Download Landscape Analysis of Patent Dataset

Journal of Intellectual Property Rights
Vol 21, July 2016, pp 211-225
Landscape Analysis of Patent Dataset
Deepti Mehrotra, Sai Sabitha†, Renuka Nagpal and Nisha Mattas
Amity University, Sector 125, Noida, Uttar Pradesh - 201 313, India
Received 04 January 2016; accepted 15 June 2016
With the advancement of technology in almost all sectors of industry and decreasing span of product, inventing new
ideas are required for any industry. These ideas need to be properly guarded through patents to provide inventor due
economical reward and right to control his creation. The patents are stored in large databases. The analysis of these
databases will help to get an insight into the technology sector, competitor and chronological development in the field of
technology. It also helps the inventor to understand how his invention can cater to the need of the current market, so that
viable industry collaboration can be done. Landscape analysis of patent is done to get a comprehensive view about all these
information. Various computational approaches are used to analyse the patent dataset. These approaches and their objectives
are discussed in this literature. By converging all of them on a single platform will provide complete insight at a single point
which will aid the inventor and business investor. In this research work an extensive literature on existing approaches is
discussed. A framework for a landscape analysis is proposed along with tools and techniques that can be suitably used for a
complete technological growth and patent data.
Keywords: Technology road-mapping analysis, research and development (R&D), data set attributes, preprocessing,
mining techniques
In this world of k-economy, the survival of a business
organization exists only by using the latest and
innovative technology, thereby providing them an
edge over their competitor. Encouragement of
creating innovative ideas and protecting it under
intellectual property rights (IPR)1,64 will be achieved.
A patent represents an innovation in the technological
domain that represents detailed information about the
invention, including inventors, an area to which it
belongs, publication details, assignee information,
international patent classification code (IPC code).2,3
Today scientists and technocrats are filing a large
number of patents. These patents are stored in various
databases like-United States Patent and Trademark
Office (USPTO), European Patent Office (EPO),
World Intellectual Property Organization (WIPO),
Japan Patent Office (JPO), State Intellectual Property
Office (SIPO) etc.4,5 These databases contain
terabytes of information in the unstructured form.
This information can be in text, images, numbers,
equations, special characters, etc., which can cause
hurdle in the extraction of meaningful information.
With advent of each day, this increased size of
information and different data formats of the patent
———————
†
Corresponding author: Email: [email protected]
document makes it difficult for users to take full
advantage of it.
One of the prime objectives of designing these
databases is to check the originality of innovation and
to assign the research in the name of a researcher or
its organization. Apart from novelty check, a
researcher is keenly interested in retrieving the patents
that have high industrial relevance. Patent landscape
is the comprehensive analysis that focuses on
identifying IP trends, technology leaders and
technological positioning of the companies. Thus,
there is a need to maintain and analyse this database
so that one can project on competitiveness and also on
domains where organizations can invest their
Research and Development (R&D) capital to advance
technologically.6
There is a need for an organization to perform
landscape analysis of a patent dataset before starting
any research oriented project. This analysis will give a
comprehensive view of the project. Since technology
is changing at a fast pace, it is required to forecast
technological trends, to gain quick insights into
research areas. Business units today are focusing on
technology-driven business, and are planning based
on their technology capabilities. Analysing valid
patents from the repository could save a lot of time
J INTELLEC PROP RIGHTS, JULY 2016
212
and effort and would keep organizations, abreast of
technology-driven business opportunities, thus more
competitive. Researchers have done a wide analysis of
patent datasets to find the technological trend, patent
classification, clustering similar patents, comparing the
technical trend with market analysis etc.
To analyse and visualize patent information
effectively, researchers extensively utilize text and
data mining techniques and tools.7,9 The novel and
unknown patterns obtained from these techniques
provides necessary decision support to organizations
to improve innovation and efficiency. Patent
information can provide an approximate description
of the innovation activity occurring in most fields of
technology in developing countries. It is accepted as
the only viable quantitative measure since, it
accumulates information over an extended period10 of
time. Monitoring of competitors, technological trend
analysis, organization share in technology, patent
infringement calls for ideas and time-efficient patent
analysis. Analysis of patent data can be broadly
divided into four types as follows:
Technology Roadmap Analysis11
Technology Classification Analysis
Originality/R&D Support Analysis
Technology Distribution Analysis
To do any of the above analysis, one needs a
suitable dataset. As these repositories store
information corresponding to various attributes,
selection of suitable attributes, mining technique and
appropriate preprocessing task need to be identified.
