Open Access Research Article
TEXT AND DATA MINING: COMPARITIVE ANALYSIS OF FAIR USE AND FAIR DEALING
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Article Details
TEXT AND DATA MINING: COMPARITIVE
ANALYSIS OF FAIR USE AND FAIR DEALING
AUTHORED BY - RITWIK KARMAKAR
Research Scholar, CHRIST (Deemed to
be University)
INTRODUCTION
Intellectual Property Rights plays a
vital role in protecting the rights of creators and inventors. It includes
copyright, patents, trademark, geographical indications, designs, biological
diversity, integrated circuits and so on. Basically, the original work or
invention is protected under this law. The Copyrights Act, 1957 was formulated
in order to protect the rights of a person who claims to be the first and true
owner of his work. It simply protects the right of creators so that the
originality of work is preserved. This includes literary, artistic, musical,
dramatic, sound recording and cinematography. The primary purpose of copyright
is to give authors and creators control over the use of their works, ensuring
they can benefit from their creations and prevent unauthorized exploitation.
In many jurisdictions, copyright
protection is automatic once a work is created and fixed in a tangible medium,
such as a written document or digital file. The rights typically granted under
copyright include the right to reproduce, distribute, perform, display, and
create derivative works based on the original creation. However, copyright
isn't absolute and there are limitations to the rights it provides. One
important limitation is the doctrine of "fair dealing" (or "fair
use" in some jurisdictions). This doctrine allows for the limited use of
copyrighted material without permission from the copyright holder, under
specific circumstances. The doctrine of fair dealing under the Indian Copyright
Act, 1957, allows for the limited use of copyrighted material without requiring
permission from the copyright owner, providing a balance between protecting
creators' rights and serving public interests. Section 52 of the Act specifies
various exceptions, including uses for private study, criticism, review, news
reporting, and educational purposes. This doctrine ensures that essential
activities such as academic research and public commentary can proceed without
undue restrictions, while also considering factors like the purpose of use, the
nature of the work, the amount used, and its potential market impact. However,
it does not specifically address text and data mining (TDM), a modern process
involving the extraction and analysis of large datasets to uncover patterns or
insights.
TDM often requires access to
significant portions of copyrighted material, which can be challenging under
current fair dealing provisions that may not fully accommodate the extensive
data use inherent in these activities. Text and data mining involves the extraction
and analysis of large datasets to identify patterns, trends, and relationships.
This process is increasingly important in various fields, including academic
research, machine learning, artificial intelligence, and business analytics.
TDM relies on accessing and analyzing extensive amounts of text and data, often
raising questions about copyright infringement when copyrighted materials are
used. Traditionally, copyright law does not explicitly address TDM, which
creates uncertainty for researchers and businesses that wish to engage in this
practice. The core issue is that TDM typically requires access to copyrighted
materials, which may be subject to restrictive access controls or licensing
agreements. As a result, researchers and organizations may face legal
challenges when using copyrighted content for TDM purposes. To understand how
the Indian judiciary understands the concept of Fair Dealing, let us see the
case of Kartar Singh Giani v. Ladha Singh,[1]whereby
the Hon’ble Bench observed that:
“Two points have been urged in connection
with the meaning of the expression
fair, in fair dealing. First, that in order to constitute unfairness there must
be an intention to compete and to derive profit from such competition and
secondly, that unless the motive of the infringer were unfair in the sense of being improper the dealing
would be fair.”
The Doctrine of Fair Dealing was initially sparked with an understanding
of the doctrine of equity which allows certain secondary use of certain
copyrighted work, which would amount to a clear infringement of the copyright.
In other words, if the secondary use of the
copyrighted product is fair and just, the secondary use would rather fall under
the ambit of the exception instead of the ambit of copyright infringement.[2]
FAIR DEALING IN DIGITAL AGE
With the
presence and increasing use of the Internet the wholly new global world has
emerged, and the protection of copyrighted work is the major concern.
