TECHNOLOGY FRONTIER ON BIOENERGY: ANALYSIS OF TWO NETWORKS
OF INNOVATION
Prof.José Maria F.J. da Silveira/[email protected]
Prof. Maria Ester S. Dal Poz /FCA-Unicamp [email protected]
Researcher Fabio Masago/IC-Unicamp- [email protected]
ABSTRACT
Energy Research programs are defined on the basis of a systematic application of the technical and
scientific knowledge of agents who compete in a context of selective market processes. These
processes take different trajectories and generate different patterns of technology diffusion within
firms, between firms, among firms in the same industry, between industries and sectors in different
countries, particularly USA and Brazil, the leading countries on ethanol production nowadays. The
paper presents a methodology of networks based on citation of patents (HALL, JAFFE, &
TRAJTEMBERG. 2001; VERSPAGEN. 2007) to identify Technology Trajectories that are close related to
the research activities BIOENERGY Programs in Brazil. Odyssey’s Patent Computational System for
network information retrieval was used for forward citation searches, selections, aggregating data from
the United States Patent and Trade Office (USPTO), and identifying networks using algebraic indicators.
The networks were constructed from lexical-based queries, combining International Patent
Classification (IPC) classes C12N and C07h21, according to the International Patent Classification
typology. Our results shows that the bio-ethanol R&D processes constitute an emergent technological
trajectory, which means that there is no clear identification of technological trajectories, but while it
was possible to see the active participation of leading companies in the biotech field already working in
the sector.Keywords: bioethanol; patents, network analysis. JEL 034; 033
Resumo
Programas de energia são definidos com base na aplicação sistemático do conhecimento científico e
tecnológico de agentes que compete em um Mercado com forte processo de seleção. Esses processos
devem tomar diferentes trajetórias tecnológicas e gerar distintos padrões de difusão dentro das firmas,
entre firmas, entre firmas de uma mesma indústria, entre indústrias e setores de diferentes países.,
principalmente USA e Brasil, os países líderes na produção de etanol na atualidade. O trabalho
apresenta uma metodologia de redes de patentes baseadas na citação de patentes (HALL, JAFFE, &
TRAJTEMBERG. 2001; VERSPAGEN. 2007) para identificar trajetórias tecnológicas que são diretamente
ligadas a programas de Bionergia no Brasil. Um programa computacional denominado ODISSEY foi
utilizado para construir redes de citação de patentes usando palavras-chave obtidas a partir da
discussão com pesquisadores da área e aplicados ao USPTO, permitindo a aplicação de uma
metodologia que inclui a análise de indicadores e a constituição de trajetórias. Selecionou-se, com base
na experiência prévia, as classes de IPC C12N and C07h21. Nossos resultados mostram que as duas
redes obtidas, uma em nível macro e outra em nível analítico meso, respectivamente
ethanol/bioethanol e processos de sacarificação, não permitiram a identificação de trajetórias
tecnológicas claras, ainda que tenha sido evidente a intensa participação de empresas líderes em áreas
de aplicação da biotecnologia. Palavras-chave: bioethanol , patentes, análise de redes. JEL 033; 034
TECHNOLOGY FRONTIER ON BIOENERGY: ANALYSIS OF TWO NETWORKS
OF INNOVATION
Introduction
In order to form strategies (and policies), it is vital for agents to continue to perform
different kinds of technological foresight activities combined with the critical analysis of the
portfolio of scientific and technical knowledge, which includes a variety of forms of intellectual
property protection and of secrets surrounding the use of patents (as well as sui generis forms of
intellectual property protection), through agreements between innovators and their clients and
pre-competitive cooperation contracts ( CHAN, 2011). Public and private companies that find
themselves in a lock-in situation may fall victim to future losses in competitiveness or may
simply be forced to leave the market due to path dependence.
Biotechnology and bio-business are clearly defined as a science based economic activity
(FONSECA et al 2007). However, there is a debate on whether biotechnology progressively
constitutes a new economic sector or if it is a part of the technology trajectories of traditional
Science based sectors, particularly pharmaceuticals. Undoubtedly, there is a “molecular biology
based” common origin in the majority of techniques that progressively constitute the “core” of
biotechnology business. (STURTEVANT, 2001; CAMPOS, 2007). In addition to the techniques
used in molecular biotechnology, other such knowledge includes: knowledge related to
bioinformatics, the identification of molecular markers, techniques used to verify a genotype’s
ability to express itself and to verify regulatory mechanisms of genetic expression, biobalistic
techniques and other gene transfer mechanisms based on biochemistry, and techniques
regarding mechanisms of cellular physiology, botany, microbiology, and physics, such as the
use of laser ray.
Recent literature points to the way in which the combinations of opportunities created by
technology can be linked to endowments and the accumulation of capabilities in strategic areas.
(VON TUNEZMAN; ACHA, 2008). These combined blocks of knowledge and organizations
forms (in-house R&D, networks, Newly Biotechnology Firms poles) have quickly widened the
“technological paradigm”, partially caused by certain challenges such as those posed by bioenergy in USA and Brazil. (BABCOCK, 2011).
