Three years ago, we thought this would be impossible to accomplish. Today, it is a reality : Artificial Intelligence gains more and more importance in the value chain of Intellectual Property. The « red button » allowing to obtain, with very limited interaction from human intelligence, a full technology landscape while starting from a simple technical fact sheet, is at hand. Assessment of the progress made so far.

1. IC-SDV 2018
When Artificial
Intelligence Joins
Intellectual Property
Dr. Rebeca Valledor
Dr. Raphaël Imer
Dr. Harald Jenny
IC-SDV 2018
Nice, 23 avril 2018

2. IC-SDV 2018
Three years ago, we thought this would be impossible to
accomplish. Today, it is a reality.
The « red button » allowing to obtain, with very limited
interaction from human intelligence, a full technology
landscape while starting from a simple technical fact sheet, is
at hand.
Assessment of the progress made so far in AI & IP.

3. IC-SDV 2018 3
Un monde complexe1. Inventions are multi-disciplinary
ingénieur
musicien poète
architecte
anatomiste
philosophe écrivain
botaniste
peintre
organisateur de spectacles
sculpteur
diplomate

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1. Inventions are multi-disciplinary

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2. Surroundings need to be explored

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Collect
information
Analyse
signals
Create
scenarios
Take decisions

7. IC-SDV 2018 7
Technology Monitoring Programs
• Realise a 360 Degree view
• Calculate indicators allowing rapid
understanding of technology domain and
development dynamics
• Visualise the results
• Create scenarios allowing for the projection
into the future of the technology and its use
cases.
• http://viz.envisioning.io/deftech_2017/?o=0

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Technology Monitoring Programs
• Realise a 360 Degree view
• Calculate indicators allowing rapid
understanding of technology domain and
development dynamics
• Visualise the results
• Create scenarios allowing for the projection
into the future of the technology and its use
cases.
• http://viz.envisioning.io/deftech_2017/?o=0

9. IC-SDV 2018 9
Technology Monitoring Programs
• Realise a 360 Degree view
• Calculate indicators allowing rapid
understanding of technology domain and
development dynamics
• Visualise the results
• Create scenarios allowing for the projection
into the future of the technology and its use
cases.
• http://viz.envisioning.io/deftech_2017/?o=0

10. IC-SDV 2018 10
Technology Monitoring Programs
• Realise a 360 Degree view
• Calculate indicators allowing rapid
understanding of technology domain and
development dynamics
• Visualise the results
• Create scenarios allowing for the projection
into the future of the technology and its use
cases.
• http://viz.envisioning.io/deftech_2017/?o=0

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Technology Monitoring Programs
Make the invisible visible
Make the unpredictable predictable
Transform observations in to action

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Nombrecumulédebrevets
Temps
Degré de maturité
S
t1 t2 t3
13
Maturity indicator
• In analogy with the
product life cycle in 4
phases
• Modelisation by the curve
of the Sigmoïde:
𝑌(𝑡) =
𝑆
1 + 𝑒−𝑏𝑡−𝑎
Whereas:
S: Saturation value
b: growth coefficient 0,1S 0,5S 0,9S
Développement
Croissance
Maturité
Saturation

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Maturity indicator
0
5000
10000
15000
20000
25000
30000
1980 1990 2000 2010 2020
Cumulatednumberofpatents
Publication year
0
5000
10000
15000
20000
25000
30000
1960 1980 2000 2020
Cumulatednumberofpatents
Publication year
Technologie LCD Wearable computing
1.Développement
2.Croissance
3.Maturité
4.Saturation
1.Développement
2.Croissance

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Top affiliations
• Who are the big actors of
the technology under
investigation?
326
335
342
350
371
396
504
759
790
838
0 200 400 600 800 1000
Semiconductor Co Ltd
Apple
Microsoft
LG
Google
Canon
Sony
Chineses univ
Seiko epson
Samsung
Top 10 – Technology 1

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Acceleration of affiliations
Growth factor =
No of cumulated docs of past year –No of cumulated docs of first year
No of docs of first year
• Who are the actors with a
strong growth of activities?
• Where are the small signals ?
8
9
9
10
10
11
17
22
26
31
0 10 20 30 40
Midea Group
Shenzhen Beowodeke
Wuhu Meide Kitchen
Shenzhen Qianhai
Shenzhen Yiteke
Zhou Chang An
Guangdong Xiaotiancai
Xiaomi Tech
Huizhou TCL
Nexdigm
Acceleration – Technology 1

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Diversification
Samsung
Techno 1 : Top actor
Techno 2 :
• Minor actor
• Strong acceleration
 Possible diversification
Midea
Techno 1 :
• Minor actor
• Strong acceleration
Techno 2 : Top actor
 New possible actor
Affiliations
Techno 1 Techno 2
Top Acc Top Acc
Samsung 1 0.6 134 17
Apple 2 1.2 201 1
Microsoft 3 1 --- ---
LG 4 0.4 --- ---
…
Midea Group 85 31 4 3.5
Shenzhen Beowodeke 151 26 698 0.2
Wuhu Meide Kitchen 68 22 --- ---
Shenzhen Qianhai 99 17 879 66
…

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• The quality of a landscape directly
depends on the quality of the analysed
data corpus.
• The selection and categorisation of
documents plays a key role in the
process of realisation of the analysis.
• The exponential growth of the numer of
documents makes this process
impossible to realise manually.
Data structuring

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Data structuring
A learning system :
• ALLOWS to classify rapidly a big
number of documents with high
precision by observation and imitation
of the human decision.
• DOES NOT ALLOW the replacement of
the human expertise of document
selection.
• Its the INTELLIGENT COMBINATION of
human intelligence with artificial
intelligence that makes the difference.

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FROM
NATURAL
INTELLIGENCE
TO
AUGMENTED
INTELLIGENCE

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Automatic categorisation
Iteratif process in 4 steps:
1) Training
2) Learning
3) Prediction
4) Verification

22. IC-SDV 2018
2015 : SASAS
Development of a semi-automatic process for the realization of
technologic landscapes.
2016 : (S)ASAS
• Optimization of the process in order to automate to the
maximum.
• Application of the process on three technologies
2017 : ASAS & ATL
Development of an automatic process for the realization of
technologic landscapes.
Application to technology identification

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Process
Understanding of the search
Familiarization with the subject
Topic

24. IC-SDV 2018
Process
Understanding of the search
Familiarization with the subject
Databases selection
Search queries creation
Search
Processing
Analysis
Topic
Report
Data centralization
Selection of the documents
Charts creation
Indicators extraction
Quality indicator
0
5000
10000
15000
20000
25000
30000
1960 1980 2000 2020
Maturity indicator

25. IC-SDV 2018
ATL (Automatic Technology Landscape)
Querie 2
(Large)
DATASET - KO DATASET - OK
Corpus CD
DATASET - TD
Classification
Centredoc
Querie 1
(Precise)
Querie 3
(NOK)

26. IC-SDV 2018
Conclusion: AI & IP
Knowing where to go is the pre-
requisite in combining AI with IP.
Confidence and continued efforts are
necessary to achieve the objective.
Be satisfied with small steps. Many
small steps lead further than one big
step which is never undertaken.
38

27. IC-SDV 2018
THANK YOU !