Technology and Knowledge Transfer under the Open Innovation Paradigm

Technology and Knowledge Transfer under the Open Innova8on Paradigm The Problems of Discovery and Matching Between “Technology Push and Pull”

Pedro Parraguez Ruiz Pedro@advient.net www.openinnovate.co.uk

Presenta(on Content

Research triggers Objec(ves

Areas of study

Context

From literature and interviews

Proposed models and tools

Findings

Final remarks

Conclusions

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Context

Research triggers Objec(ves

Areas of study

Context

From literature and interviews

Proposed models and tools

Findings

Final remarks

Conclusions

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Research triggers

Open Innova8on boHlenecks and unfulfilled promises

Disconnec8on between tech transfer, knowledge transfer and OI

Inadequate IT tools to deal with the data deluge in OI and tech transfer

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Research objec(ves • Review and analysis of the most common barriers to successful technology transfer as well as of the tools and methods already developed to deal with them.

• Create a new integral framework to model and understand technology and knowledge transfer processes under the open innova8on paradigm.

• Propose a process or system to improve the main T&K transfer issues iden8fied.

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Research nature

Rela(onal instead of transac6onal

T&K mapping, scou(ng and sourcing

Precursors of innova8on, the detec8on of knowledge transfer opportuni(es, collabora(on and co-­‐crea(on 6 www.openinnovate.co.uk

Areas of study Open Innova8on Models & Paradigms Management of Innova8on processes Technology and Innova8on Management

TIM

Technology & Knowledge Transfer

Innova8on/ Design Theories

C-­‐K Engineering Design Theory

Methods & Techniques

TRIZ Seman8c Analysis

Knowledge & Informa8on Management

Informa8on Technology Tools

Informa8on Aggrega8on and Clustering Data Mining 7

Context

Domain

Area

Subject

Volume of publica(ons per area and (meline Volume of publica(ons indexed in ISI Web of Knowledge per topic per year

450 400 350 300 250 200 150 100

0

1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

50

Technology Transfer

Knowledge Transfer

Open Innova8on

C-­‐K Design Theory

TRIZ

8

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Volume of ISI publica(ons about TT and OI Volume of publica(ons indexed in ISI Web of Knowledge per topic per year

450 400 350 300 250 200 150 100 50 0 2003

2004

Technology Transfer

2005

1 2006

Open Innova8on

3 2007

9 2008

6 2009

Technology Transfer & Open Innova8on

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The gaps between R, D and i offers needs Development: Increasingly in high tech SMEs (ex spin offs).

Sometimes in big corporations and universities.

i

Innovations: Due to the need of market expertise and commercialization players are usually successful mainly in global companies.

needs

marketing

D

Engineering & design

R

Science + Eng

Research: usually in Universities and Research Centres.

Motivated by scientific curiosity and disruptive discoveries.

needs offers The full R&D + i potential is highly distributed and requires collaboration and co-creation to be exploited www.openinnovate.co.uk

Visual model Technology Transfer VS Intermediated Open Innova(on

Generation

Evaluation and Selection Evaluation of the discovery/invention and its potential applications

Technology Push Technology is “packed” to be offered in the market

If it has commercial value

If it doesn’t have commercial prospects

Research Funding

Research centre infrastructure and accumulated knowledge

Transaction

If there is an interested party

Application for a patent or other IP rights If there is no interest in the offer

Scientific Discovery

Negotiations to licence, sell or create an spin-off

Final transaction and exchange of IP

Once IP is cleared it is possible to publish

Scientific Publication

TTO usually does not get involved

TTO offers support and expertise in commercial evaluation and IP

Patent becomes part of the passive portfolio of IP

Usually TTO is fully responsible for this process

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Visual model Interac(ons and Problems under Technology Push-­‐Pull Classic university technology transfer model

Open innovation through innov. intermediaries

Technology Push

Technology Pull Technology is “packed” to be offered in the market

Researchers

Final transaction and exchange of IP

If it has commercial value

If it doesn’t have commercial prospects

If there is no interest in the offer

Open i nnovation networks

Scientific Publications

Company with a need

Passive patents

Issues: • Linear process: Low itera8on and co-­‐crea8on à lack of feedback loops.

• Middle point is non existent.

• Problems of iden8fying opportuni8es and knowledge • More than 1792 ac8ve needs

(Innocen8ve + Ninesigma + Yet2.com + others. August 2010)

• Con8nuous explicit knowledge genera8on (papers, patents...)

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Visual model Interac(ons and Problems under Technology Push-­‐Pull Technology Push

Technology Pull Final transactions and exchanges of IP

Researchers

Company with a need

Researchers

Company with a need

Open i nnovation networks

Researchers

Company with a need

Researchers

Company with a need

Company with a need

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Open Innova(on Brokers

Screencast: Innocen8ve, Ninesigma and Yet2.com 14 www.openinnovate.co.uk

Open Innova(on Brokers => A fragmented landscape of technology brokers with a few big players

=> Yet2.com technology offers: 5067 15 www.openinnovate.co.uk

Findings

Research triggers Objec(ves

Areas of study

Context

From literature and interviews

Proposed models and tools

Findings

Final remarks

Conclusions

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Discovery and Matching The case for a virtual hub Technology Push

Technology Pull Final transactions and exchanges of IP

Researchers

Negotiations and collaboration

Company with a need

Researchers

Company with a need

Company with a need Researchers

Open i nnovation networks

Virtual hub for “discovery and matching” Researchers

Company with a need

Drawing the fron8er of what is possible…

Company with a need

Company with a need

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Integra(ve Framework

C-­‐K

Open Innova(on

Tech Transfer

?

