Digital Twins In Life Sciences: Patent Strategy Across Eligibility, Enforcement, And Architecture

Digital twins are no longer experimental engineering tools—they are becoming core components of medical devices, biomanufacturing systems, and AI-driven clinical platforms. A digital twin is a computer-based representation of a physical or biological system that continuously synchronizes with live data, updates a virtual state model, and predicts—or in some cases influences—physical-world behavior. In healthcare,¹ a surgeon may simulate a cardiac ablation procedure on a patient-specific digital twin to identify arrhythmia risks before performing the intervention.

Beyond procedural planning, digital twins are increasingly used to guide drug development, improve biomanufacturing, and enhance medical device performance.² Many implementations integrate artificial intelligence (“AI”) and machine learning (“ML”) to refine patient-specific simulations, generate virtual patient populations, and conduct large-scale “in silico” clinical trials without the need for live subjects.³

That predictive capability creates technical opportunity but also patent risk. At their core, digital twins rely on modeling, data analysis, and ML—areas that routinely attract patent eligibility challenges under 35 U.S.C. § 101. At the same time, these systems often operate across distributed architectures involving sensors, cloud computing, and medical instrumentation, raising difficult questions about detectability and divided infringement.

Companies investing in digital twin platforms must therefore think strategically about how their innovations are claimed and protected. The distinction between an abstract model and a patent-eligible technological improvement may turn on how the feedback loop is implemented, how data is processed, or how the system alters physical-world behavior. Organizations must also consider how to structure claims and specifications to capture the value of their modeling architecture and preserve enforceability in distributed, data-driven systems. This article outlines prosecution and enforcement strategies for securing durable patent protection in life sciences digital twin technologies.

A. Patent Eligibility: Avoiding § 101 Abstraction Pitfalls
As digital twin technologies mature within the life sciences sector, patent filings in this space have accelerated in the U.S. and abroad. Thousands of digital twin patents have issued in recent years, and continued investment suggests sustained growth in the sector.⁴ In the life sciences context, digital twin applications are frequently examined in Art Unit 3770⁵ and Art Unit 1610⁶, where eligibility and written description scrutiny can be particularly rigorous.

Because life sciences digital twins are typically implemented through software-driven modeling architectures that process dynamic biological data, they frequently sit at the intersection of computational analytics and medical application. Many systems also incorporate AI to refine simulations of organs, tissues, or drug‑response pathways in real time and to generate increasingly accurate patient‑specific or population‑level physiological models. This combination of data ingestion, modeling, and prediction—while technically sophisticated—can invite eligibility challenges if claims are framed at too high a level of abstraction.

Accordingly, applicants should ensure that draft claims do more than recite data collection, model updating, or iterative training. To withstand scrutiny under § 101, claims should articulate how the digital twin achieves a concrete technical improvement—either in the computational architecture itself or in the operation of a physical system through defined feedback mechanisms. For example, a patent directed to a digital twin of a patient's lung should articulate the particular modeling algorithms, physiological parameters, or adjustment methods that allow the system to replicate lung biomechanics, airflow patterns, or other pathology-driven challenges. These details help distinguish the invention from generalized modeling techniques and anchor the claim in specific technological implementation. Similarly, where a therapy device incorporates a digital twin, the claims may reference specific imaging inputs or treatment parameters that are updated based on the model's output. For example, the therapy device may adjust treatment intensity, output strength, or treatment location in response to predictions generated by the digital twin. Such integration with physical instrumentation can further support eligibility by tying the modeling architecture to a practical technological application.

Recent Federal Circuit decisions reinforce this distinction. In Recentive Analytics, the court held that patents directed to machine learning models were ineligible under 35 U.S.C. § 101 where the claims failed to recite a technological improvement.⁷ The court emphasized that describing models as “‘iteratively trained'” or “dynamically adjusted” does not, standing alone, demonstrate an inventive concept.⁸ Rather, the claims must explain how the software or machine learning architecture achieves a concrete technical advance.⁹ For digital twin innovations, this underscores the importance of articulating not only that a model is trained or updated, but how the specific computational techniques improve modeling performance or system functionality.¹⁰

USPTO guidance reflects a similarly nuanced approach. In Ex parte Desjardins, the Appeals Review Panel vacated a § 101 rejection upon determining that the claims recited improvements to machine learning technology.¹¹ The decision—authored in part by Director Squires—cautioned against evaluating AI-based claims at “a high level of generality” that reduces them to generic algorithms without adequately considering the claimed technological improvements.

