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NAVIGATING INTELLECTUAL PROPERTY RIGHTS IN THE ERA OF BIG DATA: CHALLENGES, SOLUTIONS, AND IMPLICATIONS

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This article is written by Aarathi Namboodiri of 10th semester of SASTRA Deemed University, Thanjavur, an intern under Legal Vidhiya

ABSTRACT

The rise of big data presents both opportunities and challenges for intellectual property (IP) rights. This paper examines the intersection of big data and IP, exploring the implications for copyright, patents, and trade secrets. It discusses how big data enhances patent analytics, aids in identifying patent infringements, and supports IP strategy formulation. However, it also delves into the threats posed by big data, including the misuse of data and ethical considerations. Legal safeguards and technological solutions are proposed to address these challenges, emphasizing the importance of navigating IP protection amidst the complexities of big data.

Keywords

Big Data, Intellectual Property Rights, Copyright, Patents, Trade Secrets, Legal Safeguards, Technology, Data Ownership, Privacy Rights, Veracity

INTRODUCTION

Big data has become a ubiquitous term in contemporary discourse, revolutionizing various industries and sectors. In the contemporary landscape of information technology, the proliferation of Big Data has transformed the way businesses operate, societies function, and individuals interact. With the exponential growth in data generation, collection, and analysis, there emerges a complex web of challenges and opportunities concerning intellectual property rights (IPR). Intellectual property (IP) assets such as patents, copyrights, trademarks, and trade secrets have traditionally served as essential tools for fostering innovation, incentivizing creativity, and protecting the fruits of human intellect. However, the advent of Big Data brings forth novel challenges that demand a reevaluation and adaptation of existing legal frameworks.
The fusion of vast datasets, advanced analytics technologies, and machine learning algorithms enables unprecedented insights and innovations across various sectors, including healthcare, finance, retail, and beyond. Yet, this data-driven paradigm presents multifaceted dilemmas regarding ownership, access, and control of information. While Big Data offers immense potential for value creation, it also raises pertinent questions about the boundaries of intellectual property protection, data privacy, and fair competition.

In this research paper, we delve into the challenges posed by Big Data to intellectual property rights, explore potential solutions, and discuss the broader implications for legal frameworks, businesses, and society at large. By examining the intersection of Big Data and intellectual property through a legal lens, we aim to navigate the complexities of this evolving landscape and provide insights into the path forward amidst the digital revolution.

DEFINING BIG DATA

Big data has become a prevalent term in contemporary discussions. But what exactly does it entail? Japan’s ‘Society 5.0’ initiative envisions a society centered around its people, leveraging artificial intelligence and Big Data capabilities to drive economic progress[1]. This initiative involves collaborative efforts between the Japanese government and industry IT to seamlessly integrate technological advancements, particularly in AI and Big Data, via cloud platforms[2]. To propel these technologies forward, the Japanese government facilitates various approaches to enhance and integrate diverse data types such as images, videos, historical data, and signals, commonly utilized in various application technologies to address societal challenges. The primary objective of this government initiative is to harmonize economic advancement with the resolution of social issues. Aligned with the era of 5G and AI, Society 5.0 presents a promising technological framework poised to revolutionize global information technology, particularly in domains like fin-tech, healthcare, mobility, and infrastructure. Japan aims to establish a “Super Smart Society” to foster sustainable improvements in people’s lives[3].
The integration of cutting-edge technology across all aspects of human life aims to generate datasets that profoundly impact individuals and institutions in numerous ways. Researchers leverage these datasets to enhance their technologies and achieve substantial advancements across various application domains. The aggregation of these datasets culminates in Big Data, which finds application across diverse industries such as medicine, banking, and Internet advertising.
In essence, big data refers to the aggregation of massive datasets, encompassing processes like collection, analysis, management, and storage of extensive data quantities. These datasets comprise various forms of digital information, including customer data, documents, medical records, government records, and internet analytics. Debates over the ownership of public data often arise concerning major tech companies like Google and Facebook, yet data collection is commonplace among various organizations.

