Introduction
AI is reshaping consumer health—powering personalized wellness platforms, predictive diagnostics, and digital therapies. Yet, as digital solutions multiply, so do the risks tied to intellectual property. Traditional IP systems—built for static, hardware-based inventions—are falling behind the pace of adaptive, software-driven innovation.
This gap exposes companies to imitation, regulatory conflict, and missed monetization opportunities. As regulatory expectations grow and digital health competition intensifies, business leaders must align AI initiatives with IP strategies designed to protect—and scale—what truly drives their advantage.
What's Changing—and Why It Matters
Consumer health is moving toward a connected, algorithm-driven model. AI tools now guide skincare routines, tailor nutrition plans, and support early symptom analysis. These solutions don't operate in a vacuum; they rely on proprietary data, evolving algorithms, and contextual insights—all of which need deliberate protection.
The challenge is that AI doesn't evolve in discrete steps—it learns continuously. This clashes with traditional IP timelines and frameworks, which assume inventions remain fixed. As a result, many companies fail to safeguard assets like training datasets, feature engineering processes, or user-derived insights.
At the same time, expansion into global markets brings legal complexity. Teams must navigate different IP regimes and data privacy expectations, often while collaborating with third parties. This challenge creates a need for sharper governance across product, legal, and data teams.
Signals from the Market
AI Adoption Is Gaining Momentum
In Q1 of 2025, AI-enabled startups raised $3.2 billion, capturing 60% of all digital health VC funding—up from 41% in Q1 2024—but that refers only to Q1 2025, not the full year 2023. This growth isn’t limited to startups. Bayer, Johnson & Johnson, and Nestlé Health Science are deploying AI across their digital offerings, making it a core driver of new product development.
Patents Reflect a Shift Toward Software-Data Hybrids
The number of AI-based patent applications in the medical technology (medtech) sector at the EPO nearly quadrupled from 2018 to 2022, reaching 2,771 medtech publications in 2022. This growth rate in AI-focused medtech patents consistently outpaced the growth rate of overall AI patent filings in that period. Many filings now focus on wearable-generated data, digital biomarkers, and algorithmic outputs—illustrating a shift from tangible products to data-powered platforms.
Trade Secrets Are Increasingly Targeted
Trade secret breaches pose a significant risk to life sciences companies, with industry and cybersecurity analyses indicating that a substantial portion of these companies have experienced such incidents in recent years. In many cases, the targets weren’t physical devices or final products—they were insights generated from AI models and the data pipelines behind them. This data underlines the need to broaden protection beyond patents alone.
Five Moves to Modernize Your IP Strategy
1. Expand the Definition of What Needs Protecting
IP audits should capture more than just patented inventions. Teams must assess and protect AI-specific assets such as:
- Labeled training datasets
- Feature extraction and engineering methods
- Prompt libraries used in generative models
- Proprietary model tuning based on user behavior
Not all of these merit patent protection, but they should be inventoried, tracked, and guarded through trade secrets, documentation, and selective filings.
2. Connect IP Processes to AI Development Workflows
IP can no longer sit downstream from product development. It must be embedded into AI build cycles. For example:
- Use provisional patents before major model updates
- Add IP reviews into your MLOps pipelines
- Treat explainability tools—like model transparency layers—as potentially protectable under software IP rules
This alignment ensures faster decisions on whether to file, publish, or retain as know-how.
3. Tailor IP Strategies to Local Market and Data Realities
AI models often rely on regional data patterns. What works in one country may not work—or be legal—in another. To reflect this:
- Adjust filings based on data localization laws
- Use utility models and design rights in jurisdictions with slower patent processing
- Prioritize IP filings in markets with a higher risk of imitation or regulatory complexity
A nuanced, country-specific strategy provides stronger, faster protection where it counts.
4. Build Cross-Functional Oversight
Capturing innovation isn’t just a legal task. A coordinated team is needed to manage IP in AI contexts. Key players include:
- Data scientists who understand what’s novel and replicable
- Legal teams fluent in AI and digital health compliance
- R&D managers documenting development milestones in real time
This structure reduces gaps between technical teams and IP workflows—and ensures valuable ideas don’t slip through the cracks.
5. Future-Proof Partnerships with Smarter IP Terms
Most consumer health companies collaborate with AI vendors, startups, or digital wellness platforms. These relationships must include:
- Joint ownership terms for co-developed tools or datasets
- Predefined rules on reusing models or outputs
- Clear licensing structures for shared or scalable digital assets
By addressing IP at the outset, companies avoid later disputes—and unlock new revenue opportunities.
To better illustrate how traditional IP strategies contrast with those optimized for AI-driven consumer health innovations, the following comparative matrix highlights key differences and necessary shifts in thinking.
|
Aspect
|
Traditional IP Approach
|
AI-Optimized IP Approach
|
|---|---|---|
|
Nature of Innovation
|
Fixed, tangible inventions (e.g., devices, drugs)
|
Dynamic, evolving algorithms and data-driven models
|
|
Protected Assets
|
Patents on physical products and discrete processes
|
Broader scope including training datasets, feature extraction, prompt libraries, and model tuning parameters
|
|
IP Process Timing
|
Post-development filings and protections
|
Integrated into AI development cycles (e.g., MLOps pipelines, provisional patents at updates)
|
|
Protection Mechanisms
|
Primarily patents and copyrights
|
Mix of patents, trade secrets, documentation, and rapid provisional filings
|
|
Global Strategy
|
Uniform patent filings, sometimes utility models
|
Tailored filings respecting data localization, regulatory environments, and market risks regionally
|
|
Cross-Functional Governance
|
Legal and R&D often operate separately.
|
Close collaboration among legal, product, data science, and R&D teams with real-time IP tracking
|
|
Partnerships/IP Terms
|
Broad contracts, less focus on joint IP ownership
|
Clear joint ownership, licensing, and reuse rules defined upfront with AI vendors and collaborators
|
|
Response to Continuous Learning
|
IP frameworks assume static inventions.
|
Ongoing IP review as models evolve; use of explainability tools as IP assets
|
|
Risk Management
|
Focus on patent enforcement and litigation.
|
Emphasis on trade secret protection and cybersecurity for data and model insights
|
|
Innovation Capture
|
Focus on invention disclosure at the filing date.
|
Continuous IP audit, including data and model artifacts throughout the AI lifecycle
|
A Clear Path Forward
Data and algorithms are now as central to consumer health as formulations and active ingredients once were. Yet many companies still rely on outdated IP assumptions that don’t account for the complexity of AI systems.
Now is the time to:
- Reassess existing IP portfolios with an AI lens
- Establish governance frameworks that integrate legal, product, and data teams
- Plan for cross-border and cross-functional readiness, especially as AI oversight tightens
Owning the intersection of software, data, and health innovation will be critical. With the right strategy, companies can secure—not just create—the value that AI makes possible.
