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.

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. 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. 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. 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. 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. CENTREDOC
Example
Data
Search
Subject
Report
Data
Processing
Data
Analysis

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. CENTREDOC
Example
Data
Search
Subject
Report
Data
Processing
Data
Analysis
4200
documents
Manual
selection
Automatic
classification
33%
67%
Relevant
Non relevant
1400
documents

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. CENTREDOC
Example
Data
Search
Subject
Report
Data
Processing
Data
Analysis

11. CENTREDOC
Example
Data
Search
Subject
Report
Data
Processing
Data
Analysis

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. 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. CENTREDOC
Deep analysis
• Relevant documents were manually categorized into
sub-categories.
• A deep analysis of all sub-categories was performed.

15. CENTREDOC
Dr. Quentin Ladetto
Research Director – Technology Foresight
Dr. Raphaël Imer
Senior Patent Analyst
THANK YOU