The term “Big Data” encompasses three fundamental features, with a fourth becoming increasingly typical, and a fifth derived from the others. These features include volume, veracity, velocity, variety, and value.[4] Therefore, an accurate definition of big data entails considering these parameters:

  1. Volume: Pertaining mainly to unstructured data from sources like social media, the increase in sensor and machine-to-machine data collection enables the use of large data volumes for analytics, facilitating relevant insights.
  2. Velocity: Tracking the real-time rate of data change, this parameter reflects the interconnectedness of existing data, measuring different data activity speeds and analyzing the relationship between data set changes.
  3. Variety: Reflecting the diverse formats of structured and unstructured data in contemporary contexts, this parameter poses challenges in managing, merging, and governing data due to its organizational-level nature and limited availability for analytics.
  4. Veracity: Given that Big Data corpora are often automatically generated, assessing the quality or trustworthiness of the data is crucial.
  5. Value: If the previous features are present, a Big Data corpus likely holds significant value.

WHERE DOES IP EMANATE FROM IN THE SAID SCENARIO?

In the context of the intersection between Big Data and intellectual property (IP) laws, it is essential to understand where intellectual property rights emanate from within this scenario. Intellectual property rights arise from the creation of original works or the development of novel ideas, which are then categorized into different forms of intellectual property, such as patents, copyrights, trademarks, and trade secrets.

Patents: In the realm of Big Data, innovations in algorithms, data processing methods, and data analysis techniques may qualify for patent protection. Patents provide inventors with exclusive rights to their inventions, granting them the authority to prevent others from making, using, or selling their patented technologies without permission. For example, algorithms designed to improve data analysis processes or enhance the efficiency of data storage systems may be eligible for patent protection.

Copyrights: Copyright protection may apply to original works of authorship fixed in a tangible medium of expression. In the context of Big Data, copyright may extend to databases, software code, data visualizations, and other creative expressions resulting from data analysis. For instance, a company that develops a unique data visualization tool or creates original content based on analyzed data may seek copyright protection for those works.

Trademarks: Trademarks are symbols, names, or designs used to distinguish the goods or services of one party from those of others. In the realm of Big Data, trademarks may be relevant to branding efforts, such as the names of data analysis platforms, software products, or data-related services. Companies operating in the Big Data space may seek trademark protection to safeguard their brand identity and prevent unauthorized use by competitors.

Trade Secrets: Trade secrets encompass confidential information that provides a competitive advantage to its owner. In the context of Big Data, trade secrets may include proprietary algorithms, data collection methods, or analytical techniques that are kept confidential to maintain a competitive edge in the market. Companies investing in research and development to create innovative data processing solutions may rely on trade secret protection to safeguard their valuable intellectual assets.

The convergence of Big Data and intellectual property gives rise to complex legal considerations regarding the scope of protection, ownership rights, and enforcement mechanisms. As data becomes increasingly valuable as an asset, navigating the intersection of Big Data and intellectual property requires careful attention to the nuances of IP laws and the evolving landscape of technological innovation.

BIG DATA AND COPYRIGHT

Copyright law serves as a fundamental tool in preserving the integrity of computer software and programs utilized in the collection and analysis of Big Data. Across various jurisdictions, including Nigeria, India, the USA, and within the European Union, legislation exists to protect the tools utilized for data analytics, encompassing functions such as mining, deletion, segregation, and transformation. Specifically, the European Union’s Database Directive aims to harmonize protection across member states, highlighting the global recognition of the importance of copyright in this domain.

For software to qualify for copyright protection, it must meet specific criteria, including a requisite degree of originality and fixation on a tangible medium. The threshold for originality varies across jurisdictions, with some adopting a lower threshold, such as the Sweat of Brow Theory, while others adhere to a higher standard, such as the Modicum of Creativity Theory. However, works generated solely by computers without human intervention generally do not qualify for copyright protection. In jurisdictions like the UK, attribution of authorship may be assigned to individuals who arrange for the creation of computer-generated works.
However, the mere collection of raw data without selection or analysis typically does not meet the threshold for copyright protection. While legislative support for software protection is more prevalent due to the inherent creativity involved in software development, terms of use agreements often serve as additional safeguards. Computer programs, typically defined using various digital codes and programming languages before being simulated for consumer use, require copyright measures to protect the program code.

