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Agent Skill · 研究检索

professional-patent-agents(专业专利 Agent 人)

professional-patent-agents 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

professional-patent-agents 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为操作浏览器;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 13,673。

总安装 9,180周安装 375GitHub Stars 公开资料未说明下载 2,940核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.2

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:professional-patent-agents(专业专利 Agent 人)
来源仓库:https://github.com/bigpipihua/professional-patent-agents
安装命令:
openclaw skills install professional-patent-agents
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install professional-patent-agents

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

提供覆盖专利申请全流程的多代理专业支持服务。professional-patent-agents 属于研究检索类 Skill,可作为该场景下的辅助能力补充。适用于从技术挖掘到权利要求优化的专利全周期管理。根据用户想法或初稿,执行检索分析、撰写优化与质量审核。需确认技术细节准确性,重要法律声明应由专业代理人最终确认。

涉及知识产权申报时,建议结合官方指南与执业资质人员协同使用。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install professional-patent-agents;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要操作浏览器,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

professional-patent-agents 怎么安装?

在终端执行 openclaw skills install professional-patent-agents。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

professional-patent-agents 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

professional-patent-agents 需要什么权限?

权限级别是操作浏览器。该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

professional-patent-agents 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

professional-patent-agents 用的人多吗?

公开数据是累计安装 9,180 次,近一周安装 375 次,下载 2,940 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

professional-patent-agents 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
professional-patent-agents

专业专利 Agent 人 · 本页

9,180—操作浏览器MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
professional-patent-agents
version
1.0.2
description
|-
dependencies
skills
python
system

Professional Patent Agents Suite

License

MIT License

Copyright (c) 2026 BigPiPiHua

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


Core Positioning

ScenarioInputOutputGoal
Scenario 1User idea (vague description)Complete patent document + Search reportGrant rate + Inventiveness
Scenario 2User draft (existing document)Optimized patent documentGrant rate + Inventiveness
Scenario 3Agency feedback (search report/prior art)Optimization suggestions / Cancellation adviceDecision support

Boundary: Does not handle Office Action (OA) responses - leave that to professional patent agencies.


Dependencies

Required Skills

SkillPurposeInstall Command
tavily-searchAI-optimized searchclawhub install tavily-search
aminer-open-academicAcademic paper searchclawhub install aminer-open-academic

Python Dependencies

pip install requests python-docx

Agent List (9 Core Agents)

AgentRoleCore CapabilityPriority
tech-minerTechnology Mining ExpertIdea analysis, innovation extraction, technical disclosure framework⭐⭐⭐⭐⭐
prior-art-researcherPrior Art Search ExpertKeyword strategy + Multi-source search + Analysis⭐⭐⭐⭐⭐
inventiveness-evaluatorInventiveness Evaluation ExpertInventiveness analysis, risk scoring, grant rate prediction⭐⭐⭐⭐⭐
patent-drafterPatent Drafting Expert7-section drafting, Mermaid diagrams, document conversion⭐⭐⭐⭐⭐
claims-architectClaims ArchitectClaims design, scope optimization⭐⭐⭐⭐
patent-analystPatent AnalystDraft analysis, issue identification, optimization suggestions⭐⭐⭐⭐
patent-auditorPatent Audit ExpertQuality review, grant rate prediction, revision suggestions⭐⭐⭐⭐⭐
patent-value-appraiserPatent Value Appraiser5-dimension value assessment, market value estimation⭐⭐⭐⭐
patent-converterDocument Conversion ExpertMarkdown→Word, Mermaid diagram embedding⭐⭐⭐⭐

Workflows

Scenario 1: User Idea → Drafting + Search

flowchart TB
    subgraph S1[Phase 1: Tech Mining]
        M1[tech-miner<br/>Understand idea] ~~~ M2[Extract innovations] ~~~ M3[Disclosure framework]
    end
    
    subgraph S2[Phase 2: Search]
        R1[prior-art-researcher<br/>Keyword strategy] ~~~ R2[Multi-source search] ~~~ R3[Analyze results]
    end
    
    subgraph S3[Phase 3: Evaluation]
        E1[inventiveness-evaluator<br/>Inventiveness analysis] ~~~ E2[Risk scoring] ~~~ E3[Differentiation advice]
    end
    
    subgraph S4[Phase 4: Drafting]
        D1[patent-drafter<br/>Draft specification] ~~~ C1[claims-architect<br/>Design claims]
    end
    
    subgraph S5[Phase 5: Review]
        A1[patent-auditor<br/>Full review] ~~~ A2[Grant rate prediction]
    end
    
