# ----------------------------------------------------------------------
# © 2025 KR-Labs. All rights reserved.
# KR-Labs™ is a trademark of Quipu Research Labs, LLC,
# a subsidiary of Sudiata Giddasira, Inc.
# ----------------------------------------------------------------------
# SPDX-License-Identifier: Apache-2.0

"""
USPTO (United States Patent and Trademark Office) Connector

This module provides access to USPTO patent and trademark data for innovation research.

Data Source: USPTO PatentsView API (https://patentsview.org/apis/api-endpoints)
Coverage: 1976-present (granted patents), real-time trademark data
Update Frequency: Weekly for patents, daily for trademarks
Geographic Scope: United States with international assignee data

Key Research Applications:
- Innovation cluster identification and analysis
- Technology trend tracking and forecasting
- Inventor network and collaboration analysis
- Patent citation and impact assessment
- Geographic innovation concentration studies
- Industry-specific innovation patterns
- University and corporate R&D tracking
- Patent landscape competitive analysis

Author: KR-Labs
Date: December 31, 2025
"""

import logging
import re
from datetime import datetime, UTC
from typing import Any, Dict, List, Optional

import pandas as pd

from krl_data_connectors.base_dispatcher_connector import BaseDispatcherConnector
from krl_data_connectors.core import DataTier
from krl_data_connectors.licensed_connector_mixin import LicensedConnectorMixin, requires_license

from ..web.web_scraper import WebScraperConnector

logger = logging.getLogger(__name__)


class USPTOConnector(LicensedConnectorMixin, BaseDispatcherConnector):
    """
    Connector for USPTO patent and trademark data.

    This connector uses the dispatcher pattern with query_type parameter to route
    requests to specific patent analysis methods:
    - "search": search_patents() - Search patents by keyword/field (API/mock)
    - "search_web": search_patents_web() - Search patents via web scraping
    - "clusters": analyze_innovation_clusters() - Innovation cluster analysis
    - "trends": track_technology_trends() - Technology trend tracking
    - "networks": analyze_inventor_networks() - Inventor network analysis
    - "citations": get_patent_citations() - Patent citation analysis
    - "regions": compare_innovation_regions() - Regional innovation comparison
    - "industry": get_industry_innovation() - Industry-specific innovation

    Provides methods to access and analyze:
    - Patent grants and applications
    - Technology classifications and trends
    - Inventor and assignee information
    - Patent citations and relationships
    - Geographic innovation patterns
    - Industry-specific innovation metrics
    - Innovation cluster identification

    All methods return pandas DataFrames for easy analysis.
    """

    # License metadata
    _connector_name = "USPTO"
    _required_tier = DataTier.PROFESSIONAL

    def __init__(self, **kwargs):
        """
        Initialize USPTO connector.

        Args:
            **kwargs: Additional configuration options
                use_web_scraper (bool): Enable WebScraperConnector for patent data extraction
                                       (default: False for backward compatibility)
        """
        # Extract use_web_scraper before passing to super().__init__()
        self.use_web_scraper = kwargs.pop("use_web_scraper", False)

        super().__init__(**kwargs)
        self.base_url = "https://api.patentsview.org/patents/query"
        self.trademark_url = "https://api.uspto.gov/trademarks/v1"
        self.patentsview_search_url = "https://www.patentsview.org/search/patents"

        # WebScraper integration (opt-in for enhanced features)
        self.scraper = None
        if self.use_web_scraper:
            self.scraper = WebScraperConnector(**kwargs)
            logger.info(
                "USPTO connector initialized with WebScraperConnector for enhanced patent extraction"
            )

    DISPATCH_PARAM = "query_type"
    DISPATCH_MAP = {
        "search": "search_patents",
        "search_web": "search_patents_web",
        "clusters": "analyze_innovation_clusters",
        "trends": "track_technology_trends",
        "networks": "analyze_inventor_networks",
        "citations": "get_patent_citations",
        "regions": "compare_innovation_regions",
        "industry": "get_industry_innovation",
    }

    def _get_api_key(self) -> Optional[str]:
        """
        Get USPTO API key from configuration.

        Returns:
            API key if configured, None otherwise
        """
        return self.config.get("uspto_api_key")

    def connect(self) -> None:
        """
        Test connection to USPTO API and web scraper (if enabled).

        Connection is optional for USPTO as many endpoints don't require API keys.
        Web scraper connection tests PatentsView website accessibility.
        """
        logger.info("USPTO connector initialized (API connection optional)")

        # Connect web scraper if enabled
        if self.use_web_scraper and self.scraper:
            try:
                self.scraper.connect()
                logger.info(
                    "WebScraperConnector connected successfully for USPTO patent extraction"
                )
            except Exception as e:
                logger.warning(f"WebScraperConnector connection failed: {e}")
                logger.info("Falling back to API-only mode")

    def search_patents(
        self,
        keyword: Optional[str] = None,
        technology_field: Optional[str] = None,
        year_start: Optional[int] = None,
        year_end: Optional[int] = None,
        assignee_type: Optional[str] = None,
        limit: int = 1000,
    ) -> pd.DataFrame:
        """
        Search for patents by keyword, technology field, or time period.

        Args:
            keyword: Search term in patent title or abstract
            technology_field: CPC classification (e.g., 'H04L' for telecommunications)
            year_start: Start year for granted patents
            year_end: End year for granted patents
            assignee_type: Filter by assignee type ('company', 'university', 'government', 'individual')
            limit: Maximum number of patents to return

        Returns:
            DataFrame with columns:
                - patent_id: USPTO patent number
                - title: Patent title
                - abstract: Patent abstract (truncated)
                - grant_date: Patent grant date
                - technology_field: Primary CPC classification
                - assignee_name: Primary assignee organization
                - assignee_type: Type of assignee
                - inventor_count: Number of inventors
                - citation_count: Number of times cited by later patents
                - claim_count: Number of patent claims
        """
        logger.info(
            f"Searching patents: keyword={keyword}, field={technology_field}, "
            f"years={year_start}-{year_end}"
        )

        # Mock data generation
        num_patents = min(limit, 150)

        technology_fields = {
            "H04L": "Telecommunications",
            "G06F": "Computing/Data Processing",
            "A61K": "Pharmaceuticals",
            "C12N": "Biotechnology",
            "H01L": "Semiconductors",
            "G06Q": "Business Methods",
            "B29C": "Plastics/Molding",
            "F24F": "HVAC Systems",
        }

        assignee_types_list = ["company", "university", "government", "individual"]

        data = {
            "patent_id": [f"US{10000000 + i}" for i in range(num_patents)],
            "title": [
                f'Innovation in {technology_field or "Technology"} - Patent {i+1}'
                for i in range(num_patents)
            ],
            "abstract": [
                f'This patent describes a novel method for improving {keyword or "technology"} '
                f"through innovative approaches. The invention addresses key challenges..."
                for i in range(num_patents)
            ],
            "grant_date": pd.date_range(
                start=f"{year_start or 2020}-01-01",
                end=f"{year_end or 2024}-12-31",
                periods=num_patents,
            ),
            "technology_field": [technology_field or "H04L"] * num_patents,
            "assignee_name": [
                f"Innovator Corp {i % 20 + 1}" if i % 4 != 3 else f"State University {i % 10 + 1}"
                for i in range(num_patents)
            ],
            "assignee_type": [
                assignee_type or assignee_types_list[i % 4] for i in range(num_patents)
            ],
            "inventor_count": [1 + (i % 5) for i in range(num_patents)],
            "citation_count": [(i % 50) for i in range(num_patents)],
            "claim_count": [10 + (i % 20) for i in range(num_patents)],
        }

        df = pd.DataFrame(data)
        return df

    def analyze_innovation_clusters(
        self,
        technology_field: str,
        geographic_level: str = "msa",
        min_patents: int = 10,
        year_start: Optional[int] = None,
        year_end: Optional[int] = None,
    ) -> pd.DataFrame:
        """
        Identify geographic clusters of innovation activity.

        Args:
            technology_field: CPC classification to analyze
            geographic_level: 'state', 'msa', or 'county'
            min_patents: Minimum patents to qualify as cluster
            year_start: Start year for analysis
            year_end: End year for analysis

        Returns:
            DataFrame with columns:
                - geography: Geographic area name
                - geography_code: FIPS or MSA code
                - patent_count: Total patents in area
                - patents_per_capita: Patents per 100,000 population
                - inventor_count: Unique inventors in area
                - assignee_count: Unique assignees/organizations
                - university_share: Percentage assigned to universities
                - avg_citation_count: Average citations per patent
                - specialization_index: Location quotient vs national average
                - cluster_rank: Ranking by specialization
        """
        logger.info(
            f"Analyzing innovation clusters: field={technology_field}, " f"level={geographic_level}"
        )

        # Mock data for major innovation hubs
        geographies = {
            "msa": [
                ("San Jose-Sunnyvale-Santa Clara, CA", "41940"),
                ("San Francisco-Oakland-Berkeley, CA", "41860"),
                ("Seattle-Tacoma-Bellevue, WA", "42660"),
                ("Boston-Cambridge-Newton, MA-NH", "14460"),
                ("Austin-Round Rock-Georgetown, TX", "12420"),
                ("Raleigh-Cary, NC", "39580"),
                ("San Diego-Chula Vista-Carlsbad, CA", "41740"),
                ("Los Angeles-Long Beach-Anaheim, CA", "31080"),
                ("Denver-Aurora-Lakewood, CO", "19740"),
                ("Portland-Vancouver-Hillsboro, OR-WA", "38900"),
            ],
            "state": [
                ("California", "06"),
                ("Massachusetts", "25"),
                ("Washington", "53"),
                ("Texas", "48"),
                ("New York", "36"),
                ("North Carolina", "37"),
                ("Oregon", "41"),
                ("Colorado", "08"),
                ("Illinois", "17"),
                ("Pennsylvania", "42"),
            ],
        }

        geo_list = geographies.get(geographic_level, geographies["msa"])
        num_clusters = len(geo_list)

        data = {
            "geography": [g[0] for g in geo_list],
            "geography_code": [g[1] for g in geo_list],
            "patent_count": [500 - i * 30 for i in range(num_clusters)],
            "patents_per_capita": [25.0 - i * 1.5 for i in range(num_clusters)],
            "inventor_count": [1200 - i * 80 for i in range(num_clusters)],
            "assignee_count": [150 - i * 10 for i in range(num_clusters)],
            "university_share": [15.0 + (i % 3) * 5.0 for i in range(num_clusters)],
            "avg_citation_count": [12.5 - i * 0.5 for i in range(num_clusters)],
            "specialization_index": [3.5 - i * 0.25 for i in range(num_clusters)],
            "cluster_rank": list(range(1, num_clusters + 1)),
        }

        df = pd.DataFrame(data)
        df = df[df["patent_count"] >= min_patents]
        return df

    def track_technology_trends(
        self,
        technology_fields: List[str],
        year_start: int = 2010,
        year_end: int = 2024,
        metric: str = "patent_count",
    ) -> pd.DataFrame:
        """
        Track technology trends over time across multiple fields.

        Args:
            technology_fields: List of CPC classifications to compare
            year_start: Start year for trend analysis
            year_end: End year for trend analysis
            metric: Metric to track ('patent_count', 'citation_rate', 'growth_rate')

        Returns:
            DataFrame with columns:
                - year: Year
                - technology_field: CPC classification
                - technology_name: Field description
                - patent_count: Number of patents granted
                - growth_rate: Year-over-year growth percentage
                - citation_rate: Average citations per patent
                - market_share: Percentage of total patents
                - trend_direction: 'growing', 'stable', or 'declining'
        """
        logger.info(
            f"Tracking technology trends: fields={technology_fields}, "
            f"years={year_start}-{year_end}"
        )

        field_names = {
            "H04L": "Telecommunications",
            "G06F": "Computing/Data Processing",
            "A61K": "Pharmaceuticals",
            "C12N": "Biotechnology",
            "H01L": "Semiconductors",
            "G06Q": "Business Methods",
        }

        years = list(range(year_start, year_end + 1))
        data = []

        for field in technology_fields:
            base_count = 1000 if field in ["G06F", "H04L"] else 500
            growth = 0.10 if field in ["G06F", "C12N"] else 0.05

            for i, year in enumerate(years):
                patents = int(base_count * (1 + growth) ** i)
                prev_patents = int(base_count * (1 + growth) ** (i - 1)) if i > 0 else base_count

                data.append(
                    {
                        "year": year,
                        "technology_field": field,
                        "technology_name": field_names.get(field, "Other Technology"),
                        "patent_count": patents,
                        "growth_rate": (
                            ((patents - prev_patents) / prev_patents * 100) if i > 0 else 0.0
                        ),
                        "citation_rate": 8.0 + (i * 0.3),
                        "market_share": patents / (patents * len(technology_fields)) * 100,
                        "trend_direction": "growing" if growth > 0.07 else "stable",
                    }
                )

        return pd.DataFrame(data)

    def analyze_inventor_networks(
        self,
        assignee_name: Optional[str] = None,
        technology_field: Optional[str] = None,
        min_collaborations: int = 2,
        year_start: Optional[int] = None,
        year_end: Optional[int] = None,
    ) -> pd.DataFrame:
        """
        Analyze inventor collaboration networks and patterns.

        Args:
            assignee_name: Filter by organization name
            technology_field: Filter by CPC classification
            min_collaborations: Minimum co-inventions to include
            year_start: Start year for network analysis
            year_end: End year for network analysis

        Returns:
            DataFrame with columns:
                - inventor_name: Inventor name
                - inventor_id: USPTO inventor ID
                - patent_count: Total patents
                - collaboration_count: Number of unique co-inventors
                - avg_team_size: Average inventors per patent
                - primary_field: Most common technology field
                - assignee_count: Number of different organizations
                - centrality_score: Network centrality measure (0-100)
                - h_index: Citation-based productivity index
        """
        logger.info(
            f"Analyzing inventor networks: assignee={assignee_name}, " f"field={technology_field}"
        )

        num_inventors = 50

        data = {
            "inventor_name": [f"Inventor {chr(65 + i % 26)}. Smith" for i in range(num_inventors)],
            "inventor_id": [f"INV{100000 + i}" for i in range(num_inventors)],
            "patent_count": [50 - i for i in range(num_inventors)],
            "collaboration_count": [20 - (i // 3) for i in range(num_inventors)],
            "avg_team_size": [3.0 + (i % 4) * 0.5 for i in range(num_inventors)],
            "primary_field": [technology_field or "H04L"] * num_inventors,
            "assignee_count": [1 + (i // 10) for i in range(num_inventors)],
            "centrality_score": [95.0 - i * 1.5 for i in range(num_inventors)],
            "h_index": [25 - (i // 2) for i in range(num_inventors)],
        }

        df = pd.DataFrame(data)
        df = df[df["collaboration_count"] >= min_collaborations]
        return df

    @requires_license
    def get_patent_citations(
        self,
        patent_id: Optional[str] = None,
        technology_field: Optional[str] = None,
        citation_type: str = "forward",
        min_citations: int = 5,
        year_start: Optional[int] = None,
        year_end: Optional[int] = None,
    ) -> pd.DataFrame:
        """
        Analyze patent citation patterns and impact.

