Deep tech, a term often used to describe cutting-edge innovations grounded in scientific and engineering advances, is reshaping industries and creating breakthroughs in fields such as artificial intelligence (AI), biotechnology, quantum computing, and robotics. These innovations are not just pushing the boundaries of technology—they are also challenging the frameworks that govern intellectual property. Patent law, a fundamental system for protecting and encouraging innovation, is being forced to evolve in response to the unique challenges posed by deep tech.
The Rise of Deep Tech and Its Impact on Patent Law
The rise of deep tech has introduced unprecedented opportunities for innovation, but it has also fundamentally reshaped the way businesses approach intellectual property protection.
As industries like AI, biotechnology, quantum computing, and robotics push the boundaries of what’s technologically possible, they also push the limits of what existing patent laws can adequately protect.
Deep tech is characterized by its complexity and the long-term research and development cycles that are required to bring innovations to market. This reality has profound implications for how patent law needs to evolve.
For businesses, the challenge lies in navigating the patent landscape while staying ahead of competitors in a field that is both rapidly evolving and highly technical.
Securing intellectual property is not just about filing patents—it’s about developing a sophisticated, strategic approach that ensures long-term protection in a highly competitive environment.
Patent law, traditionally designed for more tangible inventions like mechanical devices or chemical compositions, must now accommodate abstract ideas, complex algorithms, and biological processes. This shift is prompting both businesses and patent offices to rethink how intellectual property is defined, applied for, and defended.
Strategic Implications of Deep Tech for Patent Filing
As deep tech innovations become more abstract and multi-disciplinary, businesses must approach patent filing with a heightened level of strategy.
Unlike more traditional industries, where patent protection might focus on a single product or process, deep tech companies need to consider protecting a broader scope of their innovations, including software algorithms, machine learning models, hardware-software integrations, and even methods of data processing.
One critical area for businesses is understanding how patent law defines “patentable subject matter.” Deep tech innovations often blur the lines between abstract ideas and practical applications, especially in fields like AI and quantum computing.
For businesses, this means that when preparing patent applications, they must demonstrate not only the novelty of the invention but also its real-world utility. The more abstract the concept, the more critical it becomes to show how the innovation solves a specific technical problem in a way that hasn’t been done before.
For instance, in AI, a general algorithm is unlikely to meet patent eligibility standards. However, an AI algorithm that improves medical diagnostics by interpreting medical images with greater accuracy than current technology can be framed as a practical, novel application.
Businesses need to carefully craft their patent claims to highlight these tangible improvements and focus on the technological advancements that make their innovation valuable in a particular domain.
Additionally, the global nature of deep tech means that businesses must take a multi-jurisdictional approach to patent protection. Each country has its own interpretation of what constitutes patentable subject matter, particularly when it comes to deep tech fields like AI and biotechnology.
While the United States might allow patents for certain AI-based processes, the European Patent Office (EPO) has stricter requirements for demonstrating a “technical effect” beyond the algorithm itself. Businesses must tailor their patent strategies to accommodate these regional differences, ensuring that they are protected in all key markets where they operate.
Long-Term Investment in Research and the Role of Patents
One of the defining characteristics of deep tech is the lengthy research and development cycles required to bring innovations to market.
Unlike software or consumer products, where development timelines are relatively short, deep tech innovations often require years, if not decades, of investment before they are ready for commercialization. This long-term commitment to research has a direct impact on how businesses approach their patent strategies.
For deep tech companies, patents serve as more than just legal protections—they are strategic assets that can help secure funding, build partnerships, and maintain competitive advantages during the lengthy development phase.
Venture capital firms and other investors place a high value on robust patent portfolios because they demonstrate that the company has secured exclusive rights to innovative technologies that could disrupt entire industries.
Patents can also help businesses enter into lucrative licensing deals or form strategic alliances with larger companies that have the resources to bring deep tech innovations to market.
Given the importance of securing strong intellectual property protection early in the development process, businesses must prioritize patent filing from the outset of their R&D efforts. This proactive approach ensures that their innovations are protected, even if they take years to reach full commercial viability.
Additionally, deep tech businesses should consider filing multiple patents over time, covering various iterations and applications of their technology. This layered approach not only strengthens the overall patent portfolio but also ensures ongoing protection as the technology evolves.
Another critical consideration for deep tech businesses is the potential for future patent disputes. The more valuable a deep tech innovation becomes, the more likely it is that competitors will attempt to challenge the validity of the patents protecting it. Businesses should be prepared to defend their intellectual property through litigation if necessary.
Building a well-constructed, defensible patent portfolio from the beginning can provide a solid foundation for withstanding challenges from competitors.
In fact, having strong, enforceable patents can deter competitors from attempting to infringe or challenge the patents in the first place, giving businesses the space they need to focus on development and commercialization.
