DEIM Forum 2016 D1-2

Search for Similar Marks for Detecting Trademark Infringement and Dilution
Shuntaro NAKANO† , Hidekazu TANIGAWA†† , Masaharu MIYAWAKI††† , Atsushi YAMADA†††† , andKatsumi TANAKA†† Department of Social Informatics, Graduate School of Informatics, Kyoto University
Yoshida-honmachi, Sakyo-ku, Kyoto 606–8501 Japan†† IRD Patent Office,P.O.Box53, OMM Bldg.8F,1-7-31, Otemae, Chuo-ku, Osaka 540–0008 Japan††† Ritsumeikan University, College of Law, 56-1 Toji-in Kitamachi, Kita-ku, Kyoto 603–8577 Japan†††† Advanced Science, Technology & Management Research Institute of Kyoto
134 Chudoji Minamimachi, Shimogyo-ku, Kyoto 600–8813 Japan
E-mail: †{s.nakano,tanaka}@dl.kuis.kyoto-u.ac.jp, ††htanigawa@ird-pat.com, †††mmt23360@law.ritsumei.ac.jp,††††yamada@astem.or.jp
Abstract Trademarks are used to establish brand powers so that products or services are well known in business market.
However, there are malicious product/service names (marks) using registered trademark names intentionally or unintentionally and thus these marks cause business damages. This paper proposes a way to detect trademark infringement and dilution by
searching for similar marks from the Web. Our proposed method generates and suggests marks that are likely to be considered’similar’ to a given registered trademark. There exist some previous works regarding trademark infringement and dilution,
whereas they mainly focused on comparing and judging similarities between multiple registered trademarks. Therefore, they
are not so effective to detect suspiciously ’similar’ marks in advance. Our proposed method is 1) to generate marks thatare likely to be considered ’similar’ in all three types of similarity in the appellation,appearance and concept and 2) to use
generated marks as search queries and try to find ’similar’ marks from the Web.
Key words Trademark Similarity, Examination Guidelines for Trademarks, Similarity in appellation, Similarity in appearance, Similarity in concept

1.

Introduction

Nowadays, it is indispensable for companies to register names ofcan potentially be risk factors for companies with registered trademarks, they should have a clear strategy to protect the trademark
distinctiveness from being damages.

their products and services as trademarks, making sure that their

To solve this problem, this research aims to assist such compa-

products and services can clearly be distinguished with other prod-

nies by offering an information-technology based solution to de-ucts or services in the market from the consumers’ perspectives.

tect trademark infringement and dilution cases from the Web when

As suggested by Miaoulis and Amato [10], trademarks are also

one registered trademark is given as the input. In particular, thisin frequent needs of protection against imitators with the purpose

research focuses on detecting trademark infringement and dilution

to avoid an ordinary purchaser from being confused, mistaken orcases based on trademark-related laws and regulations in Japan.

deceived regarding the sources of the goods of services. For that

This research deals with trademark registration system in Japanreason, trademark registration scheme is available in each country

and thus conforms with ‘Examination Guidelines for Trademarks’1 ,to protect original marks owned by companies or organizations as

published by Japan Patent Office. It defines that when ‘judging thetrademarks so that they are not in danger of imitators. However, insimilarity of a trademark, decisive elements of the trademark, in-fact companies and organizations are still suffering from intentional

cluding its appearance, sound and concept need to be comprehen-or unintentional imitators that are becoming potential risk factors

sively taken into consideration’. To follow the principal regarding

regarding intellectual property management.

trademark similarity stated in it, the proposed method in this re-

Furthermore, Port [13] indicates that, there are also issues of ‘di-search handles all three types of trademark similarities; similarity

lution’ where a third party uses someone’s well known trademark

in appellation, similarity in appearance and similarity in concept.

without permission (known as ‘free riding’) and consequently dam-

The final goal of the proposed method is to generate a list ofages the distinctiveness of the trademark that are already obtained.
Based on what is discussed so far, since there are many issues that1

http://www.jpo.go.jp/tetuzuki e/t tokkyo e/tt1302-002.htm

marks that are definitely or potentially similar to the given one regis-

spective of researches related in the area of trademarks; that is how

tered trademark (hereafter defined as ‘suspicious marks’) and searchtrademarks are received in social environment and occasionally as-

the Web using generated marks to discover cases of trademark in-sociated with one particular impression.
The major approach in this research area is to use the real

fringement and dilution.
By utilizing the proposed method of this research, people work-

data representing social reception to measure the power of branding at an intellectual property department or equivalent will bene-

strength. Jamsranjav [6] obtained the review data from users of afit from being able to quickly find ‘suspicious marks’ compared tocosmetic review website and used the data to measure the power offinding them manually, and can proceed to a process of taking legal

brand power, defined as consisting of four elements of reception,action against imitators and ‘free-riders’. Doing so is necessary toperception quality, brand image and brand loyalty.
McCarthy [9] is well known as the traditional research about theeliminate the potential risks regarding trademarks.