    S1 --> S2 --> S3 --> S4 --> S5

Trigger:

"Help me write a patent: [technical idea]"
"I have an idea and want to apply for a patent"

Output Files:

  • TECH_DISCLOSURE.md - Technical disclosure framework
  • KEYWORD_STRATEGY.md - Keyword strategy
  • PATENT_SEARCH_REPORT.md - Search report
  • INVENTIVENESS_REPORT.md - Inventiveness evaluation report
  • Patent-*.md - Complete patent document (7-section standard format)
  • PATENT_AUDIT_REPORT.md - Audit report (with grant rate prediction)
  • Patent-*.docx - Word document (auto-converted)

Scenario 2: User Draft → Optimization

flowchart TB
    subgraph P1[Phase 1: Analysis]
        A1[patent-analyst<br/>Parse draft] ~~~ A2[Identify issues] ~~~ A3[Extract keywords]
    end
    
    subgraph P2[Phase 2: Search]
        R1[prior-art-researcher<br/>Targeted search] ~~~ R2[Analyze results]
    end
    
    subgraph P3[Phase 3: Evaluation]
        E1[inventiveness-evaluator<br/>Inventiveness risk] ~~~ E2[Enhancement suggestions]
    end
    
    subgraph P4[Phase 4: Optimization]
        D1[patent-drafter<br/>Optimize specification] ~~~ C1[claims-architect<br/>Strengthen claims]
    end
    
    subgraph P5[Phase 5: Review]
        Q1[patent-auditor<br/>Full review] ~~~ Q2[Grant rate prediction]
    end
    
    P1 --> P2 --> P3 --> P4 --> P5

Trigger:

"Help me optimize this patent: /path/to/patent.md"
"Review this patent and provide optimization suggestions"

Output Files:

  • PATENT_ANALYSIS_REPORT.md - Analysis report
  • PATENT_SEARCH_REPORT.md - Search report
  • INVENTIVENESS_REPORT.md - Inventiveness evaluation report
  • PATENT_OPTIMIZATION_SUGGESTIONS.md - Optimization suggestions
  • Patent-*.md - Optimized patent document
  • PATENT_AUDIT_REPORT.md - Audit report

Scenario 3: Agency Feedback → Evaluation

Use Case: User has submitted to a patent agency, received a search report or prior art, needs to evaluate whether to continue optimizing or cancel the application.

flowchart TB
    subgraph F1[Phase 1: Read Feedback]
        R1[Read search report] ~~~ R2[Read prior art] ~~~ R3[Read original draft]
    end
    
    subgraph F2[Phase 2: Comparative Analysis]
        A1[inventiveness-evaluator<br/>Comparison] ~~~ A2[Identify conflicts] ~~~ A3[Assess differentiation space]
    end
    
    subgraph F3[Phase 3: Decision]
        D1{Inventiveness space?}
        D2[🟢 Sufficient<br/>Recommend optimization]
        D3[🟡 Limited<br/>User confirmation needed]
        D4[🔴 No space<br/>Recommend cancellation]
        D1 --> D2
        D1 --> D3
        D1 --> D4
    end
    
    subgraph F4[Phase 4: Optimization]
        O1[patent-drafter<br/>Targeted optimization] ~~~ O2[Strengthen differences] ~~~ O3[patent-auditor<br/>Final review]
    end
    
    F1 --> F2 --> F3
    D2 --> F4
    D3 -->|User confirms| F4
    D4 --> X[Output cancellation report]

Trigger:

"The agency gave me a search report, help me see if I can optimize: /path/to/report.pdf"
"Here's the prior art, help me evaluate if I need to modify the patent"
"The agency says there's risk, should I continue or cancel?"

