Latest Articles & Insights
Original thinking on AI and patent work — white papers, essays, news, and the occasional strong opinion. Patent practice is changing quickly; we’re writing from inside the change.
Counting ROI or Chasing Hype: Stephanie Curcio on the True Test of AI in Patents
Stephanie Curcio Recognized in IAM Strategy 300 Global Leaders 2026
Choosing the Right AI for the Right Problem
Why generic AI misses the mark in patent work, and how a purpose-built platform delivers explainable results and roughly 90% time savings per project.
NLPatent Secures $3M to Help Patent Professionals Work Smarter, Faster, and with Greater Confidence
Draper Associates and Mighty Capital co-lead a $3M round to fund NLPatent's agentic platform for completing, not just supporting, patent workflows.
Introducing NLPatent Visualize
NLPatent Visualize turns any search or portfolio into an AI-generated topic map, surfacing white space, competitor clusters, and trends in seconds.
Say Hello to Ask NLPatent: Your AI Patent Sidekick
Ask NLPatent lets you chat with any patent document, while Bulk Ask runs one natural-language question across your entire result set at once.
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How NLPatent Was Used to Patent NLPatent
The story of how Potter Clarkson attorney Angus Gledhill used NLPatent itself to run prior art and draft the claims for NLPatent's own patent application.
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AI, Data, and the Enlightenment Era of IP Strategy
IP strategy is shifting from filing volume to AI-driven analysis, and the quality of those insights depends on how complete and structured the underlying data is.
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The End of the Keyword Era (Kinda)
Why keyword search still wins in some cases but breaks on patent language, and why true semantic search must be built from the ground up.
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Reinventing IP Workflow Without Reinventing the Wheel
Automation fails in patent work when it ignores how professionals actually work. Where AI adds value, and where domain expertise separates real tools from noise.
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The AI Terminology Mistakes Everyone Needs to Stop Making
A crash course on the real hierarchy between AI, ML, neural networks, deep learning, NLP, and LLMs, and why using the terms correctly matters.
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