2.

Related Works

distinctiveness of the mark and it states that the distinctiveness ofa trademark is consisted of its natural distinctiveness and the ob-

Current researches about trademark similarity can be generally

tained distinctiveness. As shown in Table 1, the distinctiveness ofgrouped into two groups; one group with researches that focuses on

the trademark is classified into three groups with the type of terms

comparing or analyzing multiple trademarks given and other withused to name a product or a service, and marketing efforts needed

researches regarding how trademarks are actually received and as-

to make the trademark popular in the market.
When arbitrary or fanciful terms are used to name a product orsociated with some impressions in society.
Comparisons and Analysis of Multiple Trademarks

service, it has the strong natural distinctiveness but needs relatively

In the area of trademark comparison and analysis, there are manyhigh marketing efforts to make its obtained distinctiveness strong

researches aiming to retrieve trademark images with image features.

because the coined or fanciful term is not recognized at all when it

2. 1

In researches of Kim and Kim [19], Wang and Hong [17] andis named, and vice versa can be said for generic terms.

Agrawal et al. [1], they all used Zernike moments of the images
Table 1: Distinctiveness of marks

as image features and proposed methods to suggest similar figure
trademarks when one figure trademark is given as an input.

INHERENTLYDISTINCTIVENON-INHERENTLYDISTINCTIVENODISTINCTIVENESS

No Secondary
Meaning Required

Secondary Meaning
Required

No Trademark
SignificanceDescriptive,Geographic
Personal Name

Generic

All of these researches focus on figure trademark similarity andthe goals of the researches are to retrieve image trademarks that arepotentially similar in appearance by comparing the given one figure

Arbitraryand Fanciful

trademark and the existing trademark image dataset.

Suggestive

Compared to these previous researches, the proposed method isdifferent in terms of giving one word trademark as an input and han-In this research area, there are also researches focusing on how

dling all three types of similarities in appellation, appearance andstructural elements of one word trademark are received and associ-concept as ‘suspicious marks’ generation.

ated with one particular impression from average consumers.

Recently, with the advance of machine learning, some researchers

Yamade and Haga [18] tried to quantify the subjective degree ofstarted using machine learning techniques to trademark similarity

similarity of any possible pairs of Japanese ‘katakana’ characters

judgment. For instance, Kawachi and Hiratsuka [7] applied a ma-with the motivation to eliminate the fatal errors in drug use.

chine learning technique of neural network to automatically analyze
figure trademark similarities in appearance.
There are also researches that analyze several patterns of similarity in appellation. Suga [14] [15] [16] looked at twenty years of trial

Contributions made by several researches introduced so far canbe utilized in the proposed method to construct a model of how
consumers feel about one trademark, especially with similarity inappearance and concept.

decision data regarding trademark dispute and analyzed whether in-3.

creasing or substituting one Japanese mora (unit of sounds that issmaller compared to a syllable) would make the original trademark
and a modified mark similar in appellation or not.

Understanding Trademark Similarity

In this chapter, basic concepts about trademarks and trademark
similarities used will be clarified for further understandings.

Contributions made by Kawachi and Hiratsuka [7] and Suga [14]3. 1

‘Trademark’ and ‘Mark’[15] [16] can be applied to the proposed method of this research to

So far, term ‘trademark’ is used without any clear definition

rank a list of marks definitely or potentially similar with one regis-

given. However, term ‘trademark’ actually must be clearly distin-

tered word trademark given as the input.

guished with similar term of‘mark’. In Japan, regulation regarding

2. 2

Social Reception and Association of Trademarks

trademark is defined in Trademark Act2 .

In the previous section, researches comparing and analyzing multiple trademarks are introduced. However, there is one another per-2

http://law.e-gov.go.jp/htmldata/S34/S34HO127.html

In Trademark Act, ‘mark’ is defined as ‘any character(s), fig-

3. 3

ure(s), sign(s) or three-dimensional shape(s), or any combination
thereof, or any combination thereof with colors, or any sound(s)Types of Trademark Similarity

It is generally accepted as given that there are three types of trademark similarity as shown in below.
•

Similarity in appellation

According to Trademark Act, mark can only be considered as a•

Similarity in appearance‘trademark’ when it satisfies either of the following two conditions.

