What makes 睿观-外观专利检测 notable?

1K downloads. Trust evidence available

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睿观-外观专利检测

基于睿观的外观专利侵权检测，支持25+国家/地区的图片专利检索。当用户提到外观专利检测、专利侵权检查、专利风险分析、TRO案件查询、外观设计专利搜索、设计专利相似度、产品专利排查、design patent detection, patent infringement, design patent, TRO cases, patent risk, patent search, Ruiguan时触发此技能。即使用户未明确提及"外观专利"，只要其需求涉及检查产品图片是否可能侵犯已有的外观设计专利，或提到侵权、专利、TRO、外观专利等关键词，也应触发此技能。 Skill: 睿观-外观专利检测 Owner: linkfox-ai Summary: 基于睿观的外观专利侵权检测，支持25+国家/地区的图片专利检索。当用户提到外观专利检测、专利侵权检查、专利风险分析、TRO案件查询、外观设计专利搜索、设计专利相似度、产品专利排查、design patent detection, patent infringement, design patent, TRO cases, patent risk, patent search, Ruiguan时触发此技能。即使用户未明确提及"外观专利"，只要其需求涉及检查产品图片是否可能侵犯已有的外观设计专利，或提到侵权、专利、TRO、外观专利等关键词，也应触发此技能。 Tags: latest:1.0.7 Version history: v1.0.7 | 2026-09-14T05:23:23.257Z | user Update from 1.0.6 t

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.0.7

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.7release · observed Sep 14, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s171g8b6m2khwdy9ye8bxj0wx183vd4z:linkfox-ruiguan-detection-patent-design

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-linkfox-ai-linkfox-ruiguan-detection-patent-design/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

135,354 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: linkfox-ruiguan-detection-patent-design
description: 基于睿观的外观专利侵权检测，支持25+国家/地区的图片专利检索。当用户提到外观专利检测、专利侵权检查、专利风险分析、TRO案件查询、外观设计专利搜索、设计专利相似度、产品专利排查、design patent detection, patent infringement, design patent, TRO cases, patent risk, patent search, Ruiguan时触发此技能。即使用户未明确提及"外观专利"，只要其需求涉及检查产品图片是否可能侵犯已有的外观设计专利，或提到侵权、专利、TRO、外观专利等关键词，也应触发此技能。
---

# Ruiguan Design Patent Detection

This skill guides you on how to perform design patent infringement detection using the Ruiguan engine, helping e-commerce sellers and IP professionals identify potential design patent risks before listing products.

## Core Concepts

Design patent detection compares a product image against a global design patent database using visual similarity algorithms. The system returns patents ranked by similarity score, along with TRO (Temporary Restraining Order) litigation history, helping users assess infringement risk.

**Similarity score**: A value between 0 and 1. Higher values indicate greater visual resemblance to the patent drawing. Patents with `similarity >= 0.7` or those flagged with TRO enforcement history deserve special attention and should be reviewed carefully.

**Radar analysis**: When radar is enabled, each patent result includes a `radarResult` with a `same` flag (suspected infringement: true/false) and an `exp` field (explanation). This provides an AI-powered judgment beyond raw similarity.

**LOC classification**: The Locarno Classification (LOC) is an international system for categorizing industrial designs. You can optionally specify LOC codes to narrow the search scope, or leave it unset to let the model predict the appropriate categories automatically.

## Parameters Guide

| Parameter | Required | Default | Description |
|-----------|----------|---------|-------------|
| imageUrl | Yes | - | Product image URL to check against the patent database |
| queryMode | Yes | hybrid | Search mode: `physical` (real product photo), `line` (line drawing), `hybrid` (combined) |
| topNumber | Yes | 100 | Number of patent results to return (max 100) |
| regions | No | US | Country/region codes, comma-separated for multiple (e.g., `US,EU,CN`) |
| productTitle | No | - | Product title for supplementary context |
| productDescription | No | - | Product description for supplementary context |
| patentStatus | No | 1 | Patent validity: `1` (active), `0` (expired), `1,0` (both) |
| enableRadar | No | true | Enable AI radar analysis for suspected infringement judgment |
| topLoc | No | - | LOC level-1 codes to restrict search scope (e.g., `06,07`). Omit to use auto-prediction |
| sourceLanguage | No | - | Source language code for translation (e.g., `zh-CN`). Leave empty if text is already in English |

### Supported Regions

US (United States), EU (European Union), CN (China), JP (Japan), KR (South Korea), DE (Germany), GB (United Kingdom), FR (France), IT (Italy), AU (Australia), CA (Canada), BR (Brazil), MX (Mexico), IN (India), TH (Thailand), SE (Sweden), CH (Switzerland),

