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| """Risk Engine — Automated Research Risk Assessment. | |
| Identifies potential pitfalls in research trends including patent thickets, | |
| licensing restrictions, and compute requirements. | |
| """ | |
| import logging | |
| from typing import List, Dict | |
| logger = logging.getLogger("vectormind.intelligence") | |
| class RiskEngine: | |
| """Analyzes technical, market, and legal risks for research trends.""" | |
| def __init__(self): | |
| self.risk_categories = { | |
| "legal": ["patent", "license", "copyright", "trademark"], | |
| "technical": ["compute", "memory", "latency", "dataset"], | |
| "market": ["competition", "moat", "adoption", "cost"] | |
| } | |
| def analyze_risks(self, trend_title: str, description: str, metadata: Dict) -> List[Dict]: | |
| """Assess risks based on signal content and metadata.""" | |
| risks = [] | |
| text = (trend_title + " " + description).lower() | |
| # 1. Patent Risk | |
| if "patent" in text or metadata.get("patent_number"): | |
| risks.append({ | |
| "type": "Legal", | |
| "severity": "High", | |
| "label": "Patent Thicket Detected", | |
| "detail": "Core technique may be covered by active patent filings." | |
| }) | |
| # 2. Compute Risk | |
| if any(kw in text for kw in ["transformer", "large language model", "diffusion", "gpu"]): | |
| risks.append({ | |
| "type": "Technical", | |
| "severity": "Medium", | |
| "label": "High Compute Requirement", | |
| "detail": "Likely requires A100/H100 clusters for full training." | |
| }) | |
| # 3. Licensing Risk | |
| license_id = metadata.get("license", "").upper() | |
| if "GPL" in license_id or "CC-BY-NC" in license_id: | |
| risks.append({ | |
| "type": "Legal", | |
| "severity": "Critical", | |
| "label": "Restrictive License", | |
| "detail": f"Dataset or code uses {license_id}, restricting commercial use." | |
| }) | |
| return risks | |