/prior-art-search

name: prior-art-search
description: Use when searching patents and technical literature for prior art, novelty, or patentability assessment.

Prior Art Search Skill

Search existing patents and technical literature to identify prior art for novelty and patentability assessment.

Overview

This skill integrates with multiple MCP servers (Google Scholar, USPTO, Semantic Scholar) to conduct comprehensive prior art searches across patents, academic papers, and technical documentation.

Usage

The skill is typically invoked by the `patent-landscape-analyst` agent during the RESEARCH stage of the patent workflow.

Basic Usage Pattern

// The skill is invoked through agent orchestration
// Agent: patent-landscape-analyst
// Input: topic keywords, technical domain
// Output: aggregated landscape report

Input Parameters

Required

• **query**: Search keywords and technical terms
• Example: `"homomorphic encryption privacy-preserving computation"`

Optional

• **searchScope**: Time range for results
• Default: Last 5 years
• Options: `1year`, `3years`, `5years`, `10years`, `all`

• **maxResultsPerSource**: Maximum results from each source
• Default: 5
• Range: 1-20

• **sources**: Which databases to query
• Default: All enabled MCP servers
• Options: `google_scholar`, `uspto_patent`, `semantic_scholar`, `cnipa_patent`, `patsnap_search`

• **jurisdiction**: Filter by patent jurisdiction
• Default: All jurisdictions
• Options: `CN`, `US`, `PCT`
• `EP` / `JP` are **not supported** (REQ-017): if the user asks for them, say so

explicitly, then proceed without a jurisdiction filter (or suggest `PCT` for international filings). Never pass an unsupported code downstream.

Output Format

Primary Output

**File**: `references/landscape_{topic_slug}.md`

Contains aggregated search results organized by:

• Patent references (with classification codes)
• Academic literature
• Technical standards
• Industry implementations

Secondary Output

**Files**: `references/{source}_{id}.md`

Individual evidence cards for each finding:

• Full citation
• Abstract/summary
• Relevance score
• Key technical features
• Novelty comparison notes

Examples

Example 1: Basic Prior Art Search

<!-- Invoked by patent-landscape-analyst -->

Input:
- Topic: "blockchain-based cross-border payment with privacy"
- Scope: Last 5 years
- Max results: 10 per source

Output:
references/landscape_blockchain-cross-border-payment.md
  - 8 relevant patents (USPTO, EPO, CNIPA)
  - 12 academic papers (Google Scholar, Semantic Scholar)
  - 3 technical standards (ISO, IEEE)

references/uspto_US10123456.md
references/cnipa_CN108234567.md
references/scholar_arxiv2023-12345.md
...

Example 2: Targeted Patent Search

Input:
- Query: "federated learning differential privacy medical data"
- Jurisdiction: CN
- Scope: 3 years
- Sources: uspto_patent, semantic_scholar

Output:
references/landscape_federated-learning-medical.md
  - 5 CN patents with IPC codes H04L29/06, G06N20/00
  - 8 academic papers from top conferences
  - Novelty gaps identified in medical-specific privacy

Example 3: Comprehensive Technical Search

Input:
- Query: "zero-knowledge proof identity authentication edge computing"
- Scope: All time
- Max results: 20

Output:
references/landscape_zkp-identity-edge.md
  Organized sections:
  1. Core patents (15 references)
  2. Academic foundations (25 papers)
  3. Implementation examples (8 systems)
  4. Novelty analysis summary

MCP Server Dependencies

Required MCP Servers

1. **google_scholar**

• Academic literature search
• Citation tracking
• Conference/journal papers

2. **uspto_patent** (optional but recommended)

• US patent database
• Patent classification lookup
• Full-text patent search

3. **semantic_scholar** (optional)

• Academic paper search with AI-powered relevance
• Citation graphs
• Influence metrics

4. **cnipa_patent** (recommended, required for CN jurisdiction)

• China National Intellectual Property Administration
• CN patent database with IPC classification
• Chinese patent full-text search

5. **patsnap_search** (recommended)

• 智慧芽 (Patsnap) patent + literature fusion search
• 2.1 billion+ global patent data across 174 patent offices
• Includes legal status, patent family, and citation data
• REST API + native MCP service (Streamable HTTP)

Configuration

MCP servers should be configured in `.claude/settings.json`:

{
  "mcpServers": {
    "google_scholar": {
      "command": "mcp-google-scholar",
      "args": []
    },
    "semantic_scholar": {
      "command": "mcp-semantic-scholar",
      "args": []
    },
    "patsnap_search": {
      "url": "https://connect.zhihuiya.com/mcp?apikey=YOUR_PATSNAP_MCP_KEY",
      "type": "streamableHttp"
    }
  }
}

**智慧芽 MCP Key 获取**： 1. 登录 https://open.zhihuiya.com/ 2. 在 API 密钥页面创建新的 MCP Key（格式 `sk-xxxxxxxxxxxx`） 3. 将 Key 填入 `url` 的 `apikey` 参数中

**注意**：智慧芽 MCP 使用 Streamable HTTP transport，不是传统的 stdio MCP。配置时需使用 `url` + `type: "streamableHttp"` 格式，而非 `command` + `args`。

Integration with Workflow

Stage: RESEARCH

1. User provides patent topic 2. `archimedes` routes to `patent-landscape-analyst` 3. Analyst invokes `prior-art-search` skill 4. Results written to `references/landscape_{topic_slug}.md` 5. Feature matrix written to `references/feature-matrix_{topic_slug}.md` 6. Problem map written to `references/problem-map_{topic_slug}.md` 7. Workflow advances to BRAINSTORM_R1

Outputs Used By

• `patentability-evaluator`: Assesses novelty against prior art
• `patent-innovation-architect`: Identifies gaps for innovation
• `patent-adversarial-examiner`: Challenges novelty claims

Performance Notes

• Search time: 30-90 seconds per query (depends on sources)
• Network required: MCP servers make external API calls
• Rate limits: Respect source-specific rate limits