For applicants in the life sciences digital twin space, the lesson is not that AI-based claims are insulated from eligibility scrutiny, but that clearly describing how the modeling architecture improves technical performance can meaningfully influence the analysis.

B. Designing for Enforcement: Detectability and Infringement Strategy
Even where eligibility hurdles are overcome, digital twin patents present distinct enforcement challenges, particularly when determining whether a competitor is using a patented modeling architecture. Detectability can be a central hurdle because many digital twin innovations rely on algorithmic or specialized modeling of human organs, organ systems, or cellular interactions used in drug development and testing. In the life sciences, many patented features relate to internal model behaviors, such as how a system simulates organ-level physiology, metabolic pathways, or patient-specific drug responses. These processes frequently occur within cloud-based or proprietary computational environments and are not externally visible to competitors or the public. To improve detectability, applicants should consider enforcement realities during the patent drafting stage and incorporate claim elements tied to observable system behavior. Such elements may include imaging-derived model parameters (e.g., measurements extracted from patient imaging data), integration with physical instrumentation through identifiable outputs or automatic device adjustments, or reciting a measurable output (e.g., treatment dose modulation). These claim structures can materially strengthen enforcement prospects for digital twin patents.

C. Building a Durable Digital Twin Patent Portfolio
As digital twin innovation accelerates in the life sciences sector, companies should approach patent prosecution as a portfolio-level strategy rather than a single application exercise. Because digital twin technologies often combine computational modeling, AI-driven refinement, and device-level integration, durable protection typically requires layered claim drafting supported by robust technical disclosure.

In addition to reciting concrete technical improvements, applicants should ensure that the specification comprehensively documents the digital twin lifecycle—including data ingestion methods, model update processes, AI or ML architectures, physiological parameters used in simulations, and unique system outputs. Detailed architectural disclosure not only supports § 101 eligibility and written‑description requirements, but also provides flexibility for continuation and divisional filings as digital twin systems evolve to incorporate new data sources, refinement techniques, or device integrations.

From an enforcement perspective, long-term durability also depends on claim architecture. As competitors develop parallel digital twin platforms, the ability to detect and prove infringement may hinge on how the claims are structured. While internal algorithmic innovations are often central to these systems, applicants should consider incorporating claim elements tied to observable inputs or outputs. At the same time, particularly in healthcare contexts, claims should emphasize computational architecture and physiological modeling techniques rather than patient-identifying clinical data, thereby preserving enforceability without requiring access to protected health data. Overall, digital twins are reshaping patient care, drug development, and biomanufacturing. Securing meaningful protection requires patent drafting that anticipates both eligibility scrutiny and the practical realities of enforcement in distributed, data-driven systems.

Footnotes

1. Digital twin architectures are also deployed across industries. For example, in infrastructure applications, embedded sensors may feed real-time strain data into a digital twin of a bridge to forecast structural fatigue and inform maintenance planning. See Adam, David. Digital twins come to the life sciences, Proceedings of the National Academy of Sciences of the United States of America vol. 122,49 (2025).

2. Id.

3. Id.

4. Nunwick, Alice, In data: digital twin patents exceeded 1,600 publications in 2023, Verdict (May 10, 2024) https://www.verdict.co.uk/in-data-digital-twin-patents-exceeded-1600-publications-in-2023/ (More than 3,000 digital twin patents were granted from 2016 to 2023); What is digital-twin technology, McKinsey & Company (August 26, 2024) https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-digital-twin-technology (Market predictors also indicate significant continued investment in the digital twin space, with McKinsey estimating digital twin investment will exceed $48 billion USD in 2026).

5. 3770 Medical & Surgical Instruments, Treatment Devices, Surgery and Surgical Supplies.

6. 1610 Pharmaceutical and Agricultural Compositions; Plant Growth Regulating Compositions: Bio-affecting, Body Treating, Drug Delivery, Steroids, Herbicides, Pesticides, Cosmetics, and Drugs; In vivo Diagnostic Agents.

7. See Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), cert. denied, No. 25-505, 2025 WL 3507020 (U.S. Dec. 8, 2025).

8. Id.  at 1212-1213.

9. Id.  at 1213.

10. Federal Circuit held that claims reciting “specific technical features or operations” including sensor operations are directed to patent-eligible subject matter. See CardioNet, LLC v. InfoBionic, Inc,  955 F.3d 1358, 1369 (Fed. Cir. 2020).

11. Ex parte Desjardins, Appeal 2024-000567 (Review Panel of the PTAB, Sept. 26, 2025).

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