patent-landscape-tools

Patent Landscape Tools

Use this skill for research-grade patent intelligence around drugs, targets, or mechanisms, not legal freedom-to-operate opinions.

Typical triggers:

• search US patents related to a drug compound, target, or mechanism of action
• classify pharmaceutical patents by claim type: NCE, formulation, method-of-use, polymorph, biologic, process
• build a landscape summary showing filing trends, top assignees, and claim-type distribution
• estimate patent expiry windows for one or more patent families
• cross-reference the FDA Orange Book for a marketed drug's listed patents and exclusivity codes
• identify IP white-space or under-patented mechanism classes before a new filing strategy

Working Rules

1. Always apply a pharmaceutical CPC filter (A61K, A61P, or C07) to avoid non-drug noise.
2. Distinguish granted patents from applications; PatentsView covers grants only.
3. Report the data source, query date, and coverage limitation (US-only) in every output.
4. Claim-type classification is heuristic from title and abstract keywords, not from reading claims.
5. Expiry is estimated as filing date + 20 years; PTE, PTA, SPC, and terminal disclaimers are not computed.
6. Do not present any output as freedom-to-operate analysis, legal advice, or infringement opinion.

Environment Check

which python3 || true
python3 - <<'PY'
mods = ["requests"]
for name in mods:
    try:
        __import__(name)
        print(f"{name}: ok")
    except Exception as exc:
        print(f"{name}: missing ({exc})")
PY

If network access is unavailable, say so before claiming patent data was retrieved.

Bundled Assets

• templates/patent_landscape.py

Patent Landscape Search

Use templates/patent_landscape.py for:

• keyword + CPC-filtered patent search on PatentsView
• automatic claim-type classification of each result
• assignee ranking, filing-trend extraction, and family grouping
• optional markdown landscape brief

Quick start:

python3 templates/patent_landscape.py \
  --query "KRAS G12C inhibitor" \
  --cpc-filter A61K \
  --max-results 200 \
  --output patents/kras_g12c_landscape.csv \
  --summary patents/kras_g12c_summary.json \
  --brief patents/kras_g12c_brief.md

Broad landscape with date range:

python3 templates/patent_landscape.py \
  --query "PD-1 antibody" \
  --cpc-filter A61K39 \
  --date-from 2015-01-01 \
  --max-results 500 \
  --output patents/pd1_ab_landscape.csv \
  --summary patents/pd1_ab_summary.json

Deliverables:

• per-patent CSV with patent number, title, filing date, grant date, assignee, country, CPC, claim-type guess, estimated expiry year, and abstract snippet
• summary JSON with total count, estimated family count, claim-type distribution, top assignees, country distribution, filing-year trend, and expiry window
• optional markdown brief with tables and caveats

Expiry Timeline

Use templates/patent_landscape.py --mode expiry-timeline for:

• patent expiry estimation sorted by earliest expiry
• identification of upcoming patent cliffs for a compound or class

python3 templates/patent_landscape.py \
  --query "semaglutide" \
  --mode expiry-timeline \
  --output patents/semaglutide_expiry.csv \
  --summary patents/semaglutide_expiry.json

Deliverables:

• patent CSV sorted by estimated expiry year
• summary JSON with total patents and timeline entry count

Orange Book Cross-Reference

Use --orange-book-query to add FDA Orange Book data for a marketed drug:

python3 templates/patent_landscape.py \
  --query "semaglutide" \
  --mode expiry-timeline \
  --orange-book-query "semaglutide" \
  --output patents/semaglutide_expiry.csv \
  --summary patents/semaglutide_expiry.json

Orange Book entries include: brand name, generic name, NDA number, dosage form, route, and marketing status.

CPC Filter Reference

Claim-Type Classification Heuristics

The template matches title + abstract keywords to assign one of:

• NCE / composition-of-matter — compound, molecule, chemical entity, active ingredient
• formulation — dosage form, tablet, capsule, nanoparticle, sustained release
• method-of-use — method of treating, therapeutic use, for the treatment
• polymorph / salt form — polymorph, crystal form, salt, co-crystal, hydrate
• combination — combination, co-administration, synergistic
• biologic — antibody, recombinant, fusion protein, monoclonal
• diagnostic — biomarker, companion diagnostic, imaging agent
• device / delivery — device, inhaler, auto-injector, applicator
• process / manufacturing — synthesis, preparation, method of making

First match wins. Unmatched patents are labeled unclassified. This is not claim analysis.

Output Expectations

Good answers should mention:

• the query terms, CPC filter, and date range applied
• number of patent families and individual documents retrieved
• claim-type distribution
• top assignees with filing counts
• filing trend by year
• estimated expiry window for key families
• Orange Book listings if queried
• explicit caveat: US patents only, heuristic classification, not legal counsel
• where the CSV, JSON, and optional brief were saved

Related Skills

For compound and regulatory lookups from ChEMBL, openFDA, ClinicalTrials.gov, activate pharma-db-tools. For target-level intelligence dossiers, activate target-intelligence-tools. For clinical-pipeline competitive analysis, activate competitive-intelligence-tools. For drug repurposing hypothesis generation, activate drug-repurposing-tools.

Reference

This skill queries the following public APIs:

• USPTO PatentsView API — https://api.patentsview.org/patents/query — US granted-patent search with CPC, date, and full-text filters (patentsview.org/apis)
• FDA openFDA drugsFDA API — https://api.fda.gov/drug/drugsfda.json — Orange Book product listings including brand/generic names, NDA numbers, dosage forms, and marketing status (open.fda.gov/apis/drug/drugsfda)
• CPC classification scheme — Cooperative Patent Classification maintained by EPO and USPTO; A61K/A61P/C07 subclasses are used as pharmaceutical relevance filters (cooperativepatentclassification.org)
• Claim-type classification heuristics are adapted from pharmaceutical IP landscape methodology described in: Dubey R, Maheshwari S. "Patent landscape analysis: A methodology for drug discovery." Drug Discovery Today, 2020.