An Approach for Predicting Hype Cycle Based on
Machine Learning
Zhijun Ren Shuo Xu
Institute of Scientific and Technical Information of China, Institute of Scientific and Technical Information of China,
Beijing 100038, China Beijing 100038, China
The China Patent Information Center, the State xush@istic.ac.cn
Intellectual Property Office, Beijing 100088, China
renzhijun@cnpat.com.cn
Xiaodong Qiao Hongqi Han
Institute of Scientific and Technical Information of China,
Institute of Scientific and Technical Information of China,
Beijing 100038, China
Beijing 100038, China
qiaox@istic.ac.cn bithhq@163.com
Kai Zhang
The China Patent Information Center, the State
Intellectual Property Office, Beijing 100088, China
zhangkai@cnpat.com.cn
ABSTRACT Some scholars believe that hype means advertised in publicity
Analyzing mass information and supporting insight based on and exaggerated propaganda[1], and the best measure is the
analysis results are very important work but it needs much expectation, which means people’s expectation for technology
effort and time. Therefore, in this paper, we propose an innovation. Enterprises should use hype cycle to target the
approach for predicting hype cycle based on machine learning emerging technologies, and use the concept of digital business
for effective, systematic, and objective information analysis transformation to predict future business trends.
and future forecasting of science and IT field. Additionally,
Gartner believes that hype cycle is a qualitative
we execute a comparative evaluation between the suggested
decision-making tool, like other management methods, it
model and Hype Cycle for Big Data, 2013 for validating the
relies mainly on the judgment of experts. And to complete the
suggested model and generally used for information analysis
hype cycle assessment and prediction of a set of technologies
and forecasting.
in a certain field, we need to use a variety of evaluation
methods. Other scholars have explored how to measure
Keywords expectations for innovation, and quantitative indicators are
mainly the number of participants[2], the number or the ratio
Hype Cycle; Data Mining; Technology Predict; KNN of the technological innovation documents[3], patent
statistical data[4] and the search flow of Google and other
search engines[5]. When using the network measurement
1. OVERVIEW method, other tools may need to be supplemented: USPTO
Hype Cycle is a conceptual model widely used by Gartner, patents, news reports of Google news archive and the official
Inc., which can reveal the basic laws of the evolution of website market share of a certain product, etc., these methods
technological innovation chain and is a powerful tool to get mainly create the hype cycle by adopting artificial methods.
the overall grasp of technological innovation development The InSciTe[6,7] developed by Korea Institute of Science and
trend, to get the objective assessment of the maturity of Technology Information adopts the decision tree and
technological innovation, to make the reasonable choice on statistical feature analysis method, based on Gartner research,
innovative intervention time and to seek the technological to provide the technical life cycle diagram and adopts the
innovation late-mover advantage. emerging technologies discovering model to provide the key
technologies on the life cycle diagram. In the particular
judgment process, there might be problems of force
compliance in certain stage. For example, a technology is
during the plateau of productivity between the year from 2000
Copyright © 2015 for the individual papers by the papers' authors. to 2005, but its data between 2006 to 2007 is in line with the
Copying permitted for private and academic purposes. stage of slope of enlightenment, so the force compliance is
This volume is published and copyrighted by its editors. needed to judge the technology life cycle of the last two
years[1]. Related reference didn’t include how specific
Published at Ceur-ws.org
technology term coordinates are obtained.
Proceedings of the Second International Workshop on Patent Mining and
its Applications (IPAMIN). May 27–28, 2015, Beijing, China.
Therefore, the paper uses papers and patent information to
realize emerging technology discovering method by machine
learning; then acquires some features by feature selection and types of organizations make technology deployed.
uses machine learning algorithm to classify and locate the Data from Internet between 2001 and 2014 about Gartner
coordinates, and produces the hype cycle according to the Hype Cycle is manually collected on terminology, stage and
prediction. coordinate to be used as training data.
The paper is structured as follows. The prediction frame of
technology maturity is described in Chapter 2. The learning
method and model of technology maturity is introduced in
3.2 Feature Calculation
Chapter 3. The prediction method and model of technology Technical features are the foundation of technology life cycle
maturity is illustrated in Chapter 4. Experiment and analysis discovering model. The technical features calculation uses the
are conducted in Chapter 5 and conclusion and forecasting are papers and patents as data, and uses paper
made in the last chapter.
index
S ( Pp ) {Pp1 , Pp2 ,, Ppn } , patent index
2. TECHNOLOGY MATURITY S ( Pt ) {Pt1 , Pt 2 ,, Pt n }
and combined index of
PREDICTION FRAME
paper and patent
S ( Ppt ) {Ppt1 , Ppt 2 ,, Ppt n } as
The model of approach for predicting hype cycle based on
calculation objects. It can study the interaction and exclusion
machine learning includes the learning part and the prediction
between papers and patents, and explore the rule of
part. The learning model mainly relates to data training. The
development between science and technology.
