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”

www.openinnovate.co.uk

Pedro Parraguez Ruiz [email protected]

Presenta(on Content

Findings Conclusions

From literature

and interviews

Proposed models and tools

Research triggers

Objec(ves

Areas of study

Final remarks

Context

2


Context


From literature

and interviews


Research triggers

Objec(ves

Areas of study

Final remarks

Context

3


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

4

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.

5


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


Technology and Innova8on

Management

TIM

Management of Innova8on processes

Models & Paradigms

Open Innova8on

Technology & Knowledge Transfer

Innova8on/Design Theories

C-‐K Engineering Design Theory

Methods & Techniques TRIZ

Knowledge & Informa8on Management

Informa8on Technology

Tools

Seman8c Analysis

Informa8on Aggrega8on and

Clustering

Data Mining

Context Domain Area Subject

Areas of study

7

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

Technology Transfer Knowledge Transfer Open Innova8on C-‐K Design Theory TRIZ

Volume of publica(ons per area and (meline

8


Volume of ISI publica(ons about TT and OI

1 3 9 6 0

50

100

150

200

250

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450

2003 2004 2005 2006 2007 2008 2009

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

Technology Transfer Open Innova8on Technology Transfer & Open Innova8on 9


R D

i

Research: usually in Universities and Research Centres. Motivated by scientific curiosity and disruptive discoveries.

Development: Increasingly in high tech SMEs (ex spin offs). Sometimes in big corporations and universities.

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

needs

needs

needs

offers

offers

The full R&D + i potential is highly distributed and requires collaboration and co-creation to be exploited

Science + Eng

Engineering & design

marketing

The gaps between R, D and i


If it doesn’t have commercial prospects

If there is no interestin the offer

If it hascommercial

value

If there isan interested

party

Research FundingResearch centre infrastructure and

accumulated knowledgeScientific Discovery

Evaluation of the discovery/invention and its potential applications

Scientific Publication

Application for a patent or other

IP rights

Technology is “packed” to be offered

in the market

Patent becomes part of the passive portfolio

of IP

Negotiations to licence, sell or create

an spin-off

Final transaction and exchange of IP

Generation Evaluation and Selection Technology Push Transaction

TTO usually does not get involved

TTO offers support and expertise in commercial evaluation and IP

Usually TTO is fully responsible for this process

Once IP is clearedit is possible to publish


Technology Transfer VS Intermediated Open Innova(on

Visual model

11

If it doesn’t have commercial prospects If there is no interest

in the offer

If it hascommercial

value

Scientific Publications

Technology is “packed” to be offered in the

market

Passive patents

Final transaction and exchange of IP

Open innovation networksCompany with a need

Technology Push Technology Pull

Researchers

Classic university technology transfer model Open innovation through innov. intermediaries


Interac(ons and Problems under Technology Push-‐Pull

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...)

Visual model

12

Final transactions and exchanges of

IP

Technology Push Technology Pull

Researchers

Researchers

Researchers

Researchers

Company with a need

Company with a need

Company with a need

Company with a need

Company with a need

Open innovation networks


Interac(ons and Problems under Technology Push-‐Pull

Visual model

13

Open Innova(on Brokers

14


Screencast: Innocen8ve, Ninesigma and Yet2.com

=> A fragmented landscape of technology brokers with a few big players

=> Yet2.com technology offers: 5067

Open Innova(on Brokers

15


Findings


From literature

and interviews


Research triggers

Objec(ves

Areas of study

Final remarks

Context

16


Final transactions and exchanges of

IP

Technology Push Technology PullResearchers

Researchers

Researchers

Researchers

Company with a need

Company with a need

Company with a need

Company with a need

Company with a need

Open innovation networks

Virtual hub for “discovery and matching”

Company with a needNegotiations and

collaboration


The case for a virtual hub

Discovery and Matching

17 Drawing the fron8er of what is possible…


Integra(ve Framework

C-‐K Open Innova(on

Tech Transfer ?

18

Tradi8onal Concept-‐

Knowledge Design Theory

Armand

Hatchuel and Benoît Weil

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


K(b)

K(a)

K(c)

K(d)

K(e)

C 1

C 3C 2

C 4 C 5

C 7C 6

Concept Space Knowledge Space

Conjunction C->K

Disjunction K->C

K(f) new

C->C K->K

K->C

The knowledge space contains

explicit expertise databases and technologies. It is structured as islands each of

them representing

different domains.

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).

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).

Concepts are defined and constrained by a list of

requirements (to fulfil the objectives of a required new

product or process).

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)

Tradi8onal Concept-‐

Knowledge Design Theory

Armand

Hatchuel and Benoît Weil

20


K(Papers)

K(Patents)

K(g)

K(c)

K(f)

C 1

C 3C 2

C 4 C 5

C 7C 6


Conjunction C→K

Disjunction K->C

K(j) new

C→C

K→K

T&K offer

K→C Knowledge can be

identified, clustered

and aggregated as needed, curating and

indexing relevant

databases.

Technology Needs

Concepts can evolve and interact with different sources of K till they are

mature enough to be transfered.

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.

At the individual firm level

K(h)

K(a) K(b)

K(d)K(e)

K(i)

K(α) Company

C→K

Concept-‐Knowledge

Design Theory re-‐

interpreta8on

(Firm level)

21


C Timeline. Analogue to TRL



interpreta8on

(Aggregated level)

22


K(Papers)

K(Patents)

K(g)

K(c)

K(f)

C2


K(N1, N2, N3) new

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.

Aggregated level

K(h)

K(a) K(b)

K(d)K(e)

K(i)

K(β) correlations

needs-K

K→C

C3

C1

C7

C6

C5

C10

C12

C11

C14

C17

C18

C9

C8

C4

C13

C16

C15

CN 1

CN 2

CN 3

CN

1:

Seg

men

tatio

nC

N2:

Fe

edba

ckC

N3:

S

peed

Clu

ster

s of

nee

ds(T

=2)



interpreta8on

(Aggregated level)

23



K(Papers)

K(Patents)

K(g)

K(c)

K(f)

C2


K(N1, N2, N3) new

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.

Aggregated level

K(h)

K(a) K(b)

K(d)K(e)

K(i)

K(β) correlations

needs-K

K→C

C3

C1

C7

C6

C5

C10

C12

C11

C14

C17

C18

C9

C8

C4

C13

C16

C15

CN 1

CN 2

CN 3

CN

1:

Seg

men

tatio

nC

N2:

Fe

edba

ckC

N3:

S

peed

Clu

ster

s of

nee

ds(T

=2)

Integrated Theore(cal Framework

• C-‐K Engineering Design Theory

• TRIZ, Theory for Inven8ng Problem Solving

• Informa8on Management Technologies

• Data Mining and Aggrega8on

• Seman8c Analysis

C-‐K adapted model

24

Barriers for TT

Priority

Culture

25

Exis(ng tools for TT

26

Exis(ng tools for TT

27

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


Experiment

28

Experiment

3 Main technology needs brokers

29


Experiment

3 randomly selected needs (RFPs) from different domains

30


Experiment

31

Experiment

32

Tool Proposal

NEEDS:

•  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 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.

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

33

Dashboard: Matches by need

Tool Proposal

34


Dashboard: Matches by K

Tool Proposal

35


Dashboard: High probability matches

Tool Proposal

36


Conclusions


From literature

and interviews

Proposed models

Research triggers

Objec(ves

Areas of study

Final remarks

Context

37


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. 38

Poten(al Beneficiaries

39


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Technology and Knowledge Transfer under the Open Innovation Paradigm

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