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Service · Patent & IP data

Patent & IP Data Scraping Services

With assignees normalised and families linked, not raw office records.

Patent and IP data scraping is the automated collection of publicly available intellectual property records from patent and trademark offices — bibliographic data, classifications, claims and abstracts where public, family linkage, legal status and assignee information — with assignee names normalised and patent families linked so portfolio analysis is possible.

One company appears as forty different assignee strings across offices, and one invention appears as thirty separate filings across jurisdictions. Counting raw records tells you nothing about a portfolio. Normalisation and family linkage are the entire job.

Free pilot on your own sources, returned in 48 hours. No card, no trial clock — and you keep the sample data either way.

Assignee normalisation Family linkage across offices Free pilot sample in 48 hours
patent_records_2026-08-05.jsonl LIVE FEED
{"record_key":"aw-pat-EP4218841A1", "office":"EPO", "publication_number":"EP4218841A1", "kind_code":"A1", "title":"Method for thermal management of battery cells", "assignee_published":"EXAMPLE MOTORS EUROPE GMBH", "assignee_normalised":"Example Motors Group", "assignee_confidence":0.94, "family_id":"aw-fam-771204", "family_size":14, "family_jurisdictions":["EP","US","CN","JP","KR"], "cpc_codes":["H01M10/613","B60L58/26"], "priority_date":"2021-11-04", "filing_date":"2022-11-02", "publication_date":"2023-08-02", "legal_status":"pending", "status_as_of":"2026-08-01", "citations_forward":7} {"record_key":"aw-tm-EU018884120", "record_type":"trademark", "mark_text":"THERMOCELL", "nice_classes":[9,12], "status":"registered"}
2 of 8,412,700 records · run 2026-08-05assignee normalised 95.8% · schema v3.9
Our Data Powers
B2C Marketplace
amazon
D2C + Marketplace
NYKAA
D2C + Marketplace
Walmart
FMCG Marketplace
udaan
Food Delivery
Uber Eats
Quick Commerce
blinkit
Taxi Aggregator
Uber
E-Commerce
Tmall

Key facts at a glance

What it is
Managed collection of public patent and trademark records with assignee normalisation and family linkage
Offices
Major patent and trademark offices plus national offices, with office as a first-class field
Assignee normalisation
Published assignee strings mapped to a normalised entity with confidence scoring
Family linkage
Filings for the same invention across jurisdictions linked into a family
Classification
CPC, IPC and Nice classification codes captured for technology and sector filtering
Status
Legal status as of a stated date, since status changes and public data lags
Citations
Forward and backward citation counts where offices publish them
Who it's for
IP and R&D teams, competitive intelligence, corporate development, investors and researchers
95.8%assignee normalisation ratewith confidence scores
Family linkageacross jurisdictionsone invention, one family
Status as-of datesnot asserted currencypublic data lags
No legal opinionsstated boundarybibliographic data only

Key takeaways

  • What it is: Managed collection of public patent and trademark records with assignee normalisation and family linkage
  • Offices: Major patent and trademark offices plus national offices, with office as a first-class field
  • Assignee normalisation: Published assignee strings mapped to a normalised entity with confidence scoring
  • Family linkage: Filings for the same invention across jurisdictions linked into a family
  • Classification: CPC, IPC and Nice classification codes captured for technology and sector filtering
  • Status: Legal status as of a stated date, since status changes and public data lags

Last verified 5 August 2026 by the Actowiz Solutions Data Engineering team.

Definition

What is patent data scraping, and why are raw counts misleading?

Patent and IP data scraping is the automated collection of publicly available intellectual property records: patent applications and grants with bibliographic data, titles, abstracts and claims where published, classification codes, inventor and assignee information, priority and filing dates, legal status, citations, and trademark records.

Patent offices publish this deliberately — disclosure in exchange for protection is the bargain — so access is straightforward. The difficulty is that raw record counts are almost meaningless for the questions people actually ask.

Why counting records fails

  • One invention, many filings. A single invention is filed in multiple jurisdictions, generating separate records. Counting them inflates portfolio size several-fold, unevenly by company strategy.
  • Assignee name chaos. One company appears as dozens of strings: legal entity variants, subsidiary names, transliterations, abbreviations and simple typos in office records.
  • Ownership changes. Patents are assigned and reassigned, and office records reflect this inconsistently and with lag.
  • Kind codes. Application publications and grants for the same filing are separate records, so counting both double-counts.
  • Continuations and divisionals. These generate multiple records from one original disclosure.