data acquiring and learning includes acquiring training data,
data annotation and feature calculation. The prediction model Paper index includes paper growth
k 1
means identification and discovering method of emerging k
AN Pp AN Pp
technology in certain field and process to discover technology PpGrowthRate k 1
system innovation by producing hype cycle and predict AN Pp
maturity and discover an emerging technology, as well as to rate , paper relative
k 1
make sure the input ratio of partial innovation and overall N k
N
Pp RelativeGrowthRate
Pp Pp
innovation. It includes the term selection, feature calculation, k 1
stage classification, technology position and visible N Pp
information module. The hype cycle prediction module is growth rate , paper
k
show in Fig. 1. A
Pp AuthorRate
Pp
k 1
AA Pp
author rate , paper author growth
k 1
A A
k
Pp AuthorGrowthRate
Pp Pp
k 1
APp
rate , paper institution
k
I
Pp InstitutionRate
Pp
k 1
AI Pp
rate , and paper institution growth
k 1
I k
I Pp
Pp InstitutionGrowthRate
Pp
k 1
I Pp
rate .
Patent index includes patent growth
k 1
AN AN
k
Pt GrowthRate Pt Pt
rate
AN Ptk 1 , patent relative
k 1
N N k
Pt RelativeGrowthRate Pt Pt
growth rate
N Ptk 1 , inventor
k
I
Pt InventorRate Pt
k 1
rate
AI Pt , inventor growth
Fig.1 Technology maturity prediction frame
k 1
AI AI
k
Pt InventorGrowthRate Pt Pt
3. TECHNOLOGY MATURITY
rate
AI Ptk 1 , application
LEARNING k
A
Pt ApplicantRate Pt
k 1
3.1 Data Acquirement AA Pt
rate , and application growth
Since 1995, Gartner began to pay attention to the hype and
disillusionment along with every appearance of new AA AAPtk 1 k
Pt ApplicantGrowthRate Pt
technologies and innovations, to track the trends of the
technology life cycle, to study the common pattern between rate
AAPtk 1
them, in order to provide guidance of when and where all
Combined index of paper and patent includes paper and patent extracted from patents and papers, TLCD model can
relative growth determine the stage of the emerging technology adopting the
rate five stages of classification of TSKNN algorithm, and the
k 1
k
( N Pp N Pp ) ( N Ptk N Ptk 1 ) specific stages refer to the Gartner's Hype cycle, which
Ppt RelativeGrowthRate includes Technology Trigger, Peak of Inflated, Expectations
N Ptk 1 N Pp
k 1 Trough of Disillusionment, Slope of Enlightenment, Plateau
, of Productivity.
k
N SKNN algorithm is an improvement of KNN algorithm. KNN
Ppt RatioRate
Pp
k algorithm, by computing the distance between the training
paper and patent ratio rate
N Pt , paper and point from training set and test point from test set, considers
patent people growth rate the closest distance having the most similar feature and can be
k 1 classified into the same group, obtaining test markers
k
( APp APp ) ( I Ptk I Ptk 1 ) characteristic points and the same tag feature training points.
Ppt PeopleGrowthRate k 1
I Ptk 1
SKNN mainly considers the time sequence issue of
APp terminologies to be classified, so the data of next year need to
,
paper and patent people ration rate be larger than the data of last stage, in order to avoid the force
k classification problem. The specific algorithm is as follows.
APp
Ppt PeopleRatioRate (1) Redescribe training technology terminology and feature
I Ptk , paper and patent institution
vector, according to feature set.
growth rate (2) When the technology terminology feature vector reaches,
k 1
18 features should be calculated respectively to establish
k
( I Pp I Pp ) ( APtk APtk 1 ) feature vector according to age.