_meta.json

{
  "ownerId": "kn7dmc1t4j28hem1twwyav85p182pb1j",
  "slug": "linkfox-ruiguan-detection-patent-design",
  "version": "1.0.7",
  "publishedAt": 1789363403257
}

references/api.md

# 睿观-外观专利检测 API 参考

## 调用规范

- **请求地址**：`${LINKFOX_TOOL_GATEWAY}/ruiguan/detectionPatentDesign`
- **请求方式**：POST，Content-Type: application/json
- **认证方式**：Header `Authorization: <api_key>`，api_key 从环境变量 `LINKFOX_AGENT_API_KEY`（或 `LINKFOXAGENT_API_KEY`）读取（如未配置 按 SKILL.md 的 **## 解决认证和算力问题** 处理）

## 请求参数

POST Body（JSON）：

| 参数 | 类型 | 必填 | 默认值 | 说明 |
|------|------|------|--------|------|
| imageUrl | string | 是 | - | 产品图片文件URL，用于与专利数据库进行比对（最大1000字符） |
| queryMode | string | 是 | hybrid | 检索模式：`physical`（实物图检索）、`line`（线条图检索）、`hybrid`（混合检索）。最大1000字符 |
| topNumber | integer | 是 | 100 | 召回专利数量（最大100） |
| regions | string | 否 | US | 商品所售卖国家/地区代码，多选时用逗号隔开（如 `US,EU,CN`）。支持：US、EU、CN、JP、KR、DE、GB、FR、IT、AU、CA、BR、MX、IN、TH、SE、CH、IE、IL、DK、NZ、AT、BX、FI、WO。最大1000字符 |
| productTitle | string | 否 | - | 产品标题，用于补充检索上下文（最大1000字符） |
| productDescription | string | 否 | - | 产品描述，用于补充检索上下文（最大1000字符） |
| patentStatus | string | 否 | 1 | 专利有效性筛选：`1`（有效专利）、`0`（失效专利）、`1,0`（全部）。最大1000字符 |
| enableRadar | boolean | 否 | true | 是否启用雷达图（AI侵权判定分析） |
| topLoc | string | 否 | - | 指定检索的一级LOC范围（如 `06,07`）。格式：`^(0[1-9]\|1[0-9]\|2[0-9]\|3[0-2]\|ALL)(,(0[1-9]\|1[0-9]\|2[0-9]\|3[0-2]\|ALL))*$`。不指定时使用模型LOC预测服务的结果 |
| sourceLanguage | string | 否 | - | 原语言代码，需要标记以便统一翻译成英文（如 `zh-CN`）。文本为英语时传空即可。最大1000字符 |


## 响应结构

| 字段 | 类型 | 说明 |
|------|------|------|
| total | integer | 返回的专利记录总数 |
| data | array | 专利列表（详见下方专利对象） |
| columns | array | 渲染的列定义 |
| costToken | integer | 消耗token |
| type | string | 渲染的样式 |

### 专利对象（`data` 数组中的每个元素）

| 字段 | 类型 | 说明 |
|------|------|------|
| applicationNumber | string | 专利申请号 |
| publicationNumber | string | 专利公开号 |
| patentProd | string | 专利标题（英文） |
| patentProdCn | string | 专利标题（中文） |
| similarity | string | 专利与产品图片的相似度（0-1） |
| patentImageUrl | string | 与产品图片相似度最高的专利附图URL |
| images | array | 专利图片列表 |
| abstracts | string | 专利摘要 |
| specification | string | 专利说明书 |
| inventors | array | 发明人列表 |
| applicants | array | 申请人列表 |
| applicantAddresses | array | 申请人地址 |
| troCase | boolean | 是否有TRO维权史 |
| troHolder | boolean | 是否是TRO权利人的专利 |
| radarResult | object | AI雷达分析结果 |
| radarResult.same | boolean | 是否疑似侵权 |
| radarResult.exp | string | 预期描述（雷达判定说明） |
| patentLoc | string | 该专利的LOC分类（多个用逗号隔开） |
| locOneInfo | string | LOC一级详情 |
| locTwoInfo | string | LOC二级详情 |
| patentValidity | string | 专利有效性 |
| applicationDate | string | 专利申请日 |
| publicationDate | string | 专利公开日 |
| grantDate | string | 专利授权日 |
| estimatedDueDate | string | 预估到期日 |
| registrationOfficeCode | string | 专利注册受理局 |
| patentFamily | array | 同族专利列表 |
| globalPatentId | string | 全球专利ID |
| globalImageId | string | 专利图片的ID |
| isSketchText | string | 是否线稿图 |

