Metrics question
How will you build a patent system?
- Metrics
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
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What this question tests
Tests systems and metrics thinking for a complex internal workflow tool, defining what quality and throughput mean for it.
How to approach it
- Clarify the goal: a system to help Google's legal and engineering teams file, search and track patents more efficiently, not a public patent office replacement.
- Identify the users: inventors submitting disclosures, patent attorneys drafting filings, and legal ops tracking status across jurisdictions.
- Map the pain points: duplicate or overlapping disclosures, slow attorney review queues, and poor visibility into filing status across many countries.
- Design core features: a prior-art and duplicate-disclosure checker, an attorney review queue with SLA tracking, and a unified cross-jurisdiction status dashboard.
- Define success as time from disclosure to filing, attorney review turnaround time, and reduction in duplicate or rejected filings.
What a strong answer includes
- Correctly scopes this as an internal legal-engineering tool rather than a public patent system, avoiding a much larger scope.
- Names concrete workflow stages, disclosure, review, filing, and designs for each rather than a vague database idea.
- Picks cycle-time metrics, disclosure-to-filing, review turnaround, that map to real legal-ops pain.
Common mistakes
- Confusing this with building a public national patent office system, a much larger and different scope.
- Ignoring the attorney review bottleneck, often the real constraint in patent filing speed.
Likely follow-up questions
- How would you prioritize which inventions get filed first when legal capacity is limited?
- How would you handle cross-jurisdiction filing requirements differing by country?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop