uspto-database▌
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USPTO provides specialized APIs for patent and trademark data. Search patents by keywords/inventors/assignees, retrieve examination history via PEDS, track assignments, analyze citations and office actions, access TSDR for trademarks, for IP analysis and prior art searches.
USPTO Database
Overview
USPTO provides specialized APIs for patent and trademark data. Search patents by keywords/inventors/assignees, retrieve examination history via PEDS, track assignments, analyze citations and office actions, access TSDR for trademarks, for IP analysis and prior art searches.
When to Use This Skill
This skill should be used when:
- Patent Search: Finding patents by keywords, inventors, assignees, classifications, or dates
- Patent Details: Retrieving full patent data including claims, abstracts, citations
- Trademark Search: Looking up trademarks by serial or registration number
- Trademark Status: Checking trademark status, ownership, and prosecution history
- Examination History: Accessing patent prosecution data from PEDS (Patent Examination Data System)
- Office Actions: Retrieving office action text, citations, and rejections
- Assignments: Tracking patent/trademark ownership transfers
- Citations: Analyzing patent citations (forward and backward)
- Litigation: Accessing patent litigation records
- Portfolio Analysis: Analyzing patent/trademark portfolios for companies or inventors
USPTO API Ecosystem
The USPTO provides multiple specialized APIs for different data needs:
Core APIs
-
PatentSearch API - Modern ElasticSearch-based patent search (replaced legacy PatentsView in May 2025)
- Search patents by keywords, inventors, assignees, classifications, dates
- Access to patent data through June 30, 2025
- 45 requests/minute rate limit
- Base URL:
https://search.patentsview.org/api/v1/
-
PEDS (Patent Examination Data System) - Patent examination history
- Application status and transaction history from 1981-present
- Office action dates and examination events
- Use
uspto-opendata-pythonPython library - Replaced: PAIR Bulk Data (PBD) - decommissioned
-
TSDR (Trademark Status & Document Retrieval) - Trademark data
- Trademark status, ownership, prosecution history
- Search by serial or registration number
- Base URL:
https://tsdrapi.uspto.gov/ts/cd/
Additional APIs
- Patent Assignment Search - Ownership records and transfers
- Trademark Assignment Search - Trademark ownership changes
- Enriched Citation API - Patent citation analysis
- Office Action Text Retrieval - Full text of office actions
- Office Action Citations - Citations from office actions
- Office Action Rejection - Rejection reasons and types
- PTAB API - Patent Trial and Appeal Board proceedings
- Patent Litigation Cases - Federal district court litigation data
- Cancer Moonshot Data Set - Cancer-related patents
Quick Start
API Key Registration
All USPTO APIs require an API key. Register at: https://account.uspto.gov/api-manager/
Set the API key as an environment variable:
export USPTO_API_KEY="your_api_key_here"
Helper Scripts
This skill includes Python scripts for common operations:
scripts/patent_search.py- PatentSearch API client for searching patentsscripts/peds_client.py- PEDS client for examination historyscripts/trademark_client.py- TSDR client for trademark data
Task 1: Searching Patents
Using the PatentSearch API
The PatentSearch API uses a JSON query language with various operators for flexible searching.