Data mining techniques like text mining and
visualization approaches can be suitably used to get
the desired result.12 The Organization of this research
work is as follows:a
b
c
Detailing of various types of analysis of patent
database;
Patent analysis that includes, data collection,
preprocessing and dataset attribute selection for
specific analysis;and
The comparative analysis of various approaches
of patent analysis.
This study provides the useful information for
gaining insight to explore hidden and helpful
intelligent knowledge from patents.
Analysis on Patents
Patent analysis has attracted the researchers in the
last decade and enough literature is available in which
patent data set is considered to get the desired
analysis. In this study, more than 100 papers are
refereed and approximately 71 papers are reviewed.
The discussion is broadly based on four patent analysis
approaches like, technology roadmap, classification,
originality/R&D and distribution analysis.
Technology Road-Mapping Analysis
The patented technology is an emerging field for
establishing the relationship between company plan
and current technology. For strategic planning, it is
required to identify and invest in the right technology.
With reduced product life cycle, it is becoming
necessary for the company to develop new products
for their survival. This analysis provides a
commercial perspective to patent analysis. This
dimension of analysis can be useful for business units,
researchers and educational institutions find new
areas and trends in patent. This information is vital for
understanding prospects in the field of technology, its
prior and current use. The technology roadmap
analysis can be retrospective and prospective, i.e.,
analysis of patent filed in previous decades, present
and in the future direction like time series analysis,
patent trend analysis and citation analysis. To
perform road map analysis, researchers have used
different techniques like statistical, data mining
analysis and currently text mining is one of popular
approach used.2,4
Time Series Analysis
The pull of the market encourages a push for
innovation as most of the technology innovation is
carried out due to competitive need. In time series
analysis, the growth of technology in the past and its
market implementation are studied. Based on the
current market need the future innovations are
forecasted for a particular technology. This is plotted
against the time distribution in the years.10 It helps in
understanding the growth/decline rate of various
technologies over time.13 Also, this information
requires observing the frequency and quality of R&D
activities in the particular geographical area.14
Technology Lifecycle/ Patent Trend Analysis
Companies analyse the patent document to
ascertain the latest trend in a given technology.
Designing of technology driven roadmap for the
industry using portfolio affinity map establishes the
relationship between technology and industry.11 The
characteristics of patent distribution based on the
technology development15,16 can be understood. It
SABITHA et al.: LANDSCAPE ANALYSIS OF PATENT DATASET
helps in the development of data processing
technologies and also for evaluation of novelty
performances of different countries.17 Social impact
and cross-impact analysis is performed to judge the
impact of innovation on society.18-20
Citation Analysis
Citation refers to the number of times the research
works of different assignees get cited. Citations are of
two kinds- Forward and Backward Citation. If a
patent cites another patent, then it is called Forward
Citation and a patent being cited by another patent is
called Backward Citation. Countries owning patents
with more forward citation have a strong
technological impact in comparison to a backward
citation that depicts countries with mature
technologies. It further indicates the R&D ability of
assignees. Yujin and Byungum21 designed the patent
citation network where patents are the nodes and arcs
representing a citation link between citing patent and
cited patent. Citation analysis provides the relative
importance of the domain and the patent was
proposed on the basis of understanding the technical
assessment of the patent.62
Technology Classification Analysis
Classifying patents into groups help in
smoothening the search for the required patent. The
IPC (International Patent Classification) code is
commonly used to classify patents based on the
technology domain.2,22 The data mining tool is used
for classifying patents based on similar
characteristics. Classification helps in analysing the
prospects of key technologies, research, development
trends and changes, in particular focuses on
concerned technical field.10
IPC Analysis
The IPC standard is a hierarchical classification
system developed by WIPO for organizing the subject
matter of published patents.17 It can help enterprises
understand the major categories for technological
classification for planning R&D activities.23 Besides,
IPC code, different research work uses other codes like Derwent Assignee Code (merging same assignee),24
Corporative Patent Classification (CPC code)14,25 and
the Unified Patent Court (UPC).14
Family Analysis
In order to protect their invention, inventors file
their patent in different patent offices, thus creating a
family of the same patent which is known as patent
213
family. Such analysis of patent family helps to
organize the database to avoid double counting
problem to restrict R&D budget to novel work
only and also provide an estimation of patent filling
in different offices. Authorized members assign
priority to the members usually by date of filing
patent. The patent portfolio is then built using the
relationship between priority claiming and priority
26
claimed in the patent family. This concept is further