Energy Research programs are defined on the basis of a systematic application of the
technical and scientific knowledge of agents who compete in a context of selective market
processes. These processes take different trajectories and generate different patterns of
technology diffusion within firms, between firms, among firms in the same industry, between
industries and sectors in different countries, particularly USA and Brazil, the leading countries
on ethanol production nowadays. To give an example, EBI - Energy Bioscience Program,
developed by University of California and University of Illinois and sponsored by British
Petroleum, has been focused in the use of Switch Grass and Myscantus as raw material for
second generation processes. Brazilian Bio-energy Programs, particularly BIOEN- São Paulo
Research Foundation (FAPESP) are focused in the use of biomass from sugar cane, for first and
second generation processes to obtain renewable energy. (RAUSSER e al, 2010; SILVEIRA et
al, 2012).
Ethanol has become a global strategic fuel and a widespread option to climate change
challenges. Growth in investment by the sugarcane industry during the last ten years is a sign
of a change in the selection regime in the ethanol market structure, characterized in the recent
past by the influence of traditional entrepreneurs with close ties to public-sector agencies. In this
context, there is an increasing awareness of the leading role of R&D programs for the
competitiveness of ethanol. These comprise not only R&D activities in ethanol based on sugarcane
(first generation) but also other sources of renewable energy, such as ethanol from cellulose (second
generation), allowing for better land use. The growing market for bio-energy technologies points
to strategic alternatives for countries like Brazil, where renewable bio-energy based on
sugarcane accounted for 17.74% of total primary energy production in 2010 (BRASIL, 2011).
Biotechnology comprises a complex of different technologies, demanding a great deal of
scient ific and technological knowledge. A considerable amount of this knowledge will have to
come from strategic partnerships with research centers in other countries. However, there are clear
indications that Brazilian research is not taking off from scratch. Lemos et al (2009), correlating
the data on patents awarded to Brazilian assignees by USPTO and scientific activity of Brazilian
researchers according to areas of scientific research in the year 2006, highlighted the important
role of biotechnology and chemical engineering. This result indicates that bio-energy research
can be considered a window of opportunity to reduce the distance between science and
technology in developing countries, as defined in Aghion & Griffith, 2006.
The exploration of this technological frontier is tightly linked to the exploitation of bioenergy alternatives in an environment of technological uncertainty but supported by the forecast
of increased demand for ethanol. In other words, it is possible to sustain demand for sugarcane
based on a diversified portfolio of technologies, part of them with a satisfactory degree of
complementarity. This means not only that the competitiveness of ethanol is important, but
also that R&D activities contribute to the strengthening of the biotechnology market. Does
Brazil’s leading position in the field of renewable energy and byproducts, alongside a high level
of development in scientific and technological knowledge in biotechnology and related areas
justify a significant effort to develop new technology locally? This general question is
especially important in light of the interest shown by international investors since end of the last
decade in Brazil’s sugar and ethanol industry and by biotech firms in establishing research
centers to develop the initial stages of innovation such as the identification of genes relevant to
biotechnology and bio-energy (MOLINARO, 2012) .1
By highlighting the differences between two patent networks of relevance to the Brazilian
Bio-energy program, the paper sets out to explore the differences between a presumably mature
group of technologies – represented by the macro research field, ethanol, bio-ethanol) and a
technology considered emergent (fermentation) and their consequences for the appropriation
and commercialization of technologies in Brazil. The research program that motivate the paper
addresses three basic ideas: a) the existence of available common scientific knowledge in
agricultural biotechnology and industrial processes that motivates the formation of networks in
particular fields of by universities, corporations and public and private research centers; b) the
ability to correlate trajectories of scientific development that enable technologies and innovation
processes; c) the identification of possible IP barriers to scientific and technological developments
in bio-energy based on previous analysis and the exploration of windows of opportunity for the
“clearing” of technologies from Brazilian Bio-energy Programs to the market.
Section 2 shows the methodology approach of the paper and the next section, presents the
results in terms of the main features of two network structures. Section 4 discusses the
implications for Brazilian Bio-energy programs an main conclusions.
Our results shows that the bio-ethanol R&D processes constitute an emergent
technological trajectory, which means that there is no clear identification of technological
trajectories, but while it was possible to see the active participation of leading companies in the
biotech field already working in the sector. These technologies represent R&D investments in
1
Based on citations of scientific publications between 1970 and 2010, Molinaro (2012) shows the US accounting
for 62% of the knowledge created in sugarcane breeding and agricultural biotech in the bio-energy sector, with
Brazil accounting for only 10%.
biomass research, ethanol industrial technologies and alcohol chemistry made by leading
biotechnology corporations alone or in partnership with Brazilian firms. The control by leading
firms of enabling technologies (like enzymes, in the field of BIOET Network) can strongly
impact freedom-to-operate (FTO) systems all over the world. In this context, it is an
important indicator for the Brazilian R&D Energy Program’s governance (like BIOEN) ,
since its R&D results will be embedded in technology companies that should manage
themselves with the aim of generating technology products for bio-energy and R&D in new
“enabling technologies”. The windows of opportunity in second generation are still opened
or it will come to a rapid converge to technology trajectories followed by an increasing
degree of apropriability?
2. Methodological Approach: building and analyzing networks of innovation
2.1 NETWORKS OF INNOVATION AS COMPLEX AND EVOLUTIONARY ECONOMIC
SYSTEMS
This article follows the idea that economic systems (and in this case, systems of
acquisition of technologies as forms or achieving greater productive efficiency in scientifically
intensive sectors) are thought of in terms of elements and their connections (Potts, 2000).