Given software’s status as one of the most significant information assets, copyright laws play a paramount role in safeguarding the creative efforts involved. Despite existing copyright protection methods, gaps in software and systems security persist. However, measures such as patent protection can be pursued for software design ideas closely related to hardware. Additionally, ongoing advancements, such as deep learning strategies utilizing blockchain, exemplify efforts to enhance copyright protection for digital assets like software, ensuring robust enforcement of intellectual property rights in the digital realm.

BIG DATA AND PATENTS

While big data itself may not be patentable, the algorithms and software programs integral to its processing can fall under legal jurisdiction. Algorithms play a pivotal role in transforming small data into big data and subsequently into actionable intelligence. Although the content generated from big data typically cannot be patented, exceptions arise where commercial advantage is attainable through its expression as an invention, provided it meets criteria of novelty and industrial applicability. For instance, if an analysis of big data yields a novel business method applicable to a specific commercial sector, it may qualify for patent protection.
An illustrative case is Commissioner of Patents vs. RPL Central Pty Ltd[5], wherein a computer-assisted vocational training course qualification assessment system was deemed patentable. The court distinguished between mere computer-aided schemes and innovations improving computer functionality or addressing technical issues beyond its standard use, suggesting patentability in the latter scenario.

Individual components of technology with specialized functions are typically more amenable to patenting. However, seeking patents for inventions derived from computer-generated works can be complex, particularly as unsupervised artificial intelligence outputs are not considered patentable subject matter, challenging traditional intellectual property paradigms.
Big data’s impact on patent rights manifests in the exponential growth of prior art and increased accessibility facilitated by big data analytics. This proliferation of data could lead to a decline in patentable inventions, as patent approval processes lag behind data generation rates. Consequently, the realm of patentable technology may contract, reflecting the disparity between patent granting speed and data generation velocity.

BIG DATA AND TRADE SECRETS

In the realm of Big Data, trade secrets play a crucial role in safeguarding valuable information derived from data analysis. Trade secret protection can be applied to a wide array of data-related assets, including algorithms, data processing techniques, analytical methodologies, and proprietary datasets. By meeting specific criteria, data analyzed and derived from Big Data sources can qualify for trade secret protection, offering perpetual safeguarding without the formalities and legal expenses associated with patents.

Criteria for Trade Secret Protection

To qualify as a trade secret, the information derived from Big Data must possess commercial value, derive its value from being kept confidential, be subject to reasonable efforts to maintain secrecy, and have limited access within the organization. This can encompass a broad spectrum of data elements, from proprietary algorithms used for data analysis to unique insights gleaned from extensive datasets.

Advantages of Trade Secrets

Trade secrets offer several advantages over patents in the context of Big Data. Unlike patents, which have limited durations and require public disclosure of the invention, trade secrets provide perpetual protection as long as the information remains confidential. Moreover, trade secrets do not entail the costs associated with patent registration, maintenance, and compliance obligations, making them a cost-effective option for protecting valuable data assets.

Illustrative Examples

Well-known examples of trade secrets in the realm of Big Data include Google’s closely guarded search algorithm, Kentucky Fried Chicken’s original recipe, and Coca-Cola’s iconic beverage formula. These examples highlight the diverse range of data-related assets that can be protected as trade secrets, demonstrating the versatility of trade secret protection across different industries and sectors.

Enforcement and Protection Measures

Trade secret protection can be reinforced through contractual agreements, such as non-disclosure agreements (NDAs) with employees, contractors, and business partners. Additionally, technological measures, such as encryption, access controls, and digital rights management (DRM) systems, can help prevent unauthorized access and dissemination of confidential data.

Limitations and Challenges

While trade secrets offer robust protection against illegitimate acquisition, such as espionage or theft, they do not provide immunity against reverse engineering by competitors. However, trade secrets serve as a deterrent against unauthorized access to confidential information and can be complemented by other intellectual property rights, such as patents and copyrights, to create a comprehensive IP protection strategy in the Big Data landscape.
In conclusion, trade secrets constitute a valuable tool for safeguarding data assets derived from Big Data sources, offering perpetual protection, cost-effectiveness, and flexibility in enforcement. By leveraging trade secret protection, organizations can preserve the confidentiality and competitive advantage of their proprietary data assets in an increasingly data-driven world.

