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patent-novelty-check专利新颖性检查

Agent Skill

patent-novelty-check 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

791

周安装

32

GitHub Stars

7,799

下载量

248
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:patent-novelty-check(专利新颖性检查)
来源仓库:https://github.com/wanshuiyin/auto-claude-code-research-in-sleep
仓库路径:skills/patent-novelty-check
安装命令:
npx skills add https://github.com/wanshuiyin/auto-claude-code-research-in-sleep --skill patent-novelty-check
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/wanshuiyin/auto-claude-code-research-in-sleep --skill patent-novelty-check

简介

用于查找、检索和筛选相关信息。patent-novelty-check 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

  • 适合在专利新颖性判断中快速定位对比文献。
  • 通过 GitHub 安装,支持主流 AI 开发环境。
  • 建议在使用前核实来源仓库的维护情况。
  • 注意可能触发的网络请求或外部依赖。

SKILL.md

Patent Novelty and Non-Obviousness Check

Assess patentability of: $ARGUMENTS

Adapted from /novelty-check for patent legal standards. Research novelty is NOT the same as patent novelty.

Constants

  • REVIEWER_MODEL = gpt-5.4 — Model used via Codex MCP for cross-model examiner verification
  • NOVELTY_STANDARD = patent — Always use legal patentability standard, not research contribution standard

Inputs

  1. Invention description from $ARGUMENTS
  2. patent/PRIOR_ART_REPORT.md (output of /prior-art-search)
  3. patent/INVENTION_BRIEF.md if exists

Shared References

Load ../shared-references/patent-writing-principles.md for novelty/non-obviousness standards. Load ../shared-references/patent-format-us.md for 102/103 analysis framework.

Workflow

Step 1: Define Claim Elements

From the invention description, extract the key claim elements that would define the invention's scope:

  1. List the technical features that make the invention novel
  2. Identify which features are known from prior art vs. inventive
  3. Draft preliminary claim language for 2-3 independent claims (method + system)

Step 2: Anticipation Analysis (Novelty)

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 ElementRef 1Ref 2Ref 3...
Feature AYes/No + evidence
Feature BYes/No + evidence
Feature CYes/No + evidence
Feature DYes/No + evidence

Verdict per reference:

  • ANTICIPATED: One reference discloses every element → claim is not novel
  • NOT ANTICIPATED: At least one element missing from every single reference → claim is novel

Step 3: Obviousness Analysis (Inventive Step)

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:

  1. Primary reference: Which reference is closest to the claimed invention?
  2. Secondary reference(s): Which reference(s) teach the missing element(s)?
  3. Motivation to combine: Would a POSITA have reason to combine these references?

- Explicit suggestion in the references themselves? - Same field, same problem? - Common design incentive? - Known technique for improving similar devices?

Format as a matrix:

CombinationPrimarySecondaryMissing ElementsMotivation to CombineObvious?
Ref1 + Ref2Ref1Ref2Feature DSame field, similar problemYes/No

Step 4: Cross-Model Examiner Verification

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.

Step 5: Jurisdiction-Specific Assessment

For each target jurisdiction, provide a patentability assessment:

Under 35 USC 102/103 (US):

  • Novelty: PASS / FAIL (cite specific reference if fail)
  • Non-obviousness: PASS / FAIL (cite combination if fail)

Under Article 22 CN Patent Law (CN):

  • 新颖性 (Novelty): 通过 / 未通过
  • 创造性 (Inventive Step): 通过 / 未通过

Under Article 54/56 EPC (EP):

  • Novelty: PASS / FAIL
  • Inventive step: PASS / FAIL (problem-solution approach)

Step 6: Output

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?]

Key Rules

  • Patent novelty is absolute: any public disclosure before the priority date counts as prior art, worldwide.
  • Research novelty ("has anyone published this?") is NOT the same as patent novelty ("does any single reference teach every claim element?").
  • Obviousness requires BOTH: (1) a combination of references AND (2) a motivation to combine them.
  • Never assume the invention is patentable just because no identical patent exists.
  • The assessment is advisory only -- actual prosecution may reveal different prior art.
  • If mcp__codex__codex is not available, skip cross-model examiner review and note it in the output.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.99%
按下载量换算92

Claude

30.91%
按下载量换算77

Cursor

18.44%
按下载量换算46

Gemini CLI

10.39%
按下载量换算26

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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