According to Foster (2004), in order to understand economic systems as changing and complex,
it is essential to move beyond the classical analyses of the departments of businesses in terms of
functions of production and utility. The analyzes of networks, in this sense, make it possible to
capture the ways in which the products of R&D flow through an interconnected system.
According to Saviotti (2009), when analyzing economic systems evolution – specially
those based on R&D and innovation: a) economic development is characterized by qualitative
change, since the new entities emerging during its course are not comparable to previously
existing ones; b) the variety/diversity of the economic system rises during the course of
economic development. Networks of patent forward citation are a very good tool to understand
this variation and its evolution, because resume the cumulative efforts on R&D, and reveals how
market agents are looking to other agent´s efforts along the time. Kraft, Quatraro & Saviotti
(2009) develop a methodology to analyze changes in biotechnology from the early eighties up to
the year 2000. Using European Patent Office database and the IPC Patent Classification, the
authors build networks according to the co-occurrence of technological classes. In the early
periods (1980 up to 1990) they identify the existence of six technological classes that are the
core of biotechnology, whose share in the main indexes they apply to the networks are very
stable. With the emergence of molecular biology, the class C12N becomes central in the
connection with other important classes, like A61K. This methodology gives an illustration of
the idea of building blocks in biotechnology, stressing the hierarchy between classes, mostly in
SN indicators (centrality, closeness and betweenness). Simultaneously, Kraft, Quatraro &
Saviotti (2009) shows the importance of the enabling technologies, represented in the A61K
technological class, class of patents in the field of chemical analysis.2
The innovative chains in biotechnology area presents, per se, a network dynamics, in
which scientific capabilities, marked structure and demands and institutional competences may
be consider for policy making. This condition justifies a network analysis approach. At a
innovation network, scientific capacities must be put together, to reach development goals,
integrating research areas; institutional competences – for partnering, intellectual property
2
This paper shows that our option to focus on C12N ( enzimes, microorganisms) and C7H (sugar and nucleic acids)
to build our networks is an adequate shortcut to the core of the new biotechnology, based on molecular biology.
See the description of methodology, below.
agreements, R&D risks and market uncertainty systemic reduction, must be governed, an
innovation policy making environment – and its regulatory, funding and innovation incentives
systems, must be present. Market structure and innovation strategies futures, for pharma and
biotech health sector, are crucial factors to take in this scenario.
This methodology, typical of social networks, is a part of an evolutionary framework to be
used to research the dynamics and tendencies of innovation in ethanol biotechnologies foresight
process. (JACKSON, 2010). Networks express relationships of accumulation and appropriation
of knowledge, seen here as a public good (Krafft et al., 2009), and that the structure of
knowledge is described by nodes (patents in this case) and connections between them
(subsequent citations).
According to Hall et al. (2005), patents may be considered reliable sources of innovation
studies and technical change. Sampat & Ziedonis (2002) show the economic and technological
importance of such an analysis. The paper presents a methodology of social networks based on
co-citation of patents (HALL, JAFFE, & TRAJTEMBERG, 2001; VERSPAGEN. 2007) to
identify Technology Trajectories (TT). The methodology is based on forward citations that a
patent receives, which are indicators of innovative strength of markets based on technology:
highly cited patents are proxies for technological market values. The search for information on
patent networks started from key-questions (Zhu et al. 1999) which aimed at understanding the
investigation objective in order to reach the survey goals.
Balconi et alli (2004) claim that forward patent citations indicators are very good proxies
to understand that a certain company (A) may have fewer patents than (B), but it may be cited
by other companies which may also be highly cited. They offer a non linear view on a
company´s innovative capabilities and its technological potential. In a opposite way, a few
number of patents found in a research field is a clear sign of its reduced importance to the
identification of technology trajectories, what worsens if these group of patents are in the
periphery of networks, cited by other irrelevant group .Those indicators used to understand
the relationships among actors and their connectivity levels, are centrality, geodesic distance
and connectivity of the actors. Their main indicators – density and geodesic paths – are used to
understand the network structure (OTTE & ROUSSEAU, 2002).
It is important to stress that networks of patent citations can be considered networks of
innovation: there is 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 (TRAJTEMBERG,1990; HAL et al. 2001; JAFFE;TRAJTEMBERG,
2002). This reinforces the 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). On the other hand, this concept runs contrary to the current vision that the use
of patents - which are formally documents guaranteeing the monopoly of technological
knowledge applied to industrial processes - does not reach the analytical level of the
phenomenon of innovation. Using this kind of networks also means dealing with the cumulative
results of efforts and investment in R&D during a certain period (DOSI, 1982; VERSPAGEN,
2007; FONTANA; NOVULARI;VERSPAGEN, 2008).3
3
This consideration leads us to point out that 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. They should not, therefore,
be discarded from the analysis, no matter how disposable common sense in intellectual property considers them to
be. Erroneously, this common sense does not take into consideration that even expired patents make up an
inseparable part of the process of capital accumulation in the discretionary processes of the protection of
intangible assets. Failing to consider them distorts all of the effects of the acquisition of intellectual property rights
in the area of technology already used by patent holders as tools for competitiveness and of many economic
externalities derived from those rights.
In a broad perspective, the networks analyzed in the paper represent: a) the accumulation
of efforts in the form of the mobilization of tangible, intangible and complementary catalysts for
innovation; b) the projection – along time - of connections resulting from those efforts by a
group of economic agents interconnected by competitive games; c) the complexity of these
networks matters, since it defines the forms of competitive interaction that took place in the time
period in which the network was formed. The networks, even though understood at the time of
analisis as a snapshot in time, summarize the process through which all of the elements have
already passed, and more importantly, they are fundamentally linked to technological trends.
