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A) Cookie-Policy-Architecture-Block Fallback auf DSE-Text wenn cookie via P15 deduped wurde. Erkennt jetzt auch single-doc Sites (Safetykon-Pattern). B) Konkrete-Aufgaben-Liste: Per-Doc-Cap (3) entfernt + globaler Cap 10→20. Safetykon zeigt jetzt 7 statt 4 Aufgaben. C) business_type-Klassifizierer: B2B-Service-Cluster aus P14 als Boost. Bei 2+ Service-Indikatoren (CE-Zertifizierung/Compliance/Auditierung) wird b2b_score angehoben. Safetykon: "B2C consulting" → "B2B (consulting)". D) Vendor-Extract Fallback auf DSE-Text wenn cookie deduped + keine CMP- Payloads. LLM extrahiert dann Vendors aus dem DSE-Text. Safetykon: 0 → 1 Vendor (Google Analytics aus dem DSE-Text erkannt). Smoke-Test Safetykon: alle 4 Polish-Items wirken, kein Regression. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
435 lines
19 KiB
Python
435 lines
19 KiB
Python
"""
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Business Profiler — detect business model from document texts.
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Pure keyword-based detection (deterministic, no LLM). Analyzes
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DSE, Impressum, AGB, Widerruf etc. together to build a profile
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that drives context-aware compliance checks.
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Example:
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profile = await detect_business_profile({"dse": "...", "impressum": "..."})
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profile.business_type # "b2c"
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profile.has_online_shop # True
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"""
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from __future__ import annotations
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import re
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from dataclasses import dataclass, field
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@dataclass
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class BusinessProfile:
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business_type: str = "unknown" # b2b, b2c, b2g, nonprofit, unknown
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industry: str = "unknown" # it_services, retail, healthcare, legal, craft, public, unknown
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has_online_shop: bool = False
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has_editorial_content: bool = False
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is_regulated_profession: bool = False
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regulated_profession_type: str = "" # arzt, anwalt, steuerberater, architekt, ""
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needs_odr: bool = False # Online-Streitbeilegung
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detected_services: list[str] = field(default_factory=list)
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confidence: float = 0.0
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# Wenn True: die Site selbst schliesst KEINEN Direktkauf-Vertrag
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# (typisch OEM-Konfigurator-Sites BMW/Audi/Mercedes — Vertrag laeuft
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# ueber den Vertragshaendler, nicht die Hersteller-Webseite).
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# Konsequenz: AGB/Widerruf/Nutzungsbedingungen sind NICHT PFLICHT
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# auf der Website, sondern werden beim Haendler ausgehaendigt.
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no_direct_sales: bool = False
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# ── Keyword lists ────────────────────────────────────────────────────
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_B2C_KEYWORDS = [
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"verbraucher", "warenkorb", "bestellung", "lieferung", "widerruf",
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"shop", "kaufpreis", "rueckgabe", "rückgabe", "endkunde", "kaeufer",
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"käufer", "privatkunde", "zahlungspflichtig bestellen",
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]
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_B2B_KEYWORDS = [
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# Discriminative — these don't appear in B2C consumer texts
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"geschaeftskunden", "geschäftskunden", "firmenkunde", "b2b",
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"industriekunden", "ausschliesslich gewerblich", "ausschließlich gewerblich",
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"ausschliesslich unternehmer", "ausschließlich unternehmer",
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"kein verbrauchergeschaeft", "kein verbrauchergeschäft",
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# Note: "unternehmen", "beratung", "consulting", "dienstleistung"
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# were removed — they match in any company text and bias toward B2B.
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]
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_B2G_KEYWORDS = [
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"koerperschaft des oeffentlichen rechts", "körperschaft des öffentlichen rechts",
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"gemeinde", "stadtverwaltung", "landesbehoerde", "landesbehörde",
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"kommunal", "buergerservice", "bürgerservice", "rathaus",
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"landesamt", "bundesamt", "oeffentliche verwaltung", "öffentliche verwaltung",
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"oeffentlicher dienst", "öffentlicher dienst",
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]
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_NONPROFIT_KEYWORDS = [
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"gemeinnuetzig", "gemeinnützig", "verein", "stiftung", "e.v.",
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"spende", "ehrenamtlich", "satzung",
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]
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_REGULATED_PROFESSIONS = {
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# Anwalt — nur spezifische Begriffe, nicht "anwalt" allein
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# (matcht sonst Redaktionsanwalt, Justiziar etc.)