refined in the extended family to find the possible
link between two patent document and bring them
into single family.27
Cluster Analysis
Clustering is the task of grouping objects having
several attributes into different clusters such that the
objects belonging to the same cluster have high
similarity in comparison to other patents.28 This
approach is specially used to reduce the size of the
dataset by grouping them into the subset of similar
object, thus helps in searching object in the class.70
Text mining techniques are used for cluster creation
based on evaluations done on the similarity score
between patents. This approach has been extended
by designing the patent map which is created by
the semantic network of keywords retrieved
from cluster.61 Also, Fuzzy clustering is used to
identify the overlapping patent documents and
interdisciplinary patent.20
Originality/R&D Support Analysis
The basic conditions for invention must be
statutory, must be useful, be non-obvious. Apart from
these conditions for granting a patent the most basic
condition as per patent law, the patent can be granted
exclusively if invented technology is novel. This
analysis compares new patent with existing patents in
the closely related domain. Further, the patents are
ranked based on its quality and market usage [6]. The
prior art is the technique that contains information
about patent's claims of originality. Identification of
patent conflicts is made using TRIZ-Led patent
mapping techniques.7
Infringement Analysis
Patents have been in existence since a long time
back and continue to populate databases with
exceptional works. Due to the increasing size of the
database, it becomes necessary to check for
infringements in work. This analysis is useful in
determining any similarities among patents to ensure
that a patent has not been copied in any sense. Also, it
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J INTELLEC PROP RIGHTS, JULY 2016
is important to check for infringement before
allocating R&D budget.2
Novelty Analysis
A work which has not been done in the past is
remarked as a novel or Patent in technical terms.
Patents are a huge repository of new technology,
which provides new development directions in any
domain. Thus, it is required to analyse the novelty of
work in the field of technology. It is determined by
generating the patent maps and measuring the
technological distances among patents by constructing
semantic matrix, to identify inventions in patents that
are highly novel.2
White Spot Analysis
It provides direction for research work through
the identification of gaps or white spots for problems,
for which no or better solutions are required.2
Technology Distribution Analysis
This analysis provides information about
technology regarding the prevailing domain area,
competitors, assignees, etc. It helps in analysing
countries with good R&D support.25 Also, it provides
information about the proper allocation of resources
in a particular technology domain, to enhance niche
technology and country as well.16
Patent Trend of Technology Distribution by Country
Distribution of patents about same technology
with respect to different countries. It helps in
knowing countries with most patents registered
in a particular technical field.14,15,25 Along
with citation this patent analysis helps us to
identify the knowledge flow in the different
countries.
Association Rule Analysis
It analyzes objects that associate with each other
based on following performance measures like
support, confidence and distance.29 It is used to find
the interesting hidden patterns in the patent
information; thus identifying the relationship among
technologies and patentees in a certain technical
field.10 Common association rule algorithms are C4.5
decision trees, Apriori algorithms.
Rivals/Competitor Analysis
This analysis searches all patents of the
competitors to analyse the status, scope of technology
and R&D competence to prepare guidelines for
research and development.30
Assignee Analysis
This analysis evaluates Technological innovation
capability of different organizations. It ranks the
assignees based on their patent quantity.31 Fig. 1
indicates the number of papers reviewed for each type
of analysis and their subcategories. The technology
road map analysis is most discussed in literature.
Cluster analysis is also widely used for subdividing
the dataset based on similarity.
Fig. 1− Papers reviewed – Patent analysis approaches
SABITHA et al.: LANDSCAPE ANALYSIS OF PATENT DATASET
Methodology for Patent Analysis
The methodology adopted consists of the sequence
of steps as shown in Figure 2. The first step involves
finding relevant patent data. For this objective, the
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data collection is done through commercial or public
databases. Then, patent data are pre-processed to
remove irrelevant and incorrect data. The resulting
data are in structured form. Mining techniques are
Fig. 2−Methodology for landscape patent analysis
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applied to a specific data set attribute. The results help
in visualizing R&D innovation and in intelligent
business decision making.
Data Collection from Different Sources
Data collection is the initial stage in analysis
process where the collection of patent data is made
from commercial or public databases like – USPTO,
WIPO, JPO, EPO etc as shown in Table 1 below. The
task of searching the patent databases to find relevant
patents is supported by various data and text mining
tools.2 Methods like- keywords, multi-agents
technology for information extraction based on
XML,32 patent classification methods like-IPC or CPC
Code25 are commonly used for patent information
search or patent categorization.
