II-SDV Nice, 16 April 2013
Key Success Factors in the Setup of
Cutting-Edge Patent Intelligence
Services
Gerhard Fischer
I...
22
Overview
● Syngenta at a glance
● Technology Mining in today’s Information Research landscape
● Functional excellence a...
33
Syngenta at a glance
● A uniquely broad product portfolio
- A leader in crop protection
- Third in high-value commercia...
44
Major R&D sites
located on three
continents
Other sites
● Marker-assisted and seed
breeding capabilities
● Global field...
55
The today’s Information Research landscape
Market
FromResearchtoMarket
Development
Invention
Number of information rese...
66
Technology Mining and the Gartner Hype Cycle
77
Technology Mining shapes and drives…..
Innovation
Culture
FTO Expansion
IP Acquisitions
Innovation
Protection
IP Exploi...
88
Information Research strategic direction
Innovation at scale
The Fundamentals
Functional Excellence
99
Towards the implementation of new services
• It is about creating a mindset and a team that is always
looking for areas...
1010
Functional Excellence and the delivery of the
Fundamentals are the foundations which enables shape
and explore innova...
1111
Elements in the implementation of Technology Mining service
PEOPLE
PROCESS
TECHNOLOGY
1212
Phased approach in the setup of professional Technology
Mining services
Tools Tools Tools
Target
Forefront
Technology...
1313
Required competencies
Common for all Information
Research
General Capabilities
•Communication and people skills
•Abil...
1414
The process
Search
Strategy &
Retrieval
Normalization/
Cleaning
Visualization &
Analysis
● Understand the
question & ...
1515
Customer expectations drive data and visualization
analysis
● 80:20 retrieval and quality of data sufficient
● Use of...
1616
● Import of bibliographic data into MS Excel or other
visualization tools
● “Drag and drop” creation of pivot tables ...
1717
The technology life cycle
Maximize
usage
Establish
confidence
Work on best
processes
Re-evaluation
(& Exit)
Establish...
1818
Technology Mining agenda
The deliverables
Innovation Culture
• White space analysis
redesign of patent portfolio by f...
1919
(Semi-)intellectual Text Mining
Pre-categorization of documents into an ontology with a text mining tool
followed by ...
2020
Getting the data set right: Example non-agri use of fungicides
● Compiling a comprehensive list of fungicides
● Searc...
2121
Themescape® map for non-agri use of fungicides
2222
Towards Technology Mining quality
There is no
„One tool fits all“ approach
Tech Mining
Quality
Build excellence in Te...
2323
Key Performance Indicators (KPIs) and Metrics
Driving value
Business Impact
• Sustainable innovation protection
• Eff...
2424
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II-SDV 2013 Key Success Factors in the Setup of Cutting-Edge Patent Intelligence Services

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II-SDV 2013 Key Success Factors in the Setup of Cutting-Edge Patent Intelligence Services