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"rechtsanwalt": "anwalt",
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"rechtsanwaeltin": "anwalt",
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"rechtsanwältin": "anwalt",
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"kanzlei": "anwalt",
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"rechtsanwaltskammer": "anwalt",
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"zugelassener anwalt": "anwalt",
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# Arzt — "praxis" entfernt (matcht "in der Praxis")
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"arztpraxis": "arzt",
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"zahnarzt": "arzt",
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"facharzt": "arzt",
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"aerztekammer": "arzt",
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"ärztekammer": "arzt",
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"kassenärztlich": "arzt",
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"kassenaerztlich": "arzt",
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# Steuerberater
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"steuerberater": "steuerberater",
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"steuerberaterin": "steuerberater",
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"steuerberaterkammer": "steuerberater",
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# Architekt
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"architekt": "architekt",
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"architektin": "architekt",
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"architektenkammer": "architekt",
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# Notar
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"notar": "notar",
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"notariat": "notar",
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# Apotheker
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"apotheke": "apotheker",
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"apotheker": "apotheker",
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}
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_ONLINE_SHOP_KEYWORDS = [
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"warenkorb", "checkout", "bestellung", "lieferung", "versand",
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"paypal", "kreditkarte", "klarna", "sofortueberweisung",
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"sofortüberweisung", "zahlungsarten", "versandkosten",
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"lieferzeit", "retour", "paketdienst",
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]
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_EDITORIAL_KEYWORDS = [
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"blog", "ratgeber", "news", "redaktion", "artikel", "magazin",
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"beitrag", "kommentar", "podcast", "newsletter", "autor",
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]
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_INDUSTRY_KEYWORDS = {
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# "software/cloud/hosting" are often mentioned in privacy texts of any
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# vendor (Cloud-Hosting fuer Newsletter, SaaS-Tools etc.) without making
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# the company an IT-services vendor itself. Keep the list deliberately
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# narrow: only patterns that strongly suggest IT/SaaS as the core business.
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"it_services": ["saas-anbieter", "software-as-a-service",
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"ihr saas", "ihre cloud", "hosting-provider",
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"api-anbieter", "developer-portal"],
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"retail": ["shop", "warenkorb", "versand", "lieferung", "einzelhandel"],
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"healthcare": ["arzt", "praxis", "patient", "gesundheit", "therapie", "klinik"],
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"legal": ["kanzlei", "rechtsanwalt", "mandant", "anwalt"],
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"craft": ["handwerk", "meister", "werkstatt", "montage", "gewerk"],
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"public": ["kommune", "stadtverwaltung", "buergerservice", "bürgerservice", "rathaus"],
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"finance": ["bank", "versicherung", "finanz", "kredit", "anlage"],
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"education": ["schule", "bildung", "unterricht", "lehrplan", "schueler", "schüler"],
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"consulting": ["beratung", "consulting", "schulung", "seminar", "gutachten", "audit",
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"arbeitssicherheit", "brandschutz", "sicherheitstechnik", "zertifizierung"],
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"manufacturing": ["fertigung", "produktion", "maschinenbau", "anlagenbau", "zulieferer",
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"werkzeugbau", "spritzguss", "cnc", "industrietechnik"],
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"automotive": ["fahrzeug", "kraftfahrzeug", "kfz", "automobil", "neuwagen",
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"gebrauchtwagen", "fahrzeugempfehlung", "modellreihe",
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"modellpalette", "antriebs", "motor", "reifen", "elektroauto",
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"verbrenner", "hybridfahrzeug", "leasing", "werkstatt",
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"wartung und reparatur", "probefahrt", "bmw", "mercedes",
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"audi", "volkswagen", "porsche", "opel"],
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"media": ["redaktion", "verlag", "medien", "journalismus", "presse"],
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}
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# Terms that indicate "versicherung" / "bank" is only mentioned as a
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# §34d/§34c GewO disclosure (Versicherungsvermittler / Finanzanlagenvermittler)
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# rather than the core business. Used to suppress false finance matches.