Output Files:

  • AGENCY_FEEDBACK_ANALYSIS.md - Agency feedback analysis
  • DECISION_RECOMMENDATION.md - Decision recommendation (continue/cancel)
  • PATENT_OPTIMIZATION_SUGGESTIONS.md - Targeted optimization suggestions

Decision Criteria:

Inventiveness SpaceBasisRecommendation
🟢 SufficientCore features not disclosed, clear differentiation pointsContinue optimization, strengthen differences
🟡 LimitedSome features disclosed, need repositioningUser confirmation required
🔴 No spaceCore features already disclosed, cannot circumventRecommend cancellation or redesign

Agent Details

Tech Miner (Technology Mining Expert)

Identity: Dr. Li, 15 years of technology assessment and innovation mining experience

Trigger: When user provides vague idea

Output: Technical disclosure framework

Use tech-miner to analyze the technical idea:
[User description]

Prior Art Researcher (Prior Art Search Expert)

Identity: Dr. Chen, 15 years of patent search experience

Trigger: When search is needed

Output: Search report + analysis

Use prior-art-researcher to search:
Keywords: [keywords]
Technical field: [field]

Search Channels (Default):

PriorityChannelToolPurpose
1Tavilytavily-searchQuick search, technical overview
2AMineraminer-open-academicAcademic papers + patent database
3Google Patentsweb_fetchGlobal patent full text
4GitHubtavily site:Open source projects
5Tech blogstavily site:Technical articles

⚠️ Patent Database APIs Recommended:

Default channels may not be sufficient for accurate patent prior art search. For professional patent search, recommend users to connect patent database APIs:

DatabaseAPI TypeCoverageBest For
Google PatentsPublic API100+ officesGlobal search
USPTOPublic APIUS patentsUS details
EPO (Espacenet)Public APIEuropean patentsEP search
CNIPAPublic APIChinese patentsCN search
WIPOPublic APIPCT applicationsInternational
Lens.orgFree APIGlobal patentsAcademic research

ClawHub Skill Discovery:

# Always check ClawHub for patent search skills
clawhub search patent
clawhub search "prior art"

Inventiveness Evaluator (Inventiveness Evaluation Expert)

Identity: Dr. Zhao, former examiner, 12 years of evaluation experience

Trigger: After search completion

Output: Inventiveness evaluation report (with risk score)

Use inventiveness-evaluator to evaluate inventiveness:
Patent document: /path/to/patent.md
Search report: PATENT_SEARCH_REPORT.md

Patent Drafter (Patent Drafting Expert)

Identity: Patricia, 12 years of drafting experience, 92% grant rate

Trigger: After inventiveness evaluation passes

Output: Complete patent document (7 sections)

Use patent-drafter to draft patent:
Patent title: [title]
Technical disclosure: TECH_DISCLOSURE.md
Search report: PATENT_SEARCH_REPORT.md
Inventiveness evaluation: INVENTIVENESS_REPORT.md

Claims Architect (Claims Architect)

Identity: Claude, 1000+ claims experience

Trigger: Parallel participation during drafting phase

Output: Claims document

Use claims-architect to design claims:
Patent document: /path/to/patent.md
Core inventive points: [points]

Patent Analyst (Patent Analyst)

Identity: Dr. Zhang, 12 years of patent analysis experience

Trigger: First step in optimization scenario

Output: Analysis report + optimization suggestions

Use patent-analyst to analyze patent:
Patent document: /path/to/patent.md

Patent Auditor (Patent Audit Expert)

Identity: Judge Wu, former examiner, reviewed 3000+ applications

Trigger: After drafting/optimization completion

Output: Audit report (with grant rate prediction)

Use patent-auditor to audit patent:
Patent document: /path/to/patent.md

Patent Value Appraiser (Patent Value Appraiser)

Identity: Ms. Lin, 10 years of patent valuation experience, certified IP asset appraiser

Trigger: User needs to evaluate existing patent value (transfer, pledge, financing)

Output: Patent value assessment report (5-dimension radar chart + market value range)

Use patent-value-appraiser to evaluate patent value:
Patent title: [title]
Or patent number: [number]
Purpose: pledge financing / licensing negotiation / technology transfer / M&A transaction

