How I Work
AI-assisted speed. A reviewer’s eye built from 7+ years of patent work.
A custom translation system designed for patents, so every project benefits from the system that came before it.
The Process
1. Project Setup
Terminology gets locked before translation starts, across three 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 feed forward — phrasings, term choices, sentence-level structure. Cross-filing consistency that compounds 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 MT. The draft already speaks patent language.
4. Automated Quality Checks
The system catches terminology drift, reference-sign mismatches, missing paragraphs, and numerical inconsistencies. The mechanical errors handled before I 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 read by me. I verify technical accuracy, validate terminology against your glossary, check claim scope, 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
The translation arrives with a notes file flagging issues in the source — 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. No use in AI training. AI processing runs on AWS Bedrock’s Japan-only regions (Tokyo and Osaka).
AWS infrastructure meets international security standards (including SOC 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)? Happy to walk through it before we start.
Comparison
| My Workflow | Generic MT (Google/DeepL) | ChatGPT (consumer) | |
|---|---|---|---|
| Patent-specific terminology | Yes (Glossary + TermCheck) | No | No |
| Human review | Every document | No | No |
| Translation notes | Yes — source issues + interpretation calls | No | No |
| Data used for training | Never | May be used | May be used |
| Data retention | None | Varies | Up to 30 days |
| Processing location | Japan only | Unknown/Global | Global |
| Terminology consistency | Enforced by system | None | None |