A detailed study on patent database and their
importance in prior art documentation and patent
search is discussed by Singh et al.65 The commercial
database is very expensive and public database often
do not provide complete information. Once a database
is selected the searching in these databases started
with broad area which is further refined later as per
need of search.
suitable. As done in any information retrieval system,
it requires compiling this information in the form of a
text dataset for conducting data mining analysis. The
preprocessing approaches are used for cleaning and
transforming the data, without which it is hard and
time-consuming to process collected data. Different
steps involved in preprocessing are:
1. Removal of duplicate patents
2. Cleaning – removing irrelevant and incorrect data
3. Keyword extraction
4. Text representation1
5. Data transformation and data reduction
Many authors have combined preprocessing and
text mining techniques for categorization of patents.
The popular text mining methods used are correlation
analysis, neural networks, clustering,31 that converts
unstructured text to numerical data or some structured
text data for further analysis.34 Also, as this
preprocessing reduces the unwanted and redundant
information, hence it increases accuracy, scalability
and reliability of the classification and clustering
algorithms.35
Data Preprocessing
Data Sets Attributes
Patent documents contain a variety of information
in the form of images, text, diagrams, numbers, dates,
equations and special symbols, hence directly using
the collection of patent as an input for data mining
tool or machine learning algorithm may not be
Different attributes are considered in selecting
relevant data sets for appropriate analysis. The chosen
attributes provide trend analysis, technological
contribution with respect to country and organization,
citation analysis and many other types of analysis.
Based on the need of the organization, attributes are
chosen. Table 2 shows attributes, analysis based on
data sets, research area, methods and tools used in
different case studies.
Table 1—Commercial and public databases
Commercial
Thomas Reuter
LexisNexis
Mine SoftPatBase
PatSear
ProQuest
Questel Orbit
SciFinder
Public databases
Universal
Country
USPTO
(United States Patent
and TradeMark Office)
Google Patents
CPD
(Canadian Patent
Office)
JPO (Japan Patent
Office)
INPAIRS (India)
WIPO(World
Intellectual Property
Organisation)
EAPO (Eurasian
Patent Organisation)
Europe patent
organization (EPO)
Patent.com
IP.com
IPONZ
(New Zealand patent)
IPOS
(Singapore)
AusPAT(Australia)
TIPO (Taiwan
Intellectual Property
Office)
SIPO(Chinese Patent
Office)
Data Mining Techniques
These datasets of patents are represented as
structured and unstructured data. The classification
techniques used for the analysis are broadly classified
into two categories:1. Text mining techniques for extracting information
from structured or unstructured text.
2. Visualization techniques provide a visual analysis
of the patent, so that decision makers or
technology experts can easily interpret.43
For data analysis and extracting useful information
some commonly used techniques are natural language
processing, text mining and data mining techniques.
Table 3 presents some popular techniques used
for processing data sets. Approximately 20 papers
SABITHA et al.: LANDSCAPE ANALYSIS OF PATENT DATASET
217
Table 2—Showing attributes and analysis based on them in different research fields
S. No. Attributes of data set chosen
Analysis based on chosen
data sets
Research work
Method & Tool/ Model used
1
Search period, Item, Country,
No. of patents25
Trend analysis, Technology
distribution by country,
organization, Level of patent
Green car trend analysis
Keyword, Y-code, IPC code
classification
2
Bibliography, assignee, inventor,
abstract, annotation, patent
family, citation, citation assignee,
and citation inventor 4,36
Trend analysis, Technology
distribution by country,
organization, Level of patent,
Citation Analysis
Web mining based
patent analysis and
Citation visualization
Keyword, Text mining
approach.
Tool - Patent Spider (to get
original pages of patents),
VantagePoint, Aureka and
Omniviz
3
Summary, Title, Claim2,15
Trend analysis, Technology
distribution by country,
organization, Level of patent,
Citation analysis
Technological trend
analysis of Silicon
Solar Cell
Keyword, TrendPerceptor
(Text mining approach
based on property-function
technique)
4
Claims, Title of invention, Title
of document, Technical field,
Background art, Summary of
invention, Problem, Solution to
problem, Effects of invention,
Industrial application 37
Technology specific patents
retrieved
Extraction of the effect
and the technology
terms from a patent
document
Extraction of technology
specific keywords, String
matching
5
Abstract , Title, Claim,
Inventors and applicants
names, citation etc.37,38
Clustering of similar patents
Multilingual text
mining approach
Hierarchical clustering, Selforganizing maps (SOM)
methods
Model - Unified space
vector (patents mapped
into a document)
Tool- PatViz38
6
Applicant name, Number of
patent applications34
Assignees analysis
Forecasting emerging
technologies of Low
Emission Vehicle
SVM classifier applied on
data sets
Tool - RapidMiner for text
processing
7
Patent no., Citation, Publication
date 39,40
Patents having same text
are grouped into one class
related to same technology
SIMPLE: A strategic
information mining
Platform.
