Tech Scouting Report

Tech scouting report generation for any emerging technology: enter a technology name, and this actor queries 8 academic and patent databases in parallel to produce a structured commercialization readiness assessment. It scores research momentum, patent strength, funding validation, and Technology Readiness Level (TRL), then delivers a single investment verdict — from PASS to INVEST_NOW. Designed for venture capital analysts, corporate R&D teams, and technology transfer offices who need objective, evidence-based signals fast.

Traditional tech scouting means weeks of manual literature review, patent database searches, and grant database queries spread across disparate sources. This actor collapses that process into a single run, pulling up to 20 records each from OpenAlex, Semantic Scholar, arXiv, USPTO, EPO, NIH, Grants.gov, and ClinicalTrials.gov simultaneously. Four weighted scoring models synthesize those 160 data points into a composite score with named verdict levels and actionable recommendations — no spreadsheets, no subscriptions, no analyst overhead.

What data can you extract?

Why use Tech Scouting Report?

Manual technology scouting is slow and inconsistent. A single junior analyst querying PubMed, Google Patents, NIH Reporter, and ClinicalTrials.gov by hand takes 1-3 days per technology — and the results depend heavily on individual search skill. Specialized platforms like PatSnap or Amplity charge $1,000–5,000 per month for patent analytics alone. This actor runs the equivalent analysis in under 90 seconds for $0.10.

This actor automates the entire evidence collection and synthesis pipeline. It calls 8 vetted open-data sources in parallel, applies four independently calibrated scoring models, and cross-references findings across sources — such as matching academic authors against patent inventors — to surface signals that manual reviews miss.

Running on the Apify platform means you also get:

• Scheduling — run weekly scans on technology watchlists to catch momentum shifts before competitors
• API access — trigger reports from Python, JavaScript, or any HTTP client inside your existing research workflows
• Proxy rotation — Apify's built-in proxy infrastructure handles any rate-limiting from upstream data sources
• Monitoring — get Slack or email alerts when a technology crosses a composite score threshold on a scheduled run
• Integrations — push results to Zapier, Make, Google Sheets, Airtable, or your CRM without writing custom connectors

Features

• 8 parallel data source queries — OpenAlex, Semantic Scholar, arXiv, USPTO, EPO, NIH Reporter, Grants.gov, and ClinicalTrials.gov are queried simultaneously, each returning up to 20 records, for a maximum input of 160 data points per run
• Research Momentum scoring (0-100, 20% weight) — citation velocity from OpenAlex (up to 35 points), influential citation acceleration from Semantic Scholar (up to 25 points), arXiv preprint velocity as a leading indicator (up to 25 points), and a momentum amplifier when high citations and active preprints co-occur (up to 15 points)
• Patent Commercialization scoring (0-100, 25% weight) — USPTO granted-vs-pending ratio and recency (up to 35 points), EPO international filing coverage indicating global IP strategy (up to 25 points), author-to-patent cross-referencing (up to 25 points), recency bonus for patents filed since 2022 (up to 15 points)
• Funding Validation scoring (0-100, 25% weight) — NIH grant portfolio with R01/R21/R35 and SBIR/STTR weighting (up to 35 points), Grants.gov federal opportunities by estimated funding volume (up to 25 points), clinical trial phase progression as a translation proxy (up to 25 points), funding cascade amplifier when SBIR and clinical trials co-occur (up to 15 points)
• TRL Assessment scoring (0-100, 30% weight) — keyword analysis across 9 HIGH_TRL terms (commerc, manufactur, fda approv, deploy, etc.) and 6 MED_TRL terms (prototype, validat, proof of concept, etc.) applied to patents and preprints (up to 30 points), patent grant ratio as a maturity proxy (up to 25 points), clinical trial phase as a TRL proxy — Phase 3 maps to TRL 7+ (up to 25 points), SBIR Phase II awards as commercialization validation (up to 20 points)
• Author-inventor cross-reference — matches author surnames from OpenAlex publications against inventor names on USPTO and EPO patents to detect active researcher commercialization
• TRL override rule — automatically elevates any technology to INVEST_NOW when TRL reaches 7+ and IP status is COMMERCIAL_READY, regardless of the weighted composite
• 5-level named verdicts — INVEST_NOW (75+), STRONG_CANDIDATE (55-74), MONITOR (35-54), TOO_EARLY (15-34), PASS (<15), each with named sub-level classifications per scoring dimension
• Actionable recommendations — the engine generates specific, condition-triggered text recommendations (e.g., "Breakthrough research momentum — time-sensitive acquisition opportunity") from 5 signal patterns
• Structured metadata block — every result includes the search query, optional field/region filters, run timestamp, and per-source record counts for auditability

