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UNIVERSITY OF CAMPINAS BRAZIL. 2012 ICABR Conference Ravello. Innovation Networks: Emerging Technological Trajectories on Ethanol Fermentation Processes. Dal-Poz, Ester Silveira, Jose Maria Masago, Fabio. Objectives. Analysis of the dynamics of global R&D efforts on biofuels

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2012 icabr conference ravello

UNIVERSITY OF CAMPINAS

BRAZIL

2012ICABR ConferenceRavello

Innovation Networks: Emerging Technological Trajectories on Ethanol Fermentation Processes.

Dal-Poz, Ester

Silveira, Jose Maria

Masago, Fabio

objectives
Objectives
  • Analysis of the dynamics of global R&D efforts on biofuels
  • This article: innovation markets foresight on alcoholic fermentation of second generation ethanol.
  • Understand the technological evolution and the relative position of potentially competitive technologies.
objectives1
Objectives

Three methodological approaches – throught patents

  • Innovation networks - by citation of patents (Hall et al., 2001) - are used to chart the technological trajectories
  • Patent lexicographical environments, to understand technological areas linked to second generation ethanol processes
  • The most important fields and enterprises patenting efforts, in each of the same technological areas.
four innovative industrial processes
Four “innovative” industrial processes
  • Simultaneous Saccharification and Fermentation (SSF)
  • Consolidated Bioprocessing (CBP)
  • Direct Microbial Conversion (DMC)
  • Separate Hydrolysis and Fermentation (SHF)
current vision about patent use to understand innovation
Current vision about patent use to understand innovation
  • Patents are formally documents guaranteeing the monopoly of technological knowledge applied to industrial processes, and does not reach the analytical level of the phenomenon of innovation.
  • Does not??? It is not true: when you have market agents looking for surveys of patents (citing each other), you may extract some tendencies… or trajectories, and market value of technologies.
networks of citations and market agents which invest in r d are proxies of innovation
Networks of citations and market agents – which invest in R&D – are proxies of innovation
  • Networks of patent citations can be considered networks of innovation: extensive empirical evidence demonstrating that a high degree of citation for a patent is correlated with market presence, which allows us to consider highly cited patents as examples of innovation (Trajtenberg,1990; Hall et al. 2001; Jaffe, and Trajtenberg, 2002).
  • Idea that the network (to the degree that it is promptly acquired) characterizes momentum in the development of a complex and adaptable economic system (Foster, 2004).
networks deal with the cumulative results of efforts and investment in r d during a certain period
Networks deal with the cumulative results of efforts and investment in R&D during a certain period.
  • How market agents – which have R&D efforts too – are looking for tech scenarios?
  • Highly cited patents are the “créme-de-la-créme” of these efforts – recognized by other patenting agents.
  • Strong economic valuation (intangible assets) approach – highly cited patents makes enterprises more valued.
advantages of networks of innovation
Advantages of networks of innovation
  • Emerging technologies may be detected: highly cited AND highly connected patents designs a technological trajectory
  • Expired patents are an important aspect of the many efforts making up the relations in the network that define the strategy of R&D of patent pool holders.
  • The potential blocking forces of many of the identified patent pools can be identified through the proceedings of the network (Chu 2009), considering the effects of patent blocking in R&D:
na economic evolutionary approach
Na economic evolutionary approach
  • Economic development is characterized by qualitative change, since the new entities emerging during its course are not comparable to previously existing ones.
  • The variety/diversity of the economic system rises during the course of economic development.

(Saviotti, 2009)

characteristics of networks of innovation
Characteristics of networks of innovation
  • Scale-free - very resistant to random attack: almost 80% of the links can be cut before a scale-free network is destroyed, while the corresponding percentage for an exponential network is less than 20%.
  • Good approach to understand innovation.
networks
Networks
  • Structure of knowledge networks is described by nodes (patents in this case) and connections between them (forward citations).
  • Breschi & Lissoni (2004) - a certain company (A) may have fewer patents than (B), but it may be cited by other companies which may also be highly cited.
  • A non-linear view on a company´s innovative capabilities and its technological potential.
networks algebric indicators
Networks algebric indicators*
  • density
  • geodesic distance
  • centrality
  • And using k-cores for citing indicators

* According to OTTE & ROUSSEAU, 2002, based on graph theory literature

network ssf k core 32 1
Network SSF - k-core 32 -1

Density 0,81701%.

Centrality (indegree): 0,0119191

network ssf k core 32 20 and geodesic distance 1 0
Network SSF – K-core 32-20 and Geodesic Distance = 1,0

Density 1,19191%.

Not tipical of innovation net. This is the same net of the last slide. Density does not changes, despite GD = 1,0.

Low relationships variance.

rede ssf dg 1 0 k core 32 20
Rede SSF – DG 1,0; k-core 32- 20

No tech trajectory defined.

No pattern of highly cited and highly connected nodes.

Innovation is a star war for SSF.

more cited patents and enterprises
More cited patents and enterprises

a) Solvay Enzymes, Inc. (Elkhart, IN), July 27, 1993

Process for producing ethanol

b) ZeaChem Inc. (Golden, CO), January 21, 2003

slide19
Simultaneous Saccharification and Fermentation Network – SSFSSF clusters by technological themes Patent documents topology
ssf patent top assignees by year
SSF – Patent Top Assignees by year

Source: Thomson Innovation

bioprocessing network no k core filter
Bioprocessing network – no k-core filter

Density= 0,0101807; centrality= Zero tendency (network is weakly connected)

bioprocessing top assignees by year
Bioprocessing Top Assignees by year
  • Source - Thomson Innovation
direct microbial conversion no k core filter
Direct Microbial Conversion – No K-Core Filter

Density = 0, 0735632

Centrality = 0,01091 – zero tendency

dmc patent documents topology
DMC – Patent documents topology

Legend

Source: ThomsonInnovation

shf top assignees by year
SHF – Top Assignees by year

Source – ThomsonInnovation

conclusions
Conclusions
  • Second generation ethanol, concerning its most important industrial R&D frontier, is a STAR WAR in course.
  • There is a “scale free” pattern of the nets, but no strong relationship among nodes – what means no trajectory is defined till now.
  • Although SSF has the most “complex” network, there is no technological trajectory defined (considering the double indicator of highly cited/ high k-core indicators)
conclusions1
Conclusions
  • Lexicographical patent analysis demonstrates that innovation solution for the industrial demands for using all biomass raw material is not a clear technological ensemble: a high level of uncertainty about the benefits that each type of fermentation technological adoption may represent to the industry is still non-defined.
  • 10 technologies, linked to their assignees enterprises, have been selected as potential solution to set up second generation ethanol industry.
  • Expectations about second generation ethanol may be seen as a muddy ground, concerning innovation generation and adoption.
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