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Assess patent novelty and non-obviousness against prior art. Use when user says "专利查新", "patent novelty", "可专利性评估", "patentability check", or wants to evaluate if an invention is patentable.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/ThomasMoreAI/legal-skills-open --skill patent-novelty-check命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| name | patent-novelty-check |
| title | Patent Novelty and Non-Obviousness Check |
| description | Assess patent novelty and non-obviousness against prior art. Use when user says "专利查新", "patent novelty", "可专利性评估", "patentability check", or wants to evaluate if an invention is patentable. |
| author | wanshuiyin |
| author_url | https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/patent-novelty-check |
| license | MIT |
| version | 0.1.0 |
| execution_mode | open |
| jurisdiction | us |
| practice | ip |
| language | en |
Assess patentability of: $ARGUMENTS
Adapted from /novelty-check for patent legal standards. Research novelty is NOT the same as patent novelty.
REVIEWER_MODEL = gpt-5.5 — Model used via Codex MCP for cross-model examiner verificationNOVELTY_STANDARD = patent — Always use legal patentability standard, not research contribution standard$ARGUMENTSpatent/PRIOR_ART_REPORT.md (output of /prior-art-search)patent/INVENTION_BRIEF.md if existsLoad ../shared-references/patent-writing-principles.md for novelty/non-obviousness standards.
Load ../shared-references/patent-format-us.md for 102/103 analysis framework.
From the invention description, extract the key claim elements that would define the invention's scope:
For each preliminary claim, test against EACH prior art reference in PRIOR_ART_REPORT.md:
Single-reference test: Does any single reference disclose ALL claim elements?
| Claim Element | Ref 1 | Ref 2 | Ref 3 | ... |
|---|---|---|---|---|
| Feature A | Yes/No + evidence | |||
| Feature B | Yes/No + evidence | |||
| Feature C | Yes/No + evidence | |||
| Feature D | Yes/No + evidence |
Verdict per reference:
If the invention is novel (passes Step 2), test for obviousness:
Two/three-reference combination test: Can 2-3 references be combined to render the claim obvious?
For each combination of the top references:
Format as a matrix:
| Combination | Primary | Secondary | Missing Elements | Motivation to Combine | Obvious? |
|---|---|---|---|---|---|
| Ref1 + Ref2 | Ref1 | Ref2 | Feature D | Same field, similar problem | Yes/No |
Call REVIEWER_MODEL via mcp__codex__codex with xhigh reasoning:
mcp__codex__codex:
config: {"model_reasoning_effort": "xhigh"}
prompt: |
You are a senior patent examiner at the [USPTO/CNIPA/EPO].
Examine the following invention for patentability.
INVENTION: [invention description + preliminary claims]
PRIOR ART: [prior art references with key teachings]
Please analyze:
1. Anticipation (novelty): Does any single reference anticipate any claim?
2. Obviousness: Can any combination of references render claims obvious?
3. Claim scope: Are the claims broad enough to be valuable?
4. Recommended amendments if any claim is rejected.
Be rigorous and cite specific references.
For each target jurisdiction, provide a patentability assessment:
Under 35 USC 102/103 (US):
Under Article 22 CN Patent Law (CN):
Under Article 54/56 EPC (EP):
Write patent/NOVELTY_ASSESSMENT.md:
## Patentability Assessment
### Invention Summary
[description]
### Overall Assessment
[PATENTABLE / PATENTABLE WITH AMENDMENTS / NOT PATENTABLE]
### Anticipation Analysis
[claim-by-claim matrix against each reference]
### Obviousness Analysis
[combination analysis with motivation to combine]
### Cross-Model Examiner Review
[summary of GPT-5.4 examiner feedback]
### Recommended Claim Amendments
[If claims need modification to overcome prior art, suggest specific amendments]
### Risk Factors
[What could cause rejection during actual prosecution?]
mcp__codex__codex is not available, skip cross-model examiner review and note it in the output.