search-patents

patents.google.com/search-patents-369445 · updated May 21, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$browse install patents.google.com/search-patents-369445
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summary

Search Google Patents (~140M+ worldwide publications) by free-text query, assignee, inventor, country, status, language, or date range — returns publication number, title, priority/filing/grant/publication dates, inventor, assignee, snippet, PDF URL, and canonical landing-page URL for each match.

skill.md
name
search-patents
title
Google Patents Search
description
>- Search Google Patents (~140M+ worldwide publications) by free-text query, assignee, inventor, country, status, language, or date range — returns publication number, title, priority/filing/grant/publication dates, inventor, assignee, snippet, PDF URL, and canonical landing-page URL for each match.
website
patents.google.com
category
research
tags
- patents - search - research - google - ip - api
source
'browserbase: agent-runtime 2026-05-20'
updated
'2026-05-20'
recommended_method
api
alternative_methods
- method: browser rationale: >- When the /xhr/query endpoint is unreachable or returns the Google 'Sorry...' rate-limit interstitial without a residential-proxy fallback, drive https://patents.google.com/?q=... and harvest results from the rendered SPA. ~3 browser turns per query vs. one HTTP fetch — use only on API failure.
verified
true
proxies
true

Google Patents Search

Purpose

Search Google Patents (patents.google.com) — the public index of ~140M+ worldwide patent publications — and return matching patents with their publication number, title, priority/filing/grant/publication dates, inventor, assignee, language, snippet, and canonical landing-page + PDF URLs. Read-only; never authenticates, never submits forms, never opens Google Account.

When to Use

  • Free-text patent search ("quantum computing", "lithium battery").
  • Filtered patent search by assignee, inventor, country, status (granted vs application), language, or date range.
  • Boolean / phrase / CPC queries against the title, abstract, claims, or full document.
  • Bulk patent discovery feeding downstream PDF download (the pdf field plus the patentimages.storage.googleapis.com prefix yields a stable direct link).
  • Anywhere you'd otherwise scrape the Google Patents results page — the public XHR endpoint is faster, structured, and avoids JS-rendering overhead.

Workflow

The Google Patents web UI is a thin client over an undocumented but stable public JSON endpoint at https://patents.google.com/xhr/query — no auth, no cookies, no CSRF token, no anti-bot challenge on the endpoint itself. The endpoint takes one parameter, url=, whose value is the URL-encoded form of the entire query string that would normally appear after patents.google.com/? (i.e. double-encoded relative to the outer URL). Lead with the API; the browser path is a slow fallback that pays a ~100× turn-cost premium because the search page is fully JS-rendered (browse snapshot returns no useful refs until after the SPA hydrates).

Stealth note: the Google Sorry interstitial ("your computer or network may be sending automated queries", HTTP 503) trips after ~5–10 sequential requests from the same datacenter IP. Always fetch through residential proxies (browse cloud fetch ... --proxies) — every call gets a fresh exit IP and the rate-limit never fires. A bare session (no proxies) works for a handful of one-off queries but is not safe for any sustained workload.

1. Build the inner query string

Construct the query exactly as you would type it into the URL bar of patents.google.com, then URL-encode the entire string and stuff it into url=.

Inner-query parameters (these go inside the url= value):

ParamPurposeExamples
qFree-text query. Supports phrases ("…"), boolean (AND/OR), exclusion (-term), field prefixes (TI=/AB=/CL=/TAC=), CPC codes (CPC=B60R22), chemistry (SSS=/SMARTS=). Words are AND-ed by default with left associativity (so safety OR seat belt parses as (safety OR seat) AND belt).q=quantum+computing
assigneeFilter by assignee (company / org).assignee=google
inventorFilter by inventor name.inventor=hinton
countryTwo-letter publication-office code.country=US, country=CN, country=EP
statusGRANT or APPLICATION.status=GRANT
languageLanguage of the publication.language=ENGLISH
before / afterDate filter. Prefix the date with the date type: publication:, priority:, filing:, grant:. Format is YYYYMMDD.before=publication:20240101, after=priority:20100101
sortnew (newest first) or old (oldest first). Omit for relevance (default).sort=new
numResults per page. Default 10, max 100.num=25
page1-indexed page number. Hard cap of 1000 results total across all pages (num × page ≤ 1000); beyond that the API returns an empty result set even though total_num_results may report millions.page=2

2. Fetch the endpoint

INNER='q=quantum+computing&num=25&page=1&sort=new'
URL_ENC=$(node -e "console.log(encodeURIComponent(process.argv[1]))" "$INNER")
browse cloud fetch "https://patents.google.com/xhr/query?url=${URL_ENC}&exp=" --proxies

The &exp= trailing parameter is required and always empty. The response wraps a JSON envelope around the page payload; parse .content as JSON to get the real result object.

