assess-ip-landscape
정보
이 스킬은 특정 기술에 대한 지식재산권 환경을 분석하여, 특허 클러스터 분석, 경쟁사 평가 및 사전 자유실시(FTO) 검토를 수행합니다. R&D 시작 전, 시장 진입 평가 또는 투자 실사 준비 시 활용하도록 설계되었습니다. 이 도구는 시장 공백을 식별하고 전략적 IP 포지셔닝 권고사항을 제공합니다.
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문서
Assess IP Landscape
Map IP landscape for tech domain — ID patent clusters, white spaces, key players, FTO risks. Produces strategic assessment → R&D direction, licensing, IP filing strategy.
Use When
- Before R&D in new tech area (what's claimed?)
- Market entry vs incumbents w/ strong portfolios
- Investment due diligence (IP asset assessment)
- Inform patent filing strategy (where, what to claim)
- FTO risk for new product/feature
- Monitor competitor IP for strategic positioning
In
- Required: Tech domain/product area
- Required: Geographic scope (US, EU, global)
- Optional: Specific competitors
- Optional: Own patent portfolio (gap analysis + FTO)
- Optional: Time horizon (5 years, 10 years, all)
- Optional: Classification codes (IPC, CPC)
Do
Step 1: Define Search Scope
Establish analysis boundaries.
- Tech domain precisely:
- Core (e.g., "transformer-based language models" not "AI")
- Adjacent (e.g., "attention mechanisms, tokenization, inference optimization")
- Exclude (e.g., "computer vision transformers" if focusing on NLP)
- Relevant classification codes:
- IPC (International Patent Classification) — broad, worldwide
- CPC (Cooperative Patent Classification) — more specific, US/EU std
- Search WIPO's IPC publication or USPTO's CPC browser
- Geographic scope:
- US (USPTO), EU (EPO), WIPO (PCT), specific national offices
- Most analyses start US + EU + PCT → broad coverage
- Time window:
- Recent: 3-5 years (current competitive)
- Full history: 10-20 years (mature areas)
- Watch expired patents opening design space
- Doc scope as Landscape Charter
→ Clear bounded scope specific enough actionable + broad enough captures competitive. Classification codes ID'd for systematic search.
If err: Too broad (thousands of results) → narrow via technical specificity or focus application area. Too narrow (few) → broaden adjacent tech. Right scope typically 100-1000 families.
Step 2: Harvest Patent Data
Collect data within scope.
- Query databases via Charter:
- Free: Google Patents, USPTO PatFT/AppFT, Espacenet, WIPO Patentscope
- Commercial: Orbit, PatSnap, Derwent, Lens.org (freemium)
- Combine keyword + classification codes → best coverage
- Build queries systematically:
Query Construction:
+-------------------+------------------------------------------+
| Component | Example |
+-------------------+------------------------------------------+
| Core keywords | "language model" OR "LLM" OR "GPT" |
| Technical terms | "attention mechanism" OR "transformer" |
| Classification | CPC: G06F40/*, G06N3/08 |
| Date range | filed:2019-2024 |
| Assignee filter | (optional) specific companies |
+-------------------+------------------------------------------+
- Download structured (CSV, JSON):
- Patent/app num, title, abstract, filing date
- Assignee/applicant, inventor(s)
- Classification codes, citation data
- Legal status (granted, pending, expired, abandoned)
- Dedup by family (group national filings of same invention)
- Record total family count + src databases
→ Structured dataset of families within scope, deduped + timestamped. Foundation for all subsequent.
If err: DB access limited → Google Patents + Lens.org (free) good coverage. Query returns too many (>5000) → add technical specificity. Too few (<50) → broaden keywords or add classification.
Step 3: Analyze Landscape
Map clusters, key players, trends.