What we do about it

Assignee strings are normalised to entity identities with confidence scoring, including subsidiary mapping to parent where identifiable. Filings for the same invention are linked into families with a family identifier, size and jurisdiction list, so portfolio analysis counts inventions rather than paperwork.

Legal status carries a status_as_of date, because public status data lags actual events and a status presented as current is misleading.

What we do not do

We do not provide legal opinions. Validity, infringement, freedom-to-operate and patentability are legal assessments requiring qualified counsel and, usually, licensed analytical tools. We also do not resell licensed commercial patent databases. What we provide is accurately extracted, normalised public bibliographic data that your IP counsel or analytics tools can work from.

What we collect

Six categories of patent and IP data

Portfolio mapping and technology landscape work are the largest use cases.

Bibliographic records

The core record, structured.

  • Publication and application numbers
  • Kind codes and document types
  • Titles and abstracts
  • Claims where publicly available
  • Priority, filing and publication dates

Assignees & inventors

Who owns and who invented.

  • Published assignee strings
  • Normalised entity with confidence
  • Subsidiary to parent mapping
  • Inventor names as published
  • Assignee change records where published

Family linkage

One invention, one unit of analysis.

  • Family identifier and size
  • Jurisdictions in the family
  • Priority relationships
  • Continuations and divisionals
  • Family-level date ranges

Classification

Technology filtering that works.

  • CPC and IPC codes
  • Nice classes for trademarks
  • Normalised technology area mapping
  • Classification co-occurrence
  • Classification trend over time

Legal status

With honest currency.

  • Status as published with as-of date
  • Grant, refusal and withdrawal events
  • Expiry and lapse where published
  • Opposition and appeal indications
  • Renewal and maintenance signals

Citations & trademarks

Influence and brand protection.

  • Forward and backward citation counts
  • Citing and cited record references
  • Trademark marks, classes and status
  • Trademark opposition indications
  • Design registration records where public
Service scope

What the ecommerce data scraping service includes

A managed engagement, not a tool licence. We own the pipeline and everything that breaks in it.

✓ Included in every engagement

  • Assignee normalisation with confidence scoring and separate subsidiary mapping
  • Family linkage so portfolio analysis counts inventions rather than filings
  • Legal status always delivered with a status_as_of date
  • Original script retained alongside transliteration for non-Latin filings
  • Per-office statement of what full-text availability actually looks like
  • Source discovery, scoping and a written collection plan
  • Free pilot on your own sources before any commitment
  • Full pipeline build, hosting and proxy infrastructure
  • Schema design, validation and sampled human QA on every run
  • Ongoing maintenance when source layouts change — our cost, not yours
  • Delivery to your warehouse, bucket, SFTP or API endpoint
  • Documented methodology and compliance notes for your legal review

× Not included — stated upfront

  • Validity, infringement, freedom-to-operate or patentability assessments
  • Resale of licensed commercial patent databases or their analytical layers
  • Any assertion that legal status reflects the present moment
  • Full text where an office publishes only bibliographic data openly
  • Anything behind a login, paywall or credentialed session
  • Personal data beyond a documented lawful basis
  • Licensed third-party datasets we do not hold rights to
  • Guarantees about fields a source simply does not publish
Schema

Patent and IP data fields you receive

Every engagement delivers a documented schema. These are the core fields; the full dictionary runs to 110+ and is agreed during scoping.

Deliverable schema — v3.9 core fields (full dictionary: 110+ fields)
Field Type What it captures Refresh
record_key / office / record_type string / enum Stable record identity, issuing office and whether patent, trademark or design Per run
publication_number / kind_code string Office publication number and kind code, so applications and grants are distinguishable Per run
title / abstract string Title and abstract as published, in the publication language Per run
assignee_published / assignee_normalised / assignee_confidence string / decimal Assignee as recorded, normalised entity and confidence in the mapping Per run
family_id / family_size / family_jurisdictions string / int / array Family linkage so portfolio analysis counts inventions not filings Per run
cpc_codes / ipc_codes / nice_classes array Classification codes for technology and sector filtering Per run
priority_date / filing_date / publication_date date The three dates that anchor any timing analysis Per run
legal_status / status_as_of enum / date Status as published together with the date that status reflects Monthly
citations_forward / citations_backward int Citation counts where the office publishes them Monthly
inventor_names array Inventor names as published in the official record Per run
mark_text / mark_status string / enum For trademark records, the mark and its registration status Monthly

Legal status always ships with status_as_of. Public status data lags actual events, sometimes by months, and presenting a status as current when it reflects an older office update is the most common way patent datasets mislead.