Analyzing linkage
between Industry and
technologies
Nearest Neighbour(NN)
analysis
Model - SIMPLE analytics
Network analysis using
graphical and matricial
methods
8
Patent no., Search period,
assignee, etc.41,42
Analyzes important patents by
calculating citation weight of
each patent
Analysis of patents in
MEMS-related
technologies
Payek's Search Path Count
algorithm for calculating
weight of patent41
Tool - software 'Pajek' 42
9
Patent assignee, Application
date, Abstract, Publication
number, Claims, Detailed
problems, etc.9
White spot analysis;
Extracting bibliographic data
and text information (in form
of problems and its solutions)
using keywords or short
phrases
Software-based patent
analysis
Tool - Patent Skill Cartridge
Luxid
10
Assignee, Filing date, Claims
Infringement analysis
DNA chip technology
domain by Lee et al. 43
Tool - WordNet using MDS
and Clustering algorithm
Hierarchical keyword based
approach (Tree-matching
algorithm)
(Contd.)
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218
Table 2—Showing attributes and analysis based on them in different research fields (Contd.)
S. No. Attributes of data set chosen
Analysis based on chosen
data sets
Research work
Method & Tool/ Model used
11
Patent no., Applicant name,
Filing date, IPC, Citation, etc.2
Novelty analysis
Automotive industry
Tool - Knowledgist software42
12
Patent Quantity (PQ), Revealed
Patent Advantage
(RPA), Patent Activity (PA),
Be Cited Rate (BCA), and
Relative Citation Index (RCI)
Technological analysis using
Association Rule Mining
Analysis using inference
rule based technique
Patent analysis -based
fuzzy inference system
by Yu and Lo44
Kohonen learning algorithm45
and first nearest neighbor
heuristic46
13
Citation index, Originality,
generality, and technology
cycle time
Trend analysis
using association rule
mining
Mining changes in
patent trends by
Shih et al.47
Patent Trend Change
Mining (PTCM)
14
Filing date, Assignee,
IPC codes, Titles, Abstracts,
Claims, and Description of
invention
Technological
forecasting/ Trend analysis
Patent analysis by
Wang and Cheung22
Semantic Intellectual Property
Management System (SIPMS)
uses NLP based on semantic
analysis
Naïve Bayesian algorithm
Back propagation neural
network algorithm53
Table 3—Mining techniques and Associated analysis
Mining Technique
Purpose
Applicability for analysis
MDS (Multidimensional Scaling)
in NLP2,43
To discover similarities and dissimilarities in
data.
Association Rule Mining using
apriori algorithm
Classification
(k- Nearest neighbor/ Naïve
Bayes/etc.)2,48
Clustering (k-means/
Hierarchical)43,49
Tree matching algorithm 2,43
Forming meaningful associations among
structures extracted from patent documents
Classifies patents based on similar
characteristics and thus helps in patent class
identification
Groups instances into cluster, reducing search
time
Analysis of claims made in patents
Back propagation neural network
algorithm2,50
Self-Organization Map (SOM)
technique51
To determine quality of patents
Time-Series Analysis, Trend Analysis
Technology Distribution, Citation Analysis
White spot analysis, Infringement analysis
Association Analysis, Family Analysis, Trend
Analysis
Assignee Analysis
Technology Distribution by country/domain
Trend Analysis
IPC Analysis, Infringement Analysis Cluster
Analysis
Infringement Analysis, Novelty Analysis
White spot Analysis
Patent Level Analysis, Novelty Analysis
Classification Analysis
Patent Trend Analysis
have been referred that
mining techniques.
To identify new research directions
have
been used in
Patent Analysis
Patent analysis is based on:• Technology Roadmap
• Technology Classification
• Originality/R&D Support
• Technology Distribution
Different tools and methodologies help in
analysing and visualizing patent trends, technological
innovation among countries, forecast development of
technology, citation analysis, etc. The analysis of the
information hidden in patents can provide a clear
view of the current trends of a specific technologicalscientific innovation.52-54 It helps in exploiting
potentially useful knowledge in which organization is
interested and providing a right direction for R&D to
improve research activities. Summative tables for
each type of analysis are discussed in this section.
