AI Copyright Skill

AI-native IP skill: generate patent applications, software copyright materials, or technical disclosures from AI project code/papers/docs, with direct Word and PPT output. Covers 7 AI domains, 11 claim templates, patentability check, desensitization, prior-art search, and self-check.

AI IP Document Generation

Generate Chinese patent applications, software copyright registration materials, or technical disclosure reports from AI project code, research papers, and design docs. Direct Word (.docx) + PPT (.pptx) output. Built-in 7 AI domain coverage with 11 claim templates.

Three output paths:

• Patent: CNIPA-format invention patent (claims + specification + abstract), with technical disclosure as intermediate deliverable
• Software Copyright: CPCC registration materials (manual + source code document)
• Technical Disclosure: From papers/notes to agent-deliverable disclosure

7 AI Domains x 22 Sub-Directions

Domain	Sub-Directions	Claim Template
Perception AI	2D CV · 3D Vision & Graphics · Audio/Video · Sensor Fusion	2.1 Architecture · 2.2 3D Vision
Cognitive & Language	LLM · Multimodal LLM · RAG · Knowledge Graph	2.3 Training · 2.4 MLLM · 2.5 RAG
Generative AI	Diffusion & Controlled Gen · AIGC Watermark · Style Transfer	2.6 Diffusion
Decision & Interaction	Agent · Embodied AI · RL	2.7 Agent · 2.8 VLA
AI Engineering	Training/Fine-tuning · Inference Opt · Data Engineering · Edge/IoT	2.9 Inference · 2.10 Data
AI Safety & Governance	Adversarial Defense · Watermark · Federated Learning · Alignment	2.11 Watermark
Industry Apps	Autonomous Driving · Industry QC · Healthcare · Finance · AI4Science · Digital Content	Domain-adapted

Triggers

patent / claims / specification / software copyright / disclosure / IP application / paper-to-patent / /ai-copyright / /AI知产

Iteration: When user modifies existing output, enter iterative correction flow directly.

Overall Flow

text

Phase 0  Patentability Pre-Assessment (patent path only)
Phase A  Requirement Diagnosis → path + domain + risk level
Phase B  Project Analysis → auto-detect (11 types + 6 industries) + extract key points
Phase C  Generation (branch by path)
  C1 Patent: search → layout → disclosure → claims (11 templates) → specification → abstract → self-check
  C2 Software Copyright: manual (4 templates) → source code doc → self-check
  C3 Technical Disclosure: mapping (6 general + 7 domain-specific) → drafting → self-check
Phase D  Confirmation Gate
Phase E  Iterative Correction
Phase F  Word Output (docx-js, auto)
Phase G  Briefing PPT (python-pptx, patent default)

Phase 0: Patentability Pre-Assessment

Per references/ai-patent-special.md §1, check three elements:

Element	Check	Pass Standard
Technical Problem	Anchored to specific scenario	Not "low efficiency" / "poor accuracy"
Technical Means	Algorithm steps bound to system architecture	Each step linked to HW/SW component
Technical Effect	Quantifiable	Concrete numbers or comparison baselines

Domain Risk Assessment (§1.3)

Risk	Domain	Pitfall	Countermeasure
HIGH	Generative AI (pure content gen)	Classified as "rules of mental activities"	Must bind to tech scenario + conditional control means
HIGH	Financial risk control	Classified as "business method"	Must bind to data processing means
HIGH	AI alignment / explainability	Classified as "rules of mental activities"	Must bind to safety assurance in specific app scenario
MED	Embodied AI	Pure motion control = mental activities	Bind each step to sensor input + actuator output
MED	Reinforcement Learning	Pure strategy optimization = math method	Bind to physical system + physics-constrained reward
MED	RAG	Pure information retrieval = info expression	Must show complete technical pipeline
MED	AIGC watermark	Pure info marking = info expression	Watermark embedding must bind to model internal layers

Decision: All pass + low risk → proceed; All pass + high risk → proceed with mandatory domain countermeasures; Means fail → switch to disclosure path; Effect fail → supplement quantitative comparison.

Phase A: Requirement Diagnosis

Confirm with user: path selection, tech topic, AI domain (7 domains / 22 directions, auto-detect assisted), applicant info, inventor info, existing materials.

Gate: Output 3-5 line diagnosis summary (including domain attribution and risk level).

Phase B: Project Analysis

B.1 Auto-Detection (§1.1 Decision Tree)

text

Entry file → Project type
app.py/main.py/serve.py → AI Service
train.py/trainer.py → AI Training
inference.py/predict.py → AI Inference
render.py/gaussian.py/nerf.py → 3D Vision
generate.py/diffusion.py → Generative AI
robot.py/vla.py → Embodied AI
pipeline.py + langchain → Agent
pipeline.py + rag/faiss → RAG System
package.json → Frontend/Fullstack

Also detect 6 industry categories (§1.3): autonomous driving / industry / healthcare / finance / AI4Science / digital content.