The Role of Data in Deep Tech Patent Strategies
One often overlooked aspect of deep tech innovations is the role of data in shaping patent strategies.
Many deep tech technologies, particularly those in AI and biotechnology, rely on vast amounts of data for training, development, and optimization. While data itself may not be patentable, the processes for collecting, organizing, and analyzing data can be protected by patents.
For AI-driven innovations, data is often the fuel that powers machine learning models and enables them to function effectively. Businesses developing AI technologies should consider patenting the methods they use to process and analyze data, especially if these methods offer a novel or more efficient way of producing insights or improving outcomes.
This can provide an additional layer of protection beyond the AI algorithm itself, safeguarding the entire data pipeline that supports the innovation.
In biotechnology, data is similarly critical for innovations like drug discovery, genomic analysis, and personalized medicine. Biotech companies that develop unique methods for analyzing genetic data, or for identifying new drug targets based on data-driven insights, may be able to patent these processes.
Doing so not only strengthens the overall patent portfolio but also provides protection for the data-related aspects of the innovation, which are often key differentiators in a competitive market.
How AI Is Challenging the Boundaries of Patent Law
Artificial Intelligence (AI) is not only redefining industries but also challenging the very foundations of patent law. As AI continues to advance, it raises critical questions about what can be patented, how inventorship is defined, and whether existing legal frameworks are equipped to handle innovations that blend human ingenuity with machine learning capabilities.
For businesses operating in the AI space, understanding these challenges and adopting proactive strategies is essential for protecting their intellectual property in an increasingly complex legal landscape.
At the heart of the challenge is the nature of AI itself. AI systems often involve algorithms, data processing techniques, and machine learning models that can be highly abstract in nature.
While these innovations are undoubtedly revolutionary, the abstract quality of algorithms and data structures makes them difficult to patent under traditional legal standards. Courts and patent offices have struggled to strike a balance between encouraging innovation in AI and preventing overly broad or vague patents that stifle competition.
For businesses seeking to patent AI technologies, the key to success lies in understanding how to frame their inventions to meet the evolving standards of patent eligibility.
It is not enough to simply patent an algorithm or a machine learning model—companies must demonstrate how their AI-driven invention solves a specific, real-world technical problem in a way that is novel and non-obvious.
Framing AI Inventions to Meet Patent Eligibility Standards
One of the biggest challenges businesses face when seeking to patent AI innovations is navigating the legal requirements surrounding abstract ideas. Patent law in many jurisdictions, including the U.S. and Europe, excludes abstract ideas from patent eligibility unless they can be demonstrated to have a clear technological application.
AI, by its nature, often involves algorithms and mathematical methods that courts may view as abstract. However, businesses can increase their chances of securing a patent by strategically framing their inventions to focus on the practical application of the AI technology.
For instance, rather than seeking to patent the algorithm itself, businesses should highlight how the AI system integrates with real-world systems or processes to solve specific challenges.
This could involve focusing on how the AI technology improves operational efficiency, enhances decision-making accuracy, or enables automation in industries such as healthcare, finance, or logistics. By emphasizing the practical benefits and the specific technological context in which the AI system operates, companies can make a stronger case that their invention is more than an abstract idea.
For example, an AI system used to optimize supply chain management could be framed as solving a specific technical challenge—such as reducing delivery times through predictive analytics—rather than merely processing data more efficiently.
This emphasis on practical, tangible results helps to demonstrate the invention’s value beyond the abstract concept of data processing, making it more likely to meet patent eligibility requirements.
Additionally, businesses should focus on the technical details of how the AI system achieves these results. If the innovation involves novel methods of training machine learning models, integrating multiple data sources, or creating unique data structures, these technical aspects should be clearly outlined in the patent application.
This level of specificity not only strengthens the patent but also makes it harder for competitors to design around the technology, thereby providing stronger IP protection.
Defining Inventorship in the Age of AI
Another key issue that AI presents for patent law is the question of inventorship. Traditionally, patent law requires that the inventor be a human being.
However, as AI systems become more advanced, they are increasingly capable of generating new ideas, designs, and innovations autonomously. This raises an important legal question: can AI be considered an inventor under current patent laws?
Several high-profile cases have already highlighted this issue. In 2019, an AI system called DABUS was named as the inventor on patent applications filed in multiple countries.
The invention was a novel food container design, entirely generated by the AI system without human intervention. While some jurisdictions, such as South Africa, granted the patent with DABUS listed as the inventor, others—like the U.S., Europe, and the U.K.—rejected the application, citing the requirement that only humans can be inventors.
For businesses, this debate has significant implications. As AI becomes more integrated into the innovation process, companies will need to carefully consider how they manage IP ownership when AI systems contribute to or even lead the development of new technologies.