Basic Patent Search Examples
Search by keywords in abstract:
from scripts.patent_search import PatentSearchClient
client = PatentSearchClient()
# Search for machine learning patents
results = client.search_patents({
"patent_abstract": {"_text_all": ["machine", "learning"]}
})
for patent in results['patents']:
print(f"{patent['patent_number']}: {patent['patent_title']}")
Search by inventor:
results = client.search_by_inventor("John Smith")
Search by assignee/company:
results = client.search_by_assignee("Google")
Search by date range:
results = client.search_by_date_range("2024-01-01", "2024-12-31")
Search by CPC classification:
results = client.search_by_classification("H04N") # Video/image tech
Advanced Patent Search
Combine multiple criteria with logical operators:
results = client.advanced_search(
keywords=["artificial", "intelligence"],
assignee="Microsoft",
start_date="2023-01-01",
end_date="2024-12-31",
cpc_codes=["G06N", "G06F"] # AI and computing classifications
)
Direct API Usage
For complex queries, use the API directly:
import requests
url = "https://search.patentsview.org/api/v1/patent"
headers = {
"X-Api-Key": "YOUR_API_KEY",
"Content-Type": "application/json"
}
query = {
"q": {
"_and": [
{"patent_date": {"_gte": "2024-01-01"}},
{"assignee_organization": {"_text_any": ["Google", "Alphabet"]}},
{"cpc_subclass_id": ["G06N", "H04N"]}
]
},
"f": ["patent_number", "patent_title", "patent_date", "inventor_name"],
"s": [{"patent_date": "desc"}],
"o": {"per_page": 100, "page": 1}
}
response = requests.post(url, headers=headers, json=query)
results = response.json()
Query Operators
- Equality:
{"field": "value"}or{"field": {"_eq": "value"}} - Comparison:
_gt,_gte,_lt,_lte,_neq - Text search:
_text_all,_text_any,_text_phrase - String matching:
_begins,_contains - Logical:
_and,_or,_not
Best Practice: Use _text_* operators for text fields (more performant than _contains or _begins)
Available Patent Endpoints
/patent- Granted patents/publication- Pregrant publications/inventor- Inventor information/assignee- Assignee information/cpc_subclass,/cpc_at_issue- CPC classifications/uspc- US Patent Classification/ipc- International Patent Classification/claims,/brief_summary_text,/detail_description_text- Text data (beta)
Reference Documentation
See references/patentsearch_api.md for complete PatentSearch API documentation including:
- All available endpoints
- Complete field reference
- Query syntax and examples
- Response formats
- Rate limits and best practices
Task 2: Retrieving Patent Examination Data
Using PEDS (Patent Examination Data System)
PEDS provides comprehensive prosecution history including transaction events, status changes, and examination timeline.
Installation
uv pip install uspto-opendata-python
Basic PEDS Usage
Get application data:
from scripts.peds_client import PEDSHelper
helper = PEDSHelper()
# By application number
app_data = helper.get_application("16123456")
print(f"Title: {app_data['title']}")
print(f"Status: {app_data['app_status']}")
# By patent number
patent_data = helper.get_patent("11234567")
Get transaction history:
transactions = helper.get_transaction_history("16123456")
for trans in transactions:
print(f"{trans['date']}: {trans['code']} - {trans['description']}")
Get office actions:
office_actions = helper.get_office_actions("16123456")
for oa in office_actions:
if oa['code'] == 'CTNF':
print(f"Non-final rejection: {oa['date']}")
elif oa['code'] == 'CTFR':
how to use uspto-databaseHow to use uspto-database on Cursor
AI-first code editor with Composer
1Prerequisites
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 uspto-database
2Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/davila7/claude-code-templates --skill uspto-databaseThe skills CLI fetches uspto-database from GitHub repository davila7/claude-code-templates and configures it for Cursor.
3Select 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│ • Windsurf4Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/uspto-databaseReload or restart Cursor to activate uspto-database. Access the skill through slash commands (e.g., /uspto-database) 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.
Additional Resources
List & Monetize Your Skill
Submit your Claude Code skill and start earning
GET_STARTED →Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
✓Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
✓Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
✓Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
✓Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
general reviewsRatings
4.5★★★★★69 reviews- ★★★★★Noor Abbas· Dec 28, 2024
uspto-database reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Li Rahman· Dec 20, 2024
Registry listing for uspto-database matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Luis Khanna· Dec 20, 2024
Solid pick for teams standardizing on skills: uspto-database is focused, and the summary matches what you get after install.
- ★★★★★Ishan Sanchez· Dec 16, 2024
Solid pick for teams standardizing on skills: uspto-database is focused, and the summary matches what you get after install.
- ★★★★★Noor Okafor· Dec 16, 2024
I recommend uspto-database for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Daniel Menon· Dec 8, 2024
uspto-database reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Harper Taylor· Dec 4, 2024
uspto-database has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Sophia Gonzalez· Nov 27, 2024
I recommend uspto-database for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Kiara Chen· Nov 19, 2024
I recommend uspto-database for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Liam Bansal· Nov 11, 2024
Useful defaults in uspto-database — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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