Open Innovation, Tech Mining & Competitive Technical Intelligence in the US

Transcription

1 Open Innovation, Tech Mining & Competitive Technical Intelligence in the US Alan Porter Director of Research and Development Search Technology, Inc. & Co-director Technology Policy & Assessment Center Georgia Tech

2 Outline 1. Toward Open Innovation 2. Tech Mining to deliver CTI 3. Application Example: Nano

3 What is it? Innovation? Invention is not innovation Change in function put into practice -- successfully Why is it important? The key to competitive performance of organizations, of economies

4 Technological Innovation: The Conceptual Bases Focus on changes in function -- of Products, Services, Processes, &/or Systems ( PSPS ) Draw upon models of technological change Innovation (life cycle) processes Technology substitution, transfer & diffusion See change in a contextual system Internal Factors External Forces

5 Innovation Models (Many!) Linear: sequential phases not true! but useful to benchmark development Ecological: complex interplay of phases recognition important for policy processes Understanding essential for Management Of Technology (MOT) Technology Delivery System (TDS) Identify what is needed to implement the innovation

6 A Linear view of Innovation Processes Functionality Incremental Innovation Maturation Adoption Commercial Introduction New Product Development Licensing, Collaborative Innovation Development; Patenting Basic to Applied Research Time

7 Technology Delivery System Example: What nano-electronics applications hold greatest promise for Philips? Alan Porter, Search Technology, 2006

8 Open Innovation Christenson: Innovator s Dilemma Connect and Develop = shift from NIH syndrome [Huston & Sakkab, HBR, 2006] P&G s open innovation strategy now produces >35% of their innovations ($Billions in revenue) Implies a premium on strategic competitive technical intelligence (CTI)

9 PSPS = Products, Services, Processes &/or Systems BCMCR = Business, Competitors, Markets, Customers, Regulations Research Arena The Open Innovation Model External R&D B 5 A 3 A 4 A 5 Internal R&D C 5 A 1 A 2 CTI Existing PSPS design design Contextual Arena BCMCR Knowledge Flow [via CI] New PSPS Incremental innovation Really New PSPS Radical innovation

10 How to Effect Open Innovation? Manage innovation processes systematically. MOT (Management of Technology) is presently Piecemeal Intuitive Instead, manage better based on strong competitive technical intelligence - CTI Those who do so will win Those who do not will lose Innovation Mapping can show the way Understand the system & its key leverage points Gain external research knowledge Bring into Design process to exploit business opportunities

11 Analogy Innovation Mapping You know where you are and your physical destination Get a MapQuest Map! Innovation Analyses for better Management You know capabilities and identify potential uses Build Innovation Maps!

12 2. Tools: How do you generate innovation maps? Tech Mining Alan L. Porter and Scott W. Cunningham John Wiley & Sons Inc., 2005

13 How to get Management to hear information-based knowledge products Define the Management of Technology (MOT) Issues Break out particular MOT Questions Identify candidate empirical Indicators Identify appropriate Data Source(s) Identify appropriate Analytical Tool(s) Design Effective composite Representations that can be rapidly built Answer: who, what, when, where? Tech Mining

14 MOT Issues, Questions & Innovation Indicators 13 MOT Issues 39 MOT Questions ~200 Innovation Indicators WHAT? R&D Portfolio Mgt R&D Project Initiation Engr Project Initiation New Product Development Strategic Planning, etc. 1. What s hot? 2. Fit into tech landscape? 3. Drivers? 4. Competing technologies? 5. Likely development paths? 6. etc. Mapping of topic clusters within the technology 3-D trend charts for topic clusters Ratio of conference to journal papers (benchmarked) Scorecard rate-of-change metrics for topic clusters Time slices to show evolution of topical emphases Topic growth modeling (S-curve) fit & extrapolation WHO? Pie chart: Company vs. Academic vs. Government publishing Topical main players profiles Spreading (or constricting) # of players by topic

15 Tech Mining 6 information types Technical Information A. ST&I (Science, Technology & Innovation) Databases (e.g., Web of Science, INSPEC, Micropatents) B. Internet Sources (e.g., Googling) C. Technical Expertise Contextual Information D. Business, competition, customer, popular, policy content Databases (e.g., Lexus-Nexus, Factiva) E. Internet Sources (e.g., blogs, website profiling) F. Business Expertise

