Some R&D projects generated from technological seeds produce potential applications in other fields. In many industries, huge amounts of public investment are spent continuously over long periods. Advanced technology with high levels of technology are required and achieved. Even in cases with projects that apparently failed, technological and economical spinoff effects are expected from collaterally and often serendipitously developed technologies. A few empirical studies have assessed the extent of serendipitous technological spinoffs quantitatively. This report presents a proposed methodology for predicting technological fields that have plausible and diverse applications in other industries using bibliometrics and network analyses of patent publications. The dataset is extracted from a patent database served by Thomson Reuters. To identify and predict spin-offs will contribute to science and technology policymaking and to the development of potential business partners. We were able to observe the transition of technology transfer between fields with time expanded network. Our results suggest the possibility of using our approach to detect the potential technological and industrial fields where breakthroughs by innovative seeds in other fields can open new opportunities and directions of development.

1. SERENDIPITOUS IDENTIFICATION OF FIELDS
DERIVED FROM TECHNOLOGY SPILLOVERS
FROM PATENT ANALYSIS
: CASE STUDY OF MATERIAL SCIENCE
Hajime. SASAKI
Ichiro. SAKATA
Yuya. KAJIKAWA

2. 1. Background
• Technological spin-offs refers to the spillover of government's
mission-oriented, mostly defense - related, technology
programs to the civilian sector. (J.T. Chiang, 1992)
Noise insulation materials
inside fairing panel.
Acoustic glass fiber
for room interior
Gas Initiation technology
for air bag
Ignition plug technology of
torpedoes
Deployable Structures
technology
Drinking cans package

3. 1.2. Spin-Offs Potential Industries
Aerospace Industry
Nuclear Industry
Defense Industry etc…
• Huge amount of (public) investment
• Spent long term
• Advanced technology
• Even in case where the projects which seems to be failed,
technological and economical spinoff effects are expected by
utilizing collateral technologies.
• NASA said the objective of Technological Spin-offs is to foster a
greater awareness of the practical benefits resulting from the
investment in aerospace research and development.
(www.sti.NASA.gov)

4. 1.3. Existing Researches
• U. Schmoch et al. defined areas spin-off as technology
transfer based on patent indicators. (U. Schmoch et al., 1991)
• J.T. Chiang discussed spin-off potential with national
context(economic system, country size, development
stage). (J.T. Chiang, 1992)
• A. Avadikyan and P. Cohendet examined the evolutionary
transformations as aspect of logic of spin-off and spin-in in
defense innovation policy.(A. Avadikyan and P. Cohendet, 2009)
• Few researches discussed about future prediction aspect.

5. 2. Purpose of this research
• To evaluate and discuss the prediction method of
technological spin-offs using link prediction.
• With Network of co-occurrence relationships of IPCs
2.1. Definition
• Technological spin-offs is defined as a making linkage
between two IPCs which are differ from 1st digit each
others.

6. 3. Methodology

7. Time windows (Training and Testing)

8. 3.1. Prediction Score INDEX
• WCN(Weighted Common Neighbors INDEX)
• WRA(Weighted Resource Allocations INDEX)
• WAA(Weighted Adamic-Adar INDEX)
x
y
z1
z2
Score(x,y)
4
2
1
3
1
5
3
Ex)
w(x,z1)=2, w(z1,y)=4
w(x,z2)=1, w(z2,y)=3
s(z1)=4+2+5=11
s(z2)=1+3+5+3=12
WCN(x,y)=(2+4)+(1+3)=10
WRA(x,y)=(2+4)/11+(1+3)/12=0.88
WAA(x,y)=(2+4)/log(1+11)+(1+3)/log(1+12)=3.17

9. 3.1. Prediction Score INDEX
• WCN(Weighted Common Neighbors INDEX)
• WRA(Weighted Resource Allocations INDEX)
• WAA(Weighted Adamic-Adar INDEX)
x
y
z1
z2
Score(x,y)
4
2
1
3
1
5
3
cf)
w(x,z1)=2, w(z1,y)=4
w(x,z2)=1, w(z2,y)=3
s(z1)=4+2+5=11
s(z2)=1+3+5+3=12
WCN(x,y)=(2+4)+(1+3)=10
WRA(x,y)=(2+4)/11+(1+3)/12=0.88
WAA(x,y)=(2+4)/log(1+11)+(1+3)/log(1+12)=3.17

10. 3.1. Prediction Score INDEX
• WCN(Weighted Common Neighbors INDEX)
• WRA(Weighted Resource Allocations INDEX)
• WAA(Weighted Adamic-Adar INDEX)
x
y
z1
z2
Score(x,y)
4
2
1
3
1
5
3
cf)
w(x,z1)=2, w(z1,y)=4
w(x,z2)=1, w(z2,y)=3
s(z1)=4+2+5=11
s(z2)=1+3+5+3=12
WCN(x,y)=(2+4)+(1+3)=10
WRA(x,y)=(2+4)/11+(1+3)/12=0.88
WAA(x,y)=(2+4)/log(1+11)+(1+3)/log(1+12)=3.17