Use cases for tech scouting report

Venture capital technology screening

VC firms need to evaluate dozens of emerging technologies per quarter. Analysts can run a Tech Scouting Report for each candidate technology before committing time to deeper diligence. The composite score, TRL estimate, and investment verdict give the investment committee an objective, reproducible first filter — distinguishing technologies that warrant expert interviews from those that can be deprioritized based on weak IP or early TRL.

Corporate R&D acquisition and licensing scouting

Corporate development teams scouting for acquisition targets or licensing candidates need to compare technologies across a common framework. The patent commercialization score and author-inventor cross-reference identify which academic research groups are actively commercializing — the most likely licensing partners. The TRL assessment flags technologies mature enough for near-term integration versus those requiring further incubation.

Technology transfer office prioritization

University technology transfer offices manage hundreds of invention disclosures annually with limited staff. Running a Tech Scouting Report for each disclosure provides an objective commercialization readiness score that helps offices prioritize which inventions receive patent prosecution resources, industry outreach, and SBIR application support — rather than relying entirely on faculty self-reporting.

Government R&D program evaluation

Federal program managers at agencies like DARPA, ARPA-E, or NIH need to assess which funded research programs are progressing toward deployment. The TRL assessment and SBIR Phase II tracking provide standardized readiness signals that complement subjective progress reports, enabling data-driven decisions about continued funding or program graduation to commercialization tracks.

Competitive intelligence and market entry research

Strategy teams entering a new technology market need to map the competitive landscape before committing resources. Running reports for 5-10 competing technologies surfaces which ones have strong patent portfolios, which have clinical translation underway, and which are still in basic research — giving teams a ranked view of where each competitor technology stands on the commercialization curve.

Strategic consulting technology landscape analysis

Management consulting firms preparing technology landscape reports for clients can run this actor to generate quantitative, auditable evidence for each technology in scope. The structured JSON output maps cleanly into presentation templates, and the named signals provide quotable, source-linked evidence statements without additional primary research.

How to generate a tech scouting report

1. Enter the technology name — type the technology or research area you want to scout, e.g., "solid-state batteries", "CRISPR base editing", or "GLP-1 receptor agonists". Be specific: "mRNA cancer vaccines" returns more targeted results than "cancer vaccines".
2. Optionally add a field and region — enter a scientific field (e.g., "oncology", "energy storage", "semiconductors") and/or geographic region (e.g., "United States", "Europe", "China") to filter data source queries and narrow results.
3. Click Start — the actor calls all 8 data sources in parallel. Most runs finish in 60-90 seconds.
4. Download your report — open the Dataset tab and export to JSON, CSV, or Excel. Each report is one structured record containing all four scoring dimensions, signals, verdicts, and metadata.