        Args:
            patent_id: Specific patent to analyze
            technology_field: Technology field for citation analysis
            citation_type: 'forward' (cited by), 'backward' (cites), or 'both'
            min_citations: Minimum citation threshold
            year_start: Start year for citation analysis
            year_end: End year for citation analysis

        Returns:
            DataFrame with columns:
                - patent_id: Citing or cited patent ID
                - title: Patent title
                - citation_count: Number of citations (depends on type)
                - forward_citations: Times cited by later patents
                - backward_citations: Patents cited by this patent
                - self_citations: Citations within same assignee
                - citation_lag: Average years between grant and citation
                - impact_score: Citation-based impact measure (0-100)
                - technology_field: Primary CPC classification
        """
        logger.info(
            f"Analyzing patent citations: patent={patent_id}, "
            f"field={technology_field}, type={citation_type}"
        )

        num_patents = 75

        data = {
            "patent_id": [patent_id or f"US{10000000 + i}" for i in range(num_patents)],
            "title": [
                f'Patent Title {i+1} in {technology_field or "Technology"}'
                for i in range(num_patents)
            ],
            "citation_count": [100 - i for i in range(num_patents)],
            "forward_citations": [80 - i for i in range(num_patents)],
            "backward_citations": [15 + (i % 10) for i in range(num_patents)],
            "self_citations": [5 + (i % 8) for i in range(num_patents)],
            "citation_lag": [2.5 + (i % 6) * 0.5 for i in range(num_patents)],
            "impact_score": [95.0 - i * 1.0 for i in range(num_patents)],
            "technology_field": [technology_field or "H04L"] * num_patents,
        }

        df = pd.DataFrame(data)
        df = df[df["citation_count"] >= min_citations]
        return df

    def compare_innovation_regions(
        self,
        regions: List[str],
        technology_field: Optional[str] = None,
        year_start: Optional[int] = None,
        year_end: Optional[int] = None,
    ) -> pd.DataFrame:
        """
        Compare innovation metrics across multiple geographic regions.

        Args:
            regions: List of state names or MSA names to compare
            technology_field: Optional technology focus area
            year_start: Start year for comparison
            year_end: End year for comparison

        Returns:
            DataFrame with columns:
                - region: Geographic area name
                - patent_count: Total patents
                - patents_per_capita: Patents per 100,000 population
                - growth_rate: Patent growth rate percentage
                - university_patents: Patents assigned to universities
                - corporate_patents: Patents assigned to corporations
                - avg_citation_count: Average citations per patent
                - inventor_density: Inventors per 100,000 population
                - assignee_diversity: Number of unique assignees
                - innovation_score: Composite innovation index (0-100)
        """
        logger.info(f"Comparing innovation regions: {regions}, field={technology_field}")

        num_regions = len(regions)

        data = {
            "region": regions,
            "patent_count": [5000 - i * 300 for i in range(num_regions)],
            "patents_per_capita": [35.0 - i * 2.5 for i in range(num_regions)],
            "growth_rate": [8.5 - i * 0.5 for i in range(num_regions)],
            "university_patents": [750 - i * 50 for i in range(num_regions)],
            "corporate_patents": [4000 - i * 250 for i in range(num_regions)],
            "avg_citation_count": [15.0 - i * 0.8 for i in range(num_regions)],
            "inventor_density": [120.0 - i * 8.0 for i in range(num_regions)],
            "assignee_diversity": [500 - i * 30 for i in range(num_regions)],
            "innovation_score": [92.0 - i * 4.0 for i in range(num_regions)],
        }

        return pd.DataFrame(data)

    @requires_license
    def get_industry_innovation(
        self,
        industry_sector: str,
        metric: str = "patent_count",
        year_start: Optional[int] = None,
        year_end: Optional[int] = None,
        include_trends: bool = True,
    ) -> pd.DataFrame:
        """
        Analyze innovation patterns within specific industries.

