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Patent Review

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wanshuiyin
patent-review

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.

Overview

Publisherwanshuiyin
RepositoryAuto-claude-code-research-in-sleep
Skill namepatent-review
Stars
16.3K
Forks
1.4K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by wanshuiyin on GitHub. Read the source before you install it.

Installation

Install the Patent Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git /tmp/Auto-claude-code-research-in-sleep
mkdir -p .claude/skills
cp -r /tmp/Auto-claude-code-research-in-sleep/skills/patent-review .claude/skills/patent-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Patent Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Patent Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Patent Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Patent Examiner Review via Codex MCP (xhigh reasoning)

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:
    bash
    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:

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:

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:

markdown
## 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.

Frequently asked questions

What does the Patent Review AI skill do?

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.

Why use Patent Review on TypingMind?

Because you install it once and use it with any model. Patent Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Patent Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/patent-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Patent Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Patent Review?

As many as you like. As long as a model supports skills, you can use Patent Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Patent Review AI skill free?

Yes. It is published on GitHub by wanshuiyin under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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