Input parameters

Input examples

Standard technology scout — most common use:

{
  "technology": "solid-state batteries",
  "field": "energy storage",
  "region": "United States"
}

Biomedical technology with no region filter:

{
  "technology": "CAR-T cell therapy",
  "field": "oncology"
}

Broad technology scan with minimal parameters:

{
  "technology": "perovskite solar cells"
}

Input tips

• Be specific with technology names — "solid-state lithium-ion batteries" returns more focused patent and paper results than "batteries"; the search query is constructed by concatenating the technology, field, and region inputs with spaces
• Use region to compare IP landscapes — running the same technology with "United States" and then "China" reveals divergent patent filing strategies in the two largest IP markets
• Batch via the API — if comparing 10 technologies, trigger 10 runs in parallel via the Apify API rather than running them sequentially; each run is independent and takes under 90 seconds

Output example

{
  "technology": "solid-state batteries",
  "compositeScore": 67,
  "verdict": "STRONG_CANDIDATE",
  "researchMomentum": {
    "score": 78,
    "citationVelocity": 34,
    "publicationCount": 18,
    "preprints": 9,
    "momentumLevel": "HIGH_MOMENTUM",
    "signals": [
      "High citation velocity (avg 34/paper) — strong research impact",
      "9 recent preprints — active research pipeline",
      "12 accelerating papers — citation trajectory rising"
    ]
  },
  "patentCommerc": {
    "score": 61,
    "patentCount": 22,
    "grantedPatents": 9,
    "crossRefHits": 4,
    "commercLevel": "PORTFOLIO_BUILDING",
    "signals": [
      "9 granted patents — established IP portfolio",
      "4 author-patent cross-references — researcher commercializing",
      "6 EPO filings — international patent strategy"
    ]
  },
  "fundingValidation": {
    "score": 58,
    "nihGrants": 6,
    "govGrants": 8,
    "clinicalTrials": 0,
    "fundingLevel": "VALIDATED",
    "signals": [
      "3 SBIR/STTR grants — commercialization-oriented federal funding",
      "2 R01/R21/R35 grants — substantial research validation"
    ]
  },
  "trlAssessment": {
    "score": 52,
    "estimatedTRL": 5,
    "trlLevel": "PROTOTYPE",
    "signals": [
      "Multiple commercialization keywords — advanced TRL indicators",
      "2 SBIR Phase II awards — commercialization validation"
    ]
  },
  "allSignals": [
    "High citation velocity (avg 34/paper) — strong research impact",
    "9 recent preprints — active research pipeline",
    "12 accelerating papers — citation trajectory rising",
    "9 granted patents — established IP portfolio",
    "4 author-patent cross-references — researcher commercializing",
    "6 EPO filings — international patent strategy",
    "3 SBIR/STTR grants — commercialization-oriented federal funding",
    "2 R01/R21/R35 grants — substantial research validation",
    "Multiple commercialization keywords — advanced TRL indicators",
    "2 SBIR Phase II awards — commercialization validation"
  ],
  "recommendations": [
    "Researcher-inventor overlap — strong commercialization intent",
    "Translation-stage funding secured — commercialization pathway validated"
  ],
  "metadata": {
    "field": "energy storage",
    "region": "United States",
    "searchQuery": "solid-state batteries energy storage United States",
    "timestamp": "2026-03-20T09:14:37.000Z",
    "dataSources": {
      "openalexPapers": 18,
      "semanticScholarPapers": 20,
      "usptoPatents": 14,
      "epoPatents": 8,
      "nihGrants": 6,
      "govGrants": 8,
      "clinicalTrials": 0,
      "arxivPreprints": 9
    }
  }
}

Output fields

How much does it cost to run a tech scouting report?

Tech Scouting Report uses pay-per-run pricing — you pay approximately $0.10 per report. Platform compute costs are included. Each run calls 8 sub-actors in parallel with a 120-second timeout per source, so the actor is compute-efficient relative to the breadth of data collected.

You can set a maximum spending limit per run to control costs. The actor stops when your budget is reached.

Compare this to PatSnap or Amplity at $1,000–5,000/month — with Tech Scouting Report, most teams running 50-100 reports per month spend $5-10 with no subscription commitment. Apify's free tier includes $5 of monthly credits, covering approximately 50 reports at no cost.