Legislation across the globe have incorporated technological protection measures in their copyright
regimes, so as to better
protect or rather restrict, the access and use of copyrighted work.[3]
These technologies like much innovation are both promising and potentially
harmful to various parties interested in the use and exploitation of work of
authorship- from book and music to
film and web pages.
The nature of computer programs is
difficult to determine with the idea/expression distinction. The computer
programs are utilitarian articles that accomplish a task which can me logical, structural, etc. which are
dictated by the functions in the programs with certain external inputs.[4]
But, because of the hybrid nature of computer programs, there cannot be a set
of rules or factors that can be used in identifying the unprotected idea and
the protected expression. The Third Circuit attempted to resolve the
idea/express distinction of computer
programs by observing that the “idea or
function of a computer programs is the idea in itself as a whole and everything
else which is not necessary to that purpose or function could be construed as
the expression of that idea.”[5]
Let us understand how the United
States of America have used the exception of fair use of copyrighted work in
the digital age. Let us try and understand the shift of revenue of the music
labels from selling Vinyl Records to Digital downloads to Online streaming. In
the following chart, the International Federation of Phonographic Industry
(hereby referred to as “IFPI”)
conducted research on the revenue earned by the music labels from 1999 to 2017.[6]
In the
above graph, we see that the primary source of the revenue of the music label
organization shifted from the physical sale to the digitized sale. The revenue
started dropping immensely with the introduction of the digital P2P file
sharing and downloads which can been as the orange line in the above graph.
Further, with the introduction of the online music streaming platforms like
Spotify, apple music, Sound Cloud and YouTube from 2006, the revenues again
shifted from digital downloads of sound recordings to online streaming platform
which can be seen in the grey line in
the above graph. To further prove the point,
the following graph
will show another
research by an online website
called Statista regarding how online streaming platforms came as the
messiah for the music label from the apocalypse of music industry by the demon called
piracy.[7]
TEXT AND DATA MINING
In today’s world, the amount of data
being generated is enormous, from social media interactions to online
transactions. However, this data is often in raw, unstructured forms such as
text, images, or numbers, making it hard to extract meaningful insights. This
is where Text and Data Mining (TDM) comes in. TDM involves the use of various
computational techniques to analyze large datasets and uncover hidden patterns,
relationships, and trends. The goal is to transform this raw data into useful
knowledge that can be applied to solve problems or innovate in various fields. As
data grows in volume, variety, and complexity, the importance of TDM in
research, business, healthcare, and technology continues to rise. TDM helps unlock
the value in both structured data (like spreadsheets) and unstructured data
(like social media posts or research papers), making it a powerful tool in many
different sectors.
Text and Data Mining is essentially a
way of analyzing data to uncover patterns or information that aren’t
immediately obvious. It involves two main components:Text Mining (TM): This
focuses on extracting useful information from text-based data, like research
papers, social media posts, or customer reviews. Techniques such as Natural
Language Processing (NLP) are used to analyze human language and identify
trends, sentiments, or key topics within the text. For instance, sentiment
analysis can tell whether people are happy, sad, or angry based on the language
they use in social media posts. Data Mining (DM): This focuses on identifying
patterns in structured data, such as numbers or categories, using algorithms
and statistical methods. This technique is commonly used to predict future
trends or behaviors. For example, it might be used to predict which customers
are most likely to buy a product based on their previous purchases. Together,
text mining and data mining help organizations process and make sense of the
massive amounts of data available to them, turning raw data into actionable
insights.
THE IMPORTANCE OF TDM IN RESEARCH AND
INNOVATION
TDM is not just a tool for business;
it plays a crucial role in advancing research and innovation. Whether in
academic research, medical breakthroughs, or business decisions, TDM helps researchers
and organizations gain a deeper understanding of complex problems and discover
new opportunities.
