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patent-novelty-check — Claude Code skill: what it does, how to install

Assesses patent novelty and non-obviousness against prior art using legal examination standards. Ideal before filing patent applications.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill patent-novelty-check -a claude-code
Open on GitHub

What patent-novelty-check does

The skill evaluates technical disclosures against prior art using legal criteria rather than scientific publication metrics. It reads the invention summary and patent/PRIOR_ART_REPORT.md, extracts core technical features, and drafts preliminary independent claims for method and system implementations. An anticipation matrix tests whether any single reference contains every single claimed element.

For surviving claims, the workflow runs an obviousness check across combinations of two or three references. It determines if a person of ordinary skill in the art would have motivation to combine them. Cross-model examiner verification invokes gpt-6-astra via mcp__codex__codex with extra-high reasoning effort. If that reviewer route fails, the execution records REVIEW_UNAVAILABLE rather than silently switching backends.

Jurisdiction checks cover US 35 USC 102/103, China Patent Law Article 22, and European EPC Articles 54 and 56. The complete report lands in patent/NOVELTY_ASSESSMENT.md with suggested claim amendments. The skill will fail without an existing patent/PRIOR_ART_REPORT.md file.

What it helps with

  • verify worldwide patent novelty against prior art references
  • test single-reference claim anticipation element by element
  • evaluate obviousness combinations and motivation to combine under 35 USC 103
  • draft claim amendments to bypass cited prior art documents

Example prompts for the agent

  • /patent-novelty-check patent/INVENTION_BRIEF.md
  • Check patent novelty and non-obviousness using patent/PRIOR_ART_REPORT.md
  • Evaluate patentability of this memory allocation architecture under US and EPC standards

How to install patent-novelty-check

1Skills CLI (Vercel)

npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill patent-novelty-check -a claude-code

2Manually: put the files in place

~/.claude/skills/patent-novelty-check/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/patent-novelty-check/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about patent-novelty-check

How to install patent-novelty-check in Claude Code?

Run npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill patent-novelty-check -a claude-code in your terminal. The configuration installs to ~/.claude/skills/patent-novelty-check/SKILL.md.

What does patent-novelty-check do?

It conducts novelty anticipation and obviousness analyses under US, Chinese, and European patent standards. It generates an assessment report with claim matrices and amendment suggestions in patent/NOVELTY_ASSESSMENT.md.

What prerequisites are required for the assessment?

You must supply an invention description alongside patent/PRIOR_ART_REPORT.md. Cross-model verification also expects access to the Codex MCP running gpt-6-astra.

Source text

SKILL.md · MIT
---
name: patent-novelty-check
description: "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."
argument-hint: "[invention-description-or-brief-path]"
allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, WebSearch, WebFetch, mcp__codex__codex, mcp__antigravity__antigravity, mcp__antigravity__antigravity-reply, mcp__grok__grok, mcp__grok__grok-reply, mcp__claude-review__review, mcp__claude-review__review_reply, mcp__claude-review__review_start, mcp__claude-review__review_reply_start, mcp__claude-review__review_status
---

# Patent Novelty and Non-Obviousness Check

> **Before each reviewer slot**, follow [Selectable Reviewers](../shared-references/reviewer-backends.md):
> resolve invocation → `.aris/reviewer.json` → Claude Code default (Codex MCP)
> with `reviewer_route.py` through the canonical helper resolver, preserving this
> slot's tier, fresh contexts, source/image inputs and deterministic gates.
> Codex examples/pins below apply only to `backend=codex`; other routes use their
> own first-call/reply contract. Save the route and actual returned identity;
> unavailable selections are `REVIEW_UNAVAILABLE`. Classify acceptance from actual
> families/completion/scope. Forward invocation reviewer/model/effort overrides
> to reviewer-bearing children. Generation workers are unchanged.