Tech scouting report using the API

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("ryanclinton/tech-scouting-report").call(run_input={
    "technology": "solid-state batteries",
    "field": "energy storage",
    "region": "United States"
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"Technology: {item['technology']}")
    print(f"Verdict: {item['verdict']} (score: {item['compositeScore']}/100)")
    print(f"TRL estimate: {item['trlAssessment']['estimatedTRL']} — {item['trlAssessment']['trlLevel']}")
    for signal in item.get("allSignals", []):
        print(f"  Signal: {signal}")

JavaScript

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: "YOUR_API_TOKEN" });

const run = await client.actor("ryanclinton/tech-scouting-report").call({
    technology: "solid-state batteries",
    field: "energy storage",
    region: "United States"
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
for (const item of items) {
    console.log(`Technology: ${item.technology}`);
    console.log(`Verdict: ${item.verdict} (score: ${item.compositeScore}/100)`);
    console.log(`TRL: ${item.trlAssessment.estimatedTRL} — ${item.trlAssessment.trlLevel}`);
    item.allSignals.forEach(signal => console.log(`  Signal: ${signal}`));
}

cURL

# Start the actor run
curl -X POST "https://api.apify.com/v2/acts/ryanclinton~tech-scouting-report/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"technology": "solid-state batteries", "field": "energy storage", "region": "United States"}'

# Fetch results (replace DATASET_ID from the run response)
curl "https://api.apify.com/v2/datasets/DATASET_ID/items?token=YOUR_API_TOKEN&format=json"

How Tech Scouting Report works

Phase 1: Parallel data collection

The actor constructs a unified search query by joining the technology, field, and region inputs (e.g., "solid-state batteries energy storage United States"). It then calls all 8 sub-actors simultaneously via Promise.all, each with a 512 MB memory allocation and a 120-second timeout. The 8 sources — OpenAlex, Semantic Scholar, arXiv, USPTO, EPO, NIH Reporter, Grants.gov, and ClinicalTrials.gov — are queried for up to 20 records each. Failed calls return an empty array and do not abort the run, so a single unavailable data source degrades gracefully without losing the other 7.

Phase 2: Four independent scoring models

Once all data is collected, four scoring functions run against the raw dataset in sequence:

Research Momentum calculates citation velocity as (totalCitations / publicationCount) * 2, capped at 35 points, with a 10-point bonus when more than 50% of citations are from papers published in 2023 or later. Semantic Scholar's influentialCitationCount field adds 2× weight per influential paper. ArXiv preprint volume contributes 3 points per preprint, capped at 25. A 15-point amplifier fires when both average citations exceed 10 and recent preprints exceed 3.

Patent Commercialization assigns 4 points per granted USPTO patent, 2 per application, and 2 per patent filed since 2022, capped at 35. EPO filings score 4 points each for kind codes B (granted) or A (published), capped at 25. The author-inventor cross-reference iterates every author surname from OpenAlex publications and checks whether it appears in the inventors, applicant, or assignee fields of any USPTO or EPO patent, scoring 5 points per match, capped at 25.

Funding Validation scores 3 points per NIH grant, with additional 4 points per R01/R21/R35 and 5 points per SBIR/STTR award, capped at 35. Grants.gov records contribute 3 points each plus a 10-point bonus when estimated funding exceeds $1M, capped at 25. Clinical trials score 4 points each with a 5-point Phase 2+ bonus, capped at 25. A 15-point cascade amplifier fires when SBIR and clinical trial records co-occur.

TRL Assessment performs keyword scanning across the concatenated title, abstract, and description of all patents and arXiv papers. Nine HIGH_TRL keywords (commerc, manufactur, fda approv, market, deploy, etc.) score 4 points each; 6 MED_TRL keywords (prototype, validat, proof of concept, etc.) score 2 points each; LOW_TRL keywords (discover, fundamental, theoretical, etc.) subtract 1 point each. This text score is capped at 30. Patent grant ratio contributes up to 25 points. Clinical trial phase maps to TRL (Phase 3 → TRL 7+), capped at 25. SBIR Phase II awards add 8 points each, capped at 20.

