# Research to Patent Commercialization Radar (`scrapemint/research-patent-radar-pipeline`) Actor For each technology topic, correlates academic research momentum from Google Scholar with patent filing activity from Google Patents, scores convergence 0 to 100, and tiers topics commercializing, emerging, or watch. Finds early leads before the IP race starts. No API keys. - **URL**: https://apify.com/scrapemint/research-patent-radar-pipeline.md - **Developed by:** [Ken M](https://apify.com/scrapemint) (community) - **Categories:** Business, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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 ## Research to Patent Commercialization Radar Tells you how far a technology has moved from the lab toward the market. For each topic you pass, the radar pulls recent **academic research** (Google Scholar) and recent **patent filings** (Google Patents), then scores the convergence between the two. A field where papers are accelerating *and* patents are being filed is commercializing right now. A field where research is spiking but patents are still thin is an early lead, before the IP race begins. Built for deep-tech and biotech investors, IP licensing and tech-transfer scouts, and competitive-intelligence teams who need to know where a technology sits on the research to revenue curve. ### What you get One row per topic, tiered: - **commercializing** — strong research momentum and active patent filing at the same time. Includes top papers, top assignees, recent filings, and the convergence score. - **emerging** — research accelerating, patent activity still thin. The early-lead signal. - **watch** — limited convergence so far. Each row carries the research side (paper count, recent papers, total citations, top venues, top papers), the patent side (filing count, recent filings, distinct assignees, top assignees), the component scores, and a one-line plain-language signal. ### Input ```json { "topics": ["solid state battery electrolyte", "perovskite solar cell", "mRNA cancer vaccine"], "yearFrom": 0, "maxTopics": 8, "papersPerTopic": 15, "patentsPerTopic": 20, "minScore": 0 } ```` `yearFrom` 0 means the last three years. Each topic triggers one Scholar pull and one Patents pull. ### How scoring works - **Research score (0-45):** paper volume, how many are from the last two years, and total citations. - **Patent score (0-35):** filing volume, distinct assignees, and how many filings are recent. - **Momentum (0-20):** the share of all activity that is recent. `commercializing` needs both sides strong (research >= 18, patents >= 12, total >= 55). `emerging` is research-led with thin patents (research >= 18, patents < 12). Everything else is `watch`. ### Pricing and cost Pay per row: watch $0.04, emerging $0.09, commercializing $0.15. The first commercializing row per run is free so you can validate output. This is a **pipeline**. It calls two of our other actors under the hood, once per topic: `google-scholar-scraper` and `google-patents-scraper`. Those children also bill you for their own per-row usage (one paper row, one patent row). Both run on plain HTTP with a residential proxy and no browser, so the added child cost per topic is small, but it is real and on top of the per-row prices above. Budget for it when you set `papersPerTopic` and `patentsPerTopic`. No API keys, no accounts. Just topics in, a commercialization read-out per topic out. # Actor input Schema ## `topics` (type: `array`): Free text technology or research topics. One row per topic. Example: \["solid state battery electrolyte", "perovskite solar cell", "mRNA cancer vaccine"]. ## `yearFrom` (type: `integer`): Lower bound on paper publication and patent filing year. Defaults to three years ago. 0 also means three years ago. ## `maxTopics` (type: `integer`): Hard cap on topics scanned per run. Each topic triggers one Scholar call and one Patents call. ## `papersPerTopic` (type: `integer`): How many Scholar papers to pull per topic for the research-momentum signal. ## `patentsPerTopic` (type: `integer`): How many patent filings to pull per topic for the patent-activity signal. ## `minScore` (type: `integer`): Drop topics scoring below this convergence score (0 to 100). 0 keeps everything with any data. ## `proxyConfiguration` (type: `object`): Proxy passed through to the Scholar and Patents children. Both default to Apify residential, which is required for Google. ## Actor input object example ```json { "topics": [ "solid state battery electrolyte", "perovskite solar cell" ], "yearFrom": 0, "maxTopics": 8, "papersPerTopic": 15, "patentsPerTopic": 20, "minScore": 0, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # 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 = { "topics": [ "solid state battery electrolyte", "perovskite solar cell" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scrapemint/research-patent-radar-pipeline").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 = { "topics": [ "solid state battery electrolyte", "perovskite solar cell", ] } # Run the Actor and wait for it to finish run = client.actor("scrapemint/research-patent-radar-pipeline").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 '{ "topics": [ "solid state battery electrolyte", "perovskite solar cell" ] }' | apify call scrapemint/research-patent-radar-pipeline --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapemint/research-patent-radar-pipeline", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Research to Patent Commercialization Radar", "description": "For each technology topic, correlates academic research momentum from Google Scholar with patent filing activity from Google Patents, scores convergence 0 to 100, and tiers topics commercializing, emerging, or watch. Finds early leads before the IP race starts. No API keys.", "version": "0.1", "x-build-id": "34c4ixbtZPYWORVg1" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapemint~research-patent-radar-pipeline/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapemint-research-patent-radar-pipeline", "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/scrapemint~research-patent-radar-pipeline/runs": { "post": { "operationId": "runs-sync-scrapemint-research-patent-radar-pipeline", "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/scrapemint~research-patent-radar-pipeline/run-sync": { "post": { "operationId": "run-sync-scrapemint-research-patent-radar-pipeline", "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": [ "topics" ], "properties": { "topics": { "title": "Technology topics", "type": "array", "description": "Free text technology or research topics. One row per topic. Example: [\"solid state battery electrolyte\", \"perovskite solar cell\", \"mRNA cancer vaccine\"].", "default": [], "items": { "type": "string" } }, "yearFrom": { "title": "Earliest year", "minimum": 0, "maximum": 2100, "type": "integer", "description": "Lower bound on paper publication and patent filing year. Defaults to three years ago. 0 also means three years ago.", "default": 0 }, "maxTopics": { "title": "Max topics", "minimum": 1, "maximum": 40, "type": "integer", "description": "Hard cap on topics scanned per run. Each topic triggers one Scholar call and one Patents call.", "default": 8 }, "papersPerTopic": { "title": "Papers per topic", "minimum": 5, "maximum": 40, "type": "integer", "description": "How many Scholar papers to pull per topic for the research-momentum signal.", "default": 15 }, "patentsPerTopic": { "title": "Patents per topic", "minimum": 5, "maximum": 50, "type": "integer", "description": "How many patent filings to pull per topic for the patent-activity signal.", "default": 20 }, "minScore": { "title": "Minimum score", "minimum": 0, "maximum": 100, "type": "integer", "description": "Drop topics scoring below this convergence score (0 to 100). 0 keeps everything with any data.", "default": 0 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Proxy passed through to the Scholar and Patents children. Both default to Apify residential, which is required for Google.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "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 } } } } } } } } } } ```