The document proposes a model and tool to enhance open innovation and technology/knowledge transfer by applying semantic technologies, C-K design theory, and TRIZ to systematically match technology needs with offers in order to address challenges such as high transaction costs, information overload, and a lack of integrative approaches for aggregating needs and offers. It reviews relevant theories, resources like public databases of needs and offers, and technical tools that could be utilized. The proposed tool would cluster needs and offers, create relationships between them, and provide a dashboard with alerts to probabilistically match them.
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Technology and Knowledge Transfer Under the Open Innovation Paradigm
1. Technology and Knowledge Transfer under
the Open Innovation Paradigm
A model and tool proposal to understand and enhance
collaboration-based innovations applying semantic
technologies, C-K Design Theory and TRIZ
2. The need and motivations
Problems to capitalize and apply the knowledge and
skills behind expensive publicly funded research in
universities and other R&D institutions
Missed collaboration potential and opportunities to
reduce duplicated efforts due to transactional costs to
identify partners in academia and the industry
3. The need and motivations
Difficulties to translate the potential of hundreds of
active technology needs (already in the public domain)
into value. Some reasons are
fragmentation, information overflow and an inexistent
integrative approach for aggregating and matching
them with technology offers
Lack of tools with a robust theoretical background to
help technology transfer officers and other innovation
agents to bring new technologies to the market
4. Problem contextualization
UK is ranked as having the second-strongest research
base in the world behind only the US. The UK also
produces 8% of the world’s scientific papers and has a
citation share of 12%, ranking second in the world,
BUT in spite of that its commercialization results are
very poor (as it happens in general in Europe)
The public UK R&D spending is over £3.0 billion in
2009-10 and is set to be 2.5% of GDP by 2014*
meaning that the impact of that research and its ROI
has to increase significantly to maintain the public
support.
Source: http://www.rcuk.ac.uk
5. Areas of study Open Innovation
Models &
Paradigms
Technology &
Knowledge
Transfer
Management of
Innovation
processes Innovation/Design C-K Engineering
Theories Design Theory
Methods &
TRIZ
Technology and Techniques
Innovation
Management
Semantic Analysis
Knowledge & Information
Information
Information Aggregation and
Technology Tools
Management Clustering
Data Mining
Context Domain Area Subject
6. Volume of publications per area and timeline
Volume of publications indexed in ISI Web of Knowledge per
topic per year
450
400
350
300
250
200
150
100
50
0
Technology Transfer Knowledge Transfer Open Innovation
C-K Design Theory TRIZ
7. The traditional tech-transfer
Generation Evaluation and Selection Technology Push Transaction
Evaluation of the Technology is
discovery/invention and “packed” to be offered
its potential applications in the market
If it has If there is
commercial an interested
value party
Application for a
If it doesn’t Negotiations to
patent or other Final transaction and
have commercial prospects licence, sell or create
IP rights exchange of IP
Research centre If there is no interest an spin-off
Research Funding infrastructure and Scientific Discovery in the offer
accumulated knowledge
Once IP is cleared
it is possible to publish Patent becomes part
Scientific Publication of the passive portfolio
of IP
TTO usually does not TTO offers support and expertise Usually TTO is fully
get involved in commercial evaluation and IP responsible for this process
9. Tech transfer meets open innovation
Classic university technology transfer model Open innovation through innov. intermediaries
Technology Push Technology Pull
Technology is
“packed” to be Final transaction and
offered in the exchange of IP
Researchers market
If it has
commercial
value
If it doesn’t
If there is no interest
have commercial prospects
in the offer Open innovation networks
Company
Scientific Publications
with a need
Passive patents
10. …Unfortunately the communication does not work properly
Technology Push Technology Pull
Final transactions
Researchers and exchanges of
IP
Company
with a need
Researchers
Company
with a need
Researchers Open innovation networks
Company
with a need
Company
Researchers
Company with a need
with a need
11. Research Question
Can an integrated theoretical
framework, composed by C-K design
theory, open innovation and TRIZ help to
understand and model a better approach
to systematically match technology needs
with technology offers?
12. RESOURCES FOR THIS STUDY
Theories and models
Open Innovation Overall paradigm
The assumption is that closed models of innovation are
very limited and thus is important to understand how to
effectively incorporate external sources of
knowledge/technologies to solve organizational problems
(In addition to internal R&D)
The existence of the open innovation model for technology
and knowledge transfer facilitates the identification of
common barriers, implementation problems, best practices
and existent tools
13. RESOURCES FOR THIS STUDY
Theories and models
C-K Theory Structure and framework
Open innovation lacks a robust theory and a higher level of
abstraction that C-K theory can contribute with
In the context of technology transfer the concept space can
be understood as the technology requirements, while the
knowledge space represent technology offers (expressed
for example in patents)
C K and K C “movements” are critical for technology
transfer and they define the success (or not) of a process
triggered by a new technology need
14. RESOURCES FOR THIS STUDY
Theories and models
TRIZ Model and tool for matching technology needs
with technology offers
Facilitatesusing analogies for clustering and identifying
potential areas of matching
It provides a good starting point to identify common
problems (contradictions) and their solution principles
There are several available tools that make use of its
principles to solve problems starting from an specific
“technology need”
15. RESOURCES FOR THIS STUDY
Public Databases of Technology Needs
Hundreds of technology needs published every month
in websites like www.innocentive.com,
www.ninesigma.com and
www.innovationexchange.com
Classic example:
“Damping Materials for Low-Frequency Vibrations: damping materials
that can suppress low-frequency torque fluctuations and vibrations at
a high-precision powertrain in electronic equipment.” Extract from
ninesigma.com
16. RESOURCES FOR THIS STUDY
Public Databases of Technology Offers
Open scientific repositories of papers
Funding agencies such as research councils and other
governmental organizations are rapidly implementing
opendata as a way of operation. This releases important
amounts of new information about research projects
with high potential impact
Patent databases are by definition public and contain
vast amounts of “solution principles”. More importantly
some patents have already expired or do not apply in
certain regions and they still contain valuable knowledge
to use in a wide arrange of technology needs
17. RESOURCES FOR THIS STUDY
Technical Tools
Data Mining and Semantic Analysis
Web Mashups (data aggregation from different online
sources using RSS and indexing techniques)
Searching and ranking algorithms to match needs with
offers and provide an organize dashboard of alerts
displaying areas of matching potential
18. THE DIFFICULT MIDDLE GROUND
“between C and K”
One of the objectives is to explore the
technical and social barriers in the technology
transfer process. By doing so the proposed
tool and model will incorporate those inputs
in its design.