B.2 Key Points Extraction

Priority: model definition → training/inference scripts → rendering/generation/control scripts → papers → design docs → README.

Output: Key Points List (architecture/algorithm/engineering-level innovations, scheme skeleton, key params, distinctions from prior art, quantifiable effects, domain attribution).

Gate: Present key points list for user confirmation.

Phase C1: Patent Application

C1.1 Prior Art Search

Online search 2-3 rounds: CNIPA patent DB, Google Patents, arXiv. Suggest CPC classification (§6, 6 groups / 30+ codes).

C1.2 Layout Advisory (§2)

Innovation Distribution	Layout Strategy
Core innovation in architecture	Single application (arch + training + inference)
Architecture + training independent	2 applications
Independent deployment optimization	3 applications (arch + training + deployment)
3D reconstruction + rendering independent	2 applications
Generation model + control independent	2 applications
Embodied perception + control independent	2 applications (perception-decision + control-execution)

C1.3 Technical Disclosure (Intermediate Deliverable)

7-chapter structure: Invention Name → Tech Field → Background & Prior Art Defects → Invention Content (purpose + scheme + effects) → Figure Description → Specific Embodiments (optimal + alternative) → Key Innovation Summary (3-5 points).

Data source: Phase B key points + C1.1 search results + C1.2 layout advice.

C1.4 Claims

Per references/ai-patent-claims-guide.md. Structure: Method (1 independent + 3-8 dependent) + System (1 independent + 3-8 dependent, step-by-step correspondence) + Storage Medium (1 independent).

11 Claim Templates by Domain (§2):

#	Type	Domain	Key Structure
2.1	Model Architecture	Perception (2D)	Sub-module → processing action → intermediate result chain
2.2	3D Vision & Graphics	Perception (3D)	Capture → sparse reconstruction → dense optimization → rendering (4-stage) + rendering computation sub-steps
2.3	Training Method	AI Engineering	Data construct → init → forward → loss → optimize → converge (6-step)
2.4	Multimodal LLM	Cognitive & Language	Multi-encoder → projection layer → fusion → decode
2.5	RAG	Cognitive & Language	Query parse → retrieve → rerank → context reconstruct → generate (5-stage)
2.6	Diffusion & Controlled Gen	Generative AI	Condition encode → inject → denoise → decode + condition injection sub-steps
2.7	Agent	Decision & Interaction	Intent → plan → tool execute → observe/reflect → respond
2.8	Embodied AI (VLA)	Decision & Interaction	Sensor perception → encode+fuse → policy → actuator drive + feedback
2.9	Inference Optimization	AI Engineering	Original model → optimize → inference strategy → result
2.10	Data Processing	AI Engineering	Acquire → preprocess → augment → downstream
2.11	AI Watermark & Traceability	AI Safety	Embed phase (position → generate → inject) + Verify phase (extract → match)

Domain-Specific Requirements (§1.3):

1. Every step must link to system component ("executed via GPU parallel computing unit")
2. 3D Vision: Must include full 4-stage pipeline; rendering step must expand rendering formula
3. Generative AI: Condition injection step must specify injection method (cross-attention/adapter/ControlNet) to avoid "pure content generation" rejection
4. Embodied AI: Every step must bind to sensor input + actuator output; include safety constraint dependent claim
5. RAG: Must show complete 5-stage technical pipeline to avoid "pure information retrieval" rejection
6. AI Watermark: Embed step must specify injection layer/position/encoding; verify step must quantify robustness metrics
7. Training: Must include training data construction, loss function, optimization strategy
8. Multimodal: Must include modality encoding → cross-modal alignment → fusion → decoding

Dependent Claim Expansion: General 5-layer (module structure → computation method → param range → preferred embodiment → training features) + 5 domain-specific expansion tables (§3.2).

C1.5 Specification

5-chapter structure: Tech Field → Background (prior art + defects) → Invention Content (problem + scheme + beneficial effects, must be quantified) → Figure Description (13-type figure requirements in §4) → Specific Embodiments.

Desensitization Rules (§5): 6 general replacements + 14 industry-specific + 4 3DGS/NeRF-specific.

Figures: Use fenced mermaid (flowchart TB/LR). 13-type AI system figure requirements per §4.

C1.6 Abstract

≤300 chars. Covers: tech domain + core scheme + main effect. No commercial terms. Replace algorithm names with generic expressions.

C1.7 Quality Self-Check (100-point)

Item	Points	Standard
Independent claim completeness	15	-5 per missing feature
Dependent claim back-reference correctness	10	-3 per error
Method + System + Medium triple complete	10	-5 per missing type
Specification sufficient disclosure	15	Enabling for skilled person
Embodiments cover all claim features	15	-3 per uncovered feature
Beneficial effects quantified	10	-3 per vague assertion
Terminology consistency	5	-1 per synonym
Abstract corresponds to claim 1	5	-5 if not
Desensitization completeness	10	-3 per leak
Figure numbering consistency	5	-1 per inconsistency

≥80: deliver. 60-80: auto-fix and deliver. <60: rewrite.