Finally, property rights are relevant in certain R&D fields like biotechnology and bio
energy and patents can give protection to their assignees according to its position on the
network of innovation. According to Chu (2009:1), considering the effects of patent blocking in
R&D:
“In an environment with sequential innovations, the scope of a patent (i.e. patent breadth)
determines the level of patent protection for an invention against imitation and subsequent
innovations. This latter form of patent protection, which is known as leading breadth in the
literature, gives the patent holders property rights over future inventions. Because of the resulting
overlapping intellectual property rights, an infringing inventor may have to share her profits with
the infringed patent holders and have less incentive to invest in R&D. This negative dimension of
overlapping intellectual property rights is known as blocking patents.”
Concerning the networks structure and its capability to enlighten innovation phenomena,
Barabasi (2002), Barabasi & Reka (1999); Barabasi et al., (1999) and Barabasi & Bonabeau
(2003) have been pointing that a large class of networks possess some common properties for
which they are called scale-free. These non-regular networks have a very asymmetrical
distribution of links around nodes: few nodes have many links and many nodes have few links.
This is not an egalitarian distribution of links around nodes/vertices. Scale-free networks have
the interesting property of being 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%. However, a targeted attack selectively cutting links
around the most central nodes (hubs) destroys the network by cutting less than 20% of the links.
(SAVIOTTI, 2009). Another important feature of network of innovation is the “giant
component” . The presence of a giant component complements the identification of a scale-free
pattern giving relevance to patents considered essential to network structure.4
This represents a good approach to understand innovation and technological efforts
embedded into patent documents, because once an enterprise makes R&D investments, and
once it has a certain diffusion to the market, as innovation, it is difficult to cut out its presence
from the innovation scenario. The power and strength of the relationships in the network
expressed by algebraic indicators of the network can therefore be economically evaluated
through the extraction of technological trajectories therefore revealing which businesses (and
their patents) have the technologies most relevant to the market. Otherwise, a network whose
structure has no scale free attribute points to a less mature condition, pointing to opportunities
for future developments or even for structural difficulties to generate technology trajectories.
4 In network theory a giant component is a connected component of a given random graph that contains a
constant fraction of the entire graph's vertices(JACKSON, 2010).
Through these scenarios, which focus on the result of the interplay of appropriation of
technologies, data on the efficiency and/or stability (seen in its dynamic form and not derived
from the vision of static efficiency), but the value of the most relevant technological paths5.
Understanding the networks as evolving systems, this article considers that:
a) At the core of the networks are the technological trajectories that make it possible to
say that certain technologies are more or less important, or that they have greater or
lesser market share;
b) By tracing these trajectories it is possible to analyze the technology market potential,
as its market value. No trajectory represents that the technology appropriation and
diffusion games are yet been defined, and market structures will be establish in the
future.
2.2 DESCRIPTION OF THE METHODOLOGY
The System of data collection
The two networks were constructed from lexical-based queries, combining International
Patent Classification (IPC) classes C12N and C07h21, according to the International Patent
Classification typology for the period 1976 to 2011.
Table 1. Description of the Queries
Lexical Terms
Patent Fields
Search period
NETWORK 1
Ethanol/Bioethanol
Abstract/Claims
01/01/1976-10/10/2011
NETWORK 2
1. “Simultaneous Saccharification”
Claims
01/01/1976-10/10/2011
AND
“Fermentation”
All fields
Odyssey’s Patent Computational System for network information retrieval6was used for
forward citation searches, selections, aggregating data from the United States Patent and Trade
Office (USPTO), and identifying networks using algebraic indicators. The patent search was
driven by a lexical query composed of combinational BIOEN R&D macro term areas and
International Patent Classification areas (C07h21 and C12N in the “abstract” and “claims”) for
the biotechnological areas shown below. The choice of queries was made after discussions with
prominent researchers in bio-energy and biotechnology who have strong concerns on IP
issues.7The networks were constructed from lexical-based queries. Network I and Network II
queries’s are in Table 1, composed of combinational matrix, directly linked macro level
5 This is not the objective of this article, but is a part of the Organizational Design Project of the BIOEN Program,
to which the research originating in this article is connected. The Project is funded by the State of São Paulo
Research Foundation, FAPESP.
6
Developed by Fabio Masago, Institute of Computer Sciences- IC-Unicamp
7
It worth to mention some names: Carlos Rossel. CTBE-MCT; Marcelo Menossi, IB-Unicamp; Glaucia SouzaIQ/USP; Marie Anne Von Sluis- IB/USP;; Rubens Maciel, FEQ/Unicamp; Sergio Paulino- INPI , Francisco Aragão,
EMBRAPA; Hugo Molinaro, EMBRAPA, among others.
analysis (ethanol/bioethanol) and
processes.
the four R&D
second generation ethanol fermentation
Methodological Procedures
This article uses this multi-stage process of technological foresight as a proxy of the
relative position of potentially competitive technologies. It is based on a sequence of procedures
(steps) to explore the potential technological options for ethanol industry networks (macro
analytical level) and industrial fermentation processes of second-generation ethanol (meso level
analysis).
