AgentSkillsCN

Evaluate Patent Value

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SKILL.md

Skill: Evaluate Patent Value

Domain

technology

Description

Evaluates patent portfolio value using citation analysis, market relevance, and licensing potential for IP management decisions.

Tags

IP, patents, valuation, licensing, technology, legal

Use Cases

  • •Patent valuation
  • •Portfolio optimization
  • •Licensing strategy
  • •M&A IP assessment

Proprietary Business Rules

Rule 1: Citation Analysis

Forward and backward citation evaluation.

Rule 2: Market Relevance Assessment

Technology market alignment scoring.

Rule 3: Legal Strength Evaluation

Claim breadth and validity analysis.

Rule 4: Licensing Potential

Revenue generation opportunity assessment.

Input Parameters

  • •patent_id (string): Patent identifier
  • •patent_data (dict): Patent information
  • •citation_data (dict): Citation metrics
  • •market_data (dict): Relevant market data
  • •legal_status (dict): Legal standing
  • •comparable_licenses (list): Licensing comparables

Output

  • •estimated_value (dict): Value range estimate
  • •citation_score (dict): Citation analysis
  • •market_relevance (dict): Market alignment
  • •legal_assessment (dict): Legal strength
  • •licensing_recommendation (dict): Strategy recommendation

Implementation

The evaluation logic is implemented in patent_evaluator.py and references data from CSV files:

  • •valuation_methods.csv - Reference data
  • •royalty_rates_by_industry.csv - Reference data
  • •strength_factors.csv - Reference data
  • •life_cycle_factors.csv - Reference data
  • •discount_rates.csv - Reference data
  • •quality_scores.csv - Reference data
  • •jurisdiction_factors.csv - Reference data
  • •cost_components.csv - Reference data
  • •parameters.csv - Reference data.

Usage Example

python
from patent_evaluator import evaluate_patent

result = evaluate_patent(
    patent_id="US10123456",
    patent_data={"filing_date": "2020-01-15", "claims": 25, "technology_area": "AI"},
    citation_data={"forward_citations": 45, "backward_citations": 30},
    market_data={"tam": 50000000000, "growth_rate": 0.25},
    legal_status={"status": "granted", "remaining_life_years": 12},
    comparable_licenses=[{"technology": "AI", "royalty_rate": 0.03}]
)

print(f"Estimated Value: ${result['estimated_value']['midpoint']:,.0f}")

Test Execution

python
from patent_evaluator import evaluate_patent

result = evaluate_patent(
    patent_id=input_data.get('patent_id'),
    patent_data=input_data.get('patent_data', {}),
    citation_data=input_data.get('citation_data', {}),
    market_data=input_data.get('market_data', {}),
    legal_status=input_data.get('legal_status', {}),
    comparable_licenses=input_data.get('comparable_licenses', [])
)