  1. 1. II-SDV Nice, 16 April 2013 Key Success Factors in the Setup of Cutting-Edge Patent Intelligence Services Gerhard Fischer Intellectual Property Dept Information Research
  2. 2. 22 Overview ● Syngenta at a glance ● Technology Mining in today’s Information Research landscape ● Functional excellence and Innovation at scale ● People, Process & Technology ● Experiences & Lessons learned ● Built-in quality and KPIs
  3. 3. 33 Syngenta at a glance ● A uniquely broad product portfolio - A leader in crop protection - Third in high-value commercial seeds ● World-class science - $ 14 billion sales in 2012 - $ 1.3 billion R&D investments in 2012 - 5,000 people in R&D around the world ● Global reach and experience - Over 27,000 employees in more than 90 countries
  4. 4. 44 Major R&D sites located on three continents Other sites ● Marker-assisted and seed breeding capabilities ● Global field station network Global R&D capabilities GREENSBORO Formulation Environmental Science SBI Biotechnology R&D JEALOTT’S HILL Chemical Discovery Weed Control Formulation Bioscience Environmental Science STEIN Fungicides, Insecticides & Professional Products GOA Chemistry BEIJING Biotechnology R&D
  5. 5. 55 The today’s Information Research landscape Market FromResearchtoMarket Development Invention Number of information research projects Patentability Validity Technology Mining Freedom to Operate
  6. 6. 66 Technology Mining and the Gartner Hype Cycle
  7. 7. 77 Technology Mining shapes and drives….. Innovation Culture FTO Expansion IP Acquisitions Innovation Protection IP Exploitation (“Intellectual Capital”) IP Enforcement & Anti- counterfeiting Accelerates R&D Efficient patent portfolio management Reducing risk / exposure Identifying opportunities Generating revenue External growth / leverage
  8. 8. 88 Information Research strategic direction Innovation at scale The Fundamentals Functional Excellence
  9. 9. 99 Towards the implementation of new services • It is about creating a mindset and a team that is always looking for areas in which to further enhance capability. • Functional excellence creates the foundation from which to explore and innovate. Drive Functional Excellence Innovation at scale • Making the time to explore, discover and create. • Proactive partnering and integrated ways of working • Managing dilemmas and tensions as a source of insight, innovation and energy. Classification: Internal use only Continue to deliver the fundamentals • Cost efficiency and optimized processes are enablers to reinvest in new services. • Reliability and responsiveness: a lean and agile service
  10. 10. 1010 Functional Excellence and the delivery of the Fundamentals are the foundations which enables shape and explore innovation at scale. Threshold vs distinctive capabilities THRESHOLD CAPABILITIES • What are the minimum functional deliverables? • What improvements are necessary to keep pace with advances in information research? DISTINCTIVE CAPABILITIES • What would give us a competitive advantage? • What innovations would add significant value? •What do we need to be efficient, effective and able to expand services? •What is needed to reliably and sustainably deliver the fundamentals?
  11. 11. 1111 Elements in the implementation of Technology Mining service PEOPLE PROCESS TECHNOLOGY
  12. 12. 1212 Phased approach in the setup of professional Technology Mining services Tools Tools Tools Target Forefront Technology Mining PHASE 3 • Develop best processes • Link people, tools and processes PHASE 2 • Link people and tools • Training PHASE 1 • People capabilities people • Evaluation and selection of tools • Limitation is the budget Tools People
  13. 13. 1313 Required competencies Common for all Information Research General Capabilities •Communication and people skills •Ability to interpret information requirements and analyze data Core Skills •Excellent scientific background (ability to fully understand the subject matter) •Proficiency with professional information resources and retrieval technologies Specific for Technology Mining Knowledge •Fully understands the Technology Mining process and concepts •Ability to ‘sell’ Technology Mining work products Technical Skills •Natural adaption to IT •Expert knowledge of Technology Mining tools PEOPLE
  14. 14. 1414 The process Search Strategy & Retrieval Normalization/ Cleaning Visualization & Analysis ● Understand the question & translate into search strategies ● Chose appropriate data resources with analytic tools in mind ● Interactive retrieval, ”Piece meal” approach ● Remove irrelevant documents (Garbage- in/Garbage-out) ● Application of thesauri (company and inventor) ● One document per patent family ● Man-made abstracts preferred over original abstracts ● No ”one tool fits all” approach ● Collaborate and communicate PROCESS
  15. 15. 1515 Customer expectations drive data and visualization analysis ● 80:20 retrieval and quality of data sufficient ● Use of Patent Classifications and database specific codes for retrieval Business Development Research Medium-range complete retrieval and quality of data Use of classifications, keywords for retrieval; removal of obvious false drops Intellectual Property Comprehensive and high quality data set Retrieval includes generic query expansion Manual categorization of documents Search Strategy & Retrieval
  16. 16. 1616 ● Import of bibliographic data into MS Excel or other visualization tools ● “Drag and drop” creation of pivot tables and related charts Pivot table analysis ● Built-in analysis tools ● Convenient for occasional users ● Drilling down option Tools Data source integrated ● Import of data and text via various filters ● Focused on text mining, black box ● Specialized on statistics; data is imported from various resources ● Provides a plethora of analysis and visualization functionalities Data Mining Text Mining HostintegratedTechnologyMining
  17. 17. 1717 The technology life cycle Maximize usage Establish confidence Work on best processes Re-evaluation (& Exit) Establish budget Tool evaluation & selection
  18. 18. 1818 Technology Mining agenda The deliverables Innovation Culture • White space analysis redesign of patent portfolio by filing in identified gaps • Open Innovation external sourcing of inventions/know- how/skills acceleration of R&D Innovation Protection • Patent valuation • Patent portfolio management where to create IP barriers licensing-out vs. licensing-in • Tracking fundamental inventions vis-a-vis incremental innovations • Life-cycle management IP Exploitation • 2nd uses of technologies adjacent technologies • Identify new value capture models • Niche market identification • Discover new technologies and processes and their use for product development FTO Expansion & IP Acquisition • Understand potential risks and benefits of new approaches or entering new markets • Identify acquisition targets • Competitor patent profiling understand strategies of competitors IP Enforcement & Anticounter-feiting • Infringement detection • Understand potential risks and benefits of new approaches or entering new markets • Identify activities of real and potential competitors
  19. 19. 1919 (Semi-)intellectual Text Mining Pre-categorization of documents into an ontology with a text mining tool followed by manual adjustments Source: Dolcera (2012)
  20. 20. 2020 Getting the data set right: Example non-agri use of fungicides ● Compiling a comprehensive list of fungicides ● Search fungicides compounds in database covering all technologies ● Identify typical database and patent classifications for use as fungicides in the agrochemical field ● Exclude agrochemical use patents via identified database and patent classifications ● Text mining of the remaining document set
  21. 21. 2121 Themescape® map for non-agri use of fungicides
  22. 22. 2222 Towards Technology Mining quality There is no „One tool fits all“ approach Tech Mining Quality Build excellence in Tech Mining Technology Man-made abstracts preferred over original text Data quality Documents Cleaning of data Thesauri in place Precise searches or pre-evaluation of unspecific retrieval The data drives the tool Question triggers document set One document per patent family Budget
  23. 23. 2323 Key Performance Indicators (KPIs) and Metrics Driving value Business Impact • Sustainable innovation protection • Effective IP exploitation • Open Innovation • Efficient IP portfolio management • FTO Expansion & IP Acquisition Business Partnering (Shape & Drive) • Technology Mining as part of business strategy • Effective processes & feedback • No. of iterations to agree • No. of impact / total time in meeting Value Creation • % Technology Mining reports effectively used • Value add analysis • Value capture beyond traditional business models Operations & Costs • Costs per project and overall • No “one tool fits all” • Time to deliver • Balance in-house vs outsourcing
  24. 24. 2424

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