In search of anti-commons: Academic patenting and patent-paper pairs in biotechnology. An analysis of citation flows.

1. In search of anti-commons: Academic patenting and patent-paper pairs in biotechnology. An analysis of citation flows. Tom Magerman, Bart Van Looy, Koenraad Debackere (tom.magerman@econ.kuleuven.be) INCENTIM (International Centre for Studies in Entrepreneurship and Innovation Management) K.U.Leuven Managerial Economics, Strategy & Innovation ECOOM (Centre for R&D Monitoring) ESF-APE-INV workshop Scientists & Inventors 10-11/5/2012

2. 1957

10. University-Industrylinkages

11. University-Industrylinkages Commercialization of science (Entrepreneurial University) Scientification of technology

12. University-Industry linkages

13. University-Industry linkages

14. University-Industrylinkages Commercialization of science (Entrepreneurial University) Scientification of technology

15. Anti-commonsand the end of open science If I have seen a littlefurther [thenyouand Descartes] it is by standing on the shoulders of Giants. Isaac Newton, letter to Robert Hoode (originatedfrom John of Salisbury)

16. Anti-commonsand the end of open science

17. Anti-commons and the end of open science Tragedy of the anticommons: underuse of scarce resources because too many owners can block each other => more intellectual property rights may lead paradoxically to fewer useful products On the one hand incentive to undertake risky research On the other hand too many owners hold rights in previous discoveries that constitute obstacles to future research => high transaction costs lead to inefficiencies Biomedical research has been moving from a commons model toward a privatization model => risc of anticommons tragedy Influenced by patent system: what is patentable (e.g. patents on gene fragments) Influenced by patent owner: licensing behavior (e.g. use of reach-through license agreements) Transition or tragedy? Find ways to lower transactions costs of bundling rights (intermediate organizations; patent pools; cross-licensing) Tom Magerman – ENID 2011

18. Anti-commons and the end of open science Expansion of IPR is privatizing the scientific commons and limiting scientific progress • Heller and Eisenberg (1998); Argyres and Liebskind (1998); David (2000); Lessig (2002); Etzkowitz (1998); Krimsky (2003) Murray and Stern (2007): “Do formal intellectual property rights hinder the free flow of scientific knowledge? An empirical test of the anti-commons hypothesis” • How does IPRs affect propensity of future researchers to build upon knowledge? • Compare citation patterns of publications in pre-grant period and after grant • 169 patent-paper pairs (Nature Biotechnology) • Modest anti-commons effect: decline in citation rate by 10 to 20%

19. Detection of patent-publication pairs

20. Text Mining • Text mining refers to the automated extraction of knowledge and information from text by means of revealing relationships and patterns present, but not obvious, in a document collection. Related to data mining, but additional issues: • other scale of dimensionality (100,000+ ‘variables’) • different kind of variables (not really independent, and very, very sparse – 99.99%) • language issues (homonymy/polysemy and synonymy)

21. Latent Semantic Analysis (LSA) LSA was developed late 1980s at BellCore/Bell Laboratories by Landauer and his team of Cognitive Science Research: “Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the meaning of words. Meaning is estimated using statistical computations applied to a large corpus of text. The corpus embodies a set of mutual constraints that largely determine the semantic similarity of words and sets of words. These constraints can be solved using linear algebra methods, in particular, singular value decomposition.” • LSA is a technique for analyzing text: extract (underlying or latent) meaning from text • LSA is a theory of meaning: meaning is acquired by solving an enormous set of simultaneous equations that capture the contextual usage of words • LSA is a new approach to cognitive science: use large text corpora to test cognitive theories

22. Linear algebra problem The meaning of passages of text must be sums of the meaning of its words. LSA models a large corpus of text as a large set of simultaneous equations. The solution is in the form of a set of vectors, one for each word and passage, in a semantic space Similarity of meaning of two words is measured by the cosine between the vectors, and the similarity of two passages as the same measure on the sum or average of all its contained words

23. SVD dimensionality reduction Singular Value Decomposition rank-k approximation: Dimensionality reduction by taking first k singular values: with a diagonal matrix of singular values

24. Practical application? Even when using LSA/SVD as text mining method, many options remain!

25. Assessment of 40 measure variants

26. Full process

27. Expert validation

28. University-Industrylinkages Commercialization of science (Entrepreneurial University) Scientification of technology

29. Methodology and dataPublication data Selection of biotechnology publications from the Web of Science based on the subject classification (1991-2008): • Core set of 243,361 publications : subject category Biotechnology & Applied Microbiology • Extended set of 683,674 publications : publications of following subject categories citing or cited by a publication of the core set: Biochemical Research Methods; Biochemistry & Molecular Biology; Biophysics; Plant sciences; Cell Biology; Developmental Biology; Food sciences & Technology; Genetics & Heredity; Microbiology Materials • Multidisciplinary set of 97,970 publications : publications from multidisciplinary journals Nature; Science; and Proceedings of the National Academy of Sciences of the United States of America 1,025,005 publications in total (948,432 suited for text mining) 478,361 publications published between 1991 and 2000

30. Methodology and data Patent data Selection of all granted EPO and USPTO biotechnology patents, applied for between 1991 and 2008, from PATSTAT using IPC-codes as listed in OECD definition of biotechnology (‘A Framework for Biotechnology Statistics’, OECD, Paris, 2005) 27,241 EPO patents and 91,775 USPTO patents 119,016 patents in total (88,248 suited for text mining)

31. Methodology and data Matching

32. Methodology and data Pairs 584 patent-publication pairs identified • 17 patent linked to multiple publications (up to 3) • 115 publications linked to multiple patents (up to 7) (patent families) • 566 distinct patents paired with publication • 400 distinct publications paired with patent Patentee type • 292 University • 128 Government / Non profit • 126 Company • 38 Hospital • 21 Individual (42 patents have multiple patenteesfrom different sectors)

33. Publication and citation numbers

34. Citation analysis

35. Match publications to deal with quality differences 328 pairedpublications versus 106,027 biotechnologypublications

36. Before and after publication and grant

37. Paired sample t-tests

38. Multivariate analysis (negativebinomial)

39. Sector analysis

40. Sector analysis

41. Sector analysis

42. Conclusionsscience-technologyinteractions

43. Overview

44. In search of anti-commons: Academic patenting and patent-paper pairs in biotechnology. An analysis of citation flows. Tom Magerman, Bart Van Looy, Koenraad Debackere (tom.magerman@econ.kuleuven.be) INCENTIM (International Centre for Studies in Entrepreneurship and Innovation Management) K.U.Leuven Managerial Economics, Strategy & Innovation ECOOM (Centre for R&D Monitoring) ESF-APE-INV workshop Scientists & Inventors 10-11/5/2012