1. TDM in Scientific Research
One of the primaries uses of TDM is
in scientific research. With the explosion of academic publications in recent
years, researchers often struggle to keep up with the sheer volume of new
information being published. TDM allows researchers to mine academic papers,
books, and journals quickly to identify key trends, discover new research
topics, or track the evolution of particular fields. For instance, in medicine,
researchers use TDM to analyze clinical trial data, medical records, and
research papers to find patterns that could lead to new treatments. This helps
researchers make discoveries faster than ever before, potentially saving lives.
2. TDM in Business and Market Analysis
TDM is also revolutionizing the
business world. Companies use TDM techniques to understand customer behaviors,
identify market trends, and improve their products or services. For example,
online retailers like Amazon analyze customer reviews and feedback to identify
which products are popular and which might need improvements. Additionally, TDM
can help businesses understand how customers feel about their brand or
products. Sentiment analysis of social media posts, product reviews, and online
comments can help businesses improve customer satisfaction by addressing
negative feedback quickly and adapting to market preferences.
3. TDM in Healthcare
In healthcare, TDM is transforming
patient care and medical research. By analyzing electronic health records
(EHR), doctors can identify at-risk patients, predict disease outbreaks, and
even detect early signs of diseases like cancer or heart disease. Researchers
use TDM to analyze medical literature, clinical trials, and patient data to
find new treatments and drug interactions that can improve patient outcomes. For
example, in cancer research, TDM helps identify patterns in genetic data,
potentially leading to breakthroughs in cancer treatment. By mining these large
datasets, researchers can find insights that may not be obvious from just
looking at a few individual cases.
APPLICATION OF TDM ACROSS SECTORS
TDM is used in various industries and
sectors to solve problems, improve services, and drive innovation. Below are
some key examples of how TDM is applied:
1. Academic Research
TDM is a great tool for researchers
who need to sift through thousands of academic papers. It can be used to track
research trends, analyze citations, and even identify potential research gaps.
Researchers in fields like climate change or physics use TDM to analyze vast
datasets and uncover new trends or findings that might otherwise be missed.
2. Social Media and Sentiment Analysis.
Social media platforms generate
enormous amounts of data every day. Companies and researchers use TDM to
analyze social media posts to understand public opinion. Sentiment analysis can
help businesses understand how people feel about a product, service, or
political event based on their online comments. By monitoring social media in
real-time, companies can make quick decisions to address customer concerns or
capitalize on positive trends.
3. Fraud Detection in Financial Services
TDM is widely used in financial
services to detect fraud and monitor transactions. By analyzing patterns in
transaction data, banks can spot unusual behavior that might suggest fraudulent
activity, such as a sudden change in spending patterns or transactions from
unfamiliar locations. TDM helps financial institutions quickly identify
potential fraud before it becomes a serious issue.
4. Artificial Intelligence and Machine
Learning
Text and data mining also play a
crucial role in developing artificial intelligence (AI) and machine learning
(ML) models. AI systems like voice assistants (Siri, Alexa) or chatbots rely on
TDM to understand human language and respond accordingly. Machine learning
models also use TDM techniques to train on large datasets, allowing them to
make predictions or decisions based on historical patterns. For example, AI can
predict customer behavior or recommend products based on past interactions.
5. E-commerce and Personalization
In the e-commerce world, TDM is used
to improve customer experiences and increase sales. Online retailers use TDM to
analyze past purchasing behavior and recommend products that a customer is
likely to buy next. This helps create a more personalized shopping experience,
leading to higher customer satisfaction and increased sales. For example,
Amazon uses TDM to recommend products based on what a customer has viewed or
purchased in the past, while Netflix uses TDM to suggest movies and TV shows
based on viewing history.
