Technology and Knowledge Transfer Under the Open Innovation Paradigm

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More information and the complete report is available at http://www.openinnovate.co.uk/

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Technology and Knowledge Transfer Under the Open Innovation Paradigm

  1. 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. 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. 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. 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. 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. 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. 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
  8. 8. Open innovation via brokers
  9. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
  21. 21. Knowledge Sourcing Dynamics Source: NESTA report “Sourcing knowledge for innovation” May 2010
  22. 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. 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. 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. 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. 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. 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.
  28. 28. Potential Beneficiaries
  29. 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. 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. 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.

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