These tables help to understand how a particular
approach is used in literature, on which data set the
technique is applied, what attributes were considered
for that particular analysis and also the computational
approach used for analysis of the collected data.
Each of the techniques is classified in one of the
four categories.
For upgrading any technology, it is very much
required to understand how the technologies of the
SABITHA et al.: LANDSCAPE ANALYSIS OF PATENT DATASET
past had improved and upgraded to the current state of
technology. The road map analysis involves arranging
the patents related to the core technology in
chronological order, to visualize its evolution. In
219
Table 4 different approaches used for road map
analysis are discussed. 12 papers cited in literature
were road map analysed which is done for different
domains and also the technology is discussed. It is
Table 4—Technology road-mapping analysis
Type of
analysis
Result of analysis
Time series
analysis
Analysing the distribution
The Analysers can learn
of the patent quantity changing the future trend of various
over time
technologies by analysing
historical data related to
the number of patent
applications during
different time periods.
This information reflects
the degree of technological
development with time60
Technology
life cycle /
Patent Trend
analysis
The graph represents a
comparison among
patent application and
granted patent publications
in respect of time
distribution.
Purpose served to
organization
It indicates the rate of
growth/ decline rate in
patent deployment. It helps
in judging the frequency
and quality of R&D
activities.
Research areas
Attributes / Database
chosen
Mininig
technique
Patent analysis for
technology
forecasting5
Community innovation
survey (CIS) and patent
data from the United
States Patent, Trade
Organization (USPTO)
Attributes chosen year
of filing, patent no,
assignee etc.
USPTO and pub MED
dataset
Technology field and
investment analysis
IPC code
Non-linear
regression
(Bass Model)
Bibliography, assignee,
inventor, abstract,
annotation, patent
family, citation, citation
assignee, and citation
inventor
Search Period, Item,
Country, No. of patents
Patent no, search
period, assignee etc.
Citation graph
structure
The year of filing,
assignee, plan no,
pub_appln no etch
Number of patents
issued, patents filed,
assignee etc.
Patent no, IPC code,
citation etc.
Statistical
analysis
Technology field
of health13
New Energy,
Auto Industry23
Technology
forecasting in
Bio-industry55
Web Mining
based Patent
Analysis4
Green Car Trend
Analysis25
Analysis of
MEMS-related
technologies41
Analysis of
China versus
US patents56
Patent analysis
Biotech Industry57
Citation
analysis41,33
It indicates countries with
more forward citation have a
stronger technological impact.
Having more backward
citation depicts that countries
have mature technologies.
Patent citation determines
intensity of technology ,
linkage between
technology and industry20
Patent analysis for
technology
forecasting5
Silicon Cell Trend
Analysis15
Inventory
management with
Patent analysis17
Integrating patent
family and patent
citation27
The linkage
between industries
and technologies40
Biotech industry
analysis57
Analysing industry
convergence60
Summary, title, claim
No. of patents issued,
patents filed, assignee,
IPC no, citation,
patent no.
Title, abstract, citation,
publishing authority,
pub. Date
Patent no, Citation,
Publication date
USPTO database
USPTO database
Neural
Network
Statistical
analysis
Clustering
Statistical
analysis
Statistical
analysis
Statistical
analysis
Nonlinear
Regression,
Correlation,
t-test
Statistical
analysis done
Statistical
analysis done
TRIZ
parameter
chart method
Statistical
analysis done
Statistical
analysis done
Statistical
analysis
J INTELLEC PROP RIGHTS, JULY 2016
220
very important to identify the particular technologies
which are of commercial interest. This analysis helps
to evaluate the patent portfolios using time and
magnitude indicator.63 The relevance of a particular
patent with current prospective can be interpreted
with citation analysis.
Table 4 manifests that researchers have widely used
statistical analysis, but as data mining techniques
(Table 5) provide more knowledge of hidden patterns
that exists in dataset hence the choice of suitable data
mining technique to enhance the overall quality of
analysis. The more advanced approaches apply
nonlinear models based mainly on artificial neural
networks (NNs), support vector machine (SVM), and
other machine learning methods.25,32,36,39 It reports that
NNs are nonlinear structures, capable of taking into
account more complex relations existing among the
analysed data, thus making prediction more
accurate.13,25 Usage of Natural language processing and
other text mining approaches further reduces the efforts
involved in searching the required patent. Almost all
datasets classify the patent as it reduces the searching
task and also makes the understanding of patent
simpler. Discussing the patent dataset after
classification, will definitely reduce the complexity of
the research paper and make it easier for inventor to
understand the patent in its domain. Inventors are filing
a patent in different agencies for various reasons may
lead to duplication of the database, the patent family
analysis may help to handle these redundant datasets.
