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5/11 Making Generative AI Work for Patent Practice: Key Judgement Points in Patent Translation and Know-how in Patent Analytics

Banner image for the May 11 seminar

"If we let AI read the patent, it can handle the rest."

Or perhaps:

"Once we started using AI translation, quality review could no longer keep up."

If those statements sound familiar, this seminar is designed for you. As generative AI becomes embedded in patent practice, failures are accumulating quietly but steadily. The common problem on both the translation and analytics sides is the same: the output often looks good at first glance.

Why failures happen in patent work with AI

Generative AI is undeniably powerful. It can summarize large volumes of publications, expand keyword variants, and produce first-pass translations of foreign-language patents at remarkable speed.

But AI is an engine for producing plausible text, not guaranteed correctness.

In patent translation, close review can reveal legally significant errors hidden inside otherwise fluent output. In patent analytics, taking AI-generated classifications and summaries at face value can lead to misunderstandings of claim scope and business risk.

So how should teams use AI? What should be delegated to AI, and what must remain with people?

This seminar addresses those questions directly from two different practice settings.

Speakers and topics

Ayaka Fukuda (AI Patent Trans Inc.)

Topic: Judgement points in patent translation - how to protect translation quality in the AI era

The environment around patent translation has changed dramatically with the rapid evolution of generative AI. As AI becomes a powerful support tool, the translator's role is shifting toward deeper technical and legal verification.

AI output can look very strong on first review, but hidden issues emerge when examined carefully. To use it well, teams need to understand where those problems occur and how to catch them reliably.

This talk will share practical know-how, key cautions in AI-assisted patent translation, and internal quality management approaches drawn from real project work.

Yutaro Kamimura (LeXi/Vent / GrIP)

Topic: Know-how in patent analytics - designing AI use by risk level

Misconceptions such as "AI can build the patent map by itself" or "there is a magic prompt" are still common. At the same time, organizations that fail with AI adoption often repeat the same patterns.

This talk will explain how to separate the areas that should be assigned to AI from those that should not, and how to design AI-assisted workflows based on business risk. It will also cover prompt design to reduce hallucinations, AI-in-the-loop workflow patterns, and practical barriers such as tool selection, standardization, and internal guideline design.

Recommended for

  • Teams considering AI for patent translation or patent search but unsure how to manage quality
  • Practitioners who have started using LLMs such as ChatGPT or Claude and are unsure how far to trust them
  • Organizations where AI usage varies by individual and has not yet been standardized
  • Analysts considering what skills they need to build in the AI era
  • Anyone who wants to incorporate AI into patent translation and search work in a disciplined way

There is no magic prompt and no universal tool. What matters is workflow design and quality management rules that fit the work.

We invite you to join us for practical lessons drawn from both translation and analytics practice.

Date and time: May 11, 2026, 14:00-15:00
Organizer: AI Patent Trans Inc. x GrIP (LeXi/Vent)

Registration: Webinar registration page

For questions, please contact the GrIP editorial team at info@lexi2vent.com.