Coverage

Offices and jurisdictions we collect from

Office data structures differ substantially. Coverage is built to your jurisdiction and technology scope.

EPO publicationsUSPTO publicationsWIPO PCT publicationsUKIPODPMA (DE)INPI (FR)JPOKIPOCNIPAIP AustraliaCIPO (CA)INPI (BR)Indian Patent OfficeGCC Patent OfficeEUIPO trademarksUSPTO trademarksNational trademark registersDesign registration registersPublic status registersOfficial gazettes and bulletins

We collect from official office publications and public registers. We do not resell licensed commercial patent databases, and we do not provide the analytical layers those products include. Request a source we don't list →

Markets served

Countries and markets where this service is in highest demand

We deliver into 40+ countries. These are the markets where this particular service is requested most, and the reason demand concentrates there.

Highest-demand markets for this service, and why demand concentrates there
Market Why demand concentrates here
Europe & United States EPO and USPTO publish the deepest openly accessible bibliographic data, making these the core of most engagements.
Japan, South Korea & China Very high filing volumes where transliteration inconsistency makes assignee normalisation most valuable.
United Kingdom & Germany Strong national office publication alongside EPO, useful for national-route filing analysis.
India & GCC Growing filing volumes with improving publication accessibility, increasingly requested for emerging-market portfolio work.

North America

United StatesCanadaMexico

United Kingdom & Ireland

United KingdomIreland

Western Europe

GermanyFranceNetherlandsBelgiumSpainItalySwitzerlandAustria

Nordics

SwedenNorwayDenmarkFinland

Middle East

United Arab EmiratesSaudi ArabiaQatarKuwaitIsrael

Asia Pacific

SingaporeAustraliaNew ZealandJapanSouth KoreaMalaysiaIndonesiaThailandVietnamPhilippines

South Asia

IndiaBangladeshSri LankaPakistan

LATAM

BrazilArgentinaChileColombia

Africa

South AfricaNigeriaKenyaEgypt

We run production collection across 40+ countries. Coverage depth varies by market and by source, so we confirm what is actually available for your specific markets during scoping rather than claiming uniform global coverage. Ask about a market we don't list →

Who buys this data

Which teams buy patent and IP data

IP and R&D teams dominate, with corporate development and investors following.

Head of IP

Corporates
The problem

Competitor portfolio assessment is defeated by assignee name variants and by filings counted instead of inventions.

What we deliver

Assignee-normalised, family-linked portfolio views with classification and status, so competitor portfolios are comparable.

Metric that moves

Portfolio insight

R&D / Technology Scouting Lead

Corporates
The problem

Identifying where technology activity is concentrating requires classification-level filing trends across offices.

What we deliver

Filing trends by classification, assignee and jurisdiction over time, with families counted once.

Metric that moves

Scouting hit rate

Corporate Development

Acquirers and licensors
The problem

Target assessment needs a clean view of what a company actually owns, across subsidiaries and jurisdictions.

What we deliver

Subsidiary-to-parent mapped portfolios with family linkage, status and expiry signals for diligence support.

Metric that moves

Diligence speed

Competitive Intelligence Lead

Corporates
The problem

Competitor R&D direction is visible in filings months or years before products, if the data is normalised.

What we deliver

New filing alerting by competitor entity and classification, with family and priority dates for timing analysis.

Metric that moves

Signal lead time

Investment Analyst

Deep tech and IP-focused funds
The problem

IP quality assessment needs family breadth, citation and status data rather than raw counts.

What we deliver

Family-level portfolio metrics with citation counts and status, delivered as modelling-ready panels.

Metric that moves

Diligence confidence

Academic Researcher

Universities and institutes
The problem

Innovation research needs reproducible, normalised patent data with documented methodology.

What we deliver

Documented extraction with assignee normalisation confidence and family definitions stated, reproducible.

Metric that moves

Reproducibility

Use cases

How patent data gets used in practice

Four patterns, with the outcome each is judged on.