Phase 3: Composite scoring and verdict assignment

The composite score is the weighted average: (momentumScore × 0.20) + (patentScore × 0.25) + (fundingScore × 0.25) + (trlScore × 0.30). TRL carries the highest weight because technology maturity is the most direct predictor of near-term commercializability. An override rule elevates any technology to INVEST_NOW when estimatedTRL >= 7 and commercLevel === 'COMMERCIAL_READY', regardless of the weighted composite. The engine then collects all named signal strings from each model, evaluates 5 condition-triggered recommendation templates, and assembles the final structured result for Actor.pushData.

Tips for best results

1. Specificity improves all four scoring models. A query for "CAR-T cell therapy" retrieves more relevant patent and grant records than "cell therapy". The search query is sent verbatim to all 8 sources, so terminology precision has a multiplicative effect on data quality.

2. Use the field parameter for cross-disciplinary technologies. For a technology like "graphene" that spans electronics, energy, and biomedical applications, setting field: "energy storage" narrows all 8 queries simultaneously and prevents irrelevant cross-domain records from diluting the scores.

3. Compare technologies head-to-head. Run the same field and region for competing technologies (e.g., "solid-state batteries" vs. "lithium-sulfur batteries" vs. "sodium-ion batteries") and rank by composite score. The structured JSON output makes this comparison scriptable.

4. TRL score carries 30% weight — understand what drives it. For non-biomedical technologies, the clinical trial component of TRL contributes zero; the score is driven entirely by keyword analysis and patent grant ratios. For biomedical technologies, even a single Phase 2 trial adds 10+ TRL points.

5. Schedule weekly runs for technologies under active monitoring. Use Apify's scheduling feature to run reports on a watchlist of 10-20 technologies every Monday. A sudden jump in composite score — driven by new patent grants or SBIR awards — surfaces investment timing signals before they appear in news or analyst reports.

6. Supplement with company-level data for mature technologies. For technologies with TRL 6+ and a STRONG_CANDIDATE or INVEST_NOW verdict, combine this actor with Company Deep Research to identify specific commercializing companies and assess them individually.

7. Review dataSources record counts in metadata. If usptoPatents: 0 or nihGrants: 0, those scoring components returned empty and the composite score should be interpreted as a lower bound. Re-run with a broader or different technology name formulation to verify.

Combine with other Apify actors

Limitations

• Data freshness depends on upstream sources. OpenAlex and Semantic Scholar index papers with a lag of days to weeks. Very recent publications, patents filed in the last 30-60 days, and newly opened clinical trials may not appear in results and will not affect scores.
• Clinical trial data is most relevant for biomedical technologies. Non-biomedical technologies (energy, semiconductors, software) will typically return zero clinical trial records, which reduces the funding validation score and TRL assessment relative to their actual maturity.
• Patent text analysis is keyword-based, not semantic. The TRL keyword classifier uses substring matching (e.g., text.includes('commerc')) rather than NLP. Ambiguous or technical language can produce false positive or negative TRL signal hits.
• Author-inventor cross-reference uses surname matching. The cross-reference checks whether a researcher's surname appears anywhere in the patent's inventor/applicant/assignee fields. Common surnames and abbreviated names can produce false positives; uncommon surnames may produce false negatives.
• Up to 20 records per source. Each of the 8 data sources is queried for a maximum of 20 records. For very active research areas with hundreds of publications, the scoring models operate on a sample, not the full corpus. Higher-volume technologies may require narrowing the query with field and region parameters.
• No document-level full-text access. Patent and paper abstracts are used for TRL keyword analysis; full texts are not retrieved. Claims sections of patents, which often contain stronger TRL indicators, are not analyzed.
• Sub-actor failures degrade gracefully but silently. If a data source is unavailable during a run, its score component defaults to zero. Check the dataSources metadata block to verify that each source returned records.
• Not a substitute for expert diligence. The scores are evidence-based leading indicators, not investment recommendations. Technologies with strong composite scores still require expert domain assessment, freedom-to-operate analysis, and business model evaluation before any investment or licensing decision.