Table 5—Technology Classification Analysis
Type of
analysis
Result of analysis
Purpose served to
organization
Research area where analysis
technique is applied
IPC Analysis
Organizes groups
or subgroups into
categories, making
it easy for
identification
It can help enterprises
understand the major
categories for
technological
classification for
planning R&D
activities.
Inventory management
with Patent analysis17
It helps in removing
redundant patents in
specific technology
domain.
It is vital for
organizations doing
R&D work. It helps in
knowing redundant
research works and
thus, provides decision
support while
allocating R&D
budget.
Family
Analysis57
On the basis of
certain attributes
like- IPC category,
title and abstract of
the patent document,
classification of
patents is done, to
identify interesting
correlations.
Time reduced in
finding patents that
are similar in context
to particular
technologies.
Mining technique
Patent no., Direct
citation, indirect
citation.
Classify the research paper
IPC Code,
according to IPC classification relevance score
to understand the research
trend in particular technology67
Analysis of innovative
IPC code, “patent
rehabilitation technologies52
no
Patent no,
A Model for Measuring the
R&D Projects Similarity
“abstract, IPC no
Using patent information18
Statistical analysis
done
Patent analysis by integrating
patent family and patent
citation27
Citation, title,
abstract, “IPC
code, assignee/
applicant name
LexisNexis
Database
Text mining
Multilingual Patent
Text-Mining Approach37
Patent no, type of
patent, the patent
age, number of
claims.
IPC code,
abstract, claim
Ontology-based patent
network analysis48
Patent based analysis of
technologies52
Technology forecasting in
Bio-industry55
Naïve Bayes
Classifier, selforganizing mapping
algorithm
Hierarchical
clustering
(single link) and
self-organizing
Maps (SOM)
methods
(for text mining)
Statistical analysis
Patent no, IPC
code
Hierarchical
Patent no, IPC
code, title, abstract clustering
Patent no, IPC
Statistical analysis
code
Patent priority network26
Cluster
Analysis
Attributes /
Database chosen
Forecasting Dental Implant
Technologies Using Patent
Analysis14
K-NN based for
classification of
document and SVM
for retrieval
SLINK hierarchical
clustering
KNN (K-Nearest
neighbors)
Statistical analysis
SABITHA et al.: LANDSCAPE ANALYSIS OF PATENT DATASET
In a recent study Y.N. Choi et al.66 has discussed
about technology convergence to a common unity of
technology. It is the need of the day as most of the
technology are heterogeneous in nature and cover a
wide range of interdisciplinary domain.
The originality analysis (Table 6) is a complex
mechanism as it involves a high level of technical
knowledge to justify how new invention is different
from the previous one. This study is dependent on the
dataset and in many database the older inventions are
not stored which may create unnecessary conflicts.
The patent search is widely conducted before starting
any R&D based project.64 The objective of landscape
analysis is providing a complete innovation
management. An effort is required to understand all
the social, economic and requirement of industrial
partner for any new emergent technology so that new
technology can reach the mass.
Understanding the emergent technology with
respect to countries, understanding how new
technologies differ from existing patents are the
objective of the study. Also for making emergent
221
technology to be practical rather just a theoretical
research, it is required for analysis, the competitive
business rivals and also assignee evaluation.
Conclusion
The patent dataset is a large source of information
which has both technical as well as commercial value.
Apart from utilizing the patent records for just
checking the novelty of new research work, patent
mining is highly advantageous to perform complete
landscape analysis of the patent data set. This analysis
helps to analyse the patents across a particular
technology field which includes the study of scientific
literature, its changing importance with respect to
time and market, thus contributes to forecast the
industry requirement which is very useful input in
business intelligence. This study may help to identify
the hotspots and get an opinion about validity and
other legal issues related to patent. There are various
approaches in which the patent data are searched and
whole reservoir is analysed using suitable data mining
and statistical techniques.
Table 6—Originality/R&D Support Analysis
Type of analysis Result of analysis Purpose served to
organization
Research area where analysis
technique is applied
Attributes/Database Mining technique
chosen
A literature review on the
state-of-the-art in patent
analysis2
Risk analysis of patent
infringement68
Patent number,
Hierarchical
search period,
keyword vectors,
assignee etc.
tree matching
Metrics were
algorithm
proposed which
Statistic and data
involve the cost
mining
involved in litigation,
estimated settlement
and judgment
Similarity
Patent records to
calculation using
evaluate the
text mining and
semantic similarity developing product
for technologies
patent map
Analyses
similarities
among patents.