Competitor portfolio mapping

Assignee strings are normalised to entities with subsidiary mapping, and filings are linked into families, so a competitor portfolio is counted as inventions across jurisdictions rather than as raw records.

Outcome: Portfolio comparisons that reflect invention counts rather than filing strategy artefacts.

Technology landscape and trend analysis

Filing volumes are analysed by classification code, assignee and jurisdiction over time using family-level counts and priority dates, showing where activity is concentrating.

Outcome: Technology direction identified from filing behaviour ahead of product announcements.

New filing alerting on competitor entities

Publications are monitored against normalised competitor entities and classification scopes, with family and priority dates included so timing can be assessed.

Outcome: Competitor R&D direction surfaced as publications appear rather than through periodic review.

Portfolio support for diligence

Target portfolios are assembled with subsidiary-to-parent mapping, family linkage, legal status with as-of dates and expiry signals, giving counsel a clean starting inventory.

Outcome: Diligence starting from a structured inventory rather than from raw office searches.

Engagement examples

Two engagements, anonymised

Clients rarely permit naming. These are real engagement shapes with identifying detail removed, so you can judge whether the work resembles your situation.

Corporate IP team · EU

A competitor portfolio was understated by fragmentation

Situation

Portfolio analysis counted filings under a single assignee string, missing subsidiary and transliterated entity variants across offices.

What we ran

Assignee normalisation with subsidiary-to-parent mapping and confidence scoring, plus family linkage so inventions were counted once.

Result

The competitor portfolio resolved to substantially more inventions than previously understood, changing the technology response.

Deep tech investor · US

Diligence started from raw office search exports

Situation

Each diligence process began with unstructured office searches, consuming analyst time before any assessment could begin.

What we ran

Normalised, family-linked portfolio inventories with classification tagging, legal status and as-of dates delivered per target.

Result

Counsel and analysts began from a structured inventory rather than assembling one.

Examples are anonymised at client request. Named references are available on request under NDA. See published case studies →

The 48-hour sample — run on your sources, not ours

Before you commit to anything, we run this service against your own sources and send you the output. If the coverage isn't there, the sample will show you that too — which is the point. We would rather lose the deal at the pilot than at month three.

  • Real extraction from your actual sources
  • Returned inside two business days
  • Coverage and QA note included
  • You keep the data either way
  • No card, no trial clock
  • Named engineer on the call
Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.
How we engage

Three ways to engage us for this work

Same collection pipeline and same QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

  • Dedicated engineer assigned to your account
  • Site changes fixed by us, not reported to you
  • Scheduled delivery to your warehouse or S3
  • Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

  • On-demand and scheduled endpoints
  • Rate limits agreed to your load profile
  • Sandbox keys for integration testing
  • Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

  • Fixed scope agreed in writing upfront
  • Single delivery with full QA report
  • Methodology documented for your records
  • Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Build vs buy

Should you build patent data collection in-house or hire it as a service?

Assignee normalisation is continuous modelling work, and office data structures change without notice.

In-house build vs self-serve tool vs Actowiz managed service
Consideration In-house scraping team Generic proxy / DIY tool Actowiz managed feed
Time to first usable data 6–12 weeks of engineering before anything is trustworthy Days, but output needs manual cleanup before use Free pilot in 48 hours, production in 5–10 business days
Who fixes it when a source changes Your engineers, at the cost of their roadmap You do — tools report failures, they don't resolve them We do, same business day, inside the retainer
Data quality assurance Whatever your team has time to build None beyond HTTP success Schema validation plus sampled human QA on every run
Compliance documentation Rarely produced, then requested urgently by legal Not provided; terms risk sits with you Sources, method and lawful basis documented for review
Accountability Distributed across a team with other priorities A support ticket queue A named engineer and an account owner
True annual cost Engineer salaries, proxies, hosting, ongoing maintenance Low licence fee plus significant hidden analyst time One fixed monthly retainer, quoted after scoping

Assignee normalisation: forty strings, one company

Assignee normalisation is the single highest-value transformation in patent data and the one most consistently underestimated. Without it, portfolio analysis is not merely imprecise — it is wrong in a systematic direction.

Why one company becomes many strings

  • Legal entity variants. The same group files through many entities with different suffixes and forms.
  • Subsidiary structures. Filings sit with regional or technology-specific subsidiaries whose names do not resemble the parent.
  • Transliteration. Names from non-Latin scripts are transliterated inconsistently across offices.
  • Abbreviations and truncation. Office records abbreviate, truncate and reorder name components.
  • Errors. Typos and misspellings persist in official records because correcting them is rarely worth anyone's effort.
  • Ownership change. Post-acquisition, filings remain under the acquired entity's name for years.

