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uspto-database

用于在美国专利商标局(USPTO)系统中查询官方专利和商标记录的工作流,涵盖 PatentSearch 查询、TSDR 检查、转让数据以及可复现的知识产权研究日志。

uspto-database 是什么?

该技能旨在帮助开发者或研究人员高效地访问和利用美国专利商标局(USPTO)的官方数据。它整合了多个 USPTO 官方数据源,包括专利检索(PatentSearch)、商标状态与文件检索(TSDR)、专利与商标转让查询以及专利文件封装(File Wrapper)等。技能强调数据来源的官方性和可复现性,要求每次查询都记录详细的日志,如检索日期、查询参数和结果数量。它支持 Python 等编程语言进行 API 调用,并指导用户安全地管理 API 密钥。该技能适用于构建专利组合分析、现有技术检索、知识产权 landscape 研究等场景,同时明确警告不要用于法律建议,并建议对重要结论进行人工复核。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/scientific-db-uspto-database

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文档

USPTO 数据库

当任务需要从 USPTO 系统获取美国官方专利或商标记录时,请使用此技能。

使用时机

  • 检索已授权的专利或提前公开的申请。
  • 检查专利申请状态、案卷资料、转让记录或公开的审查历史。
  • 查询商标状态、文件或转让历史。
  • 构建可复现的现有技术、专利组合或知识产权 landscape 研究日志。
  • 将 USPTO 记录与 Google Patents、Lens.org、Semantic Scholar 或公司专利页面等辅助工具进行比较。

请勿使用此技能提供法律建议。将其视为一个数据收集和记录验证工作流。

数据源选择

优先使用官方 USPTO 或 USPTO 支持的数据源:

  • 开放数据门户 (ODP):当前已迁移的 USPTO 数据集和 API 的所在地。
  • 专利文件封装:公共专利申请书目数据和案卷记录。
  • PatentSearch API:用于检索授权专利和提前公开专利数据集的 PatentsView 搜索 API。
  • TSDR 数据 API:商标状态和文件检索。
  • 专利和商标转让检索:所有权转移记录。
  • ODP 中的 PTAB 数据:专利审判与上诉委员会的程序记录。

仅将辅助来源作为便捷索引使用。当结果至关重要时,请通过官方记录进行交叉验证。

身份验证与密钥管理

许多 USPTO API 流程需要 API 密钥。请将密钥存储在环境变量或密钥管理器中,切勿存储在已提交的文件或粘贴的对话记录中。

常用的环境变量名称:

export USPTO_API_KEY="..."
export PATENTSVIEW_API_KEY="..."

对于 PatentSearch,请通过 X-Api-Key 标头发送密钥。对于 TSDR,请遵循当前 USPTO API 管理器说明和速率限制指南。

PatentSearch 工作流

当问题涉及趋势、发明人、受让人、分类、日期或专利组合切片时,请使用 PatentSearch 进行广泛的专利和提前公开专利检索。

工作流:

  1. 从当前的 PatentSearch 参考文档或 Swagger UI 中确定端点。
  2. 构建一个包含明确过滤条件的 JSON 查询。
  3. 仅请求分析所需的字段。
  4. 以确定性的方式排序和分页。
  5. 记录端点、查询体、日期、数据时效性说明和结果数量。

Python 请求框架:

import os
import requests

API_KEY = os.environ["PATENTSVIEW_API_KEY"]
BASE = "https://search.patentsview.org/api/v1"

payload = {
    "q": {
        "_and": [
            {"patent_date": {"_gte": "2024-01-01"}},
            {"assignees.assignee_organization": {"_text_any": ["Google", "Alphabet"]}},
        ]
    },
    "f": ["patent_id", "patent_title", "patent_date"],
    "s": [{"patent_date": "desc"}],
    "o": {"per_page": 100, "page": 1},
}

response = requests.post(
    f"{BASE}/patent/",
    headers={"X-Api-Key": API_KEY, "Content-Type": "application/json"},
    json=payload,
    timeout=30,
)
response.raise_for_status()
print(response.json())

在重用查询之前,请验证当前端点名称、字段路径、请求参数以及实时 PatentSearch 文档中的 API 密钥可用性。

商标/TSDR 工作流

当任务需要商标案例状态、文件、图片、所有权历史或审查事件时,请使用 TSDR。

工作流:

  1. 标准化序列号或注册号。
  2. 查看当前的 TSDR API 说明和所需的 API 密钥标头。
  3. 首先获取状态,仅在需要时才获取文件。
  4. 对于 PDF、ZIP 和多案例下载,请遵守较低的速率限制。
  5. 在输出中捕获检索日期和序列/注册标识符。

对于大规模商标数据拉取,请优先使用文档化的批量数据流,而不是从公共页面进行屏幕抓取。

文件封装和审查历史

对于申请状态、交易历史和审查文件:

  • 从 ODP 专利文件封装检索开始。
  • 尽可能使用准确的标识符:申请号、公开号、专利号或当事人名称。
  • 记录该记录是授权专利、提前公开申请还是待审申请。
  • 在引用之前,请将文件日期和状态与记录详情页进行交叉验证。

转让工作流

对于专利或商标所有权:

  1. 根据专利/申请/注册号、转让人、受让人或(如可用)卷宗/帧号检索官方转让数据。
  2. 记录转让文本、签署日期、备案日期和当事人。
  3. 区分转让记录与当前法律所有权结论。
  4. 如果所有权问题涉及实质性内容,请将结果标记为需要律师或主题专家审查。

可复现的输出

每次 USPTO 研究遍历都应包含一个日志表格:

| 数据源 | 检索日期 | 标识符/查询 | 过滤条件 | 结果数量 | 备注 |
| --- | --- | --- | --- | ---: | --- |
| PatentSearch | 2026-05-11 | `assignee=Alphabet AND date>=2024` | 专利端点 | 118 | 运行前已检查 API 文档 |
| TSDR | 2026-05-11 | `serial=90000000` | 仅状态 | 1 | API 密钥流程,未批量拉取文件 |

对于最终报告,请将以下内容分开:

  • 官方记录事实
  • 推断分析
  • 辅助来源的便捷匹配
  • 未解决的问题或需要法律审查的记录

审查清单

  • 您是否首先使用了官方 USPTO 或 USPTO 支持的数据源?
  • 在运行代码之前,您是否验证了当前端点和字段名称?
  • API 密钥是否已妥善保管,未出现在文件、shell 历史记录和输出日志中?
  • 查询日志是否包含检索日期和精确的请求形状?
  • 是否遵守了速率限制?
  • 是否避免了法律结论,或者是否已明确上报?
  • 辅助来源是否已标注为辅助来源?

参考

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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