CHALLENGES OF TEXT AND DATA MINING
While Text and Data Mining
(TDM) has many benefits, it also faces several challenges that can
make it difficult to implement and use effectively. These challenges include
issues related to data privacy, data quality, and the complexity of the
techniques involved. Understanding these challenges is important for anyone who
wants to use TDM in their work, whether in research, business, or healthcare. Data
Privacy and Security. One of the biggest challenges in TDM is ensuring
data privacy and security. When mining data,
especially personal data, there are serious concerns about how this data is
collected, stored, and used. In many cases, TDM involves analyzing large
datasets that may contain sensitive information, such as customer details,
medical records, or financial data. For example, companies that use TDM to
analyze customer behavior need to make sure they are not violating privacy
laws, such as the General Data Protection Regulation (GDPR) in
Europe. GDPR sets strict rules on how personal data should be handled, ensuring
that individuals' data is protected. Organizations must be careful to anonymize
data, get proper consent, and make sure that their methods comply with privacy
regulations. Without this, there could be legal issues, loss of trust, and
reputational damage.
Another major challenge is the quality
of the data being analyzed. For TDM to work effectively, the data must
be clean, complete, and accurate. Unfortunately, many datasets are messy, with
missing values, errors, or inconsistencies. This can make it difficult to draw
accurate conclusions from the analysis. For example, in healthcare, if medical
records are incomplete or have mistaken, TDM could lead to wrong predictions
about a patient’s health. Similarly, if a company is using social media data for
sentiment analysis, poorly written posts, slang, or misspellings might make it
harder to analyze the data accurately. Improving data quality often requires
significant effort in cleaning and preparing the data before it can be
analyzed. This process can be time-consuming and costly, but it is essential to
ensure that the results of TDM are reliable and trustworthy. TDM involves using
advanced algorithms, statistical methods, and machine learning models to
analyze large datasets. These techniques can be very complex, and it often
requires specialized knowledge to use them correctly.
The algorithms used in TDM are not
always easy to understand, and choosing the right approach for a particular
type of data or problem can be difficult. For example, text mining, which
involves analyzing text data, requires techniques such as Natural
Language Processing (NLP). NLP helps computers understand and process
human language, but it is a challenging task, especially when dealing with
languages that have many nuances, slang, or informal terms. Similarly, when
analyzing data from different sources, it may be hard to choose the right data
mining technique, as different types of data (such as numerical, textual, or
image data) require different approaches. To overcome this challenge, it’s
important to have experts in data science and machine learning who understand
the techniques involved and can apply them effectively. Unfortunately, not all
organizations have access to such specialized talent, making TDM more difficult
for some to implement. Once TDM techniques have been applied to a dataset,
interpreting the results can be challenging. While TDM can find patterns and
trends in data, these findings may not always be easy to understand or
actionable. For example, a company may use TDM to find patterns in customer
feedback, but the results may be too complex or vague to lead to clear business
decisions. In many cases, TDM results need to be explained and contextualized
by experts to make sense of them. This requires both technical expertise in
data science and a deep understanding of the subject matter (e.g., healthcare,
business, or social science) to ensure that the findings are useful and
meaningful.
OVERVIEW OF SECTION 52
Section 52 of the Indian Copyright Act explicitly lists several
actions that do not constitute copyright infringement. These actions, termed as
exceptions to infringement, allow users to access and use
copyrighted works without seeking permission from the copyright holders, under
certain conditions. These exceptions are designed to serve public policy
objectives, including education, research, and the free flow of
information. For Text and Data Mining,
this section is particularly important because TDM often involves the use of large
amounts of copyrighted text and data to extract patterns, insights, or
build models. By considering these practices under specific exceptions, Section
52 helps foster an environment conducive to innovation and academic research. Section
52 of the Indian Copyright Act, 1957 provides several important exceptions that
allow the use of copyrighted works without infringement. These exceptions are
significant in the context of Text and Data Mining (TDM), especially when the
use is for research or educational purposes. Subsection 1(a) allows for the
reproduction of a copyrighted work for private and personal use without being
considered infringement. This exception permits individuals to copy works for
personal, non-commercial purposes, such as studying or creating a personal
archive. However, it is not directly applicable to TDM, as TDM generally
involves the large-scale use of texts, which extends beyond personal,
non-commercial use.
