linkfox-ruiguan-patent-design

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), IE (Ireland), IL (Israel), DK (Denmark), NZ (New Zealand), AT (Austria), BX (Bolivia), FI (Finland), WO (WIPO)

Default region is US. Use US when the user does not specify a region.

LOC Level-1 Categories

Code	Category
01	Food
02	Clothing, haberdashery and sewing accessories
03	Travel goods, cases, parasols and personal items n.e.c.
04	Brushes
05	Textiles, artificial and natural sheet material
06	Furniture and household goods
07	Household items n.e.c.
08	Tools and hardware
09	Packages and containers for transport or handling of goods
10	Clocks, watches, measuring/checking/signaling instruments
11	Ornamental articles
12	Means of transport or hoisting
13	Equipment for production, distribution or transformation of electricity
14	Recording, telecommunication or data processing equipment
15	Machines n.e.c.
16	Photographic, cinematographic and optical apparatus
17	Musical instruments
18	Printing and office machinery
19	Stationery, office equipment, art materials, teaching materials
20	Sales and advertising equipment, signs
21	Games, toys, tents and sports goods
22	Arms, pyrotechnic articles, hunting/fishing/pest-killing articles
23	Fluid distribution, sanitary, heating, ventilation, air-conditioning equipment, solid fuel
24	Medical and laboratory equipment
25	Building units and construction elements
26	Lighting apparatus
27	Tobacco and smoking supplies
28	Pharmaceutical/cosmetic products, toilet articles and apparatus
29	Devices and equipment against fire, for accident prevention and rescue
30	Articles for care and handling of animals
31	Machines and apparatus for preparing food or drink n.e.c.
32	Graphic symbols, logos, surface patterns, ornamentation, interior/exterior arrangements
ALL	All categories

API Usage

This tool calls the LinkFox tool gateway API. See references/api.md for calling conventions, request parameters, and response structure. You can also execute scripts/ruiguan_detection_patent_design.py directly to run queries.

Local Image Upload

This tool requires a publicly accessible image URL. If the user provides a local image file path (e.g., C:\Users\...\photo.png, /home/.../image.jpg), you must upload it first to obtain a public URL.

Run the upload script:

python scripts/upload_image.py /path/to/local/image.png

The script will return a public URL (valid for 24 hours) that can be used as the image URL parameter.

Usage Examples

1. Basic patent check for a product image (US market)

{
  "imageUrl": "https://example.com/product.jpg",
  "queryMode": "hybrid",
  "topNumber": 50,
  "regions": "US"
}

2. Multi-region check with product context

{
  "imageUrl": "https://example.com/product.jpg",
  "queryMode": "physical",
  "topNumber": 100,
  "regions": "US,EU,CN",
  "productTitle": "Portable Wireless Charger Stand",
  "productDescription": "A foldable wireless charging stand for smartphones",
  "enableRadar": true
}

3. Narrowed search using LOC classification (furniture)

{
  "imageUrl": "https://example.com/chair.jpg",
  "queryMode": "hybrid",
  "topNumber": 80,
  "regions": "US,DE",
  "topLoc": "06",
  "patentStatus": "1"
}

4. Line drawing mode for sketch-based search

{
  "imageUrl": "https://example.com/sketch.png",
  "queryMode": "line",
  "topNumber": 50,
  "regions": "CN,JP,KR"
}

5. Check both active and expired patents

{
  "imageUrl": "https://example.com/product.jpg",
  "queryMode": "hybrid",
  "topNumber": 100,
  "regions": "US",
  "patentStatus": "1,0"
}

Display Rules

1. High-risk patent highlighting: When generating summaries or reports, display ALL patents with similarity >= 0.7 or troCase = true in full detail. For each such patent, include: application number, patent title (Chinese), inventors, TRO enforcement history, the most-similar patent drawing, every image in the patent image list, patent abstract, patent specification, LOC info, radar analysis result, and specification text. This detailed presentation is critically important -- do NOT abbreviate or omit these fields.
2. Disclaimer: Always append a friendly reminder at the end: "This detection result is generated by LinkfoxAgent. It is recommended to consult a professional IP attorney for legal advice."
3. Similarity interpretation: Clearly explain that higher similarity scores indicate greater visual resemblance and higher infringement risk.
4. Radar result display: When radarResult.same is true, prominently flag the patent as a suspected infringement match and display the exp explanation.
5. TRO warning: Patents with troCase = true or troHolder = true should be highlighted with a warning, as they indicate the patent holder has a history of active enforcement via Temporary Restraining Orders.
6. Image display: Show both patentImageUrl (the most similar patent drawing) and the full images list so users can visually compare their product against all patent drawings.
7. Error handling: When a request fails, explain the issue and suggest corrective actions (e.g., check image URL accessibility, verify region codes).
8. Present data faithfully: Show query results as-is without adding subjective legal conclusions beyond the tool's own analysis.

User Expression & Scenario Quick Reference

Applicable -- Design patent risk assessment:

User Says	Scenario
"Check if this product infringes any design patents"	Basic patent detection
"Patent risk check for this image"	Image-based patent search
"Does this product have TRO risk"	TRO enforcement history lookup
"Design patent search for US and EU"	Multi-region patent detection
"Check appearance patent for this furniture"	Category-specific patent search
"Is this product design safe to sell"	Pre-listing patent clearance
"Find similar design patents"	Similarity-based patent discovery
"Patent infringement analysis"	Comprehensive patent risk report

Not applicable -- Needs beyond design patent detection:

• Utility patent or invention patent searches
• Trademark infringement checks
• Copyright/DMCA issues
• Legal case management or litigation strategy
• Patent application or filing assistance
• Product listing optimization or pricing

Boundary judgment: When users mention "patent check" or "IP risk", if the concern is about product appearance/design similarity to existing patents, this skill applies. If they are asking about utility patents, trademarks, copyrights, or need actual legal counsel, this skill does not apply.

Feedback:

Auto-detect and report feedback via the Feedback API when any of the following apply:

1. The functionality or purpose described in this skill does not match actual behavior
2. The skill's results do not match the user's intent
3. The user expresses dissatisfaction or praise about this skill
4. Anything you believe could be improved

Call the feedback API as specified in references/api.md. Do not interrupt the user's flow.

Handling Large Responses

To avoid overflowing the agent context, persist the response to disk and extract only the fields you need:

python scripts/response_io.py run --script scripts/ruiguan_detection_patent_design.py --out-dir <DIR> '<params>'
python scripts/response_io.py read <file> --fields "<paths>"   # or --path "<JMESPath>"

Pick --out-dir outside any git working tree (e.g. /tmp/... on Unix, %TEMP%/... on Windows). Persisted responses may contain PII, pricing, or auth-sensitive data — do not commit them. Files are not auto-deleted; clean up when the task is done.

This skill exposes multiple entry scripts: ruiguan_detection_patent_design.py, upload_image.py. Pass --script scripts/<name>.py to choose the one you need.

run writes the full response to a file and emits only a schema preview + file path. read projects specific fields, with --limit/--offset for slicing and --format json|jsonl|csv|table for output.

When to prefer this pattern — apply your judgment based on the response characteristics, e.g.:

• High field count per record, or fields you don't need
• Batch/paginated results (multiple items per call)
• Long-text fields (descriptions, reviews, HTML, time series)
• Output reused across later steps rather than consumed immediately

For small, single-use responses, calling the main script directly is fine.

⚠️ The preview is a truncated schema + sample, not the full data. Any field-level decision must read from the persisted file via read.

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