/prior-art-search

name: prior-art-search
description: Systematic 7-step methodology for comprehensive patent prior art searches and patentability assessments using BigQuery and CPC classification
tools: Bash, Read, Write
model: sonnet

Prior Art Search Skill

Systematic 7-step methodology for comprehensive patent prior art searches and patentability assessments.

When to Use

Invoke this skill when users ask to:

• Conduct prior art search for an invention
• Assess patentability of an idea
• Perform freedom-to-operate analysis
• Find blocking patents
• Research patent landscapes
• Prepare for patent filing

What This Skill Does

Implements a professional 7-step prior art search methodology combining:

• Keyword searches across 100M+ patents (BigQuery)
• CPC classification searches
• USPTO API searches
• Timeline analysis
• Patentability assessment
• IDS (Information Disclosure Statement) preparation

The 7-Step Methodology

Step 1: Invention Definition (2-3 min)

**Goal**: Extract key features and define innovation scope

**Process**: 1. Interview user about invention 2. Extract core technical elements 3. Identify novel features 4. List all components/steps 5. Define search scope

**Output**: Structured invention summary with key features

**Questions to Ask**:

• What problem does this solve?
• What are the key components/steps?
• What makes this different from existing solutions?
• What is the core innovation?

---

Step 2: Keyword Strategy (2-3 min)

**Goal**: Develop comprehensive search keyword list

**Process**: 1. Primary keywords from invention 2. Synonyms and variations 3. Technical terminology 4. Industry-specific terms 5. Synonym sets to run as separate searches

**Query syntax (important):** the keyword search splits a query into terms and requires **every term** to appear (AND), each matching the title, abstract, or claims (results are ranked by relevance, title hits weighted highest). It does **not** parse Boolean operators — `AND`/`OR`/parentheses are ignored. So:

• Use plain space-separated terms for the AND set: `blockchain authentication`.
• Wrap a phrase in double quotes to require it verbatim: `"distributed ledger"`.
• For OR / synonyms, run **separate searches** and merge the results — don't put

`OR` in one query.

**Output**: Keyword search strategy document

**Example**:

Primary: blockchain authentication
Synonyms: distributed ledger verification, cryptographic authentication
Technical: public key infrastructure, digital signature
Related: decentralized identity, trustless verification
Searches (run each separately, then merge/dedupe):
- blockchain authentication
- "distributed ledger" verification
- cryptographic authentication
- "public key infrastructure" signature

---

Step 3: Broad Keyword Search (3-5 min)

**Goal**: Cast wide net to find relevant patents

**Process**: 1. Run keyword searches on BigQuery 2. Review top 20-30 results per query 3. Identify most relevant patents 4. Refine keyword strategy based on results 5. Document relevant patents found

**Code**:

from mcp_server.bigquery_search import BigQueryPatentSearch
searcher = BigQueryPatentSearch()

results = searcher.search_by_keywords(
    query="blockchain authentication",  # terms AND-matched, ranked by relevance
    limit=30,
    country="US",
    start_year=2015,  # filing year; look back 5-10 years
)

**Output**: List of 10-20 potentially relevant patents

---

Step 4: CPC Code Identification (2-3 min)

**Goal**: Find relevant classification codes

**Process**: 1. Extract CPC codes from relevant patents found in Step 3 2. Analyze CPC code descriptions 3. Identify primary classification areas 4. Select 3-5 most relevant CPC codes 5. Note CPC hierarchies

**Common CPC Categories**:

• **G06F**: Computing/data processing
• **H04L**: Digital communication/networks
• **G06Q**: Business methods
• **H04W**: Wireless communication
• **G06N**: AI/neural networks
• **G06T**: Image processing

**Output**: List of relevant CPC codes with descriptions

---

Step 5: Deep CPC Search (5-10 min)

**Goal**: Comprehensive search within classifications

**Process**: 1. Search each CPC code identified 2. Review 50-100 patents per CPC code 3. Read abstracts and claims of top matches 4. Document closest prior art 5. Note key differences from invention

**Code**:

results = searcher.search_by_cpc(
    cpc_code="G06F21/",  # Security arrangements
    limit=100,
    country="US"
)

**Output**: Comprehensive list of potentially blocking patents

---

Step 6: Timeline Analysis (2-3 min)

**Goal**: Understand technology evolution

**Process**: 1. Filter results by date ranges 2. Identify filing trends over time 3. Find recent developments (last 2 years) 4. Check priority dates 5. Note technology progression

**Code**:

# Search by year ranges
recent = searcher.search_by_keywords(query, start_year=2022, end_year=2024)
older = searcher.search_by_keywords(query, start_year=2015, end_year=2021)

**Output**: Timeline showing technology development

---

Step 7: Patentability Report (5-10 min)

**Goal**: Professional assessment and recommendations

**Process**: 1. Analyze top 10 closest prior art 2. Assess novelty (35 USC 102) 3. Assess non-obviousness (35 USC 103) 4. Rank prior art by relevance 5. Provide claim strategy recommendations 6. Generate IDS list

**Output**: Comprehensive patentability report

---

Report Format

# PRIOR ART SEARCH REPORT

## Executive Summary
- Invention: [Brief description]
- Search Date: [Date]
- Searcher: Claude Patent Creator
- Databases: BigQuery (100M+ patents), USPTO API
- Time Period: [Year range]

## Patentability Assessment

### Novelty (35 USC 102)
[Assessment of whether invention is novel]

Score: [High/Medium/Low]

Analysis:
- No exact matches found
- Closest prior art: US10123456
- Key differences: [List]

### Non-Obviousness (35 USC 103)
[Assessment of whether invention is non-obvious]

Score: [H