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II-SDV 2016 Raphael Ilmer, Quentin Ladetto - Optimization of Patent Landscape Process for Technology Maturity Visualization

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II-SDV 2016 Raphael Ilmer, Quentin Ladetto - Optimization of Patent Landscape Process for Technology Maturity Visualization

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This joint presentation will focus on a project between CENTREDOC and the ARMASUISSE Science and Technology Foresight program to set up an optimized Patent Landscape process. The talk will outline the major bottlenecks identified in the existing process, the solutions considered and implemented by CENTREDOC, as well as the results achieved by ARMASUISSE in its capacity to anticipate and get the necessary understanding of emerging technologies. As technologies can be considered independently of the domain of application, creating a contributory platform providing structured information about technologies is of common general interest at governmental and industrial level, both national and international.

This joint presentation will focus on a project between CENTREDOC and the ARMASUISSE Science and Technology Foresight program to set up an optimized Patent Landscape process. The talk will outline the major bottlenecks identified in the existing process, the solutions considered and implemented by CENTREDOC, as well as the results achieved by ARMASUISSE in its capacity to anticipate and get the necessary understanding of emerging technologies. As technologies can be considered independently of the domain of application, creating a contributory platform providing structured information about technologies is of common general interest at governmental and industrial level, both national and international.

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II-SDV 2016 Raphael Ilmer, Quentin Ladetto - Optimization of Patent Landscape Process for Technology Maturity Visualization

  1. 1. CENTREDOC Optimization of Patent Landscape Process for Technology Maturity Visualization II-SDV 2016, Nice, 18th April 2016 Dr. Quentin Ladetto Research Director – Technology Foresight Dr. Raphaël Imer Senior Patent Analyst
  2. 2. CENTREDOC Objective • Collect intelligence about technologies • Understand their readiness level • Provide the best links towards publications and actors in the field • Achieve 202 technological landscapes, covering 30 subjects, grouped into 5 topics armasuisse
  3. 3. CENTREDOC Standard Process Creation of the search queries Selection of the databases Execution of the search queries Centralisation of data Elimination of duplicates Selection of documents Standarisation of applicants’ names Generation of the graphs Elaboration of the report • All steps are manually executed LIMITATIONS Data Search Subject Report Data Processing Data Analysis • This process is time consuming • Highly dependent on the number of documents 202 landscapes ≈ 4 years
  4. 4. CENTREDOC Standard Process CHALLENGES • Automate the process • Limit the options • Reduce time SASAS = Semi-Automatic Search and Analysis System • Ensure quality Creation of the search queries Selection of the databases Execution of the search queries Centralisation of data Elimination of duplicates Selection of documents Standarisation of applicants’ names Generation of the graphs Elaboration of the report Data Search Subject Report Data Processing Data Analysis
  5. 5. CENTREDOC SASAS Process 1. Use RAPID as centralization & exchange platform IMPLEMENTATION 2. Define databases used for all searches 3. Define the output graphs 4. Use machine learning tools to help manual categorization Professional databases, for patents, scientific articles, theses or proceedings. Machine Learning Tools for selecting documents Professional tools for document analysis and graphical representation
  6. 6. CENTREDOC Example Data Search Subject Report Data Processing Data Analysis
  7. 7. CENTREDOC Example Data Search Subject Report Data Processing Data Analysis OR 4200 documents AND 500 + 1100 documents radar*1 passive OR passives Coherent location OR Commensal OR covert*2 OR parasitic*2 OR piggyback*2 OR Hitchhiking*2 radar*1 OR IPC 1’400 + 1200 documents AND Creation of the search queries
  8. 8. CENTREDOC Example Data Search Subject Report Data Processing Data Analysis 4200 documents Manual selection Automatic classification 33% 67% Relevant Non relevant 1400 documents
  9. 9. CENTREDOC Example Data Search Subject Report Data Processing Data Analysis 1400 documents PRE-DEFINED GRAPHS 1. Temporal distribution 2. Geographical distribution 3. Top players 4. “Acceleration” of affiliations 5. Number of publications per author 6. The experts 7. Main concepts 8. Mapping Rank Affiliation Country Nb of doc 1 [CHINESE PEOPLE LIBERATION ARMY CN] 112 2 [THALES FR] 31 3 [US AIR FORCE US] 29 4 [MITSUBISHI JP] 27 5 [SELEX ES IT] 26 6 [ONERA FR] 15 7 [RaSS IT] 14 - [LOCKHEED MARTIN US] 14 9 [NORWEGIAN ARMY NO] 13 - [EADS EP] 13 11 [US NAVY US] 11 12 [AUSTRALIAN DEFENSE AU] 9 - [CONSORZIO SESM IT] 9 14 [ERA CZ] 8 - [MATRIX RESEARCH US] 8 - [CANADIAN ARMY CA] 8 - [TOSHIBA] 8 18 [CNRS FR] 7 19 [TELECOM SUDPARIS IFR] 6 - [CEMEE CN] 6
  10. 10. CENTREDOC Example Data Search Subject Report Data Processing Data Analysis
  11. 11. CENTREDOC Example Data Search Subject Report Data Processing Data Analysis
  12. 12. CENTREDOC Conclusion • SASAS process allows to reduce the realization time by a factor of 2. • The selection of the documents is facilitated by using a machine learning tool. • The selection is less dependent on the number of documents. 1. Automation of data transfer between the different tools IMPROVEMENTS 2. Fully implementation of a machine learning solution in order to reduce data handling 3. Finalize the development of the data visualization tool
  13. 13. CENTREDOC Technological landscape Conclusion Technological landscapes should be used as a starting point to generate further interest and questions for deeper analysis. Deep analysis
  14. 14. CENTREDOC Deep analysis • Relevant documents were manually categorized into sub-categories. • A deep analysis of all sub-categories was performed.
  15. 15. CENTREDOC Dr. Quentin Ladetto Research Director – Technology Foresight Dr. Raphaël Imer Senior Patent Analyst THANK YOU

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