# Ai Trademark Conflict Analyzer (`vibexcoder/ai-trademark-conflict-analyzer`) Actor Check if your brand name conflicts with existing USPTO trademarks before you launch. Get risk scores, phonetic matching, and safer alternatives. - **URL**: https://apify.com/vibexcoder/ai-trademark-conflict-analyzer.md - **Developed by:** [Nas](https://apify.com/vibexcoder) (community) - **Categories:** AI, Automation - **Stats:** 8 total users, 3 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## AI Trademark Conflict Analyzer An Apify Actor that checks if a proposed trademark conflicts with existing registered marks using multiple similarity algorithms. ### Features - **Multi-algorithm similarity analysis**: Phonetic (metaphone/soundex), visual, fuzzy (Levenshtein), word overlap, and containment matching - **Risk scoring**: 0-100 score with 4 risk levels (LOW, MEDIUM, HIGH, CRITICAL) - **USPTO trademark database**: Searches against real trademark data - **Smart recommendations**: Provides detailed recommendations and alternative name suggestions ### Input | Field | Type | Description | Default | |-------|------|-------------|---------| | proposedMark | string | The trademark/brand name to analyze | Required | | goodsServices | string | Description of goods/services | "" | | searchDepth | string | Search depth: quick/standard/comprehensive | "standard" | | includeDeadMarks | boolean | Include dead/abandoned marks | false | | maxResults | integer | Maximum similar marks to return | 20 | ### Output The actor outputs a comprehensive trademark analysis report including: - **Risk Assessment**: Overall risk level and score (0-100) - **Similar Marks**: List of potentially conflicting trademarks with similarity breakdowns - **Recommendations**: Primary recommendation and detailed action items - **Alternative Names**: Suggested safer alternatives ### Example Input ```json { "proposedMark": "TechFlow", "goodsServices": "Software, mobile applications", "searchDepth": "standard", "includeDeadMarks": false, "maxResults": 20 } ```` ### Example Output ```json { "proposedMark": "TechFlow", "conflictRisk": "HIGH", "riskScore": 75, "totalConflicts": 5, "similarMarks": [...], "recommendation": "WARNING - TechFlow faces significant conflicts...", "saferAlternatives": ["Nova TechFlow", "TechFlow Pro", ...] } ``` ### Similarity Algorithms 1. **Exact Match**: Direct string comparison 2. **Phonetic**: Metaphone and Soundex algorithms for sound-alike detection 3. **Visual**: Character-level visual similarity 4. **Fuzzy**: Levenshtein distance for typo tolerance 5. **Contains**: Substring detection 6. **Word Overlap**: Common word analysis ### Disclaimer This analysis is for informational purposes only and does not constitute legal advice. Consult a trademark attorney before filing. # Actor input Schema ## `proposedMark` (type: `string`): The trademark or brand name you want to analyze for conflicts ## `goodsServices` (type: `string`): Description of goods or services the trademark will be used for ## `searchDepth` (type: `string`): How thorough the search should be ## `includeDeadMarks` (type: `boolean`): Whether to include dead or abandoned trademarks in the search ## `maxResults` (type: `integer`): Maximum number of similar marks to return ## Actor input object example ```json { "proposedMark": "TechFlow", "searchDepth": "standard", "includeDeadMarks": false, "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 = { "proposedMark": "TechFlow", "goodsServices": "" }; // Run the Actor and wait for it to finish const run = await client.actor("vibexcoder/ai-trademark-conflict-analyzer").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 = { "proposedMark": "TechFlow", "goodsServices": "", } # Run the Actor and wait for it to finish run = client.actor("vibexcoder/ai-trademark-conflict-analyzer").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 '{ "proposedMark": "TechFlow", "goodsServices": "" }' | apify call vibexcoder/ai-trademark-conflict-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=vibexcoder/ai-trademark-conflict-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Ai Trademark Conflict Analyzer", "description": "Check if your brand name conflicts with existing USPTO trademarks before you launch. Get risk scores, phonetic matching, and safer alternatives.", "version": "0.0", "x-build-id": "J5bekOFfj8HjvX9MC" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/vibexcoder~ai-trademark-conflict-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-vibexcoder-ai-trademark-conflict-analyzer", "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/vibexcoder~ai-trademark-conflict-analyzer/runs": { "post": { "operationId": "runs-sync-vibexcoder-ai-trademark-conflict-analyzer", "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/vibexcoder~ai-trademark-conflict-analyzer/run-sync": { "post": { "operationId": "run-sync-vibexcoder-ai-trademark-conflict-analyzer", "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": [ "proposedMark" ], "properties": { "proposedMark": { "title": "Proposed Trademark", "type": "string", "description": "The trademark or brand name you want to analyze for conflicts" }, "goodsServices": { "title": "Goods/Services Description", "type": "string", "description": "Description of goods or services the trademark will be used for" }, "searchDepth": { "title": "Search Depth", "enum": [ "quick", "standard", "comprehensive" ], "type": "string", "description": "How thorough the search should be", "default": "standard" }, "includeDeadMarks": { "title": "Include Dead/Abandoned Marks", "type": "boolean", "description": "Whether to include dead or abandoned trademarks in the search", "default": false }, "maxResults": { "title": "Maximum Results", "minimum": 5, "maximum": 50, "type": "integer", "description": "Maximum number of similar marks to return", "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 } } } } } } } } } } ```