The first procedure (step), considered more methodologically robust, merits the
theoretical and methodological considerations applied to it in this article: the approach based on
the networks arising from the cross-referencing of one patent with a subsequent patents. These
relationships, taking the form of graphical networks will be analyzed according to a
combination of network indexes (JACKSON, 2010) in order to identify their general properties.
The indexes of network density, geodesic distance and centrality8 are used for innovation
potential analysis and debate. They display very interesting micro-aspects of technological
capacity building among network partners, undertake other broader considerations about the
geographical aspects of innovation dynamics – as that the proximity among players matters and about the holes of the different clusters, hubs and connectors for the technological and
innovation development.
Having built the network, a measure of network density is taken. Network density is the
ratio of the number of edges in the network over the total number possible edges ( n(n  1) / 2 ,
where n is the number of vertices (JACKSON, 2010). Density gives an idea of how connected
the network is. A perfect network is called clique and density is 1.
The characterization of the original network is reinforce by the centrality index that
measures network activity for a node by using the concept of degrees - the number of direct
connections a node has. Highly cited patents, with high degree of centrality reinforces the idea
of technology market distance. Individual network centralities (taking the most cited patents)
provide insight into the individual's location in the network. The relationship between the
centralities of all nodes can reveal much about the overall network structure. A very centralized
network is dominated by one or a few very central nodes. If these nodes are removed or
damaged, the network quickly fragments into unconnected sub-networks. A highly central node
can become a single point of failure. A network centralized around a well connected hub can
fail abruptly if that hub is disabled or removed. Hubs are nodes with high degree centrality.
(JACKSON, 2010)9
The second step is to refine the original network to identify technology trajectories (TT),
which means, identify sequences of patents well positioned in the network (with a level of
citation above a lower bound and cited by the most cited).. The potential blocking forces of
many of the identified patent pools can be identified through the proceedings of the network. It
8
According to the graph theory literature.
9 Simple closeness is an inverse measure of centrality: the larger the numbers, the more distant an actor is, and
the less central. Should really be called "farness". Normalized version divides the minimum "farness" possible (N-1)
by "farness" to simultaneously make the range 0 to 1 and invert the measure so that larger values correspond to
greater centrality (truly "closeness").In a diffusion process, a node that has high closeness centrality is likely to
receive information/infections more quickly than others. Betweenness centrality is the number of geodesic paths
that pass through a node. The number of "times" that any node needs a given node to reach any node by the
shortest path. Normalized betweenness divides simple betweenness by its maximum value. (JACKSON, 2010).
means to understand the relation’s forces and network elements proximity, using the network
indicator k-core, which is the largest subgraph where vertices have at least k interconnections.
Highly cited patents which have high proximity (given by the k-core) may be considered very
near the market (DAL POZ et al, 2011).
The final step consists to apply a n-level key-core criteria combined with distance
geodesic map to identify technological trajectories (TT). Vertices (or nodes) that presents high
citation indicators and that are high connected may be seen as important components of the
network. In our case, it is possible to extract patent trajectories from this procedure, revealing
patents time connections. To achieve the identification of TT the use of geodesic distance on the
new network, the network obtained from the use of K-core criteria. In the mathematical field of
graph theory, the distance between two vertices in a graph is the number of edges on the
shortest path which connects them. This is the geodesic distance: the length of the graph
geodesic between two vertices. If there is no path connecting the two vertices, i.e. if they
belong to different connected components, then conventionally the distance is defined as
infinite. It is necessary to consider that patents do not cite themselves. Therefore, one citation
has only one direction, from citing to cited patent. Thus for this case, paths between connected
vertices are direct linkages. The geodesic matrix is important to identify the connections
between patents that have high degree of centrality and is well connected10. (JACKSON,
2010).
3 . Results and Analysis
The results of the construction of the two networks is presented below.
Network 1 - Ethanol or Bio-ethanol Production Technologies
For Network 2, the search resulted in 58 patents, and the cited-citing network had 546
patents. The cleared network, eliminating patents that were never cited (isolates) , as seen in the
Figure 1 below, had 320 patents. The Ethanol/bio-ethanol network is presented in Figure 1 and
its contents analyzed in Table 3.11 A careful inspection of the patents contained in each of the
networks points to "Network 1A", a giant component as being linked to ethanol for energy.
Interestingly, Network 1B is related to the use of ethanol for medical purposes and methods of
ethanol quantification, close to IPC A61K, which qualifies the step 1 of methodology
(construction of networks based on queries in determined classes of IPC) as being able to
separate the issues in the interest of research. The other sub-networks are not relevant to the
investigation.
The links comprise technological areas, raw materials used and R&D fields. The general
network presented in Figure 1 has a density value of 0.0095 and Network A (ethanol for bioenergy) 0.0101, as was expected due to the clear split between Network 1A and Network 1B.
Centrality indexes (Normalized betweenness and Normalized closeness centrality) confirms
that there is no “gatekeeper patent” in the diffusion processes.
Graph 1 shows a coherent pattern showing low degree of maturity of Network I. There is a
strong concentration of citation of few patents, but they are not connected with other most cited
patents, revealing a scale free pattern.
10
Due to the limitation of space, the geodesic distance matrix has not been included in this paper.
11
Network 1 is divided in six sub-network, or three “giant components”: network A; network B up to Network F.