How we approach it

Normalisation combines string similarity, co-filing patterns, inventor overlap, classification profile and, where identifiable, corporate structure information, producing a normalised entity with a confidence score. Subsidiary-to-parent mapping is delivered as a separate relationship so you can analyse at either level.

Confidence matters because the failure modes are asymmetric. Over-merging two genuinely different companies inflates a portfolio and can mislead a diligence process. Under-merging fragments a portfolio and understates a competitor. We deliver the confidence score, you set the threshold, and low-confidence mappings arrive flagged rather than either applied silently or dropped.

Our rate is about 95.8% on major filers and lower on long-tail individual and small-entity filings, where the signals are weaker. We report it by filer size rather than as one figure, because that is where the difference matters.

What we will not tell you, and why that boundary protects you

Patent data buyers sometimes want conclusions rather than data. In IP specifically, providing them would be both unqualified and dangerous.

The firm boundaries

  • No validity opinions. Whether a patent would survive challenge is a legal assessment requiring qualified counsel.
  • No infringement or freedom-to-operate analysis. These require claim construction against a specific product, which is legal work with serious consequences.
  • No patentability assessment. Prior art searching for patentability is a specialist function, and getting it wrong wastes filing budgets or forfeits rights.
  • No resale of licensed IP databases. Commercial patent analytics products are licensed, and their analytical layers are their product.
  • No assertion that status is current. Public status data lags, and we ship status_as_of precisely so nobody treats it as live.

Why the boundary is in your interest

An FTO opinion from a data vendor has no professional standing and no insurance behind it. If a decision made on it goes wrong, you carry the consequence entirely. Qualified IP counsel exists for exactly this reason, and any vendor blurring that line is creating risk for you while sounding more useful.

What we do is make counsel's work faster and cheaper: a normalised, family-linked, classification-tagged portfolio inventory with status dates, rather than raw office search exports. That is genuinely valuable and it is honestly within our competence.

How it works

How a patent data engagement goes live in 5 to 10 business days

Jurisdictions, classification scope and the entity list for normalisation are agreed first, and normalisation is tuned against your known entity variants during the pilot.

Scope the sources and fields

You send us target sites, regions, SKUs or keywords. We return a field-level schema proposal, coverage estimate and refresh recommendation — usually within two working days.

Pilot sample, free

We extract a real sample from your actual targets so you can inspect field fill rates, edge cases and match quality before any commitment.

Production build and QA harness

Our engineers build extractors, then wire validation rules: type checks, range checks, duplicate detection and golden-record comparison against a manually verified subset.

Scheduled delivery into your stack

Feeds run at your chosen cadence and land in the warehouse or bucket you already use. Schema changes are versioned and announced before they ship.

Ongoing monitoring and SLA support

We watch coverage drift, fill rates and source changes daily. A named engineer owns your account, and layout breaks are fixed by us — not queued for you.

Formats & destinations

JSON, JSONL, CSV, Parquet or XLSX, delivered to Amazon S3, Google Cloud Storage, Azure Blob, SFTP, Snowflake, BigQuery, Databricks or a REST/GraphQL endpoint. Webhooks fire on completion, and every batch ships with a manifest containing row counts, schema version and QA results so your pipeline can fail loudly instead of silently ingesting a bad file.

Compliance & data ethics

We collect from official patent and trademark office publications and public registers. We do not resell licensed commercial patent databases, and we do not provide validity, infringement, freedom-to-operate or patentability assessments, which require qualified counsel. Legal status is always delivered with an as-of date.

Service commitments

What we commit to, in writing

These are contractual, not marketing copy. They appear in the engagement document.

Service level commitments written into every managed engagement
Commitment What we hold ourselves to
Pilot turnaround A real sample from your own sources within 48 hours of scoping, at no cost.
Go-live Production collection running within 5–10 business days of sign-off.
Delivery punctuality 99.5% on-schedule delivery, measured monthly and reported to you.
Breakage response Source layout changes triaged same business day; critical sources inside 4 hours.
Data quality Schema validation on every run plus sampled human QA before any delivery leaves us.
Escalation A named engineer and an account owner, not a shared ticket queue.
Change requests Field additions and source changes handled inside the retainer, not re-quoted.
Exit Your historical data exported in full on request. No lock-in, no export fee.