11. 3.1. Prediction Score INDEX
• WCN(Weighted Common Neighbors INDEX)
• WRA(Weighted Resource Allocations INDEX)
• WAA(Weighted Adamic-Adar INDEX)
x
y
z1
z2
Score(x,y)
4
2
1
3
1
5
3
cf)
w(x,z1)=2, w(z1,y)=4
w(x,z2)=1, w(z2,y)=3
s(z1)=4+2+5=11
s(z2)=1+3+5+3=12
WCN(x,y)=(2+4)+(1+3)=10
WRA(x,y)=(2+4)/11+(1+3)/12=0.88
WAA(x,y)=(2+4)/log(1+11)+(1+3)/log(1+12)=3.17

12. 3.2. ROC and AUC for evaluation
A Receiver Operating
Characteristics (ROC)-style
curve with x-axis and y-axis as
the percent of total possible new
links selected and the percent of
actual new links that are in the
selected links.
The Area Under the Curve
(AUC) has been shown to
exhivit a number of desirable
properties as a classification
performance measure.
感度
偽陽性率

13. 3.3. Data Set 1: Functionally Gradient Materials (FGM)
• The FGM is a composite material whose composition and microstructure vary
continuously from place to place in ways designed to provide it with the
maximum function of mitigating the induced thermal stress.
Search Query : "functionally gradient material*” OR “FGM*”
for US granted, EU granted, JP granted patents
Data coverage : Jan.1989 ~ Dec.2010
N. of patents : 219 patents
N. of unique IPCs : 43 for 2nd level IPCs, 105 for 4th level IPCs.
N. of co-occurrences : 173 for 2nd, 397 for 4th level.

14. 3.3. Data Set 2: Carbon Fiber-Reinforced Plastic (CFRP)
• CFRP is a fiber-reinforced plastic material with carbon fiber as reinforcing
material.
• It has superior properties compared to other materials, with low density, high
strength, high specific strength, high specific modulus, and high stiffness,
Search Query :“carbon fiber reinforced plastic*” OR “CFRP*”
for US granted, EU granted, JP granted patents
Data coverage : Jan.1973 ~ Dec.2012
N of patents : 1,959 patents
N of unique IPCs : 99 for 2nd level IPCs, 312 for 4th level IPCs.
N of co-occurrences: 587 for 2nd, 1,382 for 4th level.

15. 4. Result (Data set1: FGM)2ndlevelIPC
(Class)
4thlevelIPC
(Maingroup)

16. B22 and C22 in FGM data set
Time series frequency of the pair "B22 and C22" in the FGM dataset.
B22 Casting
C22 Metallurgy
Training window
Test window

17. Example of Prediction in FGM
“Method of manufacturing magnet material, ribbon-
shaped magnet material, magnetic powder and
bonded magnet”
Publication No.
US6401799 B1
Application No. US 09/636,423
Publication Date
2002/6/11
Application Date
8/10/2000
Priority Date
8/11/1999
Inventor
Akira Arai, Hiroshi Kato
Applicant
Seiko Epson Corporation
In “SUMMARY OF THE INVENTION”
“~Furthermore, even if the surface layer 52 is composed of a single layer,
its composition needs not be limited to the case where it is uniform in the
thickness direction, and it may be one in which the contents of the
components vary successively in the thickness direction (functionally
gradient material).”

18. 4. Result (Data set2: CFRP)2ndlevelIPC
(Class)
4thlevelIPC
(Maingroup)

19. B62 and F16 in CFRP data set
Time series frequency of the pair "B62 and F16" in the CFRP dataset.
B62 LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
F16 ENGINEERING ELEMENTS AND UNITS
Training window
Test window

20. Example of Spin offs in CFRP
"Structure of transmission for bicycle" (EP1564126B1)
Publication No.
EP1564126 B1
Application No.
EP20050001425
Publication Date
7/26/2006
Application Date
1/25/2005
Priority Date
2/10/2004
Inventor
Shinya Matsumoto, Kazunari Iguchi
Applicant
HONDA MOTOR CO., Ltd.
“Claim 18: As shown in Figs. 3, 4 and 5, a case 20 of the transmission T
includes a left case 20L and a right case 20R and is configured by
combining these left and right halves. The left and right cases 20L, 20R
each include a left cover 21L and a right cover 21R formed of CFRP
(Carbon Fiber Reinforced Plastic) for covering an internal device, and
a left reinforcing member 22L and a right reinforcing member 22R formed
of aluminum alloy for reinforcing the left and right covers 21L, 21R from
the outside, and the left cover 21L is adhered to the inside of the left
reinforcing member 22L and the right cover 21R is adhered to the inside of
the right reinforcing member 22R, respectively.”

21. 5. Conclusions
• Proposed a procedure to predict technological spin-offs as a problem
of predicting links on a time expanded network, making use of
IPC(International Patent Classification) given in the Patent Gazette.
• we made experiments to predict weighted links using the three indices
of WCN, WAA and WRA. In light of the estimated influences of training
data size and testing data size on the prediction results, more than one
time window was set.
• As an example of the predicted technology, we observed the
prediction of technological spin off making use of “B22 and “C22 in
FGM” and “B62” and“F16” in CRFP.

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