3. Parse the result envelope

The inner JSON has shape:

{
  "results": {
    "total_num_results": 127800,   // total hits matching the query
    "total_num_pages": 100,        // pages available (capped — see step 1)
    "many_results": false,
    "num_page": 0,                 // 0-indexed page number of THIS response
    "cluster": [                   // exactly one cluster for ungrouped searches;
      {                            // empty result[] when zero hits
        "result": [ { "id": "...", "rank": 0, "patent": { ... } }, ... ]
      }
    ],
    "summary": "...",
    "landscape": { ... }
  }
}

For each item in cluster[0].result, the patent sub-object carries the fields you actually want: title, publication_number, priority_date, filing_date, grant_date (may be absent for applications), publication_date, inventor, assignee, language, snippet, and pdf (a relative path).

Strip HTML highlighting tagstitle and snippet contain <b>…</b> around query-term matches and HTML entities (&hellip;, &#34;). A simple replace(/<[^>]*>/g, '') plus entity decode produces clean text.

4. Construct canonical URLs

  • Landing page: https://patents.google.com/patent/{publication_number}/en (the /en suffix forces the English-translated view for non-English publications).
  • PDF: https://patentimages.storage.googleapis.com/{patent.pdf} (the pdf field is the relative path, e.g. 85/df/92/f5ac6f65349817/JP7446622B2.pdf). Empty pdf field means no PDF is hosted (occasionally true for very recent applications).

5. Paginate

Increment page= until num_page >= total_num_pages - 1. Because Google caps accessible results at 1000, choose num=100 if you need depth (10 pages max) and num=10 if you only need the relevance-top results.

Browser fallback

If the XHR endpoint is unreachable (extended outage, regional block — not observed in our trace) drive the search UI:

sid=$(browse cloud sessions create --keep-alive --proxies --verified | grep -o '"id": "[^"]*"' | head -1 | sed 's/.*"\([^"]*\)"$/\1/')
browse open "https://patents.google.com/?q=quantum+computing&num=25" --remote --session "$sid"
browse get markdown body --remote --session "$sid"

Parse the rendered markdown for the result blocks (titles are h2 links, the publication number appears immediately under the title with a link to patentimages.storage.googleapis.com/.../{pubnum}.pdf). This costs ~3 browser turns per query vs. one HTTP fetch, so only fall back if the API actually fails.

Site-Specific Gotchas

  • Double URL-encoding is required. The url= parameter wraps an already-encoded query string. If you single-encode (treating url=q=quantum computing as one flat query string) the endpoint silently returns the global-relevance landing page with total_num_results from an empty query. Always: build the inner query string first, then encodeURIComponent the whole thing and assign to url=.
  • &exp= is mandatory. Omitting the empty exp parameter returns a 400 in some response paths. Always include &exp= (no value).
  • 1000-result hard ceiling. Google reports total_num_results: 127800 but total_num_pages × num never exceeds 1000. To enumerate beyond the top-1000, narrow the query (date slices, CPC codes, assignee filters) and re-page each slice. Identical-page requests beyond the ceiling return an empty cluster[0].result with the same total_num_results — there is no error, just silence.
  • Rate-limit interstitial is HTML, not JSON. After ~5–10 rapid sequential requests from a single datacenter IP, the endpoint returns a 503 with an HTML body titled "Sorry..." ("your computer or network may be sending automated queries"). Parse defensively — check Content-Type or check that the first byte is {. Use --proxies (residential rotation) and the rate-limit never fires across hundreds of queries.
  • Inventor / assignee names are not Latin-script-normalized. A patent originally filed in Japan returns its inventor and assignee in Japanese characters (e.g. プーリ,シュルティ, イェール ユニバーシティー); the Chinese-language equivalents appear in Simplified Chinese (王鑫, 北京百度网讯科技有限公司). The /en landing page exposes the English transliteration but the XHR API does not. If you need a Latin form, you must fetch the landing page.
  • HTML in title and snippet fields. Search-term matches are wrapped in <b>…</b> and the snippet ends in &hellip;. Decode entities and strip tags before display.
  • grant_date is sometimes missing. Applications that have not been granted have only priority_date, filing_date, and publication_date. Treat grant_date as optional.
  • pdf field can be empty string. Most patents have a PDF; very recent applications, withdrawn entries, and some non-US jurisdictions don't. Test for empty string, not null.
  • Default operator quirk. q=safety OR seat belt is parsed as (safety OR seat) AND belt, not safety OR (seat belt). Use explicit parens or phrase quotes ("seat belt") to disambiguate.
  • num= ignored above 100. Setting num=500 returns 100 results and shifts the cap accordingly. Do not assume larger pages give you more depth.
  • Don't bother with GraphQL discovery. Network inspection shows no GraphQL surface — the XHR endpoint is the entire API. Don't waste turns hunting /graphql or /api/v1/; they don't exist.