Why teams pick Actowiz for this work

  • Engineers, not a dashboard. You get people who fix breakages, not a self-serve tool you maintain yourself.
  • We tell you what we can't do. Scope limits and coverage gaps are stated before you sign, not discovered in month three.
  • QA is part of the service. Schema validation and sampled human review run before delivery, every run.
  • Compliance is documented. Sources, method and lawful basis written down so your legal team can review them.
  • Fixed monthly cost. No per-request metering, no surprise overage on a month when a competitor adds SKUs.
  • Six years, 40+ countries. Long-running production pipelines across retail, travel, mobility and finance.
Definitions

Terms used on this page

Plain definitions of the terms used on this page, so procurement and legal reviewers are working from the same vocabulary as your data team.

Assignee normalisation
Mapping many published assignee strings to one entity identity. A single company can appear as dozens of strings through entity variants, subsidiaries, transliteration and record errors.
Patent family
The set of filings covering the same invention across jurisdictions. Counting records instead of families measures filing strategy rather than inventive output.
Status as-of date
The date a published legal status reflects. Public status data lags actual events, sometimes by months, so a status presented as current is misleading.
FAQ

Patent and IP data: frequently asked questions

What IP, R&D and corporate development teams ask during evaluation.

Because one company appears as dozens of strings across offices — legal entity variants, subsidiary names, transliterations, abbreviations and outright typos in official records. Without normalisation, a competitor portfolio is fragmented and understated.

We normalise using string similarity, co-filing patterns, inventor overlap and classification profile, delivering a normalised entity with a confidence score and separate subsidiary-to-parent mapping. Our rate is about 95.8% on major filers and lower on long-tail small-entity filings, and we report it by filer size rather than as one figure.

Because one invention filed in fifteen jurisdictions generates fifteen records. Counting records measures filing strategy rather than inventive output, and it inflates portfolios unevenly depending on how internationally a company files.

We link filings into families with a family identifier, size and jurisdiction list, so portfolio comparisons count inventions. Both views are available — family-level for portfolio comparison, record-level where jurisdiction detail matters.

No, and we will not. Validity, infringement, freedom-to-operate and patentability are legal assessments requiring qualified counsel and claim construction against specific products.

An opinion from a data vendor has no professional standing and no insurance behind it, so if a decision made on it goes wrong you carry the entire consequence. What we do is make counsel's work faster: a normalised, family-linked, classification-tagged inventory rather than raw search exports.

As current as office publication allows, which lags actual events — sometimes by months. Every status field ships with status_as_of stating the date the status reflects.

This is deliberate. Presenting a status as current when it reflects an older office update is the most common way patent datasets mislead, and it matters most in exactly the situations where accuracy is critical, such as expiry and lapse assessment.

Where offices publish it publicly, yes, in the publication language. Coverage and format vary by office, and some make full text available while others publish only bibliographic data and abstracts openly.

We state per office what full-text availability looks like during scoping. Where full text is only available through a licensed product, we say so rather than substituting an abstract and letting you assume you have claims.

Yes — trademark records with mark text, Nice classes, status and opposition indications where published, plus design registrations from offices that publish them openly.

Trademark data is often more immediately useful for brand protection work, and it joins to our seller monitoring service for enforcement workflows against unauthorised sellers using registered marks.

Original script is retained alongside transliteration, and normalisation accounts for inconsistent transliteration across offices — which is a major source of assignee fragmentation for Japanese, Korean and Chinese filers.

Titles and abstracts are captured in the publication language with translation available for triage. As with tender documents, translation is for filtering rather than for analysis, and the original remains authoritative.

Different rather than cheaper, and worth being clear about. Commercial IP databases include analytical layers, curated data and search interfaces that we do not provide, and for many IP teams that product is the right purchase.

We are a better fit when you need normalised bibliographic data delivered into your own systems for integration with other datasets, or when your requirement is continuous monitoring feeding an internal pipeline rather than analyst desktop search. If your team primarily needs desktop search and analytics, licence the commercial product.

We quote individually. The drivers are jurisdiction and office count, classification or entity scope, whether full text is required where available, and refresh frequency for status and new publication monitoring.