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_VERMITTLER_CONTEXT_TERMS = [
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"versicherungsvermittler", "berufshaftpflichtversicherung",
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"vermittlerregister", "§34d", "§ 34 d", "§34c", "§ 34 c",
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"finanzanlagenvermittler", "ihk muenchen", "ihk münchen",
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]
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_TRACKING_SERVICES = {
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"google analytics": "Google Analytics",
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"google tag manager": "Google Tag Manager",
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"matomo": "Matomo",
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"facebook pixel": "Facebook Pixel",
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"meta pixel": "Meta Pixel",
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"hotjar": "Hotjar",
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"hubspot": "HubSpot",
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"mailchimp": "Mailchimp",
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"linkedin insight": "LinkedIn Insight",
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"google ads": "Google Ads",
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"google adsense": "Google AdSense",
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"google maps": "Google Maps",
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"youtube": "YouTube",
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"vimeo": "Vimeo",
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"cloudflare": "Cloudflare",
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"sentry": "Sentry",
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"intercom": "Intercom",
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"zendesk": "Zendesk",
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"stripe": "Stripe",
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"paypal": "PayPal",
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}
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# ── Detection logic ──────────────────────────────────────────────────
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def _count_hits(text: str, keywords: list[str]) -> int:
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return sum(1 for kw in keywords if kw in text)
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async def detect_business_profile(documents: dict[str, str]) -> BusinessProfile:
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"""Analyze all document texts together to detect business model.
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Args:
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documents: dict mapping doc_type -> text (e.g. {"dse": "...", "impressum": "..."})
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"""
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profile = BusinessProfile()
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if not documents:
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return profile
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# Merge all texts for keyword search
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full_text = "\n".join(documents.values()).lower()
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full_text = full_text.replace("\xad", "") # strip soft hyphens
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# ── Tracking services (use full service detector) ──────────
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try:
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from compliance.services.service_detector import detect_services_in_text
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detected = detect_services_in_text(full_text)
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profile.detected_services = [s["name"] for s in detected]
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except Exception:
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# Fallback to simple keyword list
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for pattern, label in _TRACKING_SERVICES.items():
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if pattern in full_text:
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profile.detected_services.append(label)
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# ── Online shop ──────────────────────────────────────────────
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shop_hits = _count_hits(full_text, _ONLINE_SHOP_KEYWORDS)
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profile.has_online_shop = shop_hits >= 3
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# ── Editorial content ────────────────────────────────────────
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editorial_hits = _count_hits(full_text, _EDITORIAL_KEYWORDS)
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profile.has_editorial_content = editorial_hits >= 2
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# ── Regulated profession ─────────────────────────────────────
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# Only check impressum text (not full text) — keywords like "rechtsanwalt"
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# appear as contact persons in DSI texts (e.g. Spiegel's "Rechtsanwalt Kruse")
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# but that doesn't mean the company IS a law firm.
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impressum_text = documents.get("impressum", "").lower().replace("\xad", "")
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if not impressum_text:
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impressum_text = full_text[:2000] # Fallback: first 2000 chars
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for keyword, prof_type in _REGULATED_PROFESSIONS.items():
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if keyword in impressum_text:
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# Extra guard: "rechtsanwalt" must appear near the company description,
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# not just as a contact person name
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if keyword in ("rechtsanwalt", "rechtsanwaeltin", "rechtsanwältin"):
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# Check if it's in the first 500 chars (company description area)
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if keyword not in impressum_text[:500]:
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continue
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profile.is_regulated_profession = True
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profile.regulated_profession_type = prof_type
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break
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# ── Business type ────────────────────────────────────────────
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b2c_score = _count_hits(full_text, _B2C_KEYWORDS)
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b2b_score = _count_hits(full_text, _B2B_KEYWORDS)
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b2g_score = _count_hits(full_text, _B2G_KEYWORDS)
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nonprofit_score = _count_hits(full_text, _NONPROFIT_KEYWORDS)
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# P17-C: B2B-Dienstleister-Cluster (P14) als Boost — wenn ein Unternehmen
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# CE-Zertifizierung / Compliance-Beratung / Auditierung / Schulungen anbietet,
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# ist es i.d.R. B2B auch wenn die strikten B2B-Keywords nicht greifen.
