Can I get an IP address for my health technology AI?

Can I get an IP address for my health technology AI?

With the rise of investments in health technology AI, companies are looking for guidance on when and how to protect this work.

Andrew (AJ) Tibbetts, Greenberg Traurig

(Image courtesy of the FDA)

Can you get intellectual property (IP) for artificial intelligence (AI)? Absolutely. Should I file a patent application? Maybe, but there are alternatives. Should you consider data options? Yes. Is there a standard strategy for AI? Not if you want to see the value of your IP address.

The health technology industry is investing heavily in software engineering and data science as it increasingly develops decision support, medical telemetry, surgical navigation, and countless other medical software applications. Software investments naturally raise questions about how best to protect against copycats. It certainly makes sense to secure the intellectual property of your AI and software, protect your position in the market, or set partnership terms. But while protecting other technologies can be relatively simple, protecting AI labor can be complex. For companies leveraging IP to advance their business interests – rather than obtaining patents as trophies – a detailed discussion with an advisor about ideas, competitive market, business goals and product design is essential to gain defensible and valued AI protection.

When establishing an IP strategy for a health technology company or product, it is important to consider AI and software alongside other technologies and products. Work is copyrightable: Despite recent uncertainty regarding the patentability of software, both in Europe and the United States, major data processing cases have concluded that health technology software (e.g., processing of cardiac signal) were patent eligible.

In addition, intellectual property is valuable. In the summer of 2022, after prevailing on the validity and infringement of its AI-related patents in a lawsuit, a health technology company asked the United States International Trade Commission to block Apple Watch import. There are many examples of the successful application of trade secrets to medical data and software. Nowhere does the phrase “data is the new currency” hold truer than with high-quality anonymized data sets that health technology companies gather and leverage in product design. Particularly where a product’s hardware is backward or largely conventional, or runs on someone else’s hardware, obtaining intellectual property for AI, software and data can be crucial to your business.

However, in this context of high value and concrete protection, businesses face challenges. Some AI systems risk being labeled as mere unpatentable automations of pre-existing manual processes. Since many AI techniques used today (including “deep” learning) are decades old, there may be related earlier work. For back-end features, detecting violations can be tricky. And in some cases, specific AI techniques are short-lived and soon replaced by improved versions.

These obstacles should not discourage you from protecting AI, but rather serve as helpful guides towards value and away from waste. A knowledgeable IP lawyer here will analyze these factors along with the product details and business goals. They will craft a bespoke IP strategy, which can lead to moving away from patents and towards other IP, or trigger in-depth discussions about aligning a value or complement patent with a other intellectual property.

Often the focus is not on the AI ​​itself. Companies’ first thoughts are often of a specific pattern they have formed, but the exact pattern can be relatively short-lived. There will be recycling and structural improvements. Although the model chosen by your team works well, alternatives may be available, and it can be difficult to detect whether a competitor is using your model or another. For some high-value models, trade secrets may be appropriate, but the IP conversation may focus more on other aspects of the product workflow that interact with AI.

AI outputs typically drive downstream processing, leading to a diagnostic output, a control system change, a recommendation to a user, and so on. This workflow is fruitful ground for AI IP. While models evolve, AI-powered workflows can persist across product iterations. They may be more visible to customers and drive sales, and it may be possible to detect use by competitors, which may increase the value of patents. The data rights and licenses for these AI outputs are also of great value, especially in the context of the corresponding inputs. Input processes may be ripe for intellectual property, such as data curation and the pre-processing of raw data into informative features. These input-output contexts can provide important support for patent protection. While diagnostic methods can be difficult to protect, improved assays that rely on different characteristics or generate different results are patentable. It may also be possible to consider trade secret protection where the characteristics or processing are not visible. Access rights to input data can be invaluable, especially for training data obtained from partners. Such unpatented IP can be important when the AI ​​exactly mimics a pre-existing manual process.

Despite the growth and importance of AI and software in healthcare technology, investment protection remains uncertain. Given the technical and legal complexities, defensible and valuable IP protection relies on experienced counsel that tailors IP strategy through a deep understanding of the product and business.

Portrait photo of Andrew AJ Tibbetts at Greenberg Traurig

Andrew (AJ) Tibbetts, shareholder of Greenberg Traurig

Andrew (AJ) Tibbetts is a shareholder in the Intellectual Property and Technology Practice Group of the Boston office of Greenberg Traurig. A former software engineer, Tibbetts works with organizations large and small, sits on the board of MassMEDIC, and guides digital health efforts for MassBio.

The opinions expressed in this article are those of the author alone and do not necessarily reflect those of or its employees.

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