# WIPO PATENTSCOPE Patent Search (`nexgendata/wipo-patentscope-search`) Actor - **URL**: https://apify.com/nexgendata/wipo-patentscope-search.md - **Developed by:** [NexGenData](https://apify.com/nexgendata) (community) - **Categories:** Business, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $150.00 / 1,000 patents This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## WIPO PATENTSCOPE Patent Search Search WIPO's PATENTSCOPE database — the world's most comprehensive international patent index, with ~110 million patent documents across PCT, EPO, USPTO, JPO, KIPO, CNIPA and 50+ national offices — and get clean structured JSON. This actor turns the WIPO PATENTSCOPE results page into a normalized dataset of publication metadata: application numbers, publication numbers, applicants (assignees), named inventors, IPC classification codes, publication dates, country of publication, and a 500-character abstract excerpt — each row carrying a `source_url` back to the canonical WIPO record for verification. Built and maintained by NexGenData. Polite by design: a 2 req/s rate limit, a contact-identifying User-Agent, and a runtime robots.txt check before every job. ### Why teams use this actor - **Patent landscape analysis** — map all PATENTSCOPE filings in a technology area (e.g. *"diffusion model training"*) over a year window and feed the dataset straight into pandas, Looker, or your in-house IP analytics stack. - **Freedom-to-operate (FTO) searches** — pull recent publications matching a product description plus claim keywords; export to your patent counsel as a structured CSV instead of a stack of PDFs. - **Competitor IP monitoring** — schedule weekly runs filtered by `applicants` (e.g. `["NVIDIA Corporation", "Alphabet Inc"]`) to track every new PCT filing from named rivals. - **Prior art research** — power R&D teams and patent examiners with a fast, structured search over the WIPO corpus, including IPC code filters and date ranges. - **M&A IP due diligence** — capture every filing tied to a target company in minutes and surface the technology stack from their patent family. - **Academic / market research** — quantify innovation trends by IPC class, geography, or filing entity over decades of WIPO data. ### Input | Field | Type | Description | |---|---|---| | `query` | string (required) | Free-text query across title, abstract, claims. Supports PATENTSCOPE field operators (e.g. `AI AND TI:transformer`). | | `applicants` | string[] | Optional applicant / assignee names. Combined as an `OR` group, then `AND`-ed with `query`. | | `yearStart` | integer | Earliest publication year (default 2024). | | `yearEnd` | integer | Latest publication year (default 2026). | | `maxResults` | integer | Hard cap on patents returned (1–1000, default 25). | | `language` | enum | Result language preference: `EN`, `FR`, `DE`, `ES`, `JA`, `ZH`, `RU`. | ### Output One JSON object per patent, written to the actor's default dataset. Example: ```json { "application_number": "PCT/US2026/012345", "publication_number": "WO2026/098765", "publication_date": "2026-04-15", "title": "Method and apparatus for distributed model inference", "applicants": ["Acme Corp", "University of Foo"], "inventors": ["Smith, John", "Doe, Jane"], "abstract_excerpt": "A computer-implemented method for distributed inference across heterogeneous accelerators ...", "ipc_codes": ["G06F 21/56", "H04L 9/06"], "country_of_publication": "WO", "source_url": "https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2026098765", "scraped_at": "2026-05-28T12:00:00Z" } ```` ### Sample Output *(Screenshot placeholder — sample dataset rendered in the Apify console.)* ### Compliance - Public bibliographic data only. Inventor names are captured as printed on the published filing; no email addresses, phone numbers, or personal mailing addresses are ever extracted. - Polite User-Agent identifying NexGenData with a contact URL/email. - Respects WIPO's robots.txt (checked at the start of every run). - Rate-limited to ≤ 2 requests/second. ### Keywords WIPO patent search, PATENTSCOPE API, international patent data, patent intelligence, PCT filings, patent landscape analysis, IPC classification search, freedom to operate, prior art discovery, competitor IP monitoring. # Actor input Schema ## `query` (type: `string`): Free-text search across title, abstract, claims. ## `applicants` (type: `array`): List of applicant/assignee names to filter by. ## `yearStart` (type: `integer`): Earliest publication year. ## `yearEnd` (type: `integer`): Most recent publication year. ## `maxResults` (type: `integer`): Hard cap on patents returned. ## `language` (type: `string`): Title/abstract language preference. ## Actor input object example ```json { "query": "artificial intelligence", "applicants": [], "yearStart": 2024, "yearEnd": 2026, "maxResults": 25, "language": "EN" } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "query": "artificial intelligence", "applicants": [], "yearStart": 2024, "yearEnd": 2026, "maxResults": 25, "language": "EN" }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/wipo-patentscope-search").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "query": "artificial intelligence", "applicants": [], "yearStart": 2024, "yearEnd": 2026, "maxResults": 25, "language": "EN", } # Run the Actor and wait for it to finish run = client.actor("nexgendata/wipo-patentscope-search").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "query": "artificial intelligence", "applicants": [], "yearStart": 2024, "yearEnd": 2026, "maxResults": 25, "language": "EN" }' | apify call nexgendata/wipo-patentscope-search --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/wipo-patentscope-search", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "WIPO PATENTSCOPE Patent Search", "version": "0.0", "x-build-id": "Pnb2jES1J0MQFIZQT" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~wipo-patentscope-search/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-wipo-patentscope-search", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~wipo-patentscope-search/runs": { "post": { "operationId": "runs-sync-nexgendata-wipo-patentscope-search", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~wipo-patentscope-search/run-sync": { "post": { "operationId": "run-sync-nexgendata-wipo-patentscope-search", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "query" ], "properties": { "query": { "title": "Search Query", "type": "string", "description": "Free-text search across title, abstract, claims." }, "applicants": { "title": "Applicant Names (optional)", "type": "array", "description": "List of applicant/assignee names to filter by.", "items": { "type": "string" } }, "yearStart": { "title": "Start Year", "minimum": 1978, "type": "integer", "description": "Earliest publication year." }, "yearEnd": { "title": "End Year", "type": "integer", "description": "Most recent publication year." }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Hard cap on patents returned." }, "language": { "title": "Result Language", "enum": [ "EN", "FR", "DE", "ES", "JA", "ZH", "RU" ], "type": "string", "description": "Title/abstract language preference." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```