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Télécharger Zip Téléchargement... Plus depuis ce dépôt fetching-arbitration-rules Use when retrieving arbitration institutional rules (ICC, LCIA, SCC, SIAC, HKIAC, VIAC, МКАС/МАК при ТПП України, UNCITRAL) — fetching current version, verifying redaction applicable to the date of arbitration agreement, constructing URLs for official rule texts
determining-pl-request-regime Use when choosing the Polish legal regime for letters, requests, applications, complaints, petitions, public-information requests, KPA filings, PPSA complaints, RODO access requests, registry extracts, court-file access, tax/ZUS/cudzoziemcy/USC procedures, or professional lawyer letters. Prevents mixing UDIP, KPA, PPSA, RODO, registry, special-procedure, and advocate/radca letter regimes.
applying-new-york-convention Use when preparing applications for recognition and enforcement of foreign arbitral awards in Poland, applications for setting aside arbitral awards under KPC art. 1205–1211, or opposing such applications — mapping Article V of the 1958 New York Convention to art. 1214–1215 of the Polish KPC, identifying grounds for refusal, structuring public policy arguments
Métiers associés SOC
Basé sur la classification professionnelle SOC
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(
"https://ops.epo.org/3.2/auth/accesstoken"
"Authorization"
f"Basic {credentials} "
"grant_type"
"client_credentials"
return
"access_token"
def
search
self, cql_query: str , max_results: int = 25
list
dict
"""
Search patents using CQL (Common Query Language).
Example queries:
ta="machine learning" AND cl="neural network"
pa="university" AND pd>=2020
"""
f"{self.BASE_URL} /published-data/search"
"Authorization"
f"Bearer {self.token} "
"Accept"
"application/json"
"q"
"Range"
f"1-{max_results} "
return
Patent Classification Systems
Cooperative Patent Classification (CPC) The CPC hierarchy has five levels: Section > Class > Subclass > Group > Subgroup.
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)
IPC to CPC Mapping 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,
}
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" ,
}
Patent Landscape Analysis
Building a Patent Landscape A patent landscape maps the technology and competitive environment in a domain:
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
"""
patents["filing_year" ] = pd.to_datetime(patents.filing_date).dt.year
annual_filings = patents.groupby("filing_year" ).size()
top_assignees = patents.assignee.value_counts().head(20 )
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_stats = patents.citations_received.describe()
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(),
}
Citation Network Analysis 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()
for _, row in patents.iterrows():
G.add_node(row.patent_id, assignee=row.assignee,
year=row.filing_year, cpc=row.cpc_codes[0 ][:4 ])
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)
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]
Claim Analysis
Parsing Patent Claims Patent claims define the legal scope of protection. Independent claims are the broadest; dependent claims narrow them:
def parse_claims (claims_text: str ) -> list [dict ]:
"""
Parse patent claims text into structured claim objects.
Identifies independent vs dependent claims and extracts dependencies.
"""
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()
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
Prior Art Search Strategy Systematic prior art search methodology:
Define the invention : Break the invention into key technical featuresKeyword search : Use synonyms, broader terms, and technical variantsClassification search : Identify relevant CPC/IPC codes and search within themCitation search : Forward and backward citation tracking from known relevant patentsAssignee search : Search patents from known competitors and research groupsNon-patent literature : Check academic papers, standards, product documentation
Tools and Resources
PatentsView API : Free US patent data with assignee disambiguationGoogle Patents : Full-text search with CPC browsing and citation linksLens.org : Scholarly and patent search with linking between patents and papersDerwent Innovation : Commercial tool for comprehensive patent analyticsPatSnap : AI-powered patent intelligence platformWIPO Pearl : Multilingual patent terminology database 