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patent-review

Review patent claims and specifications with an AI acting as a patent examiner. It identifies clarity, novelty, and disclosure issues, applies revisions, and conducts a second review, producing a report of changes and remaining concerns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Get an external patent examiner review of a patent application. Use when user says "专利审查", "patent review", "审查意见", "examiner review", or wants critical feedback on patent claims and specification.

실행 점수 미검증

설치 방법 보기

이럴 때 유용합니다

  • Check claims and specifications before filing
  • Find unclear terms and missing technical explanations
  • Review novelty against known prior art
  • Recheck amendments and document remaining issues

포함된 파일(1)

  • SKILL.md7.7 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Patent Examiner Review via Codex MCP (xhigh reasoning)

Before each reviewer slot, follow Selectable Reviewers: resolve invocation → .aris/reviewer.json → Claude Code default (Codex MCP) with reviewer_route.py through the canonical helper resolver, preserving this slot's tier, fresh contexts, source/image inputs and deterministic gates. Codex examples/pins below apply only to backend=codex; other routes use their own first-call/reply contract. Save the route and actual returned identity; unavailable selections are REVIEW_UNAVAILABLE. Classify acceptance from actual families/completion/scope. Forward invocation reviewer/model/effort overrides to reviewer-bearing children. Generation workers are unchanged.

Get a multi-round patent examiner review of the patent application based on: $ARGUMENTS

Adapted from /research-review. The reviewer persona is a patent examiner, not a paper reviewer.

Constants

  • REVIEWER_MODEL = gpt-6-astra — Model used via Codex MCP
  • REVIEW_ROUNDS = 2 — Number of review rounds
  • EXAMINER_PERSONA = "patent-examiner" — GPT-6-Astra persona

Prerequisites

  • Codex MCP Server configured:
    claude mcp add codex -s user -- python3 "$HOME/aris_repo/mcp-servers/codex-exec/server.py"   # your ARIS clone's path
    

Inputs

  1. patent/CLAIMS.md — all drafted claims
  2. patent/specification/ — all specification sections
  3. patent/figures/numeral_index.md — reference numeral mapping
  4. patent/PRIOR_ART_REPORT.md — known prior art
  5. patent/INVENTION_DISCLOSURE.md — invention structure

Workflow

Step 1: Gather Patent Context

Before calling the external reviewer, compile a comprehensive briefing:

  1. Read all claims (independent + dependent)
  2. Read specification sections (at least summary and detailed description)
  3. Read prior art report for context
  4. Identify: core inventive concept, claim scope, known prior art, target jurisdiction

Step 2: Round 1 — Full Examiner Review

Send to REVIEWER_MODEL via mcp__codex__codex with xhigh reasoning:

Resolve this reviewer slot first; the following is the backend=codex example.

mcp__codex__codex:
  model: gpt-6-astra
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    You are a senior patent examiner at the [USPTO/CNIPA/EPO].
    Examine this patent application and issue a detailed office action.

    CLAIMS:
    [all claims]

    SPECIFICATION SUMMARY:
    [key sections: title, technical field, background, summary, abstract]

    PRIOR ART KNOWN:
    [prior art references]

    PATENTABILITY STANDARDS TO APPLY:
    [US: 35 USC 101/102/103/112 | CN: Articles 22, 26 | EP: Articles 54, 56, 83, 84]

    Please issue an office action covering:

    1. CLAIM CLARITY (112(b)/Art 84):
       - Are all terms definite?
       - Any indefinite functional language?
       - Antecedent basis issues?

    2. WRITTEN DESCRIPTION (112(a)/Art 83 first para):
       - Does the spec support ALL claim scope?
       - Any claim elements without spec support?

    3. ENABLEMENT (112(a)/Art 83):
       - Can a POSITA practice the invention?
       - Any missing algorithm/structure for functional claims?

    4. NOVELTY (102/Art 54):
       - Would any known reference anticipate any claim?
       - Identify the closest single reference.

    5. NON-OBVIOUSNESS (103/Art 56):
       - Would any combination render claims obvious?
       - What is the motivation to combine?