5 Evaluation Dimensions:

DimensionWeightContent
Technical Value25%Innovation degree, technical complexity, substitution difficulty
Legal Value25%Claim breadth, stability, invalidation resistance
Market Value25%Application scenarios, market size, competitive alternatives
Economic Value15%Cost savings, revenue potential, licensing income
Strategic Value10%Supply chain position, barrier strength, negotiation leverage

Patent Converter (Document Conversion Expert)

Identity: Alex, document conversion expert, proficient in Markdown → Word conversion

Trigger: Auto-triggered after patent-auditor review passes

Output: Word document (.docx), with embedded Mermaid diagrams

Conversion Flow:

  1. Parse 7 sections of patent Markdown
  2. Extract Mermaid code blocks and render to PNG
  3. Use Pandoc to convert section content
  4. Fill into Word template at corresponding positions
  5. Embed images into document
  6. Output to same directory as source file

Dependencies:

ToolPurpose
PandocMarkdown → Word conversion
mmdc (mermaid-cli)Mermaid → PNG rendering
python-docxWord document operations

Standard Patent Template (7 Sections)

# [Patent Title]

## 1. Related Prior Art and Their Defects or Deficiencies
### 1.1 Description of Prior Art
### 1.2 Defects or Deficiencies of Prior Art

## 2. Technical Improvements to Overcome the Above Defects

## 3. Alternative Solutions for Technical Improvements

## 4. Detailed Embodiments of the Technical Solution
### 4.1 System Architecture
### 4.2 Signal Logic Relationships
### 4.3 Implemented Functions
### 4.4 Specific Implementation Steps
### 4.5 Embodiment 1
(Describe included components/modules and their connections, signal logic; implemented functions and specific implementation steps)
## 5. Advantages of This Proposal Over Prior Art
(Achieved technical effects)
## 6. Related Drawings
(Structure diagrams with labeled component/module names; flowcharts with clear steps and process directions)
## 7. Claims
(Optional)

Language Adaptation

The agents automatically detect and use the user's language for output.

User Input LanguageOutput LanguageTemplate Format
EnglishEnglish7-Section Standard (English)
中文中文7章节标准模板(中文)
Other languagesUser's language7-Section Standard (translated)

Chinese 7-Section Template (中文七章节模板)

# [专利名称]

## 一、相关的现有技术及现有技术的缺陷或不足
### 1.1 现有技术描述
### 1.2 现有技术的缺陷或不足

## 二、为克服上述缺陷本提案的技术改进点

## 三、技术改进点的其他替代方案

## 四、详细的技术方案具体实施例
### 4.1 系统架构
### 4.2 信号逻辑关系
### 4.3 实现功能
### 4.4 具体实现步骤
### 4.5 实施例一

## 五、本提案相对现有技术的优点

## 六、相关附图

## 七、权利要求书

Key Rules for All Languages

  1. 7-Section Structure — Must follow the standard template regardless of language
  2. No Executable Code — Use pseudocode or flowcharts instead
  3. Quantified Technical Effects — Always quantify improvements (e.g., "30% efficiency increase")
  4. Comparison Table — Include comparison with prior art
  5. Mermaid Diagrams — Use flowcharts and architecture diagrams

Output Files Summary

Scenario 1: User Idea → Drafting

FileContentPhase
TECH_DISCLOSURE.mdTechnical disclosure frameworkTech mining
KEYWORD_STRATEGY.mdKeyword strategySearch
PATENT_SEARCH_REPORT.mdSearch reportSearch
INVENTIVENESS_REPORT.mdInventiveness evaluation reportEvaluation
Patent-*.mdComplete patent documentDrafting
PATENT_AUDIT_REPORT.mdAudit report (with grant rate)Review

Scenario 2: User Draft → Optimization

FileContentPhase
PATENT_ANALYSIS_REPORT.mdAnalysis reportAnalysis
PATENT_SEARCH_REPORT.mdSearch reportSearch
INVENTIVENESS_REPORT.mdInventiveness evaluation reportEvaluation
PATENT_OPTIMIZATION_SUGGESTIONS.mdOptimization suggestionsOptimization
PATENT_AUDIT_REPORT.mdAudit report (with grant rate)Review

