Identifying the latent technology opportunities based

on a perspective of coupling publications with patents

Munan LI 1, 2 Wenshu Wang 1 Keyu Zhou 1

2019/10/20

1. School of Business Administration, South China University of Technology

1. Guangdong Province Key Laboratory on Decision & Innovation Management

Outline

Related workIntroduction 1

2

Methodology 3

5

Case study

4

Conclusion & Discussions

• Within a specific topic, the publications and patents are the outputs ofscientific research and technology transformation respectively, and also theyare often utilized to evaluate the levels of R&D.

Introduction

Publications

Patents

R & D(Nations, Institutes

and Staffs)

Level

Performance

Efficiency

……

• Under the traditional framework on identifying the TO (technology opportunity), thepatents are the usually data source. And, the classic and emerging analytical methodson patents are the mainstream of current studies.

Introduction

PatentsIdentification or evaluation of TO

Efficacy matrix

Topic modeling

SAO(Subject-Action-Object) Analysis

……

Patent mapping

• Research questions

• RQ1: Within some topics or under the specific situations, the coupling relationsbetween publications and patents can be conducted to identify the latent technologyopportunities?

• RQ2: And how can we use these coupling relations between publications and patentsto identify TO?

• RQ3: The new TO based on the proposed coupling methods can be identified by theother methods or can be compared with the other methods including traditional oremerging?

Introduction

• The driving force of technology progress has been generally discussed since 1950s ,and it should be endogenous from the view of productivity development (Solow,1957). In consequence, to forecast the evolution of technology, or technologyforesight has become one of critical approaches for strategic planning and industrial-policy making (Martin, 1995; Hasan and Tucci, 2010; Miles, 2010; Li et al., 2018).

• In general, the data of publications is often utilized to evaluate the scientific outputand academic performance, efficiency or collaboration between different institutes orscientists, and so forth (Guenter et al., 2007; Kyle et al., 2015).

• Some latest studies have integrated social media data, e.g. Twitter, Facebook orWeChat into the framework of technological forecasting and TOA (Chen, 2018).

Related work

• Regarding the relevant studies on TOA (technology opportunity analysis), technologyforecasting, emerging technologies, TCE(technological capability evaluation) andtech mining etc., the patent data play the key role in those processing (Breitzman &Mogee, 2002; Tseng et al., 2007; Youtie et al, 2008; Lee et al, 2009; Porter &Newman, 2011; Zhang et al, 2016; Wang et al., 2017).

• Also, the technique on combing bibliometrics with patent analysis has beenconducted to forecast those emerging technologies or R&D evaluation (Hullmannand Meyer, 2003; Daim et al., 2006).

Related work

Related work

Bibliometrics & publications data

Patent data & Patent analysis

Bibliometrics & Patent analysis

Multiple data source analyses

Scientific evaluation on output, efficiency, performance and international collaborations and so forth.

TOA (Technology Opportunity Analysis), TCE (Technology Capability Evaluation), Technology Evolution Analysis and so on.

Forecasting emerging technologies,Evaluating the performance or efficiency of R&D

Social medias data(Twitter, Facebook, WeChatInternet data (homepages, search results and so on)

Basically, the general computing-framework based on the coupling relations betweenpublications and patents is still insufficient, especially for those multi-disciplinary ortrans-disciplinary topics, for example, artificial intelligence, smart cities and IndustrialInternet etc.

Methodology

Here, an hypothesized framework is proposed to forecast the latent technologyopportunities based on the coupling relations between scientific articles andcorresponding patents within a specific topic.

Publications

Patents

Publications Patents Approximate classification

More More High R(Research) & High D(Development)

More Less High R(Research) & Low D(Development)

Less More Low R(Research) & High D(Development)

Less Less Low R(Research) & Low D(Development)

Methodology

Corresponding concepts and equations for computing:Perplexity: In natural language processing, it is used to measure the quality of the trainedlanguage model and evaluate the generalization ability of the model (Blei et al., 2003).

Perplexity

= exp −(� � log(��(��

�

���

��

���

�

���

|��)�(��|��)))/(� ��)

�

���

Coherence: The coherence of topics can be measured by calculating the degree of correlationbetween the features with higher scores in the topics, which is helpful to classify the topicsinto understandable categories (Newman et al., 2010; Stevens, 2012).