•

Similarity in concept

and others defined in the government ordinance’.

•

It is used in connection with the goods of a person who pro-duces, certifies or assigns the goods as a business.
•

First one is based on an appellation of a trademark, which is about
how a trademark is pronounced as the product / service name. Al-

It is used in connection with the services of a person who

though there are words in many languages including Japanese thatprovides or certifies the services as a business (except those pro-can be pronounced in several ways, appellations 6 of a trademark

vided for in the preceding condition).

conform with the sound expressions that a owner of that trademark

Based on this definition, this research will make clear distinction
between ’trademark’ and ‘mark’ for the discussions hereafter.

registered with. Therefore, similarity in appellation is measured bycomparing registered appellations.

3. 2 Types of Trademark

Second one deals with an appearance of a trademark, that is de-

As already mentioned in 3. 1, ‘mark’ is defined as ‘any charac-

fined as how trademarks look like as shapes. This can be about either

ter(s), figure(s), sign(s) or three-dimensional shape(s), or any com-of each standard characters’ shape in word trademarks or shapes ofbination thereof, or any combination thereof with colors, or any

figure / three-dimentional / composite trademarks. As mentioned insound(s) and others defined in the government ordinance’ in Trade-the last section, this research assumes word trademarks consisted

mark Act of Japan. This definition also applies to when classifyingof ‘standard characters’ as the input, so similarity in appearance topossible type of trademarks. In general, the following list is consid-

be considered in this research will mainly be about shape of each

ered as types of trademark usually used.

standard characters.

•

word trademark

Last similarity with concept, is hard to define compared to sim-•

figure trademark

ilarity in appellation and appearance due to vagueness of concept.

•

three-dimensional trademark

Concept of a trademark is an abstract idea that people associate with•composite trademark (including color)

one particular word or phrase, or even image. This nature of concept•

sound trademark

makes it difficult to define what similarity in concept is in simple

In the area of trademark registration, there is one special regu-

definition. In this research, it only considers concepts associatedlation known as ‘standard characters’. ‘Standard characters’ are set

with one word phrase based on the premise that word trademarksof characters can be used to express word trademarks when an ap-

with ‘standard characters’ are given as the input trademarks.

plicant does not wish to express one’s trademark with any design4.

elements, such as font style, size or color of the text.

Generating and Ranking ‘Suspicious Marks’In Japan, the use of ‘standard characters’ is regulated in Arti-

This chapter will explain the first core element of the proposed

cle 5(3) of Trademark Act3 as ‘Where a person desires to regis-

method; that is how to generate ‘suspicious marks’ from the one

ter a trademark consisting solely of characters designated by theword trademark given as the initial input for three similarity types

Commissioner of the Patent Office (hereinafter referred to as ”stan-and rank by the degree of similarity against the input trademark.

dard characters”), the application shall contain a statement indicat-

4. 1

ing thereof.’.

4. 1. 1

Similarity in Appellation
Generating ‘Suspicious Marks’Furthermore, detailed explanation of how ‘standard characters’

For similarity in appellation, the detailed regulation is available

should be used is available in the corresponding section of ‘Exam-in Chapter 3, Article 4(1)(xi) of ‘Examination Guidelines for Trade-ination Guidelines for Trademark’4 ; ‘standard characters’ that canmarks’7 . Therefore, the proposed method handling similarity in ap-

be used to name word trademarks are also publicly listed as an ap-

pellation should strictly conform to the regulation.

5

pendix of ‘The Trademark Examination Manual’ .

However, this regulation is originally for comparing two trade-Among several types of trademark generally used, this researchmarks and thus can not be used with the original form to generate

focuses on tackling word trademark infringement and ‘dilution’‘suspicious marks’. In order to make this regulation compatible withproblems by giving word trademarks as the input. Also, the pro-generating ‘suspicious marks’, the proposed method first converts

posed method assumes all input word trademarks to be consisted

each regulation factors to a rule for ‘suspicious marks’ generation.

only of ‘standard characters’.
3

http://www.japaneselawtranslation.go.jp/law/detail/?id=45&vm=04&re=01

4

http://www.jpo.go.jp/tetuzuki e/t tokkyo e/pdf/tt1302-002/4.pdf

6

A registered trademark sometimes has more than one appellation.