19. Recommendations
SMEs should be provided with appropriate support to
enable them to access the knowledge they require from
home and abroad. Government could map key global
communities of practice for the benefit of SMEs.
Small firms should be helped to identify and use
international agents.
A register of global university expertise should be
compiled.
Firms need advice on effective network management.
Government must continue to fund existing network
support.
Based on NESTA report “Sourcing knowledge for innovation” May 2010
20. The gaps between R, D and i
offers Innovations: Due to the need of
market expertise and
needs commercialization players usually
successful mainly in global
companies.
Development: needs
Increasingly in high tech marketing
SMEs (ex spin offs).
Sometimes in big
corporations and
universities. Research: usually in
Engineering Universities and
& design Research Centres.
Motivated by
scientific curiosity
Science + Eng and disruptive
discoveries.
needs
offers
The full R&D + i potential is highly distributed
and requires collaboration and co-creation to be exploit
22. Tools and Methods for Tech Transfer
Innovation intermediaries
Open Innovation
Technology transfer
Knowledge transfer
Creativity and innovation methods
Spin outs
New organizational structures
....Overall diagnosis is that they are not widely used
23. Innovation Intermediaries
In this context innovation intermediaries play an
important role to smooth the relationships and
create bridges. Some examples of them are:
Challenges and Opportunities Platforms
Technology needs brokers
Technology offers brokers
Technology Transfer Offices
Knowledge Transfer/Exchange Offices
Incubators and Innovation Centres
Science, Technology and Innovation Parks
24. Coordination helped by a neutral hub increases
chances of discovery and matching
Technology Push Technology Pull
Researchers
Final transactions
and exchanges of
IP
Company
Negotiations and with a need
collaboration
Researchers
Company
with a need
Company
with a need
Researchers
Open innovation networks
Virtual hub for “discovery Company
and matching” with a need
Researchers Company
Company with a need
with a need
25. Aggregated level
Concept Space Knowledge Space
Segmentation
C1
CN1:
Integral view: C2
C9
C3
K→C
K(β)
correlations
needs-K
C4 C5
C7
K(a) K(b)
New theoretical model
Feedback
CN2:
C8 C6
C11
K(Papers)
based on C-K design
C10
C12 K(e)
K(c) K(d)
C18
theory and TRIZ C13
C14 C15
Speed
CN3:
C17
C16
K(f)
K(g)
K(Patents)
K(i)
Clusters of needs K(h)
CN 3
CN 1
(T=2)
K(N1, N2,
N3) new
CN 2
The visualization show Cs at two different stages. The
smaller nodes represent individual needs in T=1 while the
big nodes represent clustered groups of needs ready to
be matched with relevant K in T=2. The clusters “Speed”,
“Feedback” and “Segmentation” are only examples of
underlying common problems for those needs.
26. Tool Objectives
Describe alternative and more efficient ways to:
Aggregate and map needs, generating clusters of
similar emerging problems.
Map knowledge and the experts behind it.
Create meaningful relationships between sets of
needs and knowledge to provide clues about relevant
technologies, methods or experts that could solve the
problem
27. Tool Proposal
Systematically match technology needs with technology and knowledge
sources and the experts behind the knowledge.
Aggregate technology needs into one common feed.
Group technology needs into clusters with common underlying solution
principles.
Aggregate and index the different sources of knowledge and technologies
in a relational database (including author, location, citations.
Scientific publications (ie papers), patents, explicit technology offers,
governmentally funded research projects.
Cluster knowledge and technologies into common categories related with
solution principles.
Match needs and offers into a dashboard with alerts and filters.
29. Tool challenges and potential solutions
How to cluster groups of
needs:
Via semantic data mining
keywords are indentified.
Relationships are
established based on the
proximity of the problems
extracted from the analysis
of knowledge trees from
sources such as wikipedia.
(Image shows example
based on the keyword
“nanotechnology”
30. Tool challenges and potential solutions
How to aggregate sources of technology and
knowledge:
Using analogies based on known solution principles (as in
TRIZ), sources of knowledge/technologies will be grouped
under branches of K fitting similar patterns.
To expand and dynamically update solution principles,
the dataset of K will be compared with patent claims to
deduce known and new underlying solution principles
present on the body of the patent and linking them back
to groups of knowledge.
This process will be reinforced with the same technique
explained in the case of technology needs.
31. Tool challenges and potential solutions
How to probabilistically match needs with offers:
Having the groups of K and C well defined and
established using proximity filters to find nearest and
cost effective sources of knowledge/technology will
be possible to generate a dashboard with probabilistic
alerts.