Coherence=������(��,

���

�� , �)

Methodology

For a specific topic, the coupling relationships can be described as such two aspects: For a specific term, the amount of publications and the amount of patents can present

some useful information. Based on the topic modeling, some sub-topics can be extracted from the retrieved

publications or patents, and the similarities between these sub-topics can be computed andconducted to evaluate the strength of coupling relations.

Sub topic #1

Sub topic #2

Sub topic #n

Sub topic #1

Sub topic #2

Sub topic #n

Publications Patents

…… ……

Top modeling

Top modeling

Methodology

SDS (Similarity between Different Sub-topics): the Euclidean Distance between two sub-topics, which also needs some techniques for computation.

Two new concepts are proposed:TI (Transformation Intensity): we just utilized two factors: (1) the probability of a term

belonging to a specific sub-topic in publications; (2) this term has been transformed intohow many sub-topics of the corresponding patents.

TE(Transforming Efficiency): here, the concept of HLP(half-life period) is conducted tocalculate the TE.

� �, � = �� − ��� + �� − ��

� +⋯+ �� − ���

����= ����

�

���

⋅ ��

����

= ���� ⋅ ��

�

���

+����

�

���

⋅ ��⋅1

2

�

+����

�

���

⋅ �� ⋅1

2

�

March, 2016, the milestone event about Go game, AlphaGo vs Sedol Lee

(4:1)

Case study: Artificial intelligence

A mazing hand in the center from

Sedol Lee

s1

幻灯片 13

s1 scutlimn, 2019/7/23

In 1956 , the Dartmouth conference is thought as the mark event about the origins of artificial intelligence.

Case study: Artificial intelligence

In August 1956, at Dartmouth college, JohnMcCarthy , Marvin Minsky, Claude Shannon,Alan Newell , Herbert Simon and otherscientists are together, talking about acomplete out-of-touch theme: Using machinesto mimic human learning and other aspectsof intelligence.

The meeting lasted for two months, andalthough there was no general consensus, aname was given to the discussion: artificialintelligence. Thus, 1956 became the first yearof artificial intelligence.

In the 1960s, artificial intelligence evolved different theories and branches such as computational complexity, cognitivecomputing and computational linguistics. With the promotion and application of relevant models and algorithms of artificialintelligence, research related to human labor union intelligence has spread to different fields and disciplines.

Case study: Artificial intelligence

Considering that there are many researches on artificial intelligence and the scope of knowledge diffusion is wide, that is, itis more or less involved in most research fields, the author considers topic modeling to conduct more in-depth contentmining and presentation. LDA（Latent Dirichlet Allocation）LDA is a popular and mature topic mining algorithm at present.In essence, LDA is a Bayesian model including subject, document and topic, which is completely based on bayesianreasoning mechanism and has good knowledge interpretation ability (Blei 2003; 2012).

Case study: Artificial intelligence

Case study: Artificial intelligence

0

500

1000

1500

2000

2500

3000

3500

4000

Amount

CHINESE ACAD SCI

ISLAMIC AZAD UNIV

INDIAN INST TECHNOL

ZHEJIANG UNIV

UNIV TEHRAN INTELLIGENCE

SHANGHAI JIAO TONG UNIV

NANYANG TECHNOL UNIV

NATL UNIV SINGAPORE

Case study: Artificial intelligence

3770

7351

0

1000

2000

3000

4000

5000

6000

7000

8000

199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017

Pat

ents

Patentgrants

Patent applications

Rank Regions Amout of Patents

1 CHINA 140222 USA 68893 JAPAN 30854 KOREAN 1775

5 GERMANY 724

Patent applications surged 7,351 in 2017, of which 5,655 were from China, accounting for 76.93 percent of the total growth in 2017.