16 Tech Mining The Process Empirical Indicators MOT Questions Select Data Sources and Analytical Tools Understanding the Question MOT Issues Information Professional Search & Retrieval Query Refinement Technology Analysts Analysis Researchers Developing the Knowledge Product Combine with Expert Opinion Manager/ User Knowledge Product

17 The Tech Mining Process 1. Understand & scope the question, set in an Innovation Process context 2. Identify suitable databases (especially R&D publication or patent abstracts) 3. Search & download topical records [iteration likely] 4. Import into text mining software (e.g., VantagePoint or Thomson Data Analyzer) 5. Clean the data 6. Analyze, interpret & represent the information effectively to communicate well

18 Innovation Indicators Technology Life Cycle Indicators e,g, growth curve location & projection Innovation Context Indicators e.g., presence or absence of success factors (funding, standards, infrastructure, etc.) Product Value Chain and Market Prospects Indicators e.g., applications, sectors engaged

19 Nano : Illustration [Nanoscience & Nanoengineering R&D] Georgia Tech In support of NSF Center for Nano in Society [Arizona State Univ.] & NSF Partnership for Innovation [North Carolina State Univ.]; also EuroNano Project [sub to SPRU]

20 Microscopy - Scanning probe microscopy - Electron microscopy Self assembly; Directed assembly Nanomechanics Molecular simulation Scanning probe writing & fabrication Top-down processes (nano-lithography, laser nanomachining, etc.) Nanodevices & Nanoelectronics Nanocomputing devices Nanotransistors NEMS; PEBBLES Molecular electronics Nanoscale magnetics Nanotechnology Research Foci & Key Concepts Metrology & Nanoprocesses Nanostructure Chemistry & Materials Nanoscale chemical structures Nanocomposites Sol-gels; quasi-crystals Growth methods (epitaxy MBE, CBE,MOCVD) 0D Quantum dots 1D Nano/quantum tubes, rods or fibers; nanopolymers 2D graphite layers 3D -; fullerenes; nanocrystals [from Porter et al., J Nanoparticle Research, in press] Nanomedicine & Nanobiotechnology Biomolecular & biomemetic devices Biosensors Molecular motors Biomolecular fabrics Engineered enzymes & proteins Drug discovery and delivery

21 Our Nano Data: Global, ISI Web of Science [Science Citation Index - SCI] ~407,000 articles (Representing ~2.7% of SCI over the period and 4.1% of SCI for the period) EI Compendex ~381,000 articles & conference papers INSPEC [Engineering Village 2 website] ~334,000 articles & conference papers EKMS searched MicroPatent, INPADOC, and their proprietary U.S. Patent Citation database ~61,000 patent families [from ~70 patent authorities]

22 Nano : Illustration [Nanoscience & Nanoengineering R&D] Global Level National Level Company Level

23 Innovation Mapping: Topical Emphasis Trends Global Nano Patents,1990 (partial-year 2006) Georgia Tech TPAC / CNS-ASU patent analysis; refined nano definition; results subject to revision

24 Innovation Mapping: Nano Geo-Districts Georgia Tech TPAC / CNS-ASU Analysis of SCI Publications; refined nano definition; results subject to revision

25 Cumulative Nano Publications (Science Citation Index) EU27 US Japan China Germany Asian Tigers est.

26 Science Citation Index Nano Articles, 2005: Data Differences to Beware All Authors First Author EU % 26.4% US 25.4% 21.8% Japan 11.1% 9.5% China 17.6% 16.5% Germany 8.8% 6.2% Asian Tigers 9.6% 8.5%

27 Aged Nano Citations in 2000 and 2004 relative to Nano Articles (1 st Author)

28 Quantity vs. quality Accumulated publications and citation rates, The US leads in quality Citation Rate USA 14 China is the third largest publication 12 producer [now ~#1] 10 Quality of China s publications is 8 not comparable with quantity 6 Italy India UK Germany France South Korea Japan 4 2 Russi China Publications, in thousands Analysis of SCI Publications ; refined Georgia Tech nano definition; see Porter et al., 2007; count for 2006 extrapolated

29 EI Compendex Nano Publications 7 Target Areas by Country/Region Chemistry Energy Electronics Optics Medicine Medical Materials Environment EU USA China Japan SE Asia Tigers India

30 Innovation Mapping: What type of organizations are patenting? overnment Institutes 16% Government Institutes 4% Universities 6% Universities 43% Firms 19% Firms 52% Others 38% Others 22% China Universities and Gov t institutes dominating US Firms dominating Analysis of SIPO and USPTO patents ; refined Georgia Tech nano definition; see Porter et al., 2007; count for 2006 extrapolated