A patent having
content matching
with already
filed/published
patents, falls under
Infringement.
This identification helps
organizations in
approaching only
novel works,
eliminating infringed
works in the research
domain.
Novelty
Analysis57
It identifies novel
work in order to
determine the
quality of patents.
Organizations can easily A literature review on the
identify new research
state-of-the-art in patent
works done in particular analysis2
domains. This directly
enhances the economic
position of organizations
and country in a broader
sense.
Patent no, applicant Subject –action –
name, filing date,
object based
IPC, citation etc.
similarity matrix
generation
White Spot
Analysis
Extraction and
analysis of
problems and
solutions.
It helps users to
Software-based Patent
understand white spots Analysis9
and hence, provide
direction in R&D work.
Researchers look for
problems with a better
solution.
Patent assignee,
Text mining
application date,
abstract, publication
number, claims,
detailed problems
etc.
Infringement
Analysis
Use semantic analysis to
design a product patent amp
that will help to identify any
infringement of existing
patents69
J INTELLEC PROP RIGHTS, JULY 2016
222
This paper presents the different ways in which
patent data set is analysed literature. The different
analyses were grouped in four main categories namely
technology road-mapping analysis, technology
classification analysis, originality/R&D support
analysis and technology distribution analysis (Table 7).
Further analysis is done to review the attributes and
techniques to complete analysis. One of the major
concerns with landscape analysis is to design a
visualization tool that project the multiple analysis
discussed in the paper. Developing such a tool
will help technocrats and inventors will get an
Table 7—Technology Distribution Analysis
Type of analysis
Result of analysis
Purpose served to
organization
Research area where analysis Attributes /
technique is applied
Database chosen
Mining
technique
Technology
distribution by
country
It helps to know which
country has registered
more patents in which
year with respect to
other countries.
Search time reduced
by directly finding
patents from
database of countries
registering largest
patent documents
in the respective
technology / domain.
Empirical Research on
Technology Share based on
Hybrid Approach31
Ordinary least
squares (OLS)
regression
This kind of analysis
helps in predicting
patent trends which
make research work
of organization easier
and cost effective.
Patent information analysis
“for Company10
Discovering competitive
intelligence by mining
“changes in patent trends47
Association Rule
Analysis
Analyses patents
which are related to
each other
Technologies of Low
“Emission Vehicle34
Fuzzy inference system for
technological, strategic
planning50
Rival/Competitor
analysis
Assignee
Analysis58
Patent number,
classification,
inventor name,
assignee name,
citation
IPC no, assignee,
patent number
Statistical
analysis
Patent no, IPC code Apriori
algorithm
Assignee, IPC code, Association rule
and four patent
mining
indicators: citation
index, originality,
generality, and
technology cycle
time
Patent Quantity
Kohonen
(PQ), Revealed
learning
Patent Advantage
algorithm and
(RPA), Patent
first nearest
Activity (PA),
neighbour
Be Cited Rate
heuristic
(BCA), and
algorithm
Relative Citation
Index (RCI)
It helps in knowing the
network among
countries by taking into
account the proportion
of international research
work of each country
This mechanism is vital Research on Technology
for analysing technical Selection for Enterprises16
intelligence, scope of
technology and R&D
competence
Analysis of China vs US
patents in NEDD Race56
IPC no, assignee,
patent count etc.
Statistical
analysis
Patent no,
IPC code,
summary, title
Statistical
analysis
This helps in knowing
which organization
possess most patent
applications relative
to the technology
The technological
innovative capacity
of a corporation
can be evaluated
Green Car Trend
Analysis25
Search Period,
Item, Country,
No. of Patents
Statistical
analysis
Silicon Cell Trend
Analysis15
Analysis of China vs US
patents in NEDD Race56
Summary,
Title, Claim
USPTO and
Chinese patent
database
Bibliography,
assignee,
inventor, abstract,
annotation, patent
family, citation,
citation assignee,
and citation
inventor
Statistical
analysis
Statistical
analysis
Web Mining based Patent
Analysis4
Statistical
analysis
SABITHA et al.: LANDSCAPE ANALYSIS OF PATENT DATASET
overview of the need of invention in particular
technology, also it help to develop a business model
that supports the inventor get a commercial value for
their creation. This business model will assess
collaborators to identify the inventor and vice versa
for joint development of product or attaining suitable
licenses etc.
14
15
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