5

http://www.jpo.go.jp/tetuzuki e/t tokkyo e/pdf/appendix1.pdf

7

http://www.jpo.go.jp/tetuzuki e/t tokkyo e/pdf/tt1302-002/3-10.pdf

4. 1. 2

Ranking ‘Suspicious Marks’on the similarity of a trademark needs to, with consideration given

The proposed method uses the co-occurrence probability calcu-

to a class of main users (for example, professionals, senior people,

lated based on letter n-gram corpora generated from Trademark

children, women, etc.) of goods or services on which the trade-

Gazette CDs with the data offered by Japan Patent Office. The list

mark is used, be made based on attentiveness usually possessed by

below shows time periods of CDs used to compile a dataset.

the user, with consideration given to the state of transaction of the

In order to generate letter n-gram corpora, information of trade-goods or the provision of the services’. Therefore, the differences ofmark itself and its appellations in Japanese ‘katakana’ are extracted

attentiveness between classes of main users must also be considered

and compiled as the dataset used in the proposed method. The

as a part of the proposed method.

dataset based on Trademark Gazette CDs is consisted of 77,873 registered word trademarks in total.

In order to conform with this requirement, the proposed method
defines ‘similarity in concept’ by breaking it down into two differ-

The letter n-gram corpora are generated using the extracted infor-

ent large elements with one element also having two child elements,

mation of appellation of trademark. There are two n-gram corpora;

as shown in below and treat them separately and equally when gen-

one as 1-gram corpus and another as 2-gram corpus.

erating and ranking marks that are ‘similar in concept’.

The co-occurrence probability for each generated ‘suspicious
mark’ is calculated using 1-gram corpus and 2-gram corpus. The•

semantic similarity– dictionary-based similarity

calculated co-occurrence probability acts as an indicator of how a– contextual similarity‘suspicious mark’ is likely to be existed as a possible trademark.

•4. 2
4. 2. 1

Similarity in Appearance
Generating ‘Suspicious Marks’concept buildingThis classification is based on an idea that two words that aresemantically similar are not necessary being ‘similar’ in terms of

Although ‘Examination Guidelines for Trademarks’ does not of-‘similarity in concept’ regarding trademark. Even though one word

fer concrete examples of similarity in appearance, Amino [2] listedor phrase has one or several meanings available, having them does

examples available in the judicial precedents and trial decisions.

not mean that these meanings are strong enough to be widely ac-‘Suspicious marks’ in terms of ‘similarity in appearance’ can be

cepted as given concepts in everyday real usage.

generated by replacing a character used when naming products or

In other words, there must be some measure to express how aservices with other character that ‘looks’ similar to the original char-word or phrase is actually received in real society, regardless ofacter. In order to implement character substitutions, the knowledge

how it is defined in dictionary. For this purpose, ideas of ’concept

about which characters are ‘similar’ in shapes available to use.

building’ are included in a model to define ‘similarity in concept’.

4. 2. 2

Ranking ‘Suspicious Marks’The parameter of ’concept building’ indicates how socially accepted

One possible approach to calculate shape similarities between

level of two words are similar, and it compares strengths of concepts

two characters is to compare two characters as images by usingsocially formed for each of two words. This parameter serves impor-some measure and use the calculated value as the parameter indi-tant roles when ranking generated ‘suspicious marks’ in 4. 3. 3.

cating shape similarities / differences between two characters.
The proposed method uses Python Imaging Library (PIL) to generate images for three types of characters often used when naming
products or services in Japan. Three types of characters are Japanese‘hiragana’, ‘katakana’ and ‘kanji’.

For ‘semantic similarity’, which is a similarity of two words interms of meanings, it is also further divided into two child elementsof ‘dictionary-based similarity’ and ’contextual similarity’.
‘Dictionary-based similarity’ is a similarity based on meanings
defined in dictionary, and ’contextual similarity’ is for meanings

In order to calculate similarities between images of characters,

actually used in particular context. By combining two of these sim-

HOG(Histogram of Oriented Gradients) feature descriptor by Dalal

ilarities, the proposed method successfully expresses what it means

and Triggs [4] is used to express each of character-based images as aby ‘two words are similar in meanings’ into a simplified model.

feature vector. HOG feature descriptor break down one input image

Furthermore, an issue of ‘ambiguity’ must be addressed for each

into several blocks to compute gradient histograms of each blocksof ‘semantic similarity’ and ‘concept building’. The issue of ‘ambi-

and concatenate results into one vector at the end.

guity’ is based on a fact that sometimes words can have more than

Therefore, by computing inner products between HOG feature
vectors of two given images using OpenCV, it becomes possible toone meanings and it must be detected which meaning is used before
comparing meanings or concept strengths of two words.

quantify how two given images are similar; this value is used in theBased on discussions so far, ‘concept similarity’ is handled byproposed method to eliminate marks that failed to reach the pre-calculating two parameters of ‘semantic similarity’ and ‘concept

defined threshold of similarity in appearance from the final output.

building’ along with taking ambiguity issues into consideration.

