Life Science and Artificial Intelligence-related IP
Apr 17, 2021

Life Science and Artificial Intelligence-related IP

Big Data Plus Machine Learning corresponds to scientific advancement by providing massive data. Artificial Intelligence (AI) has moved in to navigate human beings through this enormous ocean of data! Analyzing the vast amounts of data that being generated through R&D can potentially re-intellectualize the way we realize life sciences.

We are at the start of a new era of technological innovation. This will completely disorder the way we observe medicine, research, and our own health. This era is being powered by advances in AI and machine learning that could let researchers develop cures faster, doctors deliver more effective care, and healthcare companies reduce costs while increasing access to care!

AI’s Role in Smarter Data Mining

As healthcare and medicine have advanced, more data on patients are collected. Researchers from all over the world, are gathering and sharing capital of data. Theoretically, this data should be an engine driving more modern decision-making and clinical insights. However, analyze and mining them for practical insights is of issues.

AI has broad-ranging applications in drug discovery, biotechnology, medical diagnosis, clinical trials, precision and personalized medicine and patient monitoring. There are numerous areas where the life-science industry uses AI effectively today. Some of them are the following:

 

  • Providing Personalized Medicine
    AI could revolutionize the current theory as “one size fits all” in terms of medicine dosing. By using patient health records, AI could digitalize and suggest the best treatment plan. Moreover, by continuously monitoring numerous parameters, AI may enable medical practitioners to adjust the dose size or, if the disease mutates, revise the therapy and introduce a more effective alternative.
  • Introducing Therapies and Drugs to the market
    It takes more than a decade and billions of dollars to introduce a new drug to the market needs vast money.  AI helps in putting all the data, obtained from many sources (hospitals and research labs) in a compatible format. Besides, AI also helps in developing better healthcare networks and protocols, speeding up their introduction in the market at a reasonable price.
  • Medicine Discovery and Manufacture
    Drug development involves a tedious, time-consuming, and expensive approach that consists of screening a large number of potential molecules. AI-based programs are able to scan and cross-reference through large and complex datasets more quickly and precisely as compared to human efforts.
  • Epidemiology and Clinical Investigations and Designing Clinical Trials

Artificial Intelligence can design clinical trials, estimating the ideal sample size, and implementing them remotely on participants across a wider geographical area. This, in turn, reduces the cost and increases the odds of obtaining relevant and accurate data.

  • Applications in Scientific Publishing
    Artificial Intelligence technologies help to address key issues such as identifying new peer reviewers, combating piracy, and identifying face data. This will not only aid in accelerating scientific communication and reducing human bias but also uphold the publishing quality.
  • Launching Robotic Surgery

Robotic surgery is a new field that is acquiring a considerable amount of interest. After training, a robot will be capable enough to perform each operation consistently and accurately. The consistency and accuracy of the surgery will be irrespective of the duration of the surgery.

  • Advancing the Next Generation of Radiology tools
    The current diagnostics processes rely on information gathered from CT scans, X-rays, or MRI machines. AI-based radiology tools will enable clinicians to gain a more precise and detailed understanding of how a disease progresses by performing virtual biopsies.

   

Artificial Intelligence (AI) and its IP related issues in Life Science 

For deploying AI technologies and machine learning (ML) technology -the most active area of AI technology being developed today-, a substantial investment is needed.

This amount of investments confirms the need of protecting intellectual property rights and investors in this field are keen to secure IP protection for their AI-related innovations. However, IP protection for AI-related innovations is dissimilar to those of traditional life sciences inventions.

This raises fundamental questions of inventorship, patent eligibility, and public disclosure, which are not readily addressed by patent laws of many countries. In the following paragraph, some IP strategies that can be adopted to overcome the complexities of each section are as follow: 

 

AI Inventorship!

One of the AI-related challenges is ownership issues. The life science companies utilizing AI can moderate potential IP ownership issues by defining ownership of AI-related IP rights in employment, licensing, or purchase agreements. In such agreements, the owner of the data, information, or results that may be generated by AI should also be specified. Moreover, such agreements should clarify who can use data, and how it will be used. Thus, the agreements and their clauses become very important in the field of artificial intelligence.

 For instance, IBM is known as a company having numerous patents in AI-related technologies; IBM could clearly retain all IP rights in the AI system itself but license a pharmaceutical company certain IP rights in the productivity of the AI system, such as new drug compounds. Further, such contracts should make clear who will be liable for the actions taken and the results obtained using AI systems now or in the future.

 

Type of IP Protection needed to secure AI innovations

Life sciences companies should consider what type of IP protection is appropriate for their AI-related inventions.

The key assets when it comes to AI are the development tools; having a platform for developing AI so tends to flow from Google as a platform anyone. There are several learning techniques to provide AI; data processing methods, because it is not possible to just put data into an AI, and processing data in various ways is necessary. Moreover cleaning up the data, reduce the number of pixels, and alike is necessary to run and train the AI model.

What is done in the AI model, is the training process which in addition may have a freestanding piece of software essentially. Then what is achieved is the products of AI. The AI model can be for example a journalist AI that's writing copy for newspapers or a creative AI that's creating paintings or just identifying targets of pharmaceutical research.

So they are intellectual assets but fitting them into traditional IP rights which simply were never designed with AI which deliberately excluded data in the form of information. How this innovative work could be protected?

