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Patent Novelty Check

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wanshuiyin
patent-novelty-check

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.

Overview

Publisherwanshuiyin
RepositoryAuto-claude-code-research-in-sleep
Skill namepatent-novelty-check
Stars
16.3K
Forks
1.4K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by wanshuiyin on GitHub. Read the source before you install it.

Installation

Install the Patent Novelty Check AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git /tmp/Auto-claude-code-research-in-sleep
mkdir -p .claude/skills
cp -r /tmp/Auto-claude-code-research-in-sleep/skills/patent-novelty-check .claude/skills/patent-novelty-check
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Patent Novelty Check in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Patent Novelty Check on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Patent Novelty Check is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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-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 ElementRef 1Ref 2Ref 3...
Feature AYes/No + evidence
Feature BYes/No + evidence
Feature CYes/No + evidence
Feature DYes/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:

CombinationPrimarySecondaryMissing ElementsMotivation to CombineObvious?
Ref1 + Ref2Ref1Ref2Feature DSame field, similar problemYes/No

Step 4: Cross-Model Examiner Verification

Call REVIEWER_MODEL via mcp__codex__codex with xhigh reasoning:

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 mcp__codex__codex is not available, skip cross-model examiner review and note it in the output.

Frequently asked questions

What does the Patent Novelty Check AI skill do?

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.

Why use Patent Novelty Check on TypingMind?

Because you install it once and use it with any model. Patent Novelty Check is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Patent Novelty Check in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/patent-novelty-check. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Patent Novelty Check?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Patent Novelty Check?

As many as you like. As long as a model supports skills, you can use Patent Novelty Check with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Patent Novelty Check AI skill free?

Yes. It is published on GitHub by wanshuiyin under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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