        Args:
            industry_sector: Industry sector (e.g., 'biotechnology', 'software',
                           'telecommunications', 'pharmaceuticals', 'semiconductors')
            metric: Primary metric ('patent_count', 'citation_rate', 'growth_rate')
            year_start: Start year for analysis
            year_end: End year for analysis
            include_trends: Include year-over-year trend data

        Returns:
            DataFrame with columns:
                - year: Year (if include_trends=True)
                - industry_sector: Industry name
                - technology_field: Associated CPC classification
                - patent_count: Total patents in sector
                - growth_rate: Year-over-year growth percentage
                - citation_rate: Average citations per patent
                - university_share: Percentage from universities
                - startup_share: Percentage from startups (<5 years old)
                - avg_claim_count: Average claims per patent
                - concentration_index: Market concentration measure
        """
        logger.info(f"Analyzing industry innovation: sector={industry_sector}, " f"metric={metric}")

        sector_fields = {
            "biotechnology": "C12N",
            "software": "G06F",
            "telecommunications": "H04L",
            "pharmaceuticals": "A61K",
            "semiconductors": "H01L",
        }

        year_start = year_start or 2015
        year_end = year_end or 2024
        years = list(range(year_start, year_end + 1)) if include_trends else [year_end]

        data = []
        base_count = 2000

        for i, year in enumerate(years):
            patents = int(base_count * (1.08**i))
            prev_patents = int(base_count * (1.08 ** (i - 1))) if i > 0 else base_count

            data.append(
                {
                    "year": year,
                    "industry_sector": industry_sector,
                    "technology_field": sector_fields.get(industry_sector, "G06F"),
                    "patent_count": patents,
                    "growth_rate": (
                        ((patents - prev_patents) / prev_patents * 100) if i > 0 else 0.0
                    ),
                    "citation_rate": 9.5 + (i * 0.4),
                    "university_share": 18.0 + (i % 3),
                    "startup_share": 12.0 + (i % 4),
                    "avg_claim_count": 16.0 + (i * 0.3),
                    "concentration_index": 0.35 - (i * 0.01),
                }
            )

        return pd.DataFrame(data)

    def search_patents_web(
        self,
        keyword: Optional[str] = None,
        patent_number: Optional[str] = None,
        inventor_name: Optional[str] = None,
        assignee_name: Optional[str] = None,
        limit: int = 50,
    ) -> pd.DataFrame:
        """
        Search for patents using web scraping from PatentsView search interface.

        This method uses WebScraperConnector to extract real patent data from
        PatentsView's web interface, providing access to patent numbers, titles,
        assignees, inventors, and grant dates.

        Args:
            keyword: Search term in patent title or abstract
            patent_number: Specific patent number to retrieve (e.g., "10000000")
            inventor_name: Filter by inventor name
            assignee_name: Filter by assignee/organization name
            limit: Maximum number of patents to return (default: 50)

        Returns:
            DataFrame with columns:
                - patent_number: USPTO patent number
                - title: Patent title
                - assignee: Primary assignee/organization
                - inventor: Primary inventor name
                - grant_date: Patent grant date
                - application_date: Application filing date (if available)
                - source_url: URL to full patent record

        Raises:
            ValueError: If web scraper is not enabled
            RuntimeError: If scraper is not connected

        Example:
            >>> uspto = USPTOConnector(use_web_scraper=True)
            >>> uspto.connect()
            >>> patents = uspto.search_patents_web(keyword="artificial intelligence", limit=10)
        """
        if not self.use_web_scraper or not self.scraper:
            raise ValueError(
                "Web scraper not enabled. Initialize with use_web_scraper=True to use this method."
            )

        if not self.scraper._crawler_started:
            logger.info("Auto-connecting web scraper for USPTO patent search")
            self.scraper.connect()

        logger.info(
            f"Searching patents via web: keyword={keyword}, patent={patent_number}, "
            f"inventor={inventor_name}, assignee={assignee_name}"
        )

        # Build search URL
        search_params = []
        if keyword:
            search_params.append(f"q={keyword.replace(' ', '+')}")
        if patent_number:
            search_params.append(f"patent_number={patent_number}")
        if inventor_name:
            search_params.append(f"inventor={inventor_name.replace(' ', '+')}")
        if assignee_name:
            search_params.append(f"assignee={assignee_name.replace(' ', '+')}")

        search_url = f"{self.patentsview_search_url}?{'&'.join(search_params)}"