IMPLICATIONS

Navigating intellectual property rights (IPR) in the era of big data necessitates a nuanced understanding of the evolving legal landscape and technological advancements. The fusion of vast datasets, advanced analytics technologies, and machine learning algorithms presents both opportunities and challenges for IP protection. Firstly, the enhanced capabilities of big data facilitate swift patent analytics, aiding in the identification of patterns and insights that were previously challenging to discern. This empowers companies to make informed decisions regarding their IP strategies. However, the proliferation of big data also raises concerns about data misuse and ethical dilemmas, potentially leading to IP theft and infringement. Addressing these challenges requires the implementation of legal safeguards governing data collection, usage, and sharing, alongside technological solutions like blockchain for tracking and verifying IP rights. Moreover, the complexity of navigating IP protection across different jurisdictions underscores the need for universal measures to ensure comprehensive IP protection in the digital age. By proactively addressing these implications, stakeholders can effectively safeguard their IP rights amidst the complexities of big data, fostering innovation while mitigating risks associated with data misuse and infringement.

THE POSSIBILITIES FOR INTELLECTUAL PROPERTY (IP) WITH BIG DATA

THE DIFFICULTIES AND LEGAL REPERCUSSIONS

POSSIBLE REMEDIES & SUGGESTIONS

CONCLUSION

In conclusion, the era of big data poses both opportunities and challenges for intellectual property rights. While big data enhances patent analytics, aids in identifying patent infringements, and supports IP strategy formulation, it also brings threats such as data misuse and ethical dilemmas. Legal safeguards and technological solutions are imperative to address these challenges effectively. By implementing rules governing data collection, usage, and sharing, and leveraging technologies like blockchain, organizations can safeguard their IP rights amidst the complexities of big data. As big data continues to proliferate, navigating IP protection will remain paramount, requiring concerted efforts to establish robust data collection mechanisms and cohesive IP protection strategies across jurisdictions.

REFERENCES

  1. Hamza, R.; Dao, M.S. Privacy-preserving deep learning techniques for wearable sensor-based Big Data applications. Virtual Real. Intell. Hardw. 2022, 4, 210–222. [Google Scholar] [CrossRef]
  2. Aquilani, B.; Piccarozzi, M.; Abbate, T.; Codini, A. The role of open innovation and value co-creation in the challenging transition from industry 4.0 to society 5.0: Toward a theoretical framework. Sustainability 2020, 12, 8943. [Google Scholar] [CrossRef]
  3. Holroyd, C. Technological innovation and building a ‘super smart’society: Japan’s vision of society 5.0. J. Asian Public Policy 2022, 15, 18–31. [Google Scholar] [CrossRef]
  4. Jenn Cano, ‘The V’s of Big Data: Velocity, Volume, Value, Variety, and Veracity’, XSNet (March 11, 2014), https://www.xsnet.com/blog/bid/205405/ (Last opened on March 17 2024).
  5. [2015] FCAFC 177

[1] Hamza, R.; Dao, M.S. Privacy-preserving deep learning techniques for wearable sensor-based Big Data applications. Virtual Real. Intell. Hardw. 2022, 4, 210–222. [Google Scholar] [CrossRef]

[2] Aquilani, B.; Piccarozzi, M.; Abbate, T.; Codini, A. The role of open innovation and value co-creation in the challenging transition from industry 4.0 to society 5.0: Toward a theoretical framework. Sustainability 2020, 12, 8943. [Google Scholar] [CrossRef]

[3] Holroyd, C. Technological innovation and building a ‘super smart’society: Japan’s vision of society 5.0. J. Asian Public Policy 2022, 15, 18–31. [Google Scholar] [CrossRef]

[4] Jenn Cano, ‘The V’s of Big Data: Velocity, Volume, Value, Variety, and Veracity’, XSNet (March 11, 2014), https://www.xsnet.com/blog/bid/205405/ (Last opened on March 17 2024).

[5] [2015] FCAFC 177

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