This provision directly supports academic research
and private study, permitting the use of
copyrighted works for analysis, study, or research without requiring permission
from the copyright holder. TDM activities conducted for research purposes are
likely covered by this exception, as researchers often use large datasets
(including text and data from books, articles, and websites) for
non-commercial, academic work. Text and Data Mining (TDM)
carried out for research purposes, such as academic
studies or data analysis, falls under this exception. If the data used is
purely for academic research or non-commercial
purposes, the mining of copyrighted content might be permitted under this
exception.
IS TEXT AND DATA MINING COVERED UNDER SECTION 52?
Text and Data Mining (TDM) involves
extracting useful information and patterns from large datasets, often
containing copyrighted material such as books, articles, or websites. TDM
typically requires significant amounts of text data to be accessed, reproduced,
and processed. Section 52's focus on research,
educational purposes, and non-commercial use makes it relevant for
TDM, especially in the following scenarios:
·
Non-Commercial Research: If TDM is conducted for academic or
research purposes, and not for commercial profit, it could potentially fall
under the exception for research and study outlined in Section 52(1)(b).
·
Use in Educational
Institutions:
If TDM is carried out within an educational institution for teaching, research,
or other academic purposes (as covered in Section 52(1)(i)),
this would also be an allowable exception.
·
Libraries and Archives: Research projects conducted by libraries or archives, particularly
those involving TDM to analyze or digitize text for preservation or study, may
be allowed under the non-commercial provisions of Section 52(1)(c).
However, TDM does not automatically fall under these exceptions. It
depends on the context of the mining activities, the nature of the data
used (whether it is copyrighted material), and whether the activities are commercial
or non-commercial.
Issues with Text and Data Mining under Section 52
While Section 52 provides several exceptions for
research and educational use, there are legal and practical challenges:
·
Scale of Use: TDM often involves large-scale
extraction and processing of data, which may extend beyond what is typically
considered private study or research.
The scale of the activity may push it into the realm of commercial use, which
would not be protected under Section 52 exceptions.
·
Type of Data: TDM typically uses digitized text and online
content. If the text is not sufficiently transformed
or is used in ways that go beyond the scope of research, it may not be
considered "fair use" under Section 52.
·
Commercial Use: If TDM is conducted with commercial objectives (e.g., to build a
proprietary database for resale or to create a commercially sold product), it
may not qualify for the research or education exceptions, making it potentially
subject to copyright infringement.
CHALLENGES OF APPLYING THE COPYRIGHT ACT TO TEXT AND DATA MINING (TDM)
Text and Data Mining (TDM) refers to computational techniques used
to extract patterns, trends, and insights from large datasets. TDM is used
across many fields like natural language processing (NLP), artificial
intelligence (AI), bioinformatics, and more. The application of TDM often
requires researchers to work with significant amounts of data, much of which is
copyrighted. However, the Indian Copyright Act of 1957
presents several challenges when it comes to reconciling the need for TDM with
copyright protection. The Indian Copyright Act provides certain exclusive
rights to authors, including the right to reproduce, distribute,
and adapt their works. These rights are meant to
protect the economic interests of creators and ensure that their works are not
used without permission.
Reproduction of Copyrighted Material: TDM tools typically involve copying
large portions of text, data, or other media from copyrighted works to analyze
them. Under the Indian Copyright Act, copying copyrighted works for any purpose
beyond personal use or research may be considered infringement.
The issue arises because, in order to perform TDM, significant chunks of
copyrighted content must be copied and analyzed. Even if the ultimate goal is
not to reproduce the content for commercial purposes, this process may still
fall under the reproduction right and thus infringe the copyright.
Adaptation and Transformation: TDM can result in the transformation of copyrighted material. For
example, researchers might use copyrighted text to generate new insights, data,
or predictions. The transformation of copyrighted content into a new form is
often seen as a derivative work, which is typically an
exclusive right of the copyright owner. In India, creating a derivative work
without the author’s permission is an infringement. Therefore, even if the goal
of TDM is purely research-oriented and non-commercial, the transformation of
copyrighted text could still be problematic from a legal standpoint.