- Cluster analysis: Group by sub-tech:
- Classification codes or keyword clustering → 5-10 sub-areas
- Count families per cluster
- ID growing (recent surges) vs mature (flat/declining)
- Key player analysis: Top 10 assignees by:
- Total family count (portfolio breadth)
- Recent filing rate (last 3 years — current activity)
- Avg citation count (quality proxy)
- Geographic breadth (US-only vs global)
- Trend analysis: Filing trends over window:
- Overall volume by year
- Volume by cluster by year
- New entrants (assignees filing 1st time in domain)
- Citation network: Most-cited patents (foundational):
- High forward citations = heavily relied upon
- Likely blocking patents or essential prior art
- Produce Landscape Map: clusters, players, trends, key patents
→ Clear picture: who owns what, where activity concentrated, how landscape evolving. Key blocking patents ID'd. White spaces visible.
If err: Dataset too small for meaningful clustering → combine into broader groups. 1 assignee dominates (>50%) → analyze portfolio as separate sub-landscape.
Step 4: ID White Spaces + Risks
Strategic insights from landscape.
- White space analysis (opportunities):
- Areas within scope w/ few/no filings
- Expired families → design space reopened
- Active areas w/ only 1 player (first-mover but no competition)
- White spaces adjacent to growing clusters (next frontier)
- FTO risk screening (threats) — adapted from
healtriage:- Critical: Granted directly covering planned product/feature
- High: Pending apps likely to grant relevant claims
- Medium: Granted in adjacent areas could be broadly interpreted
- Low: Expired, narrow claims, geographically irrelevant
- Competitive positioning:
- Where portfolio sits rel to competitors?
- Which competitors have blocking positions in target areas?
- Which interested in cross-licensing?
- Produce Strategic Assessment: white spaces, FTO risks, positioning, recs
→ Actionable strategic recs: where to file, what to avoid, who to watch, what risks need detailed FTO.
If err: FTO risks ID'd — preliminary ONLY — does NOT replace formal FTO opinion from patent attorney. Flag critical for legal review. White spaces seem too good → verify search scope not accidentally excluded.
Step 5: Doc + Recommend
Package assessment for decision-makers.
- Landscape Report sections:
- Exec summary (1 page: key findings, top risks, main recs)
- Scope + methodology (search terms, DBs, date range)
- Landscape overview (clusters, trends, key players + viz)
- White space analysis (opportunities ranked by strategic value)
- Risk assessment (FTO concerns ranked by severity)
- Recs (filing strategy, licensing targets, monitoring alerts)
- Supporting data:
- Family list (structured, sortable)
- Cluster map (visual)
- Filing trend charts
- Key patent summaries (top 10-20 most relevant)
- Ongoing monitoring:
- Alert queries for new filings in critical areas
- Review cadence (quarterly active, annually stable)
→ Complete landscape report → strategic IP decisions. Evidence-based, clearly scoped, actionable.
If err: Report too large → exec summary first + offer detailed sections on request. Exec summary always stands alone as decision doc.
Check
- Landscape Charter defines scope, classification, geography, time window
- Patent dataset harvested from multi DBs + deduped
- Clusters ID'd w/ filing counts + trend direction
- Top 10 key players profiled w/ portfolio metrics
- White spaces ID'd + ranked by strategic value
- FTO risks screened + classified by severity
- Key blocking patents ID'd w/ citation analysis
- Recs specific + actionable
- Limitations acknowledged (screening vs formal FTO opinion)
- Monitoring alerts defined for ongoing tracking
Traps
- Too broad scope: "AI patents" not landscape — ocean. Be specific about tech + app
- Single-DB reliance: No single DB complete coverage. ≥2 srcs
- Ignore families: Count individual filings vs families inflates. 1 invention in 10 countries = 1 family not 10
- Confuse apps w/ grants: Pending app not enforceable. Distinguish granted vs published apps
- White space misinterp: Empty area = "nobody tried" or "everybody tried + failed." Investigate before assuming opportunity
- Landscape as legal opinion: Strategic intelligence, not legal advice. FTO risks flagged need formal analysis by patent counsel
→
search-prior-art— detailed prior art search for specific inventions or patent validityscreen-trademark— trademark conflict screening + distinctiveness analysis for trademark sidefile-trademark— trademark filing procedures EUIPO, USPTO, Madrid Protocolsecurity-audit-codebase— risk assessment methodology parallels IP risk screeningreview-research— literature review skills apply to prior art analysis
GitHub 저장소
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