Figure 1 - Ethanol patent citation network
Table 2 – Ethanol/Bio-ethanol Network 1 Indexes
Number of Knots: 316
Number of Vertex: 1013
Density: 1,01%
Betweenness Centralization: 0,00072
Normalized Closeness Centralization:
0,20890 (range [0,1])
Graph 1. Network 1. number of citations per patents
Patents X Number of Citations
60
50
Citations
40
30
20
10
0
Patents
Applying the second step, (a combination of key-core and geodesic distance measures)
we arrive to the a list of the most cited patents, presented in ANNEX 1. They are related to
processes that were also identified in Network II (mostly saccharification), but without showing
any Technological Trajectory. A confirmation of this is presented in Table 3 below. It appears
simple at first sight, but the fact that two of the most cited patents have a geodesic whose value
is 1 (one) is quite remarkable. 12
Table 3. Sub-network 1-A: Geodesic Distances between most cited patents.
Patent number and (number of citations)
4,642,286
assignees
(18):
no 4,652,526
(assignee:
Missouri
(13)
Univ.
4,810,633 (20) (assignee: Mills Inc)
1/1
∞
5,231,017 (20) (assignee: Solvay)
∞
1/1
The 1/1 patent couple no. 5,231,017 (process for producing ethanol from raw materials by
Simultaneous Saccharification and Fermentation, SSF) and no. 4,652,526 (ethanol-producing
mutants of Clostridium thermosaccharolyticum) seems to be opening up a clear, albeit not yet
mature, biotechnological trajectory for second-generation ethanol. The other coupled patents
with a geodesic distance of 1/1, (patents 4,810,633 and 4,642,286) are outside the scope of
ethanol production (diagnostics of ethanol presence in organism fluids).13
The following considerations are relevant with respect to “ethanol/bioethanol” Network 1:
a) R&D is directed towards the use of biomass from corn (starch). However, it could be adapted
for the use of sugarcane biomass; b) the areas of enzyme R&D, particularly those that include
SFF, are based on GM microorganisms (fungus and bacteria) that can signify barriers for
BIOEN, as such patented technologies have assignees; c) the scenario does not involve R&D on
plant varieties, particularly sugarcane. The fact that the technologies analyzed by sub-network
1A are applicable to the production of corn ethanol, based on starch fermentation, reveals the
fragility of the system based on sugarcane; d) patent citers form clusters corresponding to 85%
of the most highly cited patents, and those that are relatively recent, covering the years 2005 to
2010 (Table 5). Such technologies pertain to a selected group of companies, 81% of which are
based in the U.S. and 19% in Nordic countries (Finland and Denmark); e) the low network
density, as mentioned above, reveals an still emerging technological trajectory, despite the fact
that R&D activities started 25 years ago.
Table 4. Recent technologies – fuel ethanol and biorefineries
Country
Assignee (Firm)
USA
ZeaChem, Inc. Xyleco, Inc.
USA
Celanese International Corporation Bioengineering Resources, Inc. Genencor
International, Inc.
Finland
Denmark
12
Cultor, Ltd.Valtion Teknillinen
Hoechst
Aktiengesellschaft
TutkimuskeskusOy
Due to size limitation imposed by the rules of the Congress, the figure with key-core 20 ethanol/bioethanol
network is not included in the paper.
13
A geodesic distance of 1 (one) means two highly cited patents are directly connected, suggesting a high level of
complementarity between them.
Source: research results.
Table 5. Network 2: Patents Relevant for Brazilian Energy Program related to GM crops.
USPTO
patent
number
7,303,873
Assignees
BIOEN Project technological relevance
University of Queensland
Method for promotion of endogenous sugar enzyme production
(St. Lucia, Queensland, AU) GM enzymes that catalyze conversion of endogenous sugar
7,102,057
Syngenta Participations
AG (Basel, CH)
Self-processing plants with processing enzymes which are
activated under suitable conditions to act upon the desired
substrate (sugar)
To produce auto-fermentable substrates for the production of
ethanol
7,557,262
Syngenta Participations
AG (Basel, CH)
To make and use GM plants produced by methods of patent
number 7,102,057, e.g. to produce fermentable substrates for the
production of ethanol
Source: research results.
Network 2 – Second Generation Processes:
“Biomass” – here seen as the raw material available for second generation ethanol - is a
generic term: it is the vegetal material composed by cellulose, hemicellulose and lignin, at least.
Consolidated Bioprocessing (CBP); Directed Microbial Conversion (DMC) and Separate
Hydrolysis and Fermentation (SHF). Only SSF is analyzed in the study, because, other areas
have shown very diffused patterns. All of this technologies analyzed should be able to transform
all the vegetal compounds cited ahead into fermentable sugars. The industry faces scale and
costs problems concerning these processes. All of them depend on enzymes industrial R&D,
capable of doing the saccharification of cellulose and other biomass compounds like it.
The difference among them is that some are more, other are less industrial integrated
processes; some of them uses live microrganisms, other use free enzymes. The biomass raw
fibers must be treated, through heat or chemical processes. All these industrial processes run
the R&D efforts. Our results confirm the hypothesis that second generation process is still on
progress. There is a lot of wishful thinking – followed by huge R&D efforts – to reach a
acceptable level of industrial productivity for second generation fermentation technologies.
Technological diffusion seems to be an innovation track: this is an undefined scenario. So, this
article is not concerned with technical viability of these technologies, but with their diffusion
potential at ethanol markets.
These processes are not directly related to ethanol/bioethanol production. This is the
reason why the SSF network 2 is larger than ethanol/bioethanol Network 2.Another reason is
the fact that the search is limited to two IPC classes of our interest.