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b2b_service_boost = _count_hits(full_text, _B2B_SERVICE_POSITIVE)
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if b2b_service_boost >= 2:
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b2b_score += min(3, b2b_service_boost - 1)
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# Missing documents as signal
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has_agb = "agb" in documents
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has_widerruf = "widerruf" in documents
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if not has_agb:
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b2c_score -= 1 # No AGB → less likely B2C
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if not has_widerruf:
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b2c_score -= 1 # No Widerruf → less likely B2C shop
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if profile.has_online_shop:
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b2c_score += 3 # Strong B2C signal
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scores = {
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"b2c": b2c_score,
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"b2b": b2b_score,
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"b2g": b2g_score,
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"nonprofit": nonprofit_score,
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}
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best = max(scores, key=scores.get) # type: ignore[arg-type]
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best_val = scores[best]
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if best_val >= 2:
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profile.business_type = best
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total = sum(max(0, v) for v in scores.values())
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profile.confidence = round(best_val / total, 2) if total > 0 else 0.5
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else:
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# Fallback: prefer B2C when the text mentions Verbraucherrechte,
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# editorial content, or consumer-direction signals — even without
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# checkout keywords. Only fall back to B2B if discriminative B2B
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# markers fired (which the keyword list above already filtered to
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# genuinely B2B-only terms).
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consumer_hint = (
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"verbraucher" in full_text
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or "widerruf" in full_text
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or "kunde" in full_text
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or profile.has_editorial_content
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)
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if b2b_score >= 1 and not consumer_hint:
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profile.business_type = "b2b"
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profile.confidence = 0.4
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elif consumer_hint:
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profile.business_type = "b2c"
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profile.confidence = 0.4
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else:
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profile.business_type = "unknown"
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profile.confidence = 0.2
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# ── ODR (Online-Streitbeilegung) ─────────────────────────────
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# Required for B2C with online shop (EU Regulation 524/2013)
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profile.needs_odr = (
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profile.business_type == "b2c" and profile.has_online_shop
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)
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# ── Industry ─────────────────────────────────────────────────
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industry_scores: dict[str, int] = {}
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for industry, keywords in _INDUSTRY_KEYWORDS.items():
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hits = _count_hits(full_text, keywords)
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if hits >= 1:
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industry_scores[industry] = hits
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# Suppress finance/insurance false positives caused by §34d/§34c GewO
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# disclosures (Versicherungsvermittler, Berufshaftpflicht, etc.) — these
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# are pflichtangaben for many companies (e.g. BMW AG) without being
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# actual financial services providers.
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if industry_scores.get("finance"):
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vermittler_hits = _count_hits(full_text, _VERMITTLER_CONTEXT_TERMS)
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if vermittler_hits >= 2:
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# Only the §34d boilerplate triggered the match — drop or shrink.
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non_insurance_finance = _count_hits(
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full_text, ["bank", "finanz", "kredit", "anlage"],
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)
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if non_insurance_finance == 0:
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industry_scores.pop("finance", None)
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else:
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industry_scores["finance"] = non_insurance_finance
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# Require a clear winner — if top score is 1 and there are ties, prefer
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# "unknown" over guessing.
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if industry_scores:
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top = max(industry_scores.values())
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winners = [k for k, v in industry_scores.items() if v == top]
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if top >= 2 or len(winners) == 1:
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profile.industry = winners[0]
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else:
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profile.industry = "unknown"
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elif profile.is_regulated_profession:
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prof_map = {"anwalt": "legal", "arzt": "healthcare",
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"steuerberater": "finance", "architekt": "craft"}
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profile.industry = prof_map.get(profile.regulated_profession_type, "unknown")
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# ── no_direct_sales (OEM-Konfigurator-Pattern) ───────────────
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# Hersteller-Sites die nur konfigurieren + zu Vertragshaendlern
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# weiterleiten (BMW/Audi/Mercedes/VW/Porsche) schliessen KEINEN
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# Direkt-Kaufvertrag. AGB/Widerruf/Nutzungsbedingungen sind dort
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# nicht Pflicht — werden beim Haendler ausgehaendigt.
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profile.no_direct_sales = _detect_no_direct_sales(full_text)
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return profile
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# P14: drei Cluster die jeweils unabhaengig no_direct_sales=True triggern.
