| name | patent-software-ip |
| description | Generate CN patent docs (claims, specification, abstract) and software copyright materials from AI/big-data project code or docs. Covers 7 AI domains + big data, 11 claim templates, auto domain detection, desensitization, prior-art search, and self-check. Use when: drafting CN patents from AI code/docs, generating software copyright materials, prior-art search for AI inventions, 专利撰写/软件著作权/AI知识产权/脱敏处理. |
| license | Apache-2.0 |
| user-invocable | true |
| metadata | {"version":"1.4.1","author":"jaccen","tags":["patent","software-copyright","ip","ai","big-data","3d-vision","generative-ai","embodied-ai","nlp","rag","ai-engineering","ai-safety"],"when_to_use":["Draft CN invention patents from AI code or docs","Generate software copyright materials","Perform prior-art search for AI inventions","Desensitize project code for IP submission","Auto-detect AI domain and select claim template","Self-check patent specification completeness","专利撰写 / 软件著作权 / AI知识产权 / 脱敏处理 / 现有技术检索"]} |
Patent & Software Copyright Generation (AI + Big Data)
Generate CNIPA invention patent documents or CPCC software copyright materials from AI / big-data project code, design docs, and research papers.
Covers 7 AI domains + Big Data (23 sub-directions), 11 claim templates.
Full version (Chinese, with Word/PPT output): see AI-Copyright-Skill project.
Triggers
patent / claims / specification / software copyright / disclosure / IP application / paper-to-patent / /patent-software-ip
Overall Flow
Phase A Requirement Diagnosis -> path + domain classification + risk level
Phase B Project Analysis -> auto-detect domain + extract key technical points
Phase C Generation (branch by path)
C1 Patent: prior art search -> claims (11 templates) -> specification -> abstract -> self-check
C2 Software Copyright: manual -> source code doc -> self-check
Phase D Iterative Correction
Phase A: Requirement Diagnosis
Confirm: path (patent/copyright/both), tech topic, applicant/inventor info, existing materials.
Auto domain classification (see Section "AI Domain Taxonomy" below).
Gate: 3-5 line diagnosis summary including domain + risk level.
AI Domain Taxonomy
| Domain | Sub-directions | High-Risk Flags |
|---|
| D1 Perceptual Intelligence | 2D vision, 3D vision, multi-sensor fusion | 3D vision: bind 4-stage pipeline |
| D2 Cognition & Language | NLP, multimodal LLM, RAG, knowledge graph | RAG: show full 5-stage chain |
| D3 Generative AI | Diffusion, LLM text gen, cross-modal gen, AIGC watermark | Must bind condition injection method; pure content gen = rejected |
| D4 Decision & Interaction | Embodied AI, reinforcement learning, multi-agent | Must bind sensor + actuator; RL: bind reward to concrete task |
| D5 AI Engineering | Training/fine-tuning, inference deployment, data engineering, edge IoT | Training: bind to specific model architecture; inference: bind to hardware |
| D6 AI Safety & Governance | Adversarial robustness, watermark/tracing, privacy, alignment, 3DGS provenance | Need concrete technical measure, not policy-level description. 3DGS IP: reference GaussTrace (ICML 2026) for evidence-driven provenance graphs |
| D7 Industry Applications | Autonomous driving, industrial, medical, financial, AI4Science | Must bind data processing means; financial: bind to data analysis |
| D8 Big Data | Distributed computing, data pipeline, stream processing, data quality, real-time analytics | Must bind to specific application scenario; pure platform = rejected |
Phase B: Project Analysis
B.1 Auto-Detection Decision Tree
Source files -> domain mapping:
| Key file | Detected domain |
|---|
model.py, unet.py, vae.py | D3 Generative AI |
train.py, finetune.py | D5 AI Engineering (Training) |
inference.py, triton_serve.py, onnx_export.py | D5 AI Engineering (Inference) |
render.py, gaussian.py, splat.py | D1 3D Vision |
llm.py, chat.py, rag_chain.py | D2 NLP / RAG |
robot.py, vla.py, env.py | D4 Embodied AI |
reward.py, ppo.py | D4 Reinforcement Learning |
watermark.py, embed_watermark.py | D6 AI Safety / Watermark |
spark_job.py, flink_job.py, kafka_consumer.py | D8 Big Data |
etl.py, data_pipeline.py, feature_store.py | D8 Big Data (Data Engineering) |
stream.py, realtime_analytics.py | D8 Big Data (Streaming) |
dataset.py, dataloader.py | D5 AI Engineering (Data) |
privacy.