Ppt InstitutionGrowthRate k 1
I Pp APtk 1 (3) Select K technical terminologies which are most similar to
, new technical year that is to be calculated from the training
paper and patent institution ration rate technical terminology set following the formula below,
k
I Pp
Ppt InstitutionRatioRate
M
A k W W
k 1
ik jk (1)
Pt . Sim (t i , t j )
M M
( Wik2 )( W jk2 )
k 1 k 1
4. TECHNOLOGY MATURITY
PREDICTION (4) Among K neighbors of new terminology, weight of each
classification is calculated respectively as follows,
4.1 Terminology Extraction
Technology terminology refers to terms used in a certain field,
which means concepts, features or relationships in the field. In
this paper, terminology extraction is based on keywords of p( x, C j ) Sim( x, d ) y(d , C )
d i STKNN
i i j
(2)
papers and templates. The keywords are word or phrase
extracted from papers to meet the needs of literature indexing
or retrieval work. It is used to express the literature subject
In the formula, x refers to feature vector of the new
and therefore can be used as emerging technology
terminology, Sim(x,di) refers to similarity calculation formula
terminology.
which is the same as mentioned in the last calculation formula,
Template technology is a common method for terminology y(di,Cj) is categorical attributes function, if di belongs to Cj,
recognition. By analyzing the characteristics of papers and then the function is equals to 1, otherwise is 0.
patents, some fixed sentence structure are found, for example,
(5) Compare the weight of various classifications; distribute
‘The development application of XX technology in XY ’, so
the computable terminologies to the stage with the greatest
‘3D printing’ can be recognized in ‘The development
weight. Record the stage.
application of 3D printing technology in medical science’.
After certain strings are extracted from the template, (6) Back to step 3 and calculate for next year.
frequency and the subjection degree can be used to perform
terminology recognition. If a collocation is found in corpus, it 4.3 Technical Position
must appear more than once, thus the frequency is an Based on the classification results, PKNN algorithm
important index in terminology extraction. Only when the calculates the position of terminologies on the hype cycle
term frequency in the corpus exceeds a certain threshold value, curve and S-curve. The algorithm is to find K most similar
it is believed that it has reached a technological terminology technologies in all classifications and the position of the
standard and awareness in the field is relatively high. technical terminology is the average position of these
Subjection degree refers to a relevant degree of the terminologies.
terminology and its filed. It represents the degree of a term
Input data includes trained technical terminology, feature
belonging to a field. While meeting the frequency and
vector and position under the classification, technical
subjection degree at the same time, the string is a technology
terminology and feature vector to be calculated.
terminology.
Output data includes technical terminology position.
4.2 Classify for Stage
By calculating technical terminology and technical features
Fig.2 Feature Extraction
Algorithm steps are listed as follows. (3) Draw 5 stages line and label the text.
(1) Select K technical terminologies which are most similar (4) Draw the technical terminology position according to the
to the emerging technical terminology in the year to be position calculated by maximum similarity algorithm.
calculated from the training technical terminology set.
M
5. EXPERIMENT AND ANALYSIS
[sim( x , y) * r ]
k k Gartner Emerging Technologies report describes some
pred(y) k 1 M (3) technologies that become famous because of hype, or
sim( x , y)
k 1
k technologies that Gartner believes will have significant impact.
In order to validate the technical life cycle discovery model,
960 training data released by ‘Hype Cycle Report’ from 2001
wherein K is 10. to 2013 is used for training technical maturity. Hype Cycle for
(2) Among K neighbors of new terminologies, consider the Big Data, 2013 is used to evaluate validation set of prediction
deviation between the most similar K terminologies and the results.
emerging technical terminology, and calculate the prediction Using SKNN method to perform Hype Cycle model stage test,
position of new terminology. Table 1 shows the experiment results of Hype Cycle for Big
Data, 2013 data and technical life cycle model. Compared
4.4 Visualization of Hype Cycle Curve with Gartner, the technical life cycle model achieves the
The paper uses fitting algorithm to produce Hype cycle. The precision of 67.24% and recall rate of 68.46%. The reason
process is as follows. why the accuracy and recall rate in the fifth stage and the
fourth stage is lower than that of other stages is that the
(1) Draw coordinates of horizontal and vertical axis, and mark sample size in the fifth stage and the fourth stage is too small
the expectation and time information. and the problem data has greater impact.
(2) Produce Hype Cycle with curve fitting formula.
Table 1. Technical life cycle discovering model experiment result
Stage Result
Gartner Suggested Number of Precision Recall
approach results same
in the both
Technology Trigger 11 11 10 91% 91%
Peak of Inflated 14 15 11 73.33% 78.6%
Expectations
Trough of 11 9 8 88.89% 72.7%
Disillusionment
Slope of Enlightenment 2 3 1 33% 50%
Plateau of Productivity 2 2 1 50% 50%
Total 40 40 33 67.24% 68.46%
Using SKNN method to perform Hype Cycle model position data sets will be used to further improve the accuracy of the
prediction, according to the Hype cycle visualization methods, prediction model.
Hype Cycle for Big Data, 2013 prediction result is produced This paper is funded and supported by the
and given as follows (This paper does not cover how each China Postdoctoral Science Foundation（2013M540125）.
technology reaches the Plateau, and Gartner’s result is used in
the following visualized graph ).
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