# Patent Landscape - Prior Art Intelligence (`enbotix/patent-landscape`) Actor

Mines full-text patent and paper literature for concepts that were infeasible when first disclosed but are manufacturable today.

- **URL**: https://apify.com/enbotix/patent-landscape.md
- **Developed by:** [Enbo Tix](https://apify.com/enbotix) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$25,000.00 / 1,000 prior-art landscapes

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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Patent Landscape - prior-art intelligence by full-text mining

Find out what the world has already decided is impossible - and whether that
decision still holds.

### What it does

Give it a product category. It mines the **full text** of patent and scientific
literature - not abstracts - for concepts that were described as infeasible,
impossible or prohibitively slow when they were first disclosed. It then
reports which of those barriers have since fallen, and ships the verbatim
sentence that proves it.

Abstracts say what a paper claims. Body text says what actually stopped the
work. That difference is the whole product.

### Example

**Input**

```json
{
  "queries": ["single cell sequencing"],
  "oldest_year": 2010,
  "papers_per_query": 25,
  "top_concepts": 5
}
```

**Output (abridged, real shape)**

```json
{
  "query": "single cell sequencing",
  "corpus": "biomed",
  "papers_read": 25,
  "landscape": [
    {
      "concept": "called infeasible",
      "claim": "...remains computationally prohibitive for routine clinical use.",
      "source": "PMC3520994",
      "url": "https://europepmc.org/article/PMC/PMC3520994",
      "year": 2012,
      "why_it_fell": "Sequencing costs fell roughly six orders of magnitude after this paper. The barrier did not soften - it evaporated."
    }
  ]
}
```

Every hit carries the quote. A landscape without quotes is an opinion.

### Run it

**In the Apify console** - the example input is pre-filled, so press Run.

**From your code**

```bash
curl -X POST "https://api.apify.com/v2/acts/{actorId}/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"queries":["single cell sequencing"],"papers_per_query":25}'
```

Then read the dataset:

```bash
curl "https://api.apify.com/v2/datasets/{datasetId}/items?token=YOUR_TOKEN"
```

**As an MCP server** - the actor is MCP-ready, so an agent can call it
directly.

### Why the output is trustworthy

Two rules are enforced in the pipeline, both learned the hard way:

1. **Reference lists are stripped before matching.** Otherwise the barrier
   phrase matches another author's abstract in the bibliography, and the
   precision collapses.
2. **An empty result is never silent.** If nothing matches, it says so and
   reports what it read - so you can tell "no barriers found" apart from
   "it did not look".

### Limits

- Patent results are **low confidence** and are not patent law. Treat them as
  pointers, not conclusions.
- There is no SLA. This is a research tool.
- It needs the source corpus to be reachable when it runs.

### License

MIT

## Actor input object example

```json
{}
```

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("enbotix/patent-landscape").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("enbotix/patent-landscape").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).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 enbotix/patent-landscape --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,enbotix/patent-landscape"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/1sgYb3O0YO8hRgUF6/builds/1qkM3NueilTgiP7Ex/openapi.json