## 错误码

正常情况下，接口的 HTTP 状态码均为 200，业务的成功与否通过响应体中的 errorCode 字段区分（errorCode = 200 表示成功，其他值表示业务错误）。当遇到未授权等情况时，HTTP 状态码为 401，且对应的 errorCode 也是 401。

| errcode | 含义 | 处理建议 |
|---------|------|----------|
| 200 | 成功 | 正常解析业务字段 |
| 401 | 认证失败 | HTTP 401 或 authorized error：按 SKILL.md 的 **## 解决认证和算力问题** 处理。 |
| 402 | 算力或余额不足 | HTTP 402：按 SKILL.md 的 **##

references/onboarding.md

# 解决认证和算力问题

调用本 skill 时若网关返回 **auth** 或 **billing** 错误，走本 skill 自带的 `scripts/onboarding.py` 完成引导。

**auth 场景**：`errcode=401` 或消息含 `authorized error`/`鉴权失败`/`未授权`/`unauthorized`；或 `LINKFOX_AGENT_API_KEY` 与 `LINKFOXAGENT_API_KEY` 均为空。
1. 若已配置 key → 先让用户重启会话（最常见误判），仍失败让用户重新取 key 或换手机号重注册
2. 未配置 → 询问：自助去 https://agent.linkfox.com/ 取 key，或提供手机号让脚本注册
3. 手机号路径：
   - `python scripts/onboarding.py send-code <phone>` → 展示 JSON 里的 phone/agreements
   - 收到验证码后：`python scripts/onboarding.py login <phone> <code>`
   - 拿到 `api_key` 后把下面三平台配置转发给用户，提示重启会话生效：
     - Windows PowerShell（永久）：`setx LINKFOX_AGENT_API_KEY "<key>"`
     - macOS zsh：`echo 'export LINKFOX_AGENT_API_KEY="<key>"' >> ~/.zshrc && source ~/.zshrc`
     - Linux bash：`echo 'export LINKFOX_AGENT_API_KEY="<key>"' >> ~/.bashrc && source ~/.bashrc`
     - 变量名 `LINKFOX_AGENT_API_KEY`（主推）或 `LINKFOXAGENT_API_KEY`（老规范）任一即可

**billing 场景**：`errcode=402` 或消息含 `算力/余额/quota/insufficient/充值/套餐到期`。
- `python scripts/onboarding.py list-plans` → 有 AskUserQuestion 就弹菜单，否则输出编号清单让用户选
- 校验 `plan_id` ∈ 清单、支付方式 ∈ 该套餐 `available_methods`（通常 `wechat/alipay`）
- `python scripts/onboarding.py order <plan_id> <method>` → 展示优先级 PNG > `pay_url` > `ascii_qr`（标注兜底）
- 已付款可选调 `python scripts/onboarding.py query <order_id>`，不主动轮询

排除 `errcode=403`（无权限，不归入这两类）。所有子命令输出 stdout JSON，`error` 字段已含阶段前缀，透传给用户即可。完整用法：`python scripts/onboarding.py --help`。

skill-card.md

## Description:

Checks product images with LinkFox Ruiguan design-patent detection to surface visually similar patents, TRO enforcement signals, similarity scores, and AI radar analysis across supported regions.

This skill is ready for commercial/non-commercial use.

## Publisher:

[linkfox-ai](https://clawhub.ai/user/linkfox-ai)

### License/Terms of Use:

MIT-0

## Use Case:

E-commerce sellers, IP professionals, and agents use this skill to check product image URLs or local product images for design-patent similarity, TRO enforcement signals, and region-scoped risk indicators before listing or reviewing products.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Local product images may be uploaded to a public URL for patent checking.

Mitigation: Use only images and product details that may be shared with LinkFox and potentially exposed through a public image URL; avoid confidential or regulated images.

Risk: API keys, account details, billing actions, or order data may appear in logs or transcripts during onboarding and payment flows.

Mitigation: Use onboarding and billing commands only when account management is intended, minimize transcript exposure, and rotate any API key that appears in logs or shared conversations.

Risk: Patent-check responses and sensitive product details are written to local session data files.

Mitigation: Review saved LinkFox data directories before sharing a workspace or transcript, and delete files that contain confidential product or account information when no longer needed.

Risk: Detection output can support risk assessment but is not legal advice.

Mitigation: Treat similarity scores, TRO signals, and radar analysis as screening indicators and consult a qualified IP attorney before making legal or commercial decisions.

## Reference(s):

- [ClawHub Skill Page](https://clawhub.ai/linkfox-ai/skills/linkfox-ruiguan-detection-patent-design)
- [LinkFox Publisher Profile](https://clawhub.ai/user/linkfox-ai)
- [Ruiguan API Reference](artifact/references/api.md)
- [Authentication and Billing Onboarding](artifact/references/onboarding.md)

## Skill Output:

**Output Type(s):** [text, markdown, code, shell commands, configuration, JSON, guidance]

**Output Format:** [Markdown guidance with JSON API parameters, shell commands, and saved JSON response files.]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Full API responses are saved to LinkFox session data directories; large responses are summarized unless inline output is requested.]

## Skill Version(s):

1.0.7 (source: server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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