How I Work — AI-Assisted, Human-Reviewed Patent Translation

A reviewer’s eye built from 7+ years of patent work — with AI accelerating everything except the judgment calls.

A custom translation system designed for patents, so every project benefits from everything the system has learned on the projects before it.

The Process

1. Project Setup

Terminology gets locked before translation starts, across 3 layers:

• Domain knowledge the system already carries from prior patent work
• Your glossary, if you have one — applied to every relevant occurrence
• Project-specific terms the system extracts from your source; I review them with you upfront

Whether you bring prior project assets or start from scratch, your terminology is locked in by day one.

2. Translation Memory Lookup

For repeat clients, your past filings carry forward into the next project — phrasings, term choices, sentence-level structure. Consistency builds with every project we do.

3. AI-Powered First Draft

Claude AI on AWS Bedrock, fed your project’s glossary and prior context — not generic machine translation (MT). The draft already carries your locked terminology, before I ever touch it.

4. Automated Quality Checks

The system catches terminology drift, internal reference-sign inconsistencies, missing paragraphs, and numerical inconsistencies in the source-to-draft comparison. Mechanical errors get handled before I ever read a draft.

5. AI-Assisted Review

Claude flags scope ambiguities, antecedent issues, and claim-spec misalignment for my attention. Risk-tagged so my time goes where judgment matters.

6. Human Review

This is the work. Every sentence is read by me. I verify technical accuracy, cross-check reference signs against your drawings (a step I keep in human hands, not the automated layer), validate terminology against your glossary, preserve claim scope in translation, and reconstruct intended meaning where the source is unclear or non-native.

Years of patent review experience applied to your translation — this is where the system meets a reviewer’s eye, and where the result becomes filing-ready.

With the drafting layer automated, I’m not piecing language together from scratch — I’m examining language that’s already in clean, consistent form. My attention goes where only a reviewer’s eye can go: scope ambiguities, intent gaps, and places where the source meant something the words don’t quite say.

7. Delivery with Translation Notes

I deliver a translation-notes document alongside the translation, listing source-text issues I flagged during review — original typos, reference-sign mismatches, and structurally ambiguous passages where I had to choose an interpretation. You see what I caught and how I read the ambiguous parts.

Terminology Management

A multi-sheet glossary system grows with every project. Client-specific terms locked to your preferences. Domain-specific sheets for medical devices, mechanical engineering, and electronics.

Translation memory captures every project, so consistency compounds across your filings.

Your 2nd project is more consistent than your 1st. Your 10th project benefits from everything that came before.

Confidentiality & Data Security

No data retention or training use by the AI model. AI processing runs on AWS Bedrock’s Tokyo region (ap-northeast-1), keeping your data in Japan.

AWS infrastructure meets international security standards (including SOC 2 and ISO certifications). All file transfers TLS-encrypted.

Mutual NDA on request — yours or mine.

Need the technical detail (specific certifications, Bedrock model identifier, region routing)? I’ll walk through it before we start.

Comparison

See Services for pricing and turnaround, or the FAQ for common questions. Ready to get started? →