Install
$ agentstack add skill-raja21068-autoresearch-patent-novelty-check ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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-5.4— Model used via Codex MCP for cross-model examiner verificationNOVELTY_STANDARD = patent— Always use legal patentability standard, not research contribution standard
Inputs
- Invention description from
$ARGUMENTS patent/PRIOR_ART_REPORT.md(output of/prior-art-search)patent/INVENTION_BRIEF.mdif 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:
- List the technical features that make the invention novel
- Identify which features are known from prior art vs. inventive
- 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:
- Primary reference: Which reference is closest to the claimed invention?
- Secondary reference(s): Which reference(s) teach the missing element(s)?
- 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:
mcp__codex__codex:
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:
## 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-5.4 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
mcp__codex__codexis not available, skip cross-model examiner review and note it in the output.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: raja21068
- Source: raja21068/AutoResearch
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.