Integrations

• Zapier — trigger a tech scouting report when a new technology name is added to a Notion database or Google Sheet, and route results to Slack or email
• Make — build scheduled technology watchlist workflows that run reports weekly and filter for composite score changes above a threshold
• Google Sheets — export all reports from a quarterly technology scan to a shared spreadsheet for team review and prioritization
• Apify API — embed tech scouting calls directly in your VC deal flow platform, research portal, or internal R&D tooling via REST or the official Python/JS client
• Webhooks — post structured report results to any internal endpoint when a run completes, enabling event-driven workflows in your investment or R&D management system
• LangChain / LlamaIndex — pipe structured tech scouting reports into an LLM-powered research assistant to generate investment memos, technology landscape summaries, or due diligence briefings

Troubleshooting

• Composite score seems low for a well-known technology — Check the metadata.dataSources record counts. If several sources returned 0 records, the technology name may be formulated differently than the sources expect. Try alternate naming (e.g., "mRNA vaccine" instead of "mRNA immunization"), or add a field parameter to narrow and clarify the query.
• Run takes longer than 90 seconds — The actor calls all 8 sub-actors in parallel with 120-second individual timeouts. If the Apify platform is under load or a sub-actor is slow, total run time can reach 2-3 minutes. This is normal. If runs consistently exceed 5 minutes, contact Apify support.
• TRL estimate is lower than expected for a mature technology — The TRL assessment weights clinical trial phase heavily for biomedical technologies. For materials science, energy, or software technologies without clinical trials, the TRL score is driven by patent keyword analysis and grant indicators alone. The absolute TRL estimate may understate true maturity for these domains; focus on the relative score when comparing technologies within a domain.
• Author-inventor cross-reference showing 0 despite known researcher-inventors — The cross-reference uses surname substring matching against the inventors, applicant, and assignee fields of patents. If patents list corporate assignees only (not individual inventors), or if author names use initials, the match will fail. This is a known limitation of the text-matching approach.
• INVEST_NOW verdict on a technology you know is early stage — Check whether the TRL override rule fired: if estimatedTRL >= 7 and commercLevel === 'COMMERCIAL_READY', the verdict is elevated regardless of other scores. This can occur when patent keyword analysis finds many HIGH_TRL terms in a small number of documents. Review the TRL signals for context.

Responsible use

• This actor only accesses publicly available academic, patent, grant, and clinical trial data from open government and institutional sources.
• Patent and publication data is sourced from OpenAlex, Semantic Scholar, arXiv, USPTO, and EPO — all public databases.
• Grant and clinical trial data comes from NIH Reporter, Grants.gov, and ClinicalTrials.gov — U.S. government open data portals.
• Do not use research outputs to misrepresent technology maturity to investors or regulatory bodies.
• For guidance on responsible use of open government data, see Apify's guide.

FAQ

How accurate is the Tech Scouting Report composite score? The composite score is an evidence-based indicator derived from publicly available data, not a proprietary valuation model. It performs best as a relative ranking tool — comparing 10 technologies against each other — rather than as an absolute measure of commercialization probability. Technologies in active, well-published research areas with strong U.S. patent activity score most reliably. For nascent technologies or those primarily active in non-English literature, scores will understate true momentum.

How many technologies can I scout in one run? Each run evaluates exactly one technology. To scout multiple technologies, trigger one run per technology via the API. Runs complete in 60-90 seconds each, so 10 technologies can be processed in under 15 minutes if triggered in parallel.

Does the tech scouting report work for software and AI technologies? Yes, but with differences. Software and AI technologies rarely have clinical trials, so the funding validation score is driven entirely by NIH and Grants.gov grants. TRL assessment relies on patent keyword analysis and SBIR awards. AI technologies may have very high research momentum scores but lower patent scores if the commercialization is primarily happening through trade secrets rather than patents.

