Top 5 IP Considerations for Medtech Companies Transitioning To Data-enabled Product Solutions

Top 5 IP Considerations for Medtech Companies Transitioning To Data-enabled Product Solutions


Medtech companies are evolving rapidly as more and more of them develop products that collect and leverage substantial patient and provider data.

Companies that once only developed hardware-based solutions for medical problems are now evolving into data platform companies, offering a more comprehensive glimpse into the habits and health of their patients and customers. Many of these solutions leverage artificial intelligence (AI) and machine learning (ML), for which intellectual property tends to be more challenging to protect through traditional approaches.

Investor and strategic partner mindsets have shifted toward optimal ways to protect their intellectual property (IP)  requirements, especially given evolving laws and social concerns.

Having had decades of experience working with medtech companies, we have identified the top 5 IP considerations they should be aware of in their product development lifecycle. This is especially true for medtech-enabled data platforms

Medtech companies should consider the following IP protection strategies in the emerging fields of software as a medical service (SaMD), software in a medical device (SiMD), and AI in medical technologies.

1. How to strategically diversify IP protection

      • Patent the device and consider patenting the software it utilizes.
      • Alternatively, or additionally, consider protection of methods, AI/ML mechanisms and software as a trade secret.
      • Ensure non-compete and confidentiality agreements with employees are in place, to the extent possible given jurisdictional constraints.
      • Consider exclusive licensing of training/learning databases to exclude others from developing similar solutions using these databases.
      • Albeit narrower protection, copyright and trademark as appropriate.

2. Whether to patent or keep innovations as trade secret

Generally speaking, the ability to reverse engineer is the key inquiry in deciding if patent or trade secret is the appropriate mode of protection for a company’s data-enabled invention, along with the evolving laws around eligibility of patenting of such inventions (See IP Point 4, below).

      • Patenting must reveal details, which can be challenging when protecting data-enabled software that includes AI/ML aspects, broadly. Thus, patenting is best for devices and for protecting the interplay of physical products and software.
      • Trade secret requires you to maintain and protect secrecy indefinitely, but can be imitated if your idea garners attention. It is often best for software, manufacturing methods, or products that are costly or difficult to imitate.

3. If patenting, how to balance breadth and abstraction in patent claims to maximize protection

Claiming software uses necessary functional language to describe the invention… which can be too abstract for protection by patent law if at too high of a level. Nevertheless, patentees will always seek to broadly cover their invention, thus resulting in the tension that exists between breadth and abstraction levels in software patent drafting.

*What is Functional Language? – Functional language explains what the invention does rather than what it is.

      • Strike a balance in the claims between permissible breadth and amount of abstraction in the claim. Functional abstraction through pseudocode/native code should be covered in varying claim structures.
      • Be more detailed in the specification. Draft full descriptions of examples and use pictures to depict examples and describe both the functional abstraction level of detail through the pseudocode/native code in the specification.

4. Keeping up with the evolution of subject matter eligibility in patents

U.S. law does not allow patenting of abstract ideas** or laws of nature.*** In 2014, the U.S. Supreme Court broadened this concept, giving the U.S. Patent Office additional ways to reject applications for AI-based patents and for trial courts to invalidate them. This decision has made it more challenging to obtain software and AI-based patents in the United States. There are creative ways to use the evolving case law and to present inventions to fall outside of this prohibition, but it is important to remain diligent of the volatile state of the industry.

**Abstract ideas include (i) mathematical concepts, (ii) methods of organizing human activity and (iii) mental processes.

***Laws of nature include natural phenomena and products of nature;a discovery of something that is a natural law, not an invention.

5. Artificial intelligence considerations: Permission, assignments, copyleft and open source

      • Ensure you have permission to use the data.
      • Consider inventorship and get assignments from individuals who: (i) select the data acted upon by AI, (ii) review results or outputs of an AI engine, (iii) select ML algorithms used to train an AI model, and/or write the source code to implement AI.
      • Be careful about open source usage and avoid copyleft.

Photo: stocknshares, Getty Images



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