4. 3 Similarity in Concept
4. 3. 1

Understanding Similarity in Concept‘Examination Guidelines for Trademarks’ states that ‘A judgment

4. 3. 2

Generating ‘Suspicious Marks’Among aforementioed two elements defining ‘similarity in concept’, the first element of ‘semantic similarity’, especially a childelement of ‘dictionary-based similarity’ is considered when gener-

Word2Vec, introduced in the research by Mikolov et al. [11].

ating ‘similar’ marks and others are considered for ranking.

Word2Vec uses a text corpus as the input to obtain concurrent words

Usually, word trademarks are made of combining several words

for each word and use to them to express each word as feature vector

and/or coined words. Therefore, by morphologically analyzing theso that similarity between words can be easily computed. It is known

input word trademark, a set of words used in the input word trade-to be effective for expressing the meaning of words as feature vec-

mark can be obtained. By using synonym databases for each word

tors with less time needed to learn compared to previous neural net-in the obtained set and replacing one word with corresponding syn-work language models. Word2Vec has two models of learning; one

onyms, marks likely to be ‘similar in concept’ can be generated.

is CBOW(Continuous Bag Of Words) and another is Skip-gram.

To do so, Japanese WordNet Synonyms Database8 distributed

These two learning models have different learning schemes;

as a part of Japanese WordNet Project [5] is used to obtain syn-while CBOW model is for predicting the current word based on

onyms for each word. Japanese WordNet Synonyms Database iscontext, Skip-gram model tries to maximize classification of a word

made by adding Japanese words to original Engligh WordNet [12]based on another word in the same sentence. For this proposed

and it has information about definition (‘gloss’) and synonyms of

method, an algorithm to express the meaning of one word is needed

words grouped in a unit called ‘synset’. In this dataset, ‘synset‘ cor-

to use. Therefore, Skip-gram learning model of Word2Vec can be

responds to a meaning of a word unique to that ‘synset’ ; so one

used for the purpose of this research.

word can have several different ‘synset’ IDs.

However, original Word2Vec model can not handle word ambigu-

Actual implementation is done by two steps of morphologi-

ity correctly since it generates one feature vector for one word each.

cally analyzing the input word trademark into a set of words us-

In order to make Word2Vec compatible with handling ambiguous

9

ing Mecab and substituting each words with all possible synonyms

words, Lin et al. [8] proposed a method to distinguish ambiguous

available in Japanese WordNet Synonyms Database. Substitution

words by pre-tagging all words in a given text corpus with types ofwith possible synonyms is limited to once in one trademark as sub-parts of speeches (grammatical types of words, such as noun, verb,

stituting multiple words, the original form of the input word trade-

adjective and others) based on the result of morphologically analyz-

mark will gradually be lost and becomes not similar.

ing the input text corpus.

4. 3. 3

Ranking ‘Suspicious Marks’The proposed method in this research also introduced proximity

By following processes clarified in last subsection, conceptual

weights in the sum pooling layer of CBOW and this improved al-‘suspicious marks’ for the input word trademark can now be gener-

gorithm is called as PAS (Proximity-Ambiguity Sensitive) CBOW

ated. However, some of these generated ‘suspicious marks’ are notalgorithm. They also scale the proximity weights learned with PAS

likely be marks similar in concept due to the backgrounds regarding

CBOW to make the sum of them equal to 1. These normalized

similarity in concept already explained in 4. 3. 1. Therefore, in orderweights are regarded as a pseudo probability distribution and used

to leave out generated marks that are not in fact similar in concept,

for dynamic window size in Skip-gram, as expanded Skip-gram

some ranking algorithm based on conceptual trademark similarity

model of PAS Skip-gram model.

measure is needed in the proposed method.

