patent-analysis-guide

Patent search, classification, landscape analysis, and prior art mining

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name	patent-analysis-guide
title	Patent Analysis Guide
description	Patent search, classification, landscape analysis, and prior art mining
author	wentorai
author_url	https://github.com/wentorai/research-plugins/tree/main/skills/domains/law/patent-analysis-guide
license	MIT
version	0.1.0
execution_mode	open
jurisdiction	general
practice	ip
language	en

Patent Analysis Guide

A skill for conducting patent research, landscape analysis, and prior art searches. Covers patent database APIs, classification systems, citation network analysis, claim parsing, and technology trend mapping for intellectual property research.

Patent Data Sources

Major Patent Databases

Database	Coverage	API	Cost
USPTO PatentsView	US patents and applications	REST API, bulk download	Free
EPO Open Patent Services	EP, WO, and 100+ offices	REST API (OPS)	Free (throttled)
Google Patents	120M+ documents worldwide	BigQuery (Google Patents Public)	Free (BigQuery costs)
Lens.org	130M+ patent records	REST API	Free for researchers
WIPO PATENTSCOPE	PCT applications + national	REST API	Free

Programmatic Patent Search

import requests
import xml.etree.ElementTree as ET

class EPOClient:
    """Client for the EPO Open Patent Services (OPS) API."""

    BASE_URL = "https://ops.epo.org/3.2/rest-services"

    def __init__(self, consumer_key: str, consumer_secret: str):
        self.token = self._authenticate(consumer_key, consumer_secret)

    def _authenticate(self, key: str, secret: str) -> str:
        import base64
        credentials = base64.b64encode(f"{key}:{secret}".encode()).decode()
        resp = requests.post(
            ,
            headers={: },
            data={: },
        )
         resp.json()[]

     () -> []:
        
        resp = requests.get(
            ,
            headers={: ,
                     : },
            params={: cql_query, : },
        )
         resp.json()

Example: H04L 9/3247
  H       = Electricity (Section)
  H04     = Electric communication technique (Class)
  H04L    = Transmission of digital information (Subclass)
  H04L 9/ = Cryptographic mechanisms (Group)
  H04L 9/3247 = Digital signatures (Subgroup)

def parse_cpc_code(code: str) -> dict:
    """Parse a CPC classification code into its hierarchical components."""
    code = code.strip().replace(" ", "")
    return {
        "section": code[0],
        "class": code[:3],
        "subclass": code[:4],
        "group": code.split("/")[0] if "/" in code else code[:4],
        "subgroup": code if "/" in code else None,
        "full": code,
    }

# Technology domain mapping (top-level CPC sections)
CPC_SECTIONS = {
    "A": "Human Necessities",
    "B": "Performing Operations; Transporting",
    "C": "Chemistry; Metallurgy",
    "D": "Textiles; Paper",
    "E": "Fixed Constructions",
    "F": "Mechanical Engineering; Lighting; Heating",
    "G": "Physics",
    "H": "Electricity",
    "Y": "General Tagging of New Technological Developments",
}

import pandas as pd
import numpy as np
from collections import Counter

def patent_landscape_metrics(patents: pd.DataFrame) -> dict:
    """
    Compute patent landscape metrics from a patent dataset.
    Expected columns: patent_id, filing_date, grant_date,
    assignee, cpc_codes (list), claims_count, citations_received
    """
    # Filing trend (annual)
    patents["filing_year"] = pd.to_datetime(patents.filing_date).dt.year
    annual_filings = patents.groupby("filing_year").size()

    # Top assignees
    top_assignees = patents.assignee.value_counts().head(20)

    # Technology distribution (CPC subclass level)
    all_cpc = []
    for codes in patents.cpc_codes:
        all_cpc.extend([c[:4] for c in codes])
    cpc_distribution = Counter(all_cpc).most_common(20)

    # Citation impact
    citation_stats = patents.citations_received.describe()

    # Geographic distribution (from assignee country)
    geo_dist = patents.assignee_country.value_counts()

    return {
        "total_patents": len(patents),
        "annual_filings": annual_filings.to_dict(),
        "top_assignees": top_assignees.to_dict(),
        "technology_areas": cpc_distribution,
        "citation_stats": citation_stats.to_dict(),
        "geographic_distribution": geo_dist.head(10).to_dict(),
    }

import networkx as nx

def build_citation_network(patents: pd.DataFrame,
                            citations: pd.DataFrame) -> nx.DiGraph:
    """
    Build a patent citation network.
    citations: DataFrame with columns [citing_patent, cited_patent]
    """
    G = nx.DiGraph()

    # Add patent nodes with attributes
    for _, row in patents.iterrows():
        G.add_node(row.patent_id, assignee=row.assignee,
                   year=row.filing_year, cpc=row.cpc_codes[0][:4])

    # Add citation edges
    for _, row in citations.iterrows():
        if row.citing_patent in G and row.cited_patent in G:
            G.add_edge(row.citing_patent, row.cited_patent)

    return G

def identify_seminal_patents(G: nx.DiGraph, top_n: int = 20) -> list:
    """Find the most influential patents by various centrality measures."""
    in_degree = dict(G.in_degree())
    pagerank = nx.pagerank(G)

    # Combine metrics
    scores = {}
    for node in G.nodes():
        scores[node] = {
            "citations_received": in_degree[node],
            "pagerank": pagerank[node],
        }
    ranked = sorted(scores.items(), key=lambda x: x[1]["pagerank"], reverse=True)
    return ranked[:top_n]

def parse_claims(claims_text: str) -> list[dict]:
    """
    Parse patent claims text into structured claim objects.
    Identifies independent vs dependent claims and extracts dependencies.
    """
    # Split on claim numbers
    claim_pattern = re.compile(r"\n\s*(\d+)\.\s+", re.MULTILINE)
    parts = claim_pattern.split(claims_text)

    claims = []
    for i in range(1, len(parts), 2):
        claim_num = int(parts[i])
        claim_text = parts[i + 1].strip()

        # Detect dependency
        dep_match = re.match(
            r"(?:The|A)\s+\w+\s+(?:of|according to)\s+claim\s+(\d+)",
            claim_text, re.IGNORECASE
        )
        is_independent = dep_match is None
        depends_on = int(dep_match.group(1)) if dep_match else None

        claims.append({
            "number": claim_num,
            "text": claim_text,
            "independent": is_independent,
            "depends_on": depends_on,
            "word_count": len(claim_text.split()),
        })
    return claims