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# Cluster A: OEM-Konfigurator-Pattern (Auto-Hersteller mit Vertragshaendler-Netz)
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_OEM_POSITIVE = [
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"vertragshaendler", "vertragshändler", "vertragspartner",
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"vertragswerkstatt", "haendlersuche", "händlersuche",
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"niederlassung", "vertretung", "autorisierter haendler",
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"autorisierter händler", "ihr haendler vor ort",
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"ihr händler vor ort", "haendler in ihrer naehe",
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"händler in ihrer nähe", "probefahrt vereinbaren",
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"anfrage an haendler", "anfrage an händler",
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"konfigurator", "fahrzeug konfigurieren",
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"ihre individuelle anfrage",
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"bmw vertriebs", "audi vertriebs", "mercedes-benz vertriebs",
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"volkswagen vertriebs", "porsche zentrum",
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# OEM-Markennamen im Pflichttext (Datenschutz erwaehnt Hersteller)
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"bmw ag", "audi ag", "mercedes-benz ag", "volkswagen ag",
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"porsche ag", "opel automobile gmbh",
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]
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# Cluster B: B2B-Dienstleister (Beratung / Compliance / Schulung / CE)
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_B2B_SERVICE_POSITIVE = [
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"ce-zertifizierung", "ce zertifizierung",
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"ce-konformitaet", "ce-konformität",
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"ce-kennzeichnung", "ce kennzeichnung",
|
|
"compliance-beratung", "compliance beratung",
|
|
"arbeitssicherheit", "product compliance",
|
|
"produktsicherheit", "produkthaftung",
|
|
"auditierung", "auditor", "auditierungen",
|
|
"schulungen", "workshops", "akademie",
|
|
"beratungsleistungen", "consultingleistungen",
|
|
"consulting services", "managementsystem",
|
|
"datenschutzbeauftragter (extern)",
|
|
"externer datenschutzbeauftragter",
|
|
"datenschutz-audit", "tisax", "iso 27001",
|
|
"iso 9001", "iso 14001", "iso 45001",
|
|
"gefaehrdungsbeurteilung", "gefährdungsbeurteilung",
|
|
"betriebsbeauftragter", "fachkraft fuer arbeitssicherheit",
|
|
"fachkraft für arbeitssicherheit",
|
|
]
|
|
|
|
# Cluster C: NGO / Verein / oeffentliche Verwaltung
|
|
_NONPROFIT_PUBLIC_POSITIVE = [
|
|
"spendenkonto", "vereinsregister", "gemeinnuetzig",
|
|
"gemeinnützig", "ehrenamtlich", "foerderverein",
|
|
"förderverein", "stiftung", "buergeramt", "bürgeramt",
|
|
"landratsamt", "kommunalverwaltung",
|
|
]
|
|
|
|
# Backwards-compat
|
|
_NO_DIRECT_SALES_POSITIVE = (
|
|
_OEM_POSITIVE + _B2B_SERVICE_POSITIVE + _NONPROFIT_PUBLIC_POSITIVE
|
|
)
|
|
|
|
# Indikatoren GEGEN no_direct_sales: echte Online-Shop-Funktionen.
|
|
_DIRECT_SALES_NEGATIVE = [
|
|
"in den warenkorb", "warenkorb hinzu", "zur kasse",
|
|
"jetzt kaufen", "kostenpflichtig bestellen",
|
|
"zahlungspflichtig bestellen", "sofort-kauf",
|
|
"online bestellen", "lieferadresse", "rechnungsadresse",
|
|
"versandkosten", "lieferzeit", "lieferbedingungen",
|
|
"checkout", "stueckpreis", "stückpreis",
|
|
]
|
|
|
|
|
|
def _detect_no_direct_sales(full_text: str) -> bool:
|
|
"""Heuristik: True wenn Site keinen Direkt-Vertrieb mit B2C-Kunden hat.
|
|
|
|
Trifft fuer 3 Cluster zu (jeweils mind. 2 Treffer im Cluster):
|
|
A) OEM-Konfigurator (Auto-Hersteller)
|
|
B) B2B-Dienstleister (Beratung/Compliance/Schulung)
|
|
C) NGO / oeffentliche Verwaltung
|
|
|
|
Negativ-Signale (echte Shop-Funktionen) zaehlen gegen den Cluster:
|
|
nur True wenn pos > neg.
|
|
"""
|
|
text = full_text.lower()
|
|
oem = sum(1 for k in _OEM_POSITIVE if k in text)
|
|
b2b = sum(1 for k in _B2B_SERVICE_POSITIVE if k in text)
|
|
npg = sum(1 for k in _NONPROFIT_PUBLIC_POSITIVE if k in text)
|
|
neg = sum(1 for k in _DIRECT_SALES_NEGATIVE if k in text)
|
|
# Jeder Cluster ist eigenstaendig: 2 Treffer + weniger Negativ-Signale
|
|
# als Cluster-Treffer.
|
|
if oem >= 2 and oem > neg:
|
|
return True
|
|
if b2b >= 2 and b2b > neg:
|
|
return True
|
|
if npg >= 2 and npg > neg:
|
|
return True
|
|
return False
|