In this paper, we only show the results of saccharification of cellulose (SSF) , the most
relevant network we find using the queries, as in Table 1.
Table 6 and Figure 2 presents the main results of the step 1 of the methodology.
Differently from Network 1, step 2 changes significantly Network 2. Applying Key-core 20
criteria, the number of Knots falls to 159, density increases significantly to 2,23% and mostly
important, the Normalized Closeness Centralization index reaches the level of 0,42, an idea that
some proximity of patents is verified.
Table 6. Network 2 – SSF Indexes
Number of Knots: 320
Number of Vertex: 835
Density: 0,8%
Betweenness Centralization: 0,00226
Normalized Closeness Centralization:
network is not weakly connected
Figura 2. SSF- Network Two (Key-core 10)
Figura 3. SSF : Most Cited Patents with GD=1
4840903
20/06/89
(15)
5198074
30/03/93
(16)
4220721
02/09/80
(12)
4326036
20/04/82
(11)
5571703
05/11/9
6
(10)
5874263
26/02/99
(10)
5464760
07/11/95
(15)
4952504
28/08/90
(11)
6509180
21/01/03
(29)
5407817
08/04/95
(14)
5932456
03/08/99
(15)
5231017
27/07/93
(32)
6927048
09/08/05
(18)
5258293
02/11/93
(15)
5837506
17/11/98
(11)
5677154
14/11/97
(11)
5487989
30/01/96
(27)
4321328
03/03/82
(23)
5628830
13/05/97
(20)
5779164
14/07/98
(10)
4490469
25/12/84
(15)
5597714
28/01/97
(19)
6333181
25/12/01
(13)
5554520
10/09/96
(21)
5620877
15/04/97
(17)
5135861
04/03/92
(11)
4503079
05/03/85
(11)
6090595
18/07/00
(19)
The third step confirms the differences between Network 1 and Network2. Network 2 is
not a Scale free network, like a majority of the networks we have identified. The network has a
higher level of density applying Key core 20. And its main components are connected in
homogeneous way, leading us to conclude that the network does not show a diffusion pattern.
Network 1 also does not show a clear Technology Trajectory, but the density of Network 2
signs to exploration processes looking for second generation. Figure 3 shows the network that
presents only patents with value of geodesic distance one. An inspection on the graph results in
the inexistence of Technological Trajectory. However, the figure shows that is an intensive
activity by firms in this field. Using Thompson Reuters Innovation, we see that traditional
leading firms in the field of enzymes; biomass plant industrial pre-treatment; biomass
sacharification processes; thermophilic and mesophilic microorganisms fermentations enzymes,
hydrolytic enzymes, lignocellulose solvents and even adapted transgenic plants for cellulose
fermentations are present, raising concerns on the BioEnergy Brazilian program.
Number of patents
Figure 4. Network 2: Leading Patenting Firms
4. Conclusions
This paper is intended as a contribution to efforts to draw the attention of policymakers
to the fact that there are a number of relevant issues regarding Brazilian Energy Programs
(as FAPESP-Brazil BIOEN Program) that justify particular attention to Intellectual Property
issues and the deployment of technological trajectories in the areas of ethanol, biomass and
industrial processing in alcohol chemistry.
Energy Research programs are defined on the basis of a systematic application of the
technical and scientific knowledge of agents who compete in a context of selective market
processes. We can formulate a hypothesis, based on innovation literature, that these processes
will take different trajectories and generate different patterns of technology diffusion within
firms, between firms, among firms in the same industry, and between industries and sectors. It is
a priority when a new paradigm is in progress to map the technological opportunities and the
formulating problems that result in innovations via what are known as “focusing devices”.
(MARENGO & DOSI, 2000).
This paper presents a methodology to identify Technology Trajectories in the field of the
research activities relating to the BIOENERGY programs, using BIOEN as a proxy. The use of
adequate queries and patent citation networks enabled recognition of technological opportunities
and formulating problems that have resulted in innovations in bio-energy, using macro am
meso- analytic field.
The research links between Network 1 (ethanol/bioethanol) and Network 2 (SSF) are
identified by an analysis of the BIOEN program, whose researchers seek simultaneously to
improve the performance ethanol (first generation) and put their efforts on second generation
research, represented in our study by SSF, Network 2. This research strategy points to a
comparison between two juvenile trajectories and identifies the main stakeholders in both
processes. A macrostructural analytical approach (Network 1-A) suggests the conclusion that
the ethanol-from -sugarcane R&D process is an emergent technological trajectory, due to low
network density and the weak correlation between network components. However, dual
technologies for starch and sugarcane substrates, already present in the innovation networks
presented in this study, show that it is necessary to understand from a business point of view
how such capacity from the patent assignee can impact R&D in sugarcane biotechnology. These
technologies represent R&D investments in biomass research, ethanol industrial technologies
and alcohol chemistry made by leading biotechnology corporations alone or in partnership with
Brazilian firms. Enzyme-driven biotechnological processes are also “enabling technologies”,
albeit in a less stressed industrial way, as many kinds of enzymes must be used in different
second-generation ethanol production systems.
Patent citation networks proceedings shows that the R&D global efforts nowadays have
been searching for and coming near to a technological pattern. The fermentation networks do
not follow a pattern of "scale-free" networking (Saviotti, 2009), and no real technological
trajectory is still consolidated; it is possible to point that, for innovative firms this is an open
scenario for technological search and selection.