What is Technology Readiness Level (TRL) and how is the estimate calculated? TRL is a 1-9 scale used by NASA, the U.S. Department of Defense, and the European Commission to measure technology maturity — from TRL 1 (basic research) to TRL 9 (full operational deployment). This actor estimates TRL by combining keyword analysis of patent and arXiv text for HIGH_TRL and MED_TRL language patterns, the ratio of granted to pending patents, the highest clinical trial phase found, and SBIR Phase II award counts. The estimate maps to five named levels: BASIC_RESEARCH, PROOF_OF_CONCEPT, PROTOTYPE, PILOT, and DEPLOYMENT_READY.

How is Tech Scouting Report different from PatSnap or Amplity? PatSnap and Amplity are full-featured enterprise patent analytics platforms costing $1,000–5,000/month. Tech Scouting Report costs approximately $0.10 per run, focuses on the commercialization readiness question specifically (rather than general patent analytics), and integrates non-patent signals — academic citations, federal grants, and clinical trial phases — that enterprise patent tools do not cover. It is best suited for initial screening and portfolio monitoring rather than deep freedom-to-operate or prior art analysis.

Can I schedule tech scouting to run automatically on a watchlist? Yes. Use Apify's built-in scheduling to trigger runs on a cron schedule. For a watchlist of 20 technologies, create 20 scheduled tasks each calling the actor with one technology name. When composite scores shift significantly between runs, use webhook integrations to post alerts to Slack or email.

What does the INVEST_NOW verdict mean? INVEST_NOW means the composite score is 75 or above, or TRL is 7+ with COMMERCIAL_READY IP status regardless of composite score. It indicates that across research momentum, patent portfolio, funding validation, and TRL maturity, the technology shows strong convergent signals for near-term commercializability. It is a screening flag, not a formal investment recommendation.

Is it legal to use data from USPTO, NIH, and ClinicalTrials.gov for commercial analysis? Yes. USPTO patent data, NIH Reporter grant data, Grants.gov, and ClinicalTrials.gov are all publicly funded U.S. government databases published under open access policies. Academic databases like OpenAlex and arXiv are explicitly open-access. Semantic Scholar data is available for research and non-commercial use under its API terms. Commercial use of aggregated, non-reproduced summaries derived from these sources is standard practice in patent analytics, VC research, and competitive intelligence.

What happens if one of the 8 data sources is unavailable during a run? The actor handles sub-actor failures gracefully — if any of the 8 parallel calls fail, that source returns an empty array and the run continues. The affected scoring components will score zero for that dimension. Check the metadata.dataSources block in the output to see which sources returned records. A source returning 0 records does not necessarily mean failure; the technology may simply not appear in that database.

Can I use the API to compare multiple technologies and rank them? Yes. Trigger parallel API calls for each technology, collect the results, and sort by compositeScore. The structured JSON output is designed for programmatic comparison. The Python example in this README returns one record per technology; wrapping it in a loop and building a comparison table requires only a few additional lines of code.

How do I interpret a MONITOR verdict? MONITOR means the composite score is 35-54. The technology shows meaningful signals in some dimensions but lacks the convergent strength across IP, funding, and TRL to warrant immediate investment. Recommended action is to re-run the report in 6-12 months and watch for increases in granted patents, SBIR awards, or clinical trial advancement that would push the score into STRONG_CANDIDATE range.

Help us improve

If you encounter issues, you can help us debug faster by enabling run sharing in your Apify account:

1. Go to Account Settings > Privacy
2. Enable Share runs with public Actor creators

This lets us see your run details when something goes wrong, so we can fix issues faster. Your data is only visible to the actor developer, not publicly.

Support

Found a bug or have a feature request? Open an issue in the Issues tab on this actor's page. For custom solutions or enterprise integrations, reach out through the Apify platform.