Intelligent Systems
Most valuable "AI inventions" today are not pure AI — they are larger systems in which machine learning is one component. Protecting them well requires counsel who understands the application, not merely the buzzword.
Many founders believe that using AI automatically means a patent is available. The reality after Alice Corp. v. CLS Bank and subsequent eligibility guidance is far more nuanced.
Strong protection comes from identifying the technical improvement — at the device level, the system level, the data-processing level, or in the hardware–software interaction — rather than simply claiming "use AI to do X." That kind of strategic counseling, grounded in both the medicine and the machine learning, is where experienced attorneys add the most value.
A patent attorney who understands only AI is becoming common. One who can also discuss medicine, devices, and electronics in the same meeting is not.
Where Models Meet Medicine
Computer vision in the OR, predictive analytics on implant data, decision support in the clinical workflow — protecting intelligent medical systems means understanding both the machine learning and the environment it operates in. That dual fluency is what makes the claims defensible.
Coverage
Diagnostic systems and decision aids that combine clinical data, imaging, and learned models to support detection and classification.
Image- and video-based analysis for digital pathology, radiology, surgical guidance, and visual monitoring applications.
Models that anticipate deterioration, failure, or risk — from implant monitoring to early-warning systems and population health.
Provider-facing tools, workflow systems, and LLM-powered applications that surface guidance within real clinical and operational settings.
Continuous monitoring and anomaly detection that turn sensor streams into actionable clinical signal.
Devices in which learned models are embedded in the product — surgical robotics, smart instruments, and connected therapeutics.
AI Is Included — But Not Leading
This is not a generic "AI practice." It is medical and technical counsel that happens to be genuinely fluent in machine learning — positioned for companies whose hardest problem is the domain, not the model.
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