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-6-astra` — 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 | Yes/No + evidence | | | |
| Feature C | Yes/No + evidence | | | |
| Feature D | Yes/No + evidence | | | |

**Verdict per reference**:
- ANTICIPATED: One reference discloses every element → claim is not novel
- NOT ANTICIPATED: At least one element missing from every single reference → claim is novel

### Step 3: Obviousness Analysis (Inventive Step)

If the invention is novel (passes Step 2), test for obviousness:

**Two/three-reference combination test**: Can 2-3 references be combined to render the claim obvious?

For each combination of the top references:
1. **Primary reference**: Which reference is closest to the claimed invention?
2. **Secondary reference(s)**: Which reference(s) teach the missing element(s)?
3. **Motivation to combine**: Would a POSITA have reason to combine these references?
   - Explicit suggestion in the references themselves?
   - Same field, same problem?
   - Common design incentive?
   - Known technique for improving similar devices?

Format as a matrix:

| Combination | Primary | Secondary | Missing Elements | Motivation to Combine | Obvious? |
|-------------|---------|-----------|-----------------|----------------------|----------|
| Ref1 + Ref2 | Ref1 | Ref2 | Feature D | Same field, similar problem | Yes/No |

### Step 4: Cross-Model Examiner Verification

Call `REVIEWER_MODEL` via `mcp__codex__codex` with xhigh reasoning:

> Resolve this reviewer slot first; the following is the `backend=codex` example.

```
mcp__codex__codex:
  model: gpt-6-astra
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    You are a senior patent examiner at the [USPTO/CNIPA/EPO].
    Examine the following invention for patentability.

    INVENTION: [invention description + preliminary claims]

    PRIOR ART: [prior art references with key teachings]

    Please analyze:
    1. Anticipation (novelty): Does any single reference anticipate any claim?
    2. Obviousness: Can any combination of references render claims obvious?
    3. Claim scope: Are the claims broad enough to be valuable?
    4. Recommended amendments if any claim is rejected.
    Be rigorous and cite specific references.
```

### Step 5: Jurisdiction-Specific Assessment

For each target jurisdiction, provide a patentability assessment:

**Under 35 USC 102/103 (US)**:
- Novelty: PASS / FAIL (cite specific reference if fail)
- Non-obviousness: PASS / FAIL (cite combination if fail)

**Under Article 22 CN Patent Law (CN)**:
- 新颖性 (Novelty): 通过 / 未通过
- 创造性 (Inventive Step): 通过 / 未通过

**Under Article 54/56 EPC (EP)**:
- Novelty: PASS / FAIL
- Inventive step: PASS / FAIL (problem-solution approach)

### Step 6: Output

Write `patent/NOVELTY_ASSESSMENT.md`:

```markdown
## Patentability Assessment

### Invention Summary
[description]

### Overall Assessment
[PATENTABLE / PATENTABLE WITH AMENDMENTS / NOT PATENTABLE]

### Anticipation Analysis
[claim-by-claim matrix against each reference]

### Obviousness Analysis
[combination analysis with motivation to combine]

### Cross-Model Examiner Review
[summary of GPT-6-Astra examiner feedback]

### Recommended Claim Amendments
[If claims need modification to overcome prior art, suggest specific amendments]

### Risk Factors
[What could cause rejection during actual prosecution?]
```

## Key Rules

- Patent novelty is absolute: any public disclosure before the priority date counts as prior art, worldwide.
- Research novelty ("has anyone published this?") is NOT the same as patent novelty ("does any single reference teach every claim element?").
- Obviousness requires BOTH: (1) a combination of references AND (2) a motivation to combine them.
- Never assume the invention is patentable just because no identical patent exists.
- The assessment is advisory only -- actual prosecution may reveal different prior art.
- If the selected reviewer is unavailable, record `REVIEW_UNAVAILABLE`; continue only as an explicitly unreviewed draft, without switching providers.

© wanshuiyin · MIT · wanshuiyin/Auto-claude-code-research-in-sleep

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