Case study: Artificial intelligence

1990-1999 2000-2009 2010-2017

Topic

codePublications

Topic tag Topic

code

Publicati

ons

Topic tag Topic

codePublications

Topic tag

L1-6 20957

Classificationand classifier L2-13 57945

Artificial neuralnetwork predictionmodel

L3-7

79750 Fuzzy model and fuzzysystem

L1-7 19345

Associativememory andpatternrecognition

L2-12 51028

Dynamic neuralnetwork stability

L3-18

67900 Neural network model

L1-10 13698

Nonlineardynamicsimulation

L2-8 48661

Optimization designof genetic algorithm L3-5

64369 Parametric predictionmodel

L1-14 13200Clinical medicine

L2-2 48181Protein structuresequence prediction

L3-10

63070 Support vector machineclassification

L1-9 12593Adaptive fuzzycontrol

L2-3 45952Spectral analysis ofcancer

L3-15

60883 Optimization design ofgenetic algorithm

Case study: Artificial intelligence

1990-1999 2000-2009 2010-2017

Topic

codePatents

Topic tag Topic

codePatents

Topic tag Topic

codePatents

Topic tag

P1-4 2001 Neural network P2-5 5494 Drawing methodsbased on neuralnetwork

P3-8 12276 Data transmission ofneural network

P1-1 1731 Image processing P2-3 4363 Expert system P3-10 11979 Drawing algorithm

P1-6 1686 Signal sensor P2-6 3787 Pattern recognition P3-5 11503 Vector drawing method

P1-5 1369 Computer system P2-4 3695 Image description P3-7 10980 Modular control system

P1-2 1318 System andparameters ofneural network

P2-1 3589 Traffic sensoring P3-9 10724 Signaling system

Case study: Artificial intelligence

The thickness of the line

between two topics is expressed as

follows: the smaller the distance

(the smaller the difference) is, the

thicker the line, indicating the

closeness of the relationship and

the degree of similarity.

The sub-topics evolution of

publications on artificial

intelligence

Case study: Artificial intelligence

The sub-topics evolution

of patents on artificial

intelligence

Case study: Artificial intelligence

The TI (Transformation Intensity) on those sub-topics on artificial intelligence

0.000

0.500

1.000

1.500

2.000

2.500

L1-

9L

1-7

L1

-11

L1-

1

L1-

4L

1-8

L1-

2L

1-1

2L

1-6

L1

-14

L1-

3L

1-1

0L

1-5

L1

-13

L2

-13

L2

-12

L2-

9L

2-7

L2-

4L

2-6

L2-

8L

2-3

L2

-10

L2-

1L

2-5

L2-

2L

2-1

1L

3-1

8L

3-1

L3-

2L

3-3

L3-

7L

3-1

0L

3-5

L3

-12

L3-

9L

3-1

3L

3-8

L3

-16

L3

-11

L3

-15

L3-

4L

3-6

L3

-14

L3

-17

TI

(Tra

nsfo

rmat

ion

Inte

nsit

y)

Scientific sub-topics

Case study: Artificial intelligence

The TE (Transformation Efficiency) on those sub-topics on artificial intelligence

0.000

0.200

0.400

0.600

0.800

1.000

1.200

1.400

1.600L

1-9

L1-

7

L1-

4

L1-

1

L1-

11

L1-

8

L1-

12

L1-

3

L1-

6

L1-

2

L1-

10

L1-

14

L1-

13

L1-

5

L2-

13

L2-

12

L2-

9

L2-

7

L2-

4

L2-

6

L2-

10

L2-

3

L2-

8

L2-

1

L2-

5

L2-

2

L2-

11

L3-

18

L3-

1

L3-

2

L3-

3

L3-

7

L3-

5

L3-

9

L3-

10

L3-

12

L3-

13

L3-

16

L3-

8

L3-

15

L3-

4

L3-

11

L3-

6

L3-

14

L3-

17

TE

(T

rans

form

atio

n E

ffic

ienc

y)

Scientific sub-topics

1990-2000 2001-2010 2011-2017

A framework was proposed to forecast the latent TO (technology opportunity) basedon the coupling relations between publications and patents, and the case study onartificial intelligence was conducted.

Conclusion & Discussions

However, the generalization capability of this proposed framework stillneeds more cases and verifications in the following studies.

In case study, this paper partially addresses some interesting phenomenon on artificialintelligence.

Thank you!