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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

2 www.openinnovate.co.uk


Context

Research triggers   Objec(ves  

Areas of   study  

Context

From literature   and   interviews  

Proposed models   and  tools  

Findings

Final remarks  

Conclusions

3 www.openinnovate.co.uk


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.   5 www.openinnovate.co.uk  


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

www.openinnovate.co.uk


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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www.openinnovate.co.uk


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

11 www.openinnovate.co.uk


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...)  

12 www.openinnovate.co.uk


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

13 www.openinnovate.co.uk


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

16 www.openinnovate.co.uk


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

17 www.openinnovate.co.uk


Integra(ve Framework  

C-­‐K

Open Innova(on  

Tech Transfer  

?

18 www.openinnovate.co.uk


Tradi8onal Concept-­‐ Knowledge   Design  Theory     Armand   Hatchuel  and   Benoît  Weil  

www.openinnovate.co.uk

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.

www.openinnovate.co.uk

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)  

22 www.openinnovate.co.uk


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.

24 www.openinnovate.co.uk


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.     27 www.openinnovate.co.uk  


Experiment Â

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Experiment 3  Main  technology  needs  brokers  

29 www.openinnovate.co.uk


Experiment 3  randomly  selected  needs  (RFPs)  from  different  domains  

30 www.openinnovate.co.uk


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    

34 www.openinnovate.co.uk


Tool Proposal     atches  by  K   Dashboard:  M  

35 www.openinnovate.co.uk


Tool Proposal   Dashboard:  High  p  robability  matches    

36 www.openinnovate.co.uk


Conclusions

Research triggers   Objec(ves  

Areas of   study  

Context

From literature   and   interviews  

Proposed models  

Findings

Final remarks  

Conclusions

37 www.openinnovate.co.uk


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  

39


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Technology and Knowledge Transfer under the Open Innovation Paradigm  

The Problems of Discovery and Matching Between “Technology Push and Pull”