However, this ambiguity-aware expansion to Word2Vec learning

To achieve this goal, the proposed method will define a value ofmodel is not effective for a problem in this research. That is simply

conceptual similarity degree ConceptSimilarity(w1 , w2 ) of two

because pre-tagging words with types of speeches often does notwords w1 ,w2 as shown in below.

offer any distinction between two words with different meanings.
For instance, this modified Word2Vec can not make distinctions be-

ConceptSimilarity(w1 , w2 )=2
1
1
+ ConceptBuilding(wSemanticSimilarity(w1 ,w2 )
1 ,w2 )

tween ‘bank’ as ‘a land near the river’ and ‘bank’ as ‘a financial(1)

institution’, since both of them are nouns.

The above equation is a harmonic mean of SemanticSimilarity

Another approach to make Word2Vec compatible of handling am-

and ConceptBuilding to make sure that two different elements

biguous words correctly is a research by Chen et al. [3]. They ap-

defining ‘similarity in concept’ are treated with equal weight.

proached to these problems by performing word sense disambigua-First element of conceptual similarity, SemanticSimilarity istion (WSD) based on different sense vectors created from WordNet.

a similarity measurement between meanings of two words. To cal-

Although this work seems to be more feasible compared to theculate this kind of similarity measurement, some algorithms to ex-

one by Lin et al., it still has some problems when applying to thepress the meaning of a word in a numerical vector is needed so that

purpose of judging conceptual similarity of Japanese trademarks.

meanings of two words can be compared.
A major approach to express how words actually mean isClassifications of synsets and glosses in WordNet are sometimes
not so natural as Japanese due to the characteristics of JapaneseWordnet that is compiled by translating original English WordNet.

8

http://compling.hss.ntu.edu.sg/wnja/

Therefore, it might be expected that sense vector generation and9

http://taku910.github.io/mecab/

WSD do not work as expected in original English WordNet.

In order to overcome this possible issue, a Word2Vec-based algorithm combining both of some techniques used by Lin et al. andChen et al. is proposed to calculate SemanticSimilarity.
In the proposed method, WSD is processed by mapping each oflog2(w1 )|) denotes concept similarity relationship.
Finally, as the value of SemanticSimilarity(w1 , w2 ) is normalized to the range of (0,1), same normalization should also
be made for ConceptBuilding(w1 , w2 ). To achieve the normal-

common nouns in a text corpus to one of WordNet synsets of word,

ization, the value of ConceptBuilding(w1 , w2 ) is assumed toof which cosine similarity between a tf-idf vector of gloss and an-

have the range of rangeConceptBuilding and using this range,other tf-idf vector of the line in the text corpus is the largest.

ConceptBuilding(w1 , w2 ) can be normalized into the range

The proposed method uses this ambiguity-aware Word2Vecof (0,1). This normalized value will be the final value ofmodel to calculate SemanticSimilarity defined in the beginning

ConceptBuilding(w1 , w2 ) and the value of θ1 is set to 500 andof the current subsection. As Word2Vec model is now aware of am-

θ2 is set to 10,000,000 in the proposed method.

biguity, it can have several different feature vectors for one word.

rangeConceptBuilding

Therefore, the proposed method considers all possible combinations(2)of feature vectors to calculate similarity between two words. For in-

11= ( | log 2(θ1 +1)−log,)2(θ1 )| | log 2(θ1 +θ2 )−log 2(θ1 )|stance, if word w1 has m synsets or feature vectors and word w2

The threshold value of concept similarity θconcept is determined

has n synsets or feature vectors, the proposed method will calculate

by calculating SemanticSimilarity(w1 , w2 ) for test datasets

the similarity for all possible m * n combinations and use the largest

consisting of trademark pairs that are judged as similar in the countsimilarity value as the similarity between word w1 and word w2 .

and not similar in the count. These test datasets are compiled us-

SemanticSimilarity is a cosine similarity between word vectors of w1 and w2 , so it takes the range of -1 to 1. In order toing data available by Amino [2] and contain three different types ofdataset, hereafter defined as A,B and C respectively.

calculate the harmonic mean between SemanticSimilarity andDataset A and B are datasets containing 16 pairs of trademarks

ConceptBuilding at the end, the value of SemanticSimilarity

judged as conceptually similar/not similar by the court respectively.

is normalized into range of 0 to 1. This normalized value will be theUsually, for this kind of threshold defining task, preparing two

final value of SemanticSimilarity.

