Szymon Pancewicz

AI can improve the quality and efficiency of patent work - but it can also create new confidentiality and privilege risks if you don’t control what data is shared, where it’s stored, and who can access it. The good news: you can turn “AI risk” into a repeatable review process that your leadership, IT/security, and risk teams can sign off on with confidence.

This guide gives you a practical framework and a due diligence checklist, that you can use to evaluate AI tools for patent workflows without compromising client confidentiality.

Key takeaways

• In patent work, confidentiality failures can jeopardise patent rights—treat inputs as high-risk.
• Risk is more than training: retention, access, logs, human review, and subprocessors matter.
• Use data tiers: Tier 0–1 OK; Tier 3 ‘default no’ unless explicitly approved and controlled.
• Make it auditable: approved use cases, human review, matter separation, and vendor diligence.

For further information, read the full guidance below.

The UK Supreme Court’s Emotional Perception decision moves UK practice closer to the EPO for computer implemented inventions, including AI. Claims with ordinary hardware will usually avoid the “computer program as such” exclusion, but only technical features can support inventive step. In practice, applicants should focus arguments and evidence on technical contribution and inventive step.

Key takeaways

1. UK moves closer to EPO, inventive step becomes the main battleground.
2. Ordinary hardware avoids exclusion, but may not support inventiveness.
3. Only technical features count at inventive step, not business aims.
4. Neural networks are treated as software, no special treatment either way.
5. Draft around technical contribution, measurable effects, and system level impact.