31 Innovation Mapping: Patent Aims along the Value Chain [by Simone Alencar and Adelaide Antunes, UFRJ] Main IPC [# patents] H01L-Semiconductor Devices; Electric Solid State Devices Not Otherwise Provided [2870] C01B-Non-Metallic Elements; Compounds Thereof [2716] A61K-Preparations For Medical, Dental, Or Toilet Purposes [1863] B82B-Nano-Structures; Manufacture Or Treatment Thereof Chemistry [1615] Main uses description in the nanopatents Electron device Semiconductor device Solar cell carbon nanotube fuel cell catalyst Cancer (treatment, medication) Cosmetics drugs Carbon nanotube Electron device catalyst Position along the Nano Value Chain Nanointermediate Nanointermediate Nano-products Nano-raw material Nano-products Nanointermediate Nano-products Nano-products Nano-products Nano-raw material Nanointermediate Nanointermediate

32 Innovation Mapping: Nano Research Coauthoring Network for a small firm -- via a university -- to other universities and government labs [from Jue Wang, PhD Dissertation, Georgia Tech, 2007]

33 Agriculture Geoscience Infectious diseases Clinical medicine Ecology Environ. Sci. General medicine Chemistry Biological Sciences Neurosciences Materials Sci Computer Sciences Engineering GLOBAL MAP OF SCIENCE Physics Leydesdorff&Rafols (2007, submitted) Pajek

34 Map of Science Quantum Dot 1995 Size (area) of nodes is proportional to: Log (1+Number of citations per category) Rafols Pajek

35 Map of Science Quantum Dot 2005 Rafols Pajek

36 Arnall, A.H. Singer, Daar, A.S. P.A. Mnyusiwalla, A. Co-citation Map (piece): Nano in SSCI ge, W.S. Crichton, M.P. Kurzweil, R. Joy, B. Wilsdon, J. ROCO MC Jones, R. Smalley, R.E. enn, O. Nordmann, A. DREXLER KE Whitesides, G. Wood, S. CRANDALL BC

37 10 Tech Mining Cases 1. Innovation & Application: Ceramic coatings for engines (Army) 2. Hazardous Substances Data Bank: Import to facilitate knowledge discovery and database management (NLM) 3. NSF Proposal Assessment/EPA STAR Research Evaluation 4. Measuring research Interdisciplinarity (National Academies) 5. Self-profiling one s organizational strengths & gaps (GT) 6. Generating ST&I Indicators (Sao Paulo) 7. Combining empirical & expert data: Plastic molding technologies to assess relative R&D priorities (UFSC) 8. Life Cycle Positioning Analyses: Nanopatenting (UFRJ) 9. Tracking Media coverage of an R&D organization (Embrapa) 10. Geo-mapping based on text and data mining

38 Polymer Biomaterials : fibrous structural proteins : skin (68 patents)

39 Polymer Biomaterials : fibrous structural proteins : skin (470 patents)

40 [Literature-Based Discovery for Open Innovation 1. Specify the initiating challenge (innovation opportunity A ) [Classic case: Swanson pursuing Raynaud s Disease] Note key attributes 2. Search the literature (&/or patents) Profile the core and fringe topical themes (related factors B ) Expert assessment of best prospects [Raynaud s associated with blood viscosity changes] 3. New, independent Literature search on B 1 (also possibly B 2, ) Profile the promising elements ( C 1, C 2, C 3, ) Expert assessment of interesting prospects (considering key attributes of A ) Vetting that C 1 has not been previously explored (check literature & patents) [Raynaud s case: blood viscosity lowered by eicosapentaenoic acid, not previously explored as treatment] 4. Investigate potential of C 1 to resolve the initiating challenge ( A )

41 Summary Open Innovation depends on effectively exploiting external research knowledge Treat text like Data Mine it for patterns! Patterns speak to innovation prospects: maturation, contextual forces, market prospects Answer who, what, where & when Innovation Management questions for business decision processes

42 Open Innovation Machine in practice at a Fortune 50 Company Implement Tech Mining treat text like data [apply VantagePoint and other mining tools] Standardize data, analyses & information presentations Script to expedite Make better innovation decisions for competitive advantage!

43 Resources Tech Mining by Alan Porter and Scott Cunningham, Wiley, the software - various News on text mining of S&T - in Spain, contact Triz XXI, Fernando Palop [fpalop@triz.es] aporter@searchtech.com