patent-novelty-check

Solid

Assess patent novelty and non-obviousness against prior art. Use when user says "专利查新", "patent novelty", "可专利性评估", "patentability check", or wants to evaluate if an invention is patentable.

AI & Automation 11,977 stars 1099 forks Updated yesterday MIT

Install

View on GitHub

Quality Score: 96/100

Stars 20%
100
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# Patent Novelty and Non-Obviousness Check Assess patentability of: **$ARGUMENTS** Adapted from `/novelty-check` for patent legal standards. Research novelty is NOT the same as patent novelty. ## Constants - `REVIEWER_MODEL = gpt-5.5` — Model used via Codex MCP for cross-model examiner verification - `NOVELTY_STANDARD = patent` — Always use legal patentability standard, not research contribution standard ## Inputs 1. Invention description from `$ARGUMENTS` 2. `patent/PRIOR_ART_REPORT.md` (output of `/prior-art-search`) 3. `patent/INVENTION_BRIEF.md` if exists ## Shared References Load `../shared-references/patent-writing-principles.md` for novelty/non-obviousness standards. Load `../shared-references/patent-format-us.md` for 102/103 analysis framework. ## Workflow ### Step 1: Define Claim Elements From the invention description, extract the key claim elements that would define the invention's scope: 1. List the technical features that make the invention novel 2. Identify which features are known from prior art vs. inventive 3. Draft preliminary claim language for 2-3 independent claims (method + system) ### Step 2: Anticipation Analysis (Novelty) For each preliminary claim, test against EACH prior art reference in `PRIOR_ART_REPORT.md`: **Single-reference test**: Does any single reference disclose ALL claim elements? | Claim Element | Ref 1 | Ref 2 | Ref 3 | ... | |--------------|-------|-------|-------|-----| | Feature A | Yes/No + evidence | | | | | Feature B...

Details

Author
wanshuiyin
Repository
wanshuiyin/Auto-claude-code-research-in-sleep
Created
3 months ago
Last Updated
yesterday
Language
Python
License
MIT

Integrates with

Similar Skills

Semantically similar based on skill content — not just same category