As companies pour resources into building the next generation of AI systems, securing patents on those innovations is becoming a strategic priority. But the current legal landscape for AI patents is complex — both courts and patent offices are scrutinizing claims more intensely, especially under U.S. patent-eligibility rules. The U.S. Court of Appeals for the Federal Circuit has made clear in recent decisions that simply applying machine learning in a new context isn’t enough; inventors must demonstrate technical improvements in the underlying system, not just in the results.
Under the well-known Alice framework, patent applicants face a two-step test. First, they must show that their invention isn’t just an abstract idea. Second, they must prove the claim adds something more — an “inventive concept” — that meaningfully improves how a machine works, rather than merely using AI to perform a generic task. Courts and the USPTO are increasingly focused on the second step, looking for innovations that change how a system operates (architecture, data flow, resource optimization) rather than simply what it achieves.
To navigate these challenges, the article recommends a few best practices for patent drafting. First, build a strong technical narrative: define a clear technical problem (e.g., latency, overfitting, inefficient data processing) and explain how your solution addresses it in a novel way. Then, describe your technological solution in detail — don’t treat the AI model as a black box; instead, highlight architectural tweaks, inventive training techniques, or new data-handling strategies.
Finally, it’s important to think more broadly than just patents. Depending on the nature of your innovation, trade secrets and contracts may offer more effective protection for certain AI assets, like proprietary data sets or internal tuning methods. A hybrid IP strategy — combining patents for structural innovations and secrecy for guardable processes — can be especially powerful in the rapidly evolving AI landscape.