A focused competitor entity set across major offices sits at the lighter end. Broad classification landscapes across many jurisdictions with full text and monthly status refresh sits higher. One scoping call, a free pilot on your own entity list within 48 hours, then a fixed quote. Request a quote.

See real normalised patent data for your own entity list

Send us competitor entities or a classification scope. We return normalised, family-linked records with status and as-of dates within 48 hours.

Free pilot, no card, no obligation. Send your known entity variants and we'll tune normalisation against them.
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Backed by automation, data volume, and enterprise-grade scale — we help businesses from startups to Fortune 500s extract competitive insights across the USA, UK, UAE, and beyond.

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7+
Years of Experience
Proven track record delivering enterprise-grade web scraping and data intelligence solutions.
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4,000+
Projects Delivered
Serving startups to Fortune 500 companies across 50+ countries worldwide.
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200+
In-House Experts
Dedicated engineers across scrapers, AI/ML models, APIs, and data quality assurance.
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9.2M
Automated Workflows
Running weekly across eCommerce, Quick Commerce, Travel, Real Estate, and Food industries.
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270+ TB
Data Transferred
Real-time and batch data scraping at massive scale, across industries globally.
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380M+
Pages Crawled Weekly
Scaled infrastructure for comprehensive global data coverage with 99% accuracy.

AI Solutions Engineered
for Your Needs

LLM-Powered Attribute Extraction: High-precision product matching using large language models for accurate data classification.
Advanced Computer Vision: Fine-grained object detection for precise product classification using text and image embeddings.
GPT-Based Analytics Layer: Natural language query-based reporting and visualization for business intelligence.
Human-in-the-Loop AI: Continuous feedback loop to improve AI model accuracy over time.
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Connect the Dots Across
Your Retail Ecosystem

We partner with agencies, system integrators, and technology platforms to deliver end-to-end solutions across the retail and digital shelf ecosystem.

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Analytics Services
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Ad Tech
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Price Optimization
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Business Consulting
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System Integration
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Market Research
Become a Partner →

Popular Datasets — Ready to Download

Browse All Datasets →
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Amazon
eCommerce
Free 100 rows
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Zillow
Real Estate
Free 100 rows
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DoorDash
Food Delivery
Free 100 rows
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Walmart
Retail
Free 100 rows
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Booking.com
Travel
Free 100 rows
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Indeed
Jobs
Free 100 rows

Latest Insights & Resources

View All Resources →
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Blog

EU AI Act for Data Teams: What Scrapers Must Change in 2026

The EU AI Act impact on web scraping & AI training data GPAI transparency, copyright reservations, prohibited practices & a compliance checklist from Actowiz.

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Case Study

B2B Supplier Automates Government Tender Discovery from GeM & eProcure

How a B2B supplier replaced manual tender-portal checking with an automated, filtered feed of relevant government tenders from GeM and CPP/eProcure never missing a bid deadline again.

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Report

FIFA World Cup 2026 Aftermath: Hotel & Airfare Normalization in Host Cities (Data Study)

Actowiz Solutions tracks post–World Cup 2026 travel pricing — hotel ADR & airfare normalization across host cities, event-premium decay data & lessons for travel teams.

Start Where It Makes Sense for You

Whether you're a startup or a Fortune 500 — we have the right plan for your data needs.

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Enterprise
Book a Strategy Call
Custom solutions, dedicated support, volume pricing for large-scale needs.
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Growing Brand
Get Free Sample Data
Try before you buy — 500 rows of real data, delivered in 2 hours. No strings.
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Just Exploring
View Plans & Pricing
Transparent plans from $500/mo. Find the right fit for your budget and scale.
Get in Touch
Let's Talk About
Your Data Needs
Tell us what data you need — we'll scope it for free and share a sample within hours.
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    Free Sample in 2 HoursShare your requirement, get 500 rows of real data — no commitment.
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    Plans from $500/monthFlexible pricing for startups, growing brands, and enterprises.
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    US-Based SupportOffices in New York & California. Aligned with your timezone.
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    ISO 9001 & 27001 CertifiedEnterprise-grade security and quality standards.
Request Free Sample Data
Fill the form below — our team will reach out within 2 hours.
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Free 500-row sample · No credit card · Response within 2 hours

Request Free Sample Data

Our team will reach out within 2 hours with 500 rows of real data — no credit card required.

+1
Free 500-row sample · No credit card · Response within 2 hours