciarini et al. (2013) grant grant lagshorter lag indicates higher valueCzarnitzki, Hussinger & Schneider (2009) generalityrange of later generations of inventions that have benefitted from a patent Trajtenberg, Henderson & Jaffe (1997) originalityindicates diversity of knowledge sourcesTrajtenberg, Henderson & Jaffe (1997) radicalnessradical versus incrementalShane (2001) technology cycle timepace of technological progressKayal & Waters (1999)

7. 7 | A multidimensional approach to visualising and analysing patent portfolios Ten steps in query set 2 1.Delineating an entry dataset using search criteria 2.Producing a working data set 3.Collecting basic properties of patents 4.Extracting and calculating patent quality indicators 5.Calculating similarity of patent families in the working data set 6.Finding patent clusters by applying SAINT network tools 7.Constructing tables with nodes and edges data for Gephi 8.Constructing tables for statistical analysis 9.Extracting descriptives per patent cluster 10.Visualising the portfolio in Gephi

8. 8 | A multidimensional approach to visualising and analysing patent portfolios Visualising five dimensions 1 slide portfolio of scientific output

9. 9 | A multidimensional approach to visualising and analysing patent portfolios A scientist’s lifetime publication output

10. 10 | A multidimensional approach to visualising and analysing patent portfolios Linking patents to publications

11. 11 | A multidimensional approach to visualising and analysing patent portfolios A firm’s lifetime patent portfolio: Google

12. 12 | A multidimensional approach to visualising and analysing patent portfolios A firm’s lifetime patent portfolio: Google

13. 13 | A multidimensional approach to visualising and analysing patent portfolios No conclusion without statistical analysis A visualisation can be extremely informative.......but be careful of the Rorschach effect! You must confirm what you think you see: statistical analysis interviews other methods Queries produce file with statistical information on all individual applications in the set

14. 14 | A multidimensional approach to visualising and analysing patent portfolios The quality of academic patents compared

15. 15 | A multidimensional approach to visualising and analysing patent portfolios The quality of academic patents compared N (single priority families) share of NPLRs = 0 mean share of NPLRs standard deviation median share of NPLRs general universities 853 338 (39.6%).410.392.375 technical universities 606 365 (60.2%).193.292.000 non-university PROs 2,343 1309 (55.9%).215.305.000 top-100 firms50,367 43389 (86.1%).042.138.000 other firms29,89124491 (81.9%).070.198.000 Note: Estimates for 1990-2010. University-invented patents to be added.

16. 16 | A multidimensional approach to visualising and analysing patent portfolios Technical university patents

17. 17 | A multidimensional approach to visualising and analysing patent portfolios Three examples Visualising the portfolio of a firm Identifying emerging topics in a technology area Comparing the quality of patent clusters

18. 18 | A multidimensional approach to visualising and analysing patent portfolios Limitations no quick and dirty substitute for sound empirical analysis probably many ways to improve on my queries very precise analysis will always require detailed data cleaning

19. Edwin Horlings | e.horlings@rathenau.nl Thanks you for your attention