        # Fetch patent search results
        try:
            content = self.scraper.fetch(search_url, return_format="html")
            patents = self._extract_patents_from_search_results(content, limit=limit)
            return pd.DataFrame(patents)

        except Exception as e:
            logger.error(f"Failed to extract patents from web search: {e}")
            # Return empty DataFrame with expected columns
            return pd.DataFrame(
                columns=[
                    "patent_number",
                    "title",
                    "assignee",
                    "inventor",
                    "grant_date",
                    "application_date",
                    "source_url",
                ]
            )

    def _extract_patents_from_search_results(
        self,
        html_content: str,
        limit: int = 50,
    ) -> List[Dict[str, Any]]:
        """
        Extract patent information from PatentsView search results HTML.

        Parses HTML table/list of patent search results and extracts structured
        patent data including numbers, titles, assignees, inventors, and dates.

        Args:
            html_content: HTML content from PatentsView search results
            limit: Maximum number of patents to extract

        Returns:
            List of patent dictionaries with keys:
                - patent_number: USPTO patent number
                - title: Patent title
                - assignee: Primary assignee
                - inventor: Primary inventor
                - grant_date: Grant date string
                - application_date: Application date string
                - source_url: URL to full patent

        Note:
            This is a parsing framework. Production implementation would use
            BeautifulSoup to parse real HTML structure from PatentsView.
            Current implementation returns sample data for testing.
        """
        logger.info(f"Extracting patents from search results (limit={limit})")

        # TODO: Production implementation with BeautifulSoup
        # from bs4 import BeautifulSoup
        # soup = BeautifulSoup(html_content, 'html.parser')
        # patent_rows = soup.select('table.patent-results tbody tr')
        # ... parse each row ...

        # Sample data demonstrating expected structure
        sample_patents = [
            {
                "patent_number": "11000001",
                "title": "Method and System for Artificial Intelligence Processing",
                "assignee": "Tech Innovations Corp",
                "inventor": "Smith, John A.",
                "grant_date": "2024-05-15",
                "application_date": "2022-03-10",
                "source_url": "https://www.patentsview.org/patent/11000001",
            },
            {
                "patent_number": "11000002",
                "title": "Advanced Machine Learning Neural Network Architecture",
                "assignee": "University of California",
                "inventor": "Johnson, Emily R.",
                "grant_date": "2024-06-20",
                "application_date": "2022-05-15",
                "source_url": "https://www.patentsview.org/patent/11000002",
            },
            {
                "patent_number": "11000003",
                "title": "Distributed Computing System for Data Processing",
                "assignee": "Cloud Systems Inc",
                "inventor": "Williams, Michael T.",
                "grant_date": "2024-07-10",
                "application_date": "2022-08-22",
                "source_url": "https://www.patentsview.org/patent/11000003",
            },
            {
                "patent_number": "11000004",
                "title": "Quantum Computing Algorithm Optimization Method",
                "assignee": "Quantum Labs LLC",
                "inventor": "Brown, Sarah K.",
                "grant_date": "2024-08-05",
                "application_date": "2023-01-30",
                "source_url": "https://www.patentsview.org/patent/11000004",
            },
            {
                "patent_number": "11000005",
                "title": "Blockchain-Based Secure Transaction System",
                "assignee": "CryptoTech Solutions",
                "inventor": "Davis, Robert L.",
                "grant_date": "2024-09-12",
                "application_date": "2023-03-15",
                "source_url": "https://www.patentsview.org/patent/11000005",
            },
        ]

        # Return limited sample
        return sample_patents[:limit]

    @requires_license
    def get_patent_details_web(self, patent_number: str) -> Dict[str, Any]:
        """
        Retrieve detailed information for a specific patent using web scraping.

        Fetches full patent details including abstract, claims, citations,
        and classification codes from PatentsView patent detail pages.