data of similar and not similar is enough by marking each of test

Second element of ConceptBuilding(w1 , w2 ) is based on cases

data into one of ‘true positive’,‘false positive’,‘false negative’ andclassifications using web hitcounts as shown in Figure 1. Each of‘true negative’ as shown in Table 2. However, with the task to be

w1 , w2 , θ1 placed on a horizontal line indicate level of concept pop-

solved in this research, the non-similar dataset of B is actually not soularity in the society. Therefore, in Case 1, concept of word w1 is

well binary-classified. This is due to the nature that mark/trademark

considered to having no concept similarity relationship as the poplu-

similarity cases brought to the court are cases where similarity isarity level of wi is lower than the threshold value of θ1 . If either ofdisputed between two people because they argue for opposite for

w1 , w2 is smaller than θ1 , the value of ConceptBuilding(w1 , w2 )similarity and the court is being asked for professional judgement.

will be ‘undefined’. Another θ value of θ2 , compared to |w2 − w1 |,Therefore, although dataset B does have pairs of trademarks judged

is the threshold value of concept similarity difference. If the value

as conceptually not similar by the court, it does not necessary meanof |w2 −w1 | is equal or larger than θ2 , ConceptBuilding(w1 , w2 )that pairs in dataset B are similar in concept for sure, in terms ofwill be the smallest value of 0.

binary-classification. In fact, they are somewhat ‘gray’ data between‘white (not similar)’ and ‘black (similar)’.
Table 2: binary classification of trademark similarity
Judged Similar

Judged Not Similarby the Court

by the Court

Marked Similar

True Positive

False Positivebysimilar by the court

not similar by the court

the Proposed Method

similar by the method

similar by the method

Marked Not Similar

False Negative

True Negativebysimilar by the court

not similar by the court

the Proposed Method

not similar by the method

not similar by the method

Figure 1: cases of concept buliding situations

In order to overcome this issue, another test dataset of C in added

In the actual calculation model of the proposed method, the hit-

to be used alongside with A and B. Test dataset C is made by humancount value of Bing is used to express the level of concept popular-changing one of the paired trademark in dataset A to a word that is

ity in the society. However, since the hitcount values tends take a

opposite or not related with the another original trademark in a pair.

wide range of integer values, the log value with base 2 of the hit-

Dataset C also contains 16 pairs of marks.

count is used and the inverse of the absolute value 1/(|log2(w2 ) −By using a unified test dataset containing all of A,B and C, thevalue of conceptual similarity threshold θconcept is determined.

pages in Japanese electronic shopping website of ‘Yodobashi Cam-

When two marks m1 and m2 are given,

era11 ’ are collected as webpages that are known to contain names•

if SemanticSimilarity(m1 , m2 ) is larger than or equal toθconcept , m1 and m2 are similar in concept.
•

the top page of ‘Yodobashi Camera’ as of 16th December, 2016 and

if SemanticSimilarity(m1 , m2 ) is smaller than θconcept ,m1 and m2 are not similar in concept.
•of goods or services. These 141 webpages are webpages featured inthere are many product categories of electronics, foods, toys andmany others. From the text of these webpages, tags and reserved

if SemanticSimilarity(m1 , m2 ) is undefined, conceptual

words of html/css/js/etc. are removed to obtain plain texts.

similarity between m1 and m2 is also undefined regardless of theUsing two large text of webpages from ‘Yodobashi Camera’ andvalue θconcept takes.

another from newspaper web articles, tf-idf vectors are generated

Based on this definition and binary classification in Table 2, thefor both of them using Japanese stopwords file distributed as a partvalue of Accuracy is calculated for each of θconcept starting atof Slothlib12 . Then, from the tf-idf vector of ‘Yodobashi Camera’

0.00 and incremented by 0.05 until 1.00 with the equation below.

webpage texts, 300 words with top tf-idf values are extracted as

As a result, θconcept is defined to be 0.20, which is the smallest

words that represent webpages containing names of goods or ser-value of θconcept when Accuracy becomes the largest. As trade-

vices. Words that are below 300 in the ranking of tf-idf values aremarks marked ‘undefined’ are not included in the equation, unde-ignored because 300 seemed to be the border where proper nouns

fined pairs are separately recorded. In the test dataset, 9 pairs arestart to appear, as words are examined by human.

‘undefined’ semantically, and 8 are ‘undefined’ in concept building.
Accuracyθconcept= tpθ

6. Evaluation(3)

The proposed method of this research will be evaluated separately

tpθconcept +tnθconcept+tnθconcept +f pθconcept +f nθconcept
conceptwith two different criteria stated as below.
（ 1 ） Qualities of generated ‘suspicious marks’5.

