# ™️ Trademark Search — USPTO Database Lookup (`nexgendata/trademark-search`) Actor Search USPTO trademark database for registered & pending marks. Get filing status, owner data, classifications & history. Essential for brand clearance & IP due diligence. Pay per search. - **URL**: https://apify.com/nexgendata/trademark-search.md - **Developed by:** [Stephan Corbeil](https://apify.com/nexgendata) (community) - **Categories:** AI, Automation - **Stats:** 3 total users, 0 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 results 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 ## Trademark Search by nexgendata Extract trademark registration data including mark names, registration numbers, owner names, filing dates, registration dates, current status, Nice classification codes, description of goods/services, and attorney of record at scale. Built for trademark attorneys conducting clearance searches before filing and anyone who needs structured intellectual property data without the overhead of building a custom scraper. ### What This Actor Does The Trademark Search connects to public data sources and extracts trademark registration data including mark names, registration numbers, owner names, filing dates, registration dates, current status, Nice classification codes, description of goods/services, and attorney of record. It handles pagination, rate limiting, and data normalization automatically so you get clean, structured JSON output ready for your database, dashboard, or analytics pipeline. No API keys to manage, no infrastructure to maintain. ### Who Uses This Trademark attorneys conducting clearance searches before filing, brand managers monitoring for potential infringements, entrepreneurs validating business names before launch, and ip portfolio managers tracking registration status. If you need intellectual property data at scale without building and maintaining your own extraction pipeline, this actor handles the heavy lifting. ### What You Get Back Each run produces a structured dataset in JSON format. Every record includes all available fields from the source, normalized into a consistent schema. The data is immediately available for export in JSON, CSV, or Excel format, or you can push it directly to your data warehouse via Apify integrations with Google Sheets, Slack, Webhooks, and 50+ other platforms. ### How It Compares USPTO TESS is free but has no API, breaks frequently, and sessions expire every 5 minutes. Corsearch and TrademarkNow charge enterprise pricing ($5K+/year). TMview covers EU marks but lacks US depth. This actor delivers the same data at $5 per 1,000 results with zero monthly commitment, no API key management, and results available in seconds. Pay only for what you use. ### Sample Output ```json { "source": "trademark-search", "data": "Structured intellectual property data fields", "timestamp": "2024-03-29T12:00:00Z", "url": "https://example.com/source" } ```` ### Use Cases Teams use the Trademark Search across a range of workflows. Analysts feed the output into business intelligence dashboards for real-time monitoring. Developers integrate it into automated data pipelines that run on daily or weekly schedules. Researchers use bulk exports for large-scale analysis projects. Marketing teams track competitive movements and industry trends. The structured output format means the data slots into virtually any downstream system with minimal transformation. ### Pricing: $5 per 1,000 Results At $5/1K, processing 5,000 results costs $25.00 total. A daily pipeline pulling 500 results runs $2.50/day ($75/month). Compare that to building and maintaining your own scraping infrastructure, which typically costs $500-2,000/month in proxy fees, compute, and engineering time alone. ### FAQ **How often can I run this?** As often as you need. Schedule runs hourly, daily, or weekly through Apify's built-in scheduler, or trigger runs via API from your own systems. **What format is the output?** JSON by default, with one-click export to CSV or Excel. You can also push results directly to Google Sheets, webhooks, or any HTTP endpoint via Apify integrations. **Do I need any API keys?** No. The actor handles all authentication and access internally. Just configure your search parameters and run. **Can I integrate this with my existing tools?** Yes. Apify supports integrations with Zapier, Make, Google Sheets, Slack, and direct webhook delivery. You can also use the Apify API to pull results programmatically into any system. # Actor input Schema ## `query` (type: `string`): Trademark name, owner, or registration number ## `searchType` (type: `string`): Type of search to perform ## `maxResults` (type: `integer`): Maximum results ## Actor input object example ```json { "query": "Nike", "searchType": "name", "maxResults": 20 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/trademark-search").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = {} # Run the Actor and wait for it to finish run = client.actor("nexgendata/trademark-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 '{}' | apify call nexgendata/trademark-search --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/trademark-search", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "™️ Trademark Search — USPTO Database Lookup", "description": "Search USPTO trademark database for registered & pending marks. Get filing status, owner data, classifications & history. Essential for brand clearance & IP due diligence. Pay per search.", "version": "0.0", "x-build-id": "i72N88OLwlNiJSdW4" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~trademark-search/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-trademark-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~trademark-search/runs": { "post": { "operationId": "runs-sync-nexgendata-trademark-search", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~trademark-search/run-sync": { "post": { "operationId": "run-sync-nexgendata-trademark-search", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "query": { "title": "Search Query", "type": "string", "description": "Trademark name, owner, or registration number", "default": "Nike" }, "searchType": { "title": "Search Type", "enum": [ "name", "owner", "number" ], "type": "string", "description": "Type of search to perform", "default": "name" }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum results", "default": 20 } } }, "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 } } } } } } } } } } ```