Phase C2: Software Copyright

Per references/ai-software-copyright-guide.md.

C2.1 Software Manual (10-15 pages, ≥6 screenshots)

4 templates by project type:

Project Type	Template	Key Sections
General AI service	§3.1 General	Standard 5 chapters
3D Vision/Reconstruction	§3.2 3D Vision	Capture → reconstruct → render → export
Generative AI	§3.3 Gen-AI	Condition input → generation engine → safety management
Embodied AI	§3.4 Embodied	Perception → decision → execution → simulation → monitoring

Key notes: Target non-technical reviewers; use [Screenshot: feature name] placeholders; describe deployment/config/monitoring for HCI requirement; declare open-source pre-trained weights outside protection scope.

C2.2 Source Code Document (front 30 + back 30 pages, ≥50 lines/page)

12-level file priority (§2.1): model.py(train required) → train.py(required) → inference.py(required) → render.py(3D vision required) → dataset.py → loss.py → generate.py(gen-AI required) → control.py(embodied required) → retriever.py(RAG required) → config.yaml(optional).

Desensitization (§2.2): Remove API keys, absolute paths, internal addresses, personal info, hardware models, cloud URLs, DB passwords. Retain algorithm comments.

C2.3 Quality Self-Check

Pages 15 + Screenshots 10 + Feature coverage 15 + Non-tech description 10 + GPU/CUDA info 10 + Code pages 15 + Lines per page 10 + Name consistency 5 + No secret leaks 10.

Phase C3: Technical Disclosure

C3.1 Paper-to-Disclosure Mapping (§3)

General 6-type mapping: Research Problem → Technical Problem | Contribution → Technical Solution | Method Modules → Implementation Units | Algorithm Flow → Implementation Flow | Experimental Outcome → Expected Effects | Ablation Study → Alternative Embodiments.

Domain-specific mapping (§3.2): 3D Vision (rendering formula → claim step) | Generative AI (noise schedule → claim step, condition injection → dependent claim) | Embodied AI (Sim2Real → adaptation step) | RL (reward function → computation step) | RAG (retrieval strategies → dependent claims) | AI Watermark (robustness experiments → verification step) | AI4Science (physics constraints → necessary feature).

C3.2 Drafting

7-chapter structure (same as C1.3).

C3.3 Quality Self-Check

Problem anchoring 15 + Implementability 20 + Quantified comparison 15 + Clear distinction 15 + Figure coverage 15 + Agent comprehensibility 10 + Completeness 10.

Phase D: Confirmation Gate

After each phase: present summary, options: Confirm / Modify / Switch path / Save progress (.temp/ai-ip-progress.md).

Phase E: Iterative Correction

Identify → Locate → Targeted fix → Diff annotation (<!-- Revision -->) → Save as v{N} → Re-run affected self-check items only. Do NOT re-run full pipeline.

Phase F: Word Output

Auto-executed after Phase C (unless user requests md only). Uses docx-js workflow: .temp/{case}-docx.js.

Path	Output
Patent	disclosure.docx + patent.docx (cover + claims + spec + abstract) + briefing.pptx
Software Copyright	manual.docx + source_code.docx
Technical Disclosure	single disclosure.docx

Format: Song Ti 24pt body / Hei Ti 32-28pt headings / 1.5x line spacing / 480DXA indent / 1-inch margins. Source code: Courier New 18pt, ≥50 lines/page.

Alternatives: md / docx (default) / both.

Phase G: Briefing PPT

Auto for patent path; --ppt for others. python-pptx workflow: .temp/{case}-ppt.py.

5-8 slides: Cover → Background & Pain Points → Core Innovation → System Architecture → Effects Comparison → Patent Layout → Implementation Plan. 16:9, blue theme (#1B3A5C + #3B82F6), Microsoft YaHei font.

Output Specification

text

outputs/{case-id}/
├── patent/          disclosure.docx + {case}.docx + briefing.pptx
├── software-copyright/  manual.docx + source_code.docx
└── disclosure/      disclosure.docx

Each path also retains Markdown intermediates. Self-check report: {case}_selfcheck.md.

Prohibitions: No skill name/repo path/disclaimers in deliverables. No self-check section in body. No fabricated patent numbers/links. No "approximately" in claims. No commercial terms in abstract.

Knowledge Index

Reference File	Contents
references/ai-patent-special.md	§1 Patentability + 8 domain risks §2 Layout (incl 3D/embodied) §3 6-type + 10 domain mapping §4 13-type figures §5 6+14+4 desensitization §6 6-group CPC §7 Quick reference
references/ai-patent-claims-guide.md	§1 Triple claim principle §2 11 claim templates §3 5-layer + 5 domain-dependent expansion
references/ai-software-copyright-guide.md	§1 11-type + 6 industry detection §2 12-level priority + desensitization §3 4 manual templates §4 10 pitfalls