Expected Output

JSON envelope for a normal search (10 results / page, default sort):

{
  "query": "quantum computing",
  "total_num_results": 127800,
  "total_num_pages": 100,
  "num_page": 0,
  "results": [
    {
      "title": "Quantum information processing using asymmetric error channels",
      "publication_number": "JP7446622B2",
      "priority_date": "2018-06-29",
      "filing_date": "2019-06-28",
      "grant_date": "2024-03-11",
      "publication_date": "2024-03-11",
      "inventor": "プーリ,シュルティ",
      "assignee": "イェール ユニバーシティー",
      "language": "en",
      "snippet": "For example, it is known that certain computational problems can be solved more efficiently using quantum computing rather than traditional classical computing...",
      "pdf_url": "https://patentimages.storage.googleapis.com/85/df/92/f5ac6f65349817/JP7446622B2.pdf",
      "canonical_url": "https://patents.google.com/patent/JP7446622B2/en"
    },
    {
      "title": "Quantum computing service with local edge devices supporting multiple quantum computing technologies",
      "publication_number": "US11650869B2",
      "priority_date": "2019-11-27",
      "filing_date": "2019-11-27",
      "grant_date": "2023-05-16",
      "publication_date": "2023-05-16",
      "inventor": "Jeffrey Paul Heckey",
      "assignee": "Amazon Technologies, Inc.",
      "language": "en",
      "snippet": "a photon based quantum computer. 6. The system of claim 1, wherein the one or more computing devices that implement the quantum computing service are configured to...",
      "pdf_url": "https://patentimages.storage.googleapis.com/28/a0/9d/323bb84148dd61/US11650869.pdf",
      "canonical_url": "https://patents.google.com/patent/US11650869B2/en"
    }
  ]
}

Zero-results envelope:

{
  "query": "xyzqzx99asdfblahNoMatchPossible",
  "total_num_results": 0,
  "total_num_pages": 0,
  "num_page": 0,
  "results": []
}

Rate-limited (interstitial) sentinel — agent should retry with --proxies or back off:

{
  "query": "lithium battery",
  "error": "rate_limited",
  "http_status": 503,
  "message": "Google 'Sorry...' interstitial returned. Retry through residential proxy or wait ~30s."
}
how to use search-patents

How to use search-patents on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add search-patents
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$browse install patents.google.com/search-patents-369445

The skills CLI fetches search-patents from GitHub repository patents.google.com/search-patents-369445 and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/search-patents

Reload or restart Cursor to activate search-patents. Access the skill through slash commands (e.g., /search-patents) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.457 reviews
  • Tariq Lopez· Dec 20, 2024

    search-patents is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Dhruvi Jain· Dec 16, 2024

    I recommend search-patents for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Nia Srinivasan· Dec 16, 2024

    Solid pick for teams standardizing on skills: search-patents is focused, and the summary matches what you get after install.

  • Nia Shah· Dec 16, 2024

    search-patents has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Harper Srinivasan· Dec 16, 2024

    Useful defaults in search-patents — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Isabella Verma· Nov 11, 2024

    search-patents reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Oshnikdeep· Nov 7, 2024

    Useful defaults in search-patents — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Valentina Chawla· Nov 7, 2024

    search-patents has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Aisha Sharma· Nov 7, 2024

    Solid pick for teams standardizing on skills: search-patents is focused, and the summary matches what you get after install.

  • Zaid Robinson· Nov 7, 2024

    I recommend search-patents for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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