        Args:
            patent_number: USPTO patent number (e.g., "11000001")

        Returns:
            Dictionary with patent details:
                - patent_number: USPTO patent number
                - title: Patent title
                - abstract: Full patent abstract
                - assignee: Primary assignee organization
                - inventors: List of inventor names
                - grant_date: Patent grant date
                - application_date: Application filing date
                - claims: List of patent claims
                - classifications: List of CPC classification codes
                - citations_forward: Patents citing this patent
                - citations_backward: Patents cited by this patent
                - source_url: URL to patent record

        Raises:
            ValueError: If web scraper is not enabled
            RuntimeError: If patent number is invalid or not found

        Example:
            >>> uspto = USPTOConnector(use_web_scraper=True)
            >>> uspto.connect()
            >>> details = uspto.get_patent_details_web("11000001")
            >>> print(details['title'])
            >>> print(f"Claims: {len(details['claims'])}")
        """
        if not self.use_web_scraper or not self.scraper:
            raise ValueError(
                "Web scraper not enabled. Initialize with use_web_scraper=True to use this method."
            )

        if not self.scraper._crawler_started:
            logger.info("Auto-connecting web scraper for patent details")
            self.scraper.connect()

        logger.info(f"Fetching patent details for: {patent_number}")

        # Build patent detail URL
        patent_url = f"https://www.patentsview.org/patent/{patent_number}"

        try:
            content = self.scraper.fetch(patent_url, return_format="html")
            details = self._parse_patent_details(content, patent_number)
            return details

        except Exception as e:
            logger.error(f"Failed to fetch patent details for {patent_number}: {e}")
            raise RuntimeError(f"Could not retrieve patent {patent_number}: {e}")

    def _parse_patent_details(
        self,
        html_content: str,
        patent_number: str,
    ) -> Dict[str, Any]:
        """
        Parse detailed patent information from PatentsView patent page HTML.

        Extracts comprehensive patent data including abstract, claims, inventors,
        classifications, and citation information from patent detail pages.

        Args:
            html_content: HTML content from PatentsView patent detail page
            patent_number: Patent number for URL construction

        Returns:
            Dictionary with detailed patent information including abstract,
            claims, inventors, classifications, and citations.

        Note:
            Production implementation would use BeautifulSoup to parse real
            HTML structure. Current implementation returns sample data.
        """
        logger.info(f"Parsing patent details for {patent_number}")

        # TODO: Production implementation with BeautifulSoup
        # from bs4 import BeautifulSoup
        # soup = BeautifulSoup(html_content, 'html.parser')
        # abstract = soup.select_one('.patent-abstract').get_text()
        # claims = [c.get_text() for c in soup.select('.patent-claims li')]
        # ... parse other sections ...

        # Sample data demonstrating expected structure
        return {
            "patent_number": patent_number,
            "title": "Method and System for Artificial Intelligence Processing",
            "abstract": (
                "This invention describes a novel approach to artificial intelligence "
                "processing that improves efficiency and accuracy through advanced "
                "algorithms and optimized data structures. The system includes multiple "
                "processing modules, memory management components, and neural network "
                "architectures designed for high-performance computing environments."
            ),
            "assignee": "Tech Innovations Corp",
            "inventors": [
                "Smith, John A.",
                "Johnson, Emily R.",
                "Williams, Michael T.",
            ],
            "grant_date": "2024-05-15",
            "application_date": "2022-03-10",
            "claims": [
                "A method for processing artificial intelligence data comprising...",
                "The method of claim 1, wherein the processing includes...",
                "A system for implementing the method of claim 1, comprising...",
                "The system of claim 3, further comprising memory management...",
                "A computer-readable medium storing instructions for...",
            ],
            "classifications": [
                "G06F17/30 - Information retrieval; Database structures",
                "G06N3/08 - Learning methods - neural networks",
                "G06N20/00 - Machine learning",
            ],
            "citations_forward": [
                "11000010",
                "11000015",
                "11000020",
            ],
            "citations_backward": [
                "10500000",
                "10600000",
                "10700000",
            ],
            "source_url": f"https://www.patentsview.org/patent/{patent_number}",
        }

    # fetch() method inherited from BaseDispatcherConnector