Data Scraping and Aggregation: Many
TDM methods involve web scraping, which is a technique for
extracting data from websites. This often includes scraping publicly available
text, images, and datasets. While scraping public domain content may not
infringe copyright, scraping data from copyrighted sources, such as news
articles, books, or academic papers, without permission can violate the
copyright holder’s rights. Websites often include terms of use that prohibit
scraping, and breaching these terms could lead to legal consequences for
researchers.
One of the major conflicts in this context is the tension between
the economic rights of copyright holders and the needs
of researchers engaged in digital research. Copyright law is designed
to provide protection to creators, ensuring that they can control how their
works are used and are compensated for their labor. However, TDM and other
research methods often require access to vast amounts of data, including
copyrighted materials, in order to extract useful insights. Here are the key
points of conflict:
Access to Data for Research: For
TDM to be effective, researchers often need access to large datasets
that may include copyrighted works. These datasets are frequently locked behind
paywalls or governed by restrictive licensing
agreements, which limit access. For example, academic articles,
books, and datasets hosted by publishers may only be accessible to institutions
that can afford expensive subscriptions. This creates a significant barrier for
researchers, particularly in developing countries like India, where access to
academic content may be limited. Copyright protection can thus
hinder the ability of researchers to gather data necessary for their work.
Open Access and Data Availability: Open access publishing and open data
models allow for broader dissemination of research and provide researchers with
freely accessible materials. However, not all
copyrighted content is available through open-access licenses. For researchers
involved in TDM, the lack of open-access content or permissive
licenses presents a major challenge. The need for free access to data
becomes even more critical in modern research areas like artificial
intelligence (AI), where large-scale data analysis is essential to the
development of algorithms and technologies. The conflict arises because copyright
holders, including publishers and authors, may be unwilling to freely
provide their works for such uses, fearing a loss of control and profit.
Fair Dealing vs. Exclusive Rights:
The Indian Copyright Act includes fair dealing provisions,
which allow certain uses of copyrighted works without authorization.
These include uses for research, private study, criticism, or review.
However, the application of these provisions to digital research and TDM is
problematic. The fair dealing exception in India is not explicitly designed to
accommodate modern digital research methods like TDM. The non-commercial
nature of fair dealing in India restricts its use in certain contexts,
especially when the research has commercial applications, such as in AI
development or when private companies fund research. Moreover, the lack
of clear guidelines for TDM under fair dealing provisions leaves
researchers uncertain about whether their use of copyrighted materials for TDM
is legally safe.
The Issue of Licensing: Many publishers, institutions, and content providers impose
restrictive licensing models that limit how
copyrighted works can be used. For instance, digital content
might be available for reading, but not for copying or analyzing. These
restrictions conflict with the needs of TDM, where data is often required to be
processed in ways that involve reproducing or adapting the original works.
Researchers may not be able to obtain the necessary permissions to use the
content for TDM, especially when content is locked behind exclusive licenses
or terms of service agreements that prohibit such
activities. As a result, researchers may need to engage in legally complex
negotiations or face potential litigation risks.
Another significant challenge is the legal uncertainty
surrounding TDM and copyright law. Because the Indian Copyright Act does not
contain explicit provisions for TDM or digital research, researchers are often
left with limited guidance on how the law applies to their activities. This
lack of clarity creates a situation where researchers may be uncertain about
whether their use of copyrighted works falls within the scope of fair
dealing or copyright infringement. This
uncertainty could discourage researchers from engaging in TDM or lead to the
risk of legal action from copyright holders.