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Tradi8onal Concept-­‐ Knowledge Design Theory

Armand Hatchuel and Benoît Weil

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K: Knowledge, something that is known to be true or false C: Concepts, something for which is currently not possible to say if it is true or false

Tradi8onal Concept-­‐ Knowledge Design Theory

Armand Hatchuel and Benoît Weil

Concept Space

Knowledge Space Disjunction K->C

C1

C->C Concepts evolve overtime partitioning themselves in continuous interaction with K. At the end of the process (by means of a conjunction) new knowledge (embodied for example in a new product) is produced (C7).

K(b)

K->K

C2

C3

K->C

C4

C5

The knowledge space contains explicit expertise databases and technologies. It is structured as islands each of them representing different domains.

K(a)

K(c) K(d) K(e) K(f) new

C6

C7

Conjunction C->K Concepts are defined and constrained by a list of requirements (to fulfil the objectives of a required new product or process).

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Knowledge can be internal or external to the organization. At the end of a successful design process a concept will be always transformed in new knowledge (in this case technologies are included in the definition of K)

The sourcing of the required knowledge to materialize a concept into new knowledge (or technologies) is the critical step where this study is focused.

This can be seen graphically in the disjunction K(c)->C(2).

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At the individual firm level

Concept Space

Knowledge Space Disjunction K->C

C1

C

K(α) Company

→C K(a) K(b) K

C Timeline. Analogue to TRL

Concept-­‐ Knowledge Design Theory re-­‐ interpreta8on

(Firm level)

Concepts can evolve and interact with different sources of K till they are mature enough to be transfered.

C2

→C

K(Papers)

C3

K(c) C

C4

→K

K(d)

K(e)

→K

K

K(f)

C5

K(g)

K(Patents) K(i) C6

C7

Conjunction C Technology Needs

→K

K(h)

K(j) new

T&K offer

To connect C with a relevant K, the aggregated database of each of them can be explored and matched semantically with the help of TRIZ. This generates relevant alerts through a dashboard.

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Knowledge can be identified, clustered and aggregated as needed, curating and indexing relevant databases.

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Concept-­‐ Knowledge Design Theory re-­‐ interpreta8on

(Aggregated level)

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Aggregated level

CN1: Segmentation

Concept Space

Knowledge Space

C1

K→C

C9 C2

K(β) correlations needs-K

C3

C4

C5

CN2: Feedback

C7

K(a) K(b)

C8

C6

K(Papers)

C11 C10 C12

K(c)

K(d)

K(e)

C18 C14

CN3: Speed

Concept-­‐ Knowledge Design Theory re-­‐ interpreta8on

(Aggregated level)

C15

C13 C17

K(f)

C16

K(g)

K(Patents)

Clusters of needs (T=2)

K(i)

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K(h)

CN 3 CN 1

K(N1, N2, N3) new CN 2

The visualization shows 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.

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C-­‐K adapted model Aggregated level

CN1: Segmentation

Concept Space

Knowledge Space

C1

K→C

C9 C2

K(β) correlations needs-K

C3

C4

CN2: Feedback

• TRIZ, Theory for Inven8ng Problem Solving

K(a) K(b) C6

K(Papers)

C11 C10 C12

K(c)

K(d)

K(e)

C18

CN3: Speed

C14

• Informa8on Management Technologies

C15

C13 C17

K(f)

C16

K(g)

K(Patents) K(i) Clusters of needs (T=2)

• C-­‐K Engineering Design Theory

C5

C7

C8

Integrated Theore(cal Framework

K(h)

• Data Mining and Aggrega8on • Seman8c Analysis

CN 3 CN 1

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.

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Barriers for TT

Priority

Culture

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Exis(ng tools for TT

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Exis(ng tools for TT

Screencast: TerMine, Wikimindmap, Creax Func8on Database and Seman8c Representa8ons.

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Experiment Â

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Experiment 3 Main technology needs brokers

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Experiment 3 randomly selected needs (RFPs) from different domains

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Experiment Â

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Experiment Â

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Tool Proposal

NEEDS: • SMEs should be provided w ith appropriate support to

enable them to access the knowledge they require from home and abroad. Government could map key global communi8es of prac8ce for the benefit of SMEs.

• Small firms should be helped to iden(fy and use interna(onal agents.

• A register of global university exper(se should be compiled.

• Firms need advice on effec8ve network management.

• Government must con8nue to fund exis(ng network

support.

33 Based on NESTA report “Sourcing knowledge for innovation” May 2010

Tool Proposal Dashboard: M atches by need

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Tool Proposal atches by K Dashboard: M

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Tool Proposal Dashboard: High p robability matches

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Conclusions

Research triggers Objec(ves

Areas of study

Context

From literature and interviews

Proposed models

Findings

Final remarks

Conclusions

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Conclusions • Exploit the “long tail” of technology needs and research.

• Using the pool of explicit scien(fic knowledge already available.

• Allows researchers to focus on what they are best at.

• Solu8ons from distant domains.

• Problems can be solved by an accessible expert in the same region or somebody associated in a close social network.

• SMEs have a good chance of enjoying the benefits of open innova(on networks if provided with the correct tools.

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Poten(al Beneficiaries

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