Search Using Generated ‘Suspicious Marks’

5. 1

Preparing Search Queries（ 2 ） Qualities of webpages found by web searches

For evaluating purposes, four word trademarks that each of them

The proposed method does not use each of generated ‘suspicious

is a trademark in trademark pairs that are judged as similar by themarks’ in the given form; instead a search query is prepared bycourt and another four word trademarks that each of them is a trade-

adding the keyword containing the information about how the in-mark in trademark pairs judged as not similar are prepared as total

put trademark is used with products/services, which users inputed

input of eight word trademarks.
6. 1

to the proposed system. In an actual application of this research, theEvaluating Generated ‘Suspicious Marks’search query is prepared in an expression as “generated ‘suspicu-

Qualities of generated ‘suspicious marks’ are evaluated by select-

ous mark’ AND product/service information keyword” and will be

ing about 0.5% of generated marks for each input word trademark.
Then, two people are asked to judge whether each mark is similar

given to the Web search engine of Bing10 .
5. 2

Ranking Searched Web Pages

or not similar to the original input word trademark based on theirUsing queries prepared in a way explained, searches are per-intuition. These two people are people who are chosen intentionallyformed in Bing to discover webpages containing information re-

because of their profiles as not being familiar with Trademark Act.

lated to trademark infringement and dilution cases. Although theGiven T as the input word trademark, S as the marks judged

proposed method limits number of pages to find for each query tosimilar by an evaluator, A as the numbers of generated ‘suspicious

10 pages, many webpages will be still found. Therefore, some ex-marks’ selected for evaluation, P recisionT of S/A is used as the

tra efforts to only keep the webpages likely to contain information

measurement used to evaluate generated ‘suspicious marks’. Table

related to trademark infringement and dilution cases are needed.

3 shows the calculated average value of P recisionT evaluated by

5. 2. 1

Used as Goods or Services

two evaluators for eight word trademark given as the input.
6. 2

The proposed method tries to detect webpages of which generated

Evaluating Web Search Results‘suspicious marks’ are used as names of goods or services. If such

In order to evaluate the web pages found by searching with Bing,

webpages can be detected, they are more likely to contain informa-a registered word trademark of ‘メガネの愛眼’ was used as an input

tion related to trademark infringement and dilution cases comparedto the proposed method to generate ‘suspicious marks’ and search

to webpages of which generated ‘suspicious marks’ are not used aswith Bing based using generated marks. Figure 2 shows one exam-

names of goods or services.

ple of a webpage found by the proposed method and trademark in

To achieve classification of such webpages, 141 product web

10

https://www.bing.com

logo available in a webpage found. As can be seen in this figure,11

http://www.yodobashi.com

12

http://www.dl.kuis.kyoto-u.ac.jp/slothlib/

Table 3: evaluation of generated ‘suspicious marks’
input word trademark T

average

レガシィクラブ
メガネの愛眼中古車の 110 番
ウォークバルーン
筑後の寒梅
大皿惣菜 ∞ 遊 ∞ 居酒屋
和漢研麗姿
自然健康館スーパーフコイダン

0.2757

total average of P recisionT0.16750.1533

Acknowledgements
This work was supported by the following project: Grants-in-Aid
for Scientific Research (Nos. 24240013) from MEXT of Japan.
References

0.10270.2578[1]0.10000.17360.1005[2]0.1769[3][4]
Figure 2: an example of found webpage and trademark logo

[5]

[6]

[7]

[8]

[9]
[10]

found webpage has a trademark in logo similar to the word trademark of ‘メガネの愛眼’.
Since this webpage is difficult or almost impossible to find by
searching with ‘メガネの愛眼’ as the search query, it can be concluded that the proposed method is useful for assisting companies

[11]
[12]
[13]

with registered trademarks by offering a solution to detect trademark infringement and dilution from the Web.

7.

[14]

Conclusion and Future Problems[15]

This research proposed a method to generate ‘suspicious marks’for three types of trademarks similarities and rank them by the degree of similarity against one given registered word trademark. Fur-

[16]

thermore, it also proposed a method to discover trademark infringement and dilution using generated ‘suspicious marks’ from the Web.

[17]

Application of the contribution in this research should not be only
limited to discovering a trademark infringement and dilution cases.
For instance, there might be a possibility to apply the knowledge of[18]

this research for an area of coming up with new product / service
name appealing to consumers. Therefore, broader possibilities must
be considered when moving this research further forward.

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