Risk of Litigation: The risk of copyright infringement lawsuits
poses a significant deterrent to TDM research. If researchers are unsure about
the legality of their activities, they may avoid using certain copyrighted materials,
thus stifling innovation. On the other hand, if they proceed without sufficient
legal clarity, they could face legal action from copyright holders, resulting
in penalties, costly settlements, or the destruction of research data.
CONCLUSION
The Indian Copyright Act of 1957 has
been established to protect the rights of creators by granting exclusive
control over the reproduction, distribution, and adaptation of their works. The
fair dealing exceptions contained in Section 52, which allow the use of
copyrighted material for purposes such as private study, research, criticism,
and education, serve to balance the interests of creators with those of the
public. The judicial interpretation provided in cases such as Kartar Singh
Giani v. Ladha Singh reinforces that the intention behind the use of
copyrighted material plays an important role in determining whether such use is
permissible under the law.
Text and Data Mining (TDM) represents a significant advancement in
research methodology, permitting the extraction and analysis of large volumes
of text and data. This process has become indispensable in academic inquiry,
commercial analysis, and technological development. TDM often requires the
reproduction of substantial portions of copyrighted works to uncover patterns
and insights that are not immediately apparent. When carried out for academic
or non-commercial purposes, TDM may fall within the exceptions outlined in
Section 52. However, the extensive nature of data extraction and the
transformation involved in TDM pose practical and legal challenges. The vast
scale of copying and processing large datasets raises questions regarding the
extent to which existing legal provisions can accommodate modern digital
research techniques.
Researchers and institutions encounter difficulties when access to
digitized content is limited by strict copyright controls. The ambiguity in the
law regarding the application of fair dealing to TDM creates uncertainty, which
may deter scholars from utilizing this important research tool. The current
framework does not clearly define the limits of acceptable use for digital
research, and the risk of litigation remains a serious concern for those
engaged in large-scale data analysis. This situation calls for a thorough
reexamination of the legal provisions to ensure that the rights of copyright
holders are maintained while supporting academic and commercial inquiry.
In summary, although Section 52 provides essential exceptions for
the use of copyrighted material, its application to TDM remains uncertain and
complex. A revised legal framework that clarifies these issues would enable
researchers to conduct digital analyses with confidence, ensuring that creative
rights are respected while fostering innovation. Policymakers must urgently address
these challenges with timely and clear reforms.
[1] Kartar Singh Giani v. Ladha Singh (1935) 16 ILR LAH 103
[2] Vaibhavi Pandey, India:
"Fair Dealing" In Copyrights: Is The Indian Law Competent Enough to
Meet the Current Challenges?MONDAQ(Oct. 24, 2019, 2:00PM)
http://www.mondaq.com/india/x/299252/Copyright/Fair+Dealing+In+Copyrights+Is+The+Indian+Law+Competent+Enough+To+Meet+The+Current+Challenges.
[3] Megha Nagpal, Copyright
Protection through digital Rights Management in India: A Non-Essential Imposition,
22 JOURNAL OF INTELLECTUAL PROPERTY RIGHTS 224, 227-230 (2017).
[4] Computer Association Intern.,
Inc. v. Altai, 982 F.2d 693 (2nd Cir. 1992).
[5] Whelan Associates v. Jaslow
Dental Laboratory, 797 F.2d 1222 (3d Cir. 1986).
[6] International Federation of the
Phonographic Industry, Global Music Report 2018: State of the Industry,
INTERNATIONAL FEDERATION OF THE PHONOGRAPHIC INDUSTRY (OCT. 24, 2019, 6:30 PM)
https://www.ifpi.org/downloads/GMR2018.pdf.
[7] Id.
Article Information
TEXT AND DATA MINING: COMPARITIVE ANALYSIS OF FAIR USE AND FAIR DEALING
Authors: RITWIK KARMAKAR
- Journal IJLRA
- ISSN 2582-6433
- Published 2025/03/18
- Issue 7
About Journal
International Journal for Legal Research and Analysis
- Abbreviation IJLRA
- ISSN 2582-6433
- Access Open Access
- License CC 4.0
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