 

Patenting AI-Related Technologies!

Although a massive increase in patents relating to AI is observed, (at least 800% rise), but they are mostly applications, not necessarily granted patents. Since research on AI backs to the the1940s and 50s, and a lot of the fundamental ideas are actually very old and can't be patented as they are already known.

Moreover, patent protection excludes the protection of mathematical methods, methods of doing business, and computer programs as such, where those developed many potential AI inventions. Therefore, what can be protected here as patent would be for example methods of preparing data, and methods of training on data. But certainly, not the data itself, that just not within the scope of patents. Therefore it is suggested if an AI-related technology could be reversed engineered, filing a patent application might be a wise option. However, the subject matter eligibility must be considered before any action and after that, enablement challenges must be overcome by drafters or practitioners.

 

Writing patent applications to overcome enablement challenges

Patenting AI-related inventions also presents challenges with regards to the written description and enablement requirements; patentees must disclose to the public enough information about the invention to enable one of ordinary skill in the art to visualize it and practice the claimed invention. Fulfilling such requirements can be tricky where AI has assisted in the discovery of a large type of compound or where the AI process claimed involves complex “black box” computer programming!

 Applications related to AI-assisted drug discoveries must ensure that any claimed genus is tailored or well-characterized enough. Therefore that a person of skill in the art would know how to make and use the invention without unnecessary experimentation.

It is also important to include data demonstrating activity for representative members of each drug class. For claims involving machine learning or deep learning, practitioners should include as much detail as possible in the specification about the inventions claimed (e.g., the architecture of the model, training dataset and methodology, pre-processing steps for new data, etc.).

 

Subject Matter Eligibility

The Mayo/Alice decisions have made it significantly more challenging for applicants to obtain patents on computer-implemented or software patents, and patents directed to AI algorithms. Life sciences patents have also resulted in the invalidation of certain diagnostic and method of treatment claims.

One approach to overcome such issues is to draft claims that apply the abstract idea or mental process employed by the AI in a specific or unconventional manner to achieve a practical result. For example, where AI is encompassed in a claim relating to a diagnostic method or method of treatment, the drafter may include an active step of administering a specific drug to the patient for the specific condition to be treated. Drafter also may want to consider including a description in the patent specification of how the claimed invention improves on prior art or overcomes a particular scientific problem, as it may provide an added advantage. Such description increases the chances to be granted in USPTO since it highlights the differences from the prior art to the examiner prior to any responses to office actions.

 

Protecting AI by Copyrights Law

For protecting AI under copyright law, it is worth noting that data in the field of AI is quite a broad term; when a computer scientist talks about data, they literally mean photos or video streams or handwritten notes. Although they are might well be copyright, individually – (photographs, medical descriptions, articles), but the data in the AI system is mere information or extracted data, or weights and measures of goods.

Moreover, copyright is also suitable for computer programs, so in a  written AI platform, if a human has written it, the form of expression even is a strange thing to think about it but can be protected under copyright.  With this type of protection, someone can't just copy your computer program. But sadly, they are entitled to copy its functions, so copyright is no way to protect the functions of a computer program! Therefore AI-related innovation can't be covered under copyright definition. 

 

Protecting AI-related innovations  by trade secret

Some AI technologies, including machine learning and deep learning, operate in part as a “black box” that may not be easily reverse-engineered. The corollary is that it can be difficult to detect and prove the infringement of patents claiming machine learning technology or copyright infringement.

So it can be said that there is still scope for traditional IP rights to be pursued where they are likely to work. But since AI is a new field, there is just a lot of uncertainty of how it's going to come out.

Under such circumstances, trade secret protection can offer advantages over traditional patent protection. Thus, if an AI invention is unlikely to be reverse-engineered and may be particularly vulnerable to patentability challenges, trade secret protection should be considered. Trade secrets also have the added advantage of having an immediate effect (with no waiting time at patent offices) and provide potentially indefinite duration.

However, the trade secret protection is only maintained if the owner takes “reasonable measures” to keep it secret. This secrecy requirement may prove difficult for life sciences companies given the large number of individuals involved in discovery, regulatory, product development, and manufacturing activities.

With this regard, companies should proactively establish specific security measures such as: limit the disclosure of trade secret information to only those who need access to the information and require signed NDAs that identify the trade secret information to be protected before granting access to such information.

 

Conclusion

While AI uptake and application in the life sciences continue to increase, companies, investors, and patent professionals in the life science sector will face more complexity and uncertainty surrounding the protection and ownership of AI-related IP. It is foreseen that AI will also affect intellectual property rights, in particular patent rights and their management. This is likely to be a two-way process: on the one hand, AI developments will affect and be incorporated into IP rights management; on the other hand, IP policies and practices will interact with the strategy of managing innovation in AI. 

However, by using series of strategies, some of which are listed above, it can be hoped that the challenges to using artificial intelligence in the field of healthcare. can be minimized.

 

 

Some Articles used here:

Artificial Intelligence in the Life Sciences Industry — Strategies for IP Protection

The use of artificial intelligence in life sciences and the protection of the IP rights

Intellectual Property Strategies For Data And Artificial Intelligence - ThinkHouse

Artificial Intelligence: Challenges for Intellectual Property

 

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