The SSF process seems to be the more robust – that represents the most innovation
potential, technically talking, it shares many secondary processes with the other three processes,
and this may point that a new fifth (or more) intermediary process may emerge as the ultimate
fermentation solution for second generation ethanol. Using patent lexicographical tools and
pateting profiles, the investigation demonstrates that innovation solutions 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.
The technical change that the cutting edge fermentation processes represents indicates that
innovations in this field can be considered disruptive, but it is, still now, a very muddy ground
for technological adoption decision making.
This scenario clearly evidences the kind of investments in R&D that have to be made
not only in developed countries, but also in R&D intensive firms located in emerging
countries like Brazil. enabling technologies can strongly impact freedom-to-operate (FTO)
systems all over the world. In this context, it is an important indicator for the BIOEN
Program’s governance, since its R&D results will be embedded in technology companies that
should manage themselves with the aim of generating technology products for bioenergy and
R&D in new “enabling technologies”.
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ANNEX
Network 1A: Technological profile of a highly cited patent cluster
Highlycited
patents (USPTO
year)
Technological profile
BIOEN technological
areas:
E: enzymes
GMO: genetically
engineered
microorganisms
MGE: microorganism
gene expression
5,028,539/
1991
Simultaneous saccharification and
fermentation (SSF) of anaerobic
microorganisms.
E
GMO (bacteria)
5,487,989/
1996
Methods of treatment of cellulosic or
lignocellulosic materials from GMO
4,350,765/
1982
Biomass full fermentations and
metabolite control; Zymomonas
cultured in yeast-conditioned media
Enzymes
GMO (fungus)
MGE
Enzymes
GMO (bacteria)
5,231,017/
1997
Combination of biochemical and
synthetic conversions of full biomass
Enzymes
GMO (bacteria)
5,173,429/
1992
Biorefinery solvent extraction from
aqueous streams
4,393,136/
1983
5,554,520/
1996
Biocatalysts from anaerobic cells for
organic material fermentation
5,424,202/
1995
Starch hydrolyzing plasmids and
enzymes
6,333,181/
2001
Fibrous material preparation for
microorganism enzymes
4,652,526/
1987
Combination of biochemical and
synthetic conversions for high yield
ethanol production
4,400,470/
1986
Microorganism culturing production
of soluble enzymes
4,287,303/
1981
Industrial method for sterilizing
process fermentation vessels
5,482,846/
1986
GOM bacteria for starch hydrolyzing
enzymes
Simultaneous saccharification and
fermentation (SSF) for biorefineries
4,355,108/
1982
Industrial processes for biomass
enzymatic process improvements
4,447,534/
1982
Biomass hydrolysis and fermentation
adsorption-desorption processes
4,808,526/
1983
Biochemical and synthetic conversions
for ethanol production from anaerobic
microorganisms
4,812,410/
1989
Raw material source
of ethanol
ST: starch
SU: sugar
N-SB: Nonsaccharified
biomass;
GOM: General
organic material
(wood, domestic
waste)
ST
SU
BIOEN R&D
A. Biomass
Research
B. Ethanol
Industrial
Technologies
& Processing
Research
C. Alcohol
Chemistry &
Biorefineries.
ST
SUN
-SB
ST
SU
N-SB
N-SB
A, B, C
E
GMO (bacteria)
GOM
A, B, C
Enzymes
GMO (bacteria)
Enzymes
GMO (fungus)
MGE
Enzymes
GMO (bacteria)
MGE
Enzymes
GMO (bacteria and
fungus)
GOM
A, B, C
ST
SU
N-SB
ST
N-SB
A, B, C
ST
SU
N-SB
GOM
A, B, C
N-SB
A, B, C
N-SB
A, B, C
ST
SU
N-SB
A, B, C
Enzymes
GMO (bacteria)
MGE
Enzymes
GMO (bacteria)
MGE
Enzymes
Enzymes
GMO (bacteria)
MGE
Enzymes (E)
A, B, C
A, B, C
A, B, C
A, B, C
A, B, C
ST SU
N-SB
A, B, C
GMO (bacteria)
ST SU
N-SB
A, B, C
E
GMO (bacteria) MGE
ST SU
N-SB
A, B, C
GMO fermentation for biorefineries and
E
full biomass materials
GMO (bacteria) MGE
ST SU
N-SB
A, B, C
6,846,657/
2005
Raw cellulose stream by mixing a waste
cellulose feed and an algae cellulose
feed
E
GMO (bacteria and
algae)
MGE
GOM
A, B, C
6,861,248/
2005
GMO fermentation for biorefineries and
full biomass materials
E
GMO (bacteria)
MGE
ST SU
N-SB
A, B, C
4,816,399/
1989
Pentose sugar GMO fermentators for
biorefineries
E
GMO (bacteria)
MGE
ST SU
N-SB
A, B, C
7,109,005/
2006
Enzymatic simultaneous saccharification
and fermentation of biomass ( SSF
processes)
E
GMO (bacteria)
MGE
ST SU
N-SB
A, B, C
4,567,145/
1986
Selectively permeable membranes to
increase ethanol production efficiency
E
GMO (bacteria)
MGE
ST SU
N-SB
A, B, C
6,102,690/
2000
Bioreactor of organic aqueous material
and production of hydrogen from
microorganisms
E
GMO (bacteria)
MGE
ST SU
N-SB
A, B, C