    6. CLAIM SCOPE:
       - Are independent claims broad enough to be commercially valuable?
       - Do dependent claims provide meaningful fallback positions?
       - Any claims that are too broad (likely rejected) or too narrow (not valuable)?

    7. SPECIFICATION QUALITY:
       - Language issues (subjective terms, relative terms, result-to-be-achieved)
       - Reference numeral consistency
       - Missing embodiments

    Format your response as a formal office action with:
    - GROUNDS OF REJECTION for each issue (cite statute)
    - SUGGESTED AMENDMENTS for each issue
    - OVERALL PATENTABILITY SCORE: 1-10

    Be rigorous and specific. This is a real examination.

Step 3: Implement Fixes (Round 1)

Based on the examiner's office action:

  1. CRITICAL issues (102 rejection, 112 indefiniteness, missing enablement):

    • Must be fixed before proceeding
    • Amend claims or add specification support
  2. MAJOR issues (103 obviousness, weak claim scope, missing support):

    • Should be fixed or argued
    • Consider claim amendments or specification additions
  3. MINOR issues (language quality, numeral consistency, formatting):

    • Fix if time permits
    • Document in output for later cleanup

For each fix:

  • Show the specific change (old claim -> new claim)
  • Explain how the fix addresses the examiner's concern

Step 4: Round 2 — Follow-Up Review

Use mcp__codex__codex-reply with the threadId from Round 1:

Resolve this reviewer slot first; the following is the backend=codex example.

mcp__codex__codex-reply:
  threadId: [from Round 1]
  # inherits the thread's model/effort — do not re-send
  prompt: |
    Here is the revised patent application after addressing your office action.

    CHANGES MADE:
    [list of all changes with rationale]

    REVISED CLAIMS:
    [updated claims]

    REVISED SPECIFICATION EXCERPTS:
    [changed sections]

    Please re-examine:
    1. Are the previous rejections overcome?
    2. Are there new issues introduced by the amendments?
    3. What is the updated patentability score?
    4. Any remaining grounds for rejection?

Step 5: Generate Improvement Report

Write patent/PATENT_REVIEW.md:

## Patent Review Report

### Application Summary
[Title, claims count, jurisdiction]

### Review Round 1
#### Office Action Summary
[Key findings from examiner]

#### Issues Found
| # | Type | Severity | Claim/Section | Issue | Citation | Fix Applied |
|---|------|----------|--------------|-------|----------|-------------|
| 1 | Clarity | CRITICAL | Claim 3 | Indefinite term "rapid" | 112(b) | Defined in spec |
| 2 | Novelty | MAJOR | Claim 1 | Ref X anticipates element C | 102 | Amended claim |

#### Score After Round 1: [X]/10

### Review Round 2
#### Follow-Up Assessment
[Are previous rejections overcome?]

#### Remaining Issues
[Any issues still outstanding]

#### Score After Round 2: [X]/10

### Recommendations
[Final recommendations before proceeding to jurisdiction formatting]
- [ ] All CRITICAL issues resolved
- [ ] All MAJOR issues resolved or argued
- [ ] Specification supports all claim amendments
- [ ] Ready for jurisdiction formatting

Key Rules

  • The reviewer persona must be a patent examiner, not a paper reviewer or academic.
  • Always use model_reasoning_effort: "xhigh" for maximum analysis depth.
  • Address CRITICAL and MAJOR issues before proceeding to the next phase.
  • Document all changes in the review report for traceability.
  • If the patentability score is below 5/10 after Round 2, recommend significant rework before filing.
  • The review is advisory -- actual prosecution may proceed differently.

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alphaxiv

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Compare machine learning experiment results from JSON or CSV files. Calculate improvements over a baseline, summarize variation across repeated runs, identify trends and unusual results, and suggest experiments to test possible explanations.

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Search arXiv papers by topic or paper ID, review authors and abstracts, and summarize contributions stated in the abstracts. Download selected papers or all search results as PDFs to build a local paper library.

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