While current laws do not recognize AI as an inventor, businesses can still secure patent protection by focusing on the human involvement in the development process. For example, if human researchers oversee the AI’s operations, select the training data, or make key decisions about how the AI system is used, they can be named as inventors.
In the near term, businesses should ensure that they document the role of humans in the invention process whenever AI is involved. This includes maintaining clear records of who trained the AI, who chose the data sets, and how the AI’s output was interpreted or refined by human engineers.
These records can serve as critical evidence in demonstrating that the invention meets the legal requirements for inventorship, while still acknowledging the role that AI played in the process.
However, as AI continues to evolve, it is likely that patent law will need to be updated to address the growing role of autonomous systems in innovation. Businesses should stay informed of these developments and be prepared to adapt their IP strategies as the legal framework around AI inventorship evolves.
Protecting AI-Driven Data and Algorithms
Another critical aspect of patenting AI technologies is the protection of data and algorithms. While data itself is generally not patentable, the methods and processes for collecting, analyzing, and using data can be protected.
This is especially important for AI systems, which often rely on vast amounts of data for training and optimization. The way a business structures, processes, and applies that data can be a key differentiator in a competitive market, making it an essential part of their intellectual property.
Businesses should consider patenting the unique methods their AI systems use to handle data, especially if these methods offer significant advantages over existing technologies. For instance, if a company’s AI system employs a novel method of preprocessing raw data to improve model accuracy or reduce computational costs, this process could be patented.
Similarly, innovations in how data is integrated from multiple sources, how biases are mitigated in machine learning, or how the system adapts to changing data environments can also be strong candidates for patent protection.
In addition to patenting processes, businesses should also think about broader data protection strategies. This could include the use of trade secrets for proprietary data sets, encryption methods, and unique algorithmic insights that cannot easily be reverse-engineered by competitors. In this way, businesses can safeguard the most valuable aspects of their AI systems, even if those aspects are not directly patentable.
Navigating Global Patent Regulations for AI
Given the global nature of AI development, businesses must also be prepared to navigate the different patent regulations that apply to AI technologies across various jurisdictions.
While the U.S. and Europe are the largest markets for AI patents, emerging markets in Asia, particularly China, are becoming increasingly important for AI development and patent protection.
Patent offices in different regions have varying standards for what constitutes a patentable AI invention. For example, the European Patent Office (EPO) requires that software-related patents demonstrate a “technical effect,” which can be difficult to meet for some AI applications.
By contrast, China has been more open to AI patents, offering opportunities for businesses to secure protection for software-based innovations.
For businesses operating globally, it is essential to tailor patent applications to the specific requirements of each jurisdiction. This may involve working with local patent attorneys who understand the nuances of regional patent laws and can help businesses maximize the likelihood of securing patent protection.
Taking a multi-jurisdictional approach also allows businesses to protect their innovations in key markets, reducing the risk of competitors copying or reverse-engineering their technology abroad.
Biotechnology and the Patentability of Living Systems
Biotechnology, particularly at the frontier of deep tech, poses significant challenges for traditional patent law. Innovations in gene editing, synthetic biology, personalized medicine, and cellular agriculture have opened up new possibilities in health, agriculture, and environmental sustainability.
However, these breakthroughs often involve biological processes or living organisms, raising complex questions about what can and cannot be patented. For businesses operating in this space, it is crucial to understand how patent law is evolving and to adopt strategies that secure their intellectual property in a legally and commercially viable manner.
The central challenge lies in distinguishing between naturally occurring biological materials and human-made inventions. Courts and patent offices have grappled with the question of whether genes, cells, or living organisms can be patented, leading to legal precedents that directly impact the biotechnology sector.
For businesses in biotech, navigating these legal uncertainties while protecting proprietary innovations is essential for securing market leadership and ensuring long-term profitability.
Patenting Engineered Biological Systems
One of the most significant areas of focus for businesses in biotechnology is the patenting of engineered or modified biological systems. Many biotech innovations, particularly in fields like synthetic biology, involve manipulating living cells, DNA sequences, or biological processes to create new functionalities.
These innovations, unlike naturally occurring biological materials, can often meet the criteria for patent eligibility because they result from human intervention and offer novel applications.
For businesses, the key to securing patent protection for engineered biological systems is to emphasize the technical modifications and improvements made to these systems.
Whether it involves using CRISPR for gene editing, developing synthetic organisms that can produce biofuels, or creating genetically modified crops with improved resistance to disease, businesses must clearly articulate how their invention differs from natural phenomena.
The focus should be on demonstrating the human innovation involved in engineering the biological system, as well as the practical, real-world applications of the invention.
For example, in the case of CRISPR technology, patent applications should highlight the specific techniques used to modify genetic sequences, the novel processes for delivering the gene-editing tool into cells, and the therapeutic or agricultural benefits of the technology.
By framing the innovation as a technical solution to a biological problem—such as treating genetic disorders or increasing crop yields—businesses can strengthen their patent applications and improve their chances of securing protection.
Additionally, biotech companies should take a forward-looking approach to patenting engineered biological systems by considering not just the current application of the technology, but also potential future uses.
A company that patents a synthetic organism engineered to produce biofuels, for instance, could also explore patents for other industrial applications, such as producing bioplastics or pharmaceuticals. This kind of strategic thinking can help businesses build a robust patent portfolio that extends beyond a single invention and provides protection across multiple markets.
Navigating the Legal Precedents on Patent Eligibility
Biotechnology businesses must also be aware of the legal precedents that shape the patentability of living systems. A critical turning point came with the U.S. Supreme Court’s decision in Association for Molecular Pathology v. Myriad Genetics, Inc., which ruled that naturally occurring DNA sequences could not be patented.
The court’s reasoning was that isolating a gene from its natural environment does not make it eligible for patent protection. However, the ruling also clarified that synthetic DNA, such as complementary DNA (cDNA), which is not naturally occurring, could be patented.
This decision underscores the importance of focusing on human-made modifications when pursuing biotech patents. Naturally occurring materials like DNA, proteins, or cells are generally not patentable in their natural state.
However, once these materials have been altered, synthesized, or engineered to perform new functions, they become eligible for patent protection. For businesses, this means that patent applications should focus on the innovation involved in modifying biological materials and how these modifications result in new, useful applications.
For instance, a biotech company working with stem cells could emphasize how the cells have been genetically engineered to produce specific proteins for therapeutic use.
By focusing on the human-driven innovation in creating these new functions, businesses can ensure that their patents align with existing legal precedents and stand up to scrutiny in court.
Moreover, the landscape is continually evolving. As new technologies, such as gene-editing tools and bioengineering platforms, advance, courts and patent offices are likely to face further challenges in defining what is patentable.
Businesses must stay up-to-date with these developments, as shifts in the legal interpretation of patent law can impact their intellectual property strategies.
Patent Strategies for Biotech Startups
For biotech startups, which often rely on cutting-edge research and development, building a strong patent portfolio is a key element of business strategy. Patents are not only a tool for protecting intellectual property but also a valuable asset when seeking funding or entering into partnerships.
Investors and strategic partners place a high value on companies with well-protected technologies, making a solid patent portfolio essential for attracting capital and building long-term relationships.
However, filing patents in biotechnology can be an expensive and time-consuming process. For startups with limited resources, prioritizing which aspects of the technology to patent is critical. In these cases, businesses should focus on patenting the core innovations that provide the greatest competitive advantage.
For example, if a startup has developed a new method for delivering gene therapy treatments, securing a patent on that delivery method might be more valuable than patenting the individual components of the therapy itself.
Another strategic approach for biotech startups is to consider provisional patents as a way to secure early-stage protection while continuing to refine and develop the technology. A provisional patent allows businesses to establish an early filing date without the need for a complete patent application.
This can be particularly useful in biotech, where research is ongoing, and innovations are still being refined. Once the technology is more fully developed, the company can file a non-provisional patent based on the provisional application, ensuring that they maintain the earliest possible priority date.
Additionally, biotech startups should consider the potential for future licensing opportunities when building their patent portfolio. Licensing agreements can provide an important revenue stream for startups, particularly those that may not have the resources to bring a product to market on their own.
By securing strong patent protection for their innovations, startups can enter into licensing agreements with larger companies, allowing them to generate income while continuing to invest in further research and development.
Managing Patent Protection Across Jurisdictions
The global nature of biotechnology means that businesses often need to protect their intellectual property in multiple jurisdictions. However, the standards for patent eligibility can vary significantly from one country to another, particularly when it comes to biotechnology and living systems.
For example, while U.S. patent law may allow for certain types of synthetic biology patents, European law might have stricter limitations regarding the patentability of living organisms.
Businesses must develop an international patent strategy that accounts for these regional differences. This involves filing patents in key markets where the company expects to operate, while tailoring the patent claims to meet the specific legal standards of each jurisdiction.
In some cases, businesses may need to adjust their patent applications to comply with local regulations. For example, the European Patent Office (EPO) has specific rules regarding patents on human embryonic stem cells and related technologies, which differ from U.S. regulations.
wrapping it up
Deep tech innovations, particularly in fields like artificial intelligence, biotechnology, and quantum computing, are not only revolutionizing industries but also reshaping the landscape of patent law.
As these cutting-edge technologies challenge the boundaries of what can be patented, businesses must adopt more strategic, thoughtful approaches to securing their intellectual property. The rise of AI, synthetic biology, and engineered systems brings with it unique legal complexities, from defining inventorship in AI-driven innovations to navigating the patentability of living organisms.