Scenario 3: Agency Feedback → Evaluation

FileContentPhase
AGENCY_FEEDBACK_ANALYSIS.mdFeedback analysisAnalysis
DECISION_RECOMMENDATION.mdDecision recommendationDecision
PATENT_OPTIMIZATION_SUGGESTIONS.mdOptimization suggestions (if chosen)Optimization

Patent Value Assessment

FileContent
PATENT_VALUE_REPORT.md5-dimension value assessment + market value range

Installation Location

skills/
└── professional-patent-agents/
    ├── SKILL.md
    ├── agents/
    │   ├── tech-miner/
    │   ├── prior-art-researcher/
    │   ├── inventiveness-evaluator/
    │   ├── patent-drafter/
    │   ├── claims-architect/
    │   ├── patent-analyst/
    │   ├── patent-auditor/
    │   ├── patent-value-appraiser/
    │   └── patent-converter/
    └── skills/
        └── continuous-learning/

Auto-Conversion Flow

Trigger Conditions

Auto-invoke patent-converter to convert Markdown to Word when:

ConditionRequirement
patent-auditor review resultPassed
Grant rate prediction≥ 60% (low/medium risk)
User confirmationHigh risk (< 60%) requires user confirmation

Conversion Flow

flowchart TB
    A[patent-auditor<br/>Review complete] --> B{Grant rate ≥ 60%?}
    B -->|Yes| C[Auto-invoke<br/>patent-converter]
    B -->|No| D[Prompt user confirmation]
    D -->|Confirm convert| C
    D -->|Don't convert| E[Output Markdown only]
    C --> F[Output .docx file]
    F --> G[Notify user<br/>Ready for agency submission]

Output Location

Source directory/
├── Patent-1-xxx.md      # Source Markdown
├── Patent-1-xxx.docx    # Converted output (same directory)
├── Patent-2-xxx.md
├── Patent-2-xxx.docx
└── ...

Innovation Mining & Notifications

Work Record Source

memory/
├── 2026-03-16.md    # Daily work records
├── 2026-03-17.md
├── 2026-03-18.md
└── ...

Innovation Mining Rules

  1. Extract technical points from work records: Code commits, architecture designs, problem solutions
  2. Combine with industry trends: Search for latest technical developments
  3. Evaluate grant rate: Prior art search + Inventiveness evaluation
  4. Selection criteria: Grant rate ≥ 65%, Low/Medium risk level

Notification Format

📢 Innovation Mining Report

📊 X potential innovations discovered this week:

1. 【Highly Recommended】A method for XXX
   - Grant rate prediction: 70-80%
   - Risk level: Low
   - Innovation: XXX

2. 【Recommended】A system for XXX
   - Grant rate prediction: 65-75%
   - Risk level: Medium
   - Innovation: XXX

📁 Detailed documents: /patent/weekly/

💡 Suggestion: Prioritize applying for patent #1

⚠️ Security & Installation Guide

System Dependencies

# 1. Install Pandoc (Markdown to Word conversion)
apt install pandoc

# 2. Install Node.js and mermaid-cli (Mermaid diagram rendering)
curl -fsSL https://deb.nodesource.com/setup_18.x | bash -
apt install nodejs
npm install -g @mermaid-js/mermaid-cli

# 3. Install Chromium (optional, for Playwright/Puppeteer)
playwright install chromium

Security Considerations

RiskDescriptionRecommendation
Puppeteer --no-sandboxRequired when running as root, reduces browser sandboxingRun in Docker container or non-root user
Network requestsDownloads PDFs and accesses third-party sitesRun in isolated environment
Default pathsConverter uses /root/workspace/patent/new as defaultVerify working directory before running

Recommended Usage

# Option 1: Non-root user (recommended)
python convert_patents.py /path/to/patents

# Option 2: Docker container (isolated environment)
docker run -v /path/to/patents:/data your-image python convert_patents.py /data

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*Professional Patent Agents Suite v1.0.2* *Author: BigPiPiHua* *Mail: 775262592@qq.com* *License: MIT* *Updated: 2026-03-31*

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