PREDICTING THE POTENTIAL

INDUSTRIAL FIELDS OF

TECHNOLOGICAL SPIN-OFFS

BY USING IPC IN PATENT ANALYSIS

H. SASAKI

Y. KAJIKAWA

I. SAKATA

V. Ittipanuvat

The University of Tokyo

Abstract •  R&D projects sometimes generate technological seeds which have

application potentiality in unintended field. Examples can be seen in defense,

aerospace and nuclear power industries. In these industries, huge amount of public investment is continuously spent in long term and advanced

technology with high technological level is required and achieved. Even in the case where the projects which seems to be failed, technological and

economical spinoff effects are expected by utilizing collateral technologies.

However, there were a few empirical studies to quantitatively assess the extent of technological spinoffs. And it is less effort to detect and predict the

fields where technological spinoffs will occur in the future.

•  The purpose of this paper is to evaluate spin-off prediction method in order to detect technological fields that have plausible and diverse applications in

other industries by using bibliometrics and network analyses of patent

publications. We observed transition of technology transfer between fields with time-series analysis of co-occurrence of IPC (International Patent

Classification) codes among patents.

•  The results suggested a possibility to utilize our approach so that we can

detect the potential technological and industrial fields where breakthrough by

innovative seeds in other fields can open a new direction for those fields.

Contents

•  1. Background

•  1.2. Spin-Off Potential Industries

•  1.3. Previous Researches

•  2. Purpose of this research •  2.1. Definition

•  3. Methodology

•  3.1. Prediction Score INDEX

•  3.2. ROC and AUC as evaluation index

•  3.3. Data Set: Functionally Gradient Materials(FGM)

•  4. Result

•  5. Discussion

•  6. Summary

•  7. References

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 Solid rocket booster

Deployable Structures technology

Drinking cans package

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 the 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)

1.3. Previous 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)

• However, there are few researches discussed about

future prediction aspect.

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 in this research.

3. Methodology

Thomson

Innovation®

1. Extract IPC data from Patent D.B.

2. Create IPC networks in each months

3. Prediction and Evaluation

Training period Testing period

t0=2001/01/01 t0’=2002/12/31

t1=2003/01/01 t1’=2004/12/31

Edge list Score

IPCa - IPCb ****

IPCb - IPCc ***

IPCb - IPCd **

Edge list Score Evaluation

IPCa - IPCb **** TRUE

IPCb - IPCc *** FALSE

IPCb - IPCd ** FALSE

Scoring for Spin-Offs IPC Evaluation for Spin-Offs IPC

Edge list Score

IPCa - IPCb *****

IPCb - IPCc ****

IPCb - IPCd ***

Edge list Score Evaluation

IPCa - IPCb **** TRUE

IPCb - IPCc *** TRUE

IPCb - IPCd ** FALSE

Scoring for all IPC Evaluation for all IPC

2000/04 2000/05 2000/06 2000/07

・・・ ・・・

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

denote the set of neighbors of node x.

3.2. ROC and AUC as evaluation index

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.

追加スライド

3.3. Data Set:

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*” for US granted, EU granted, JP granted patents

Time window : Jan.1988 ~ Dec.2010

N of patents : 202 patents (at Dec.2010)

N of unique IPCs : 526 nodes (at Dec.2010) N of co-occurrences : 2,663 edges (at Dec.2010)

4. Result N of unique IPCs

N of co-occurrences

4. Result

i e pa ' ' WCN WRA WAA

yea

yea

yea

yea

i e pa ' ' WCN WRA WAA

yea

yea

yea

yea

AUC: Spin-Offs IPC

AUC: IPC

5. Discussion

•  In case of prediction for spin-offs, WCN in 3years time span training and testing has most highly performance.

• On the other hand, in case of prediction for all IPC, 3years

training and testing did not work as classifier.

•  5years timespan training and testing has high

performance for all IPC prediction.

•  10years timespan training and testing did not work as

classifier in any prediction index.

• As above, long time backward information are useless for

prediction of technology area which is in wide distributing

phase, and past recent appropriate time span will provide

high performance prediction.

6. Summary

•  In this research, we compare and evaluate performances some prediction score index in each time span.

• We concluded that long time backward information are

useless for prediction of technology area which is in wide

distributing phase, and past recent appropriate time span

will provide high performance prediction.

• As further work, we will apply this method for some other

technology area, and set consecutive time span in test

and training. Then comparing and evaluation them are needed for general result and discussion .

7. References

•  [1] A. Avadikyan and P. Cohendet; “Between market forces and knowledge based motives: the governance of defence innovation in the UK”, J Technol Transf, vol. 34, pp. 490-504, 2009.

•  [2] B. Meng et al.; “Link prediction based on a semi-local similarity index”, Chin. Pys. B, vol. 20, No. 12, 128902,

2011.

•  [3] Bradley, A.P.; “The use of the area under the ROC curve in the evaluation of machine learning algorithms.”

Pattern Recognition, vol. 30 (7). pp. 1145-1159, 1997.

•  [4] Huang, Z.,; “Link Prediction Based on Graph Topology: The Predictive Value of the Generalized Clustering

Coefficient,” in Workshop on Link Analysis; Dynamics and Static of Large Networks, (LinkKDD-2006), 2006.

•  [5] J. T. Chiang; “Technological Spin-Off Its Mechanisms and National Contexts” Technological Forecasting and

Social Change, vol. 41, pp. 365-390, 1992.

•  [6] L. Lu and T. Zhou; “Link prediction in weighted networks: The role of weak ties”, Europhysics Letters, vol. 89,

18001, 2010.

•  [7] Newman, M. E., “Clustering and Preferential Attachment in Growing Net- works,” Physical Review Letters E, 64

(025102), 2001.

•  [8] Takahashi H, Hashida T “Development of an evaluation method of functionally gradent materials”, JSME

international jornal. Ser. 1, Solid mechanics, strength of materials 33-I(3), pp. 281-287, 1990.

•  [9] The National Aeronautics and Space Administration(NASA); “NASA Spinoff”, Retrieved 2/13/12 World Wide Web,

http://www.sti.nasa.gov/tto/spinfaq.htm

•  [10] T. Murata and S. Moriyasu; “Link Prediction of Social Networks Based on Weighted Proximity Measures”, at

IEEE/WIC/ACM International Conference on Web Intelligence, 2007.

•  [11] U. Schmoch et al.; “ANALYSIS OF TECHNICAL SPIN-OFF EFFECTS OF SPACE-RELATED R&D BY MEANS

OF PATENT INDICATORS”, Acta Astronautica, vol. 24, pp. 353-362, 1991.

•  [12] World Intellectual Property Organization (WIPO); “Preface to the International Patent Classification (IPC)”,

Retrieved 2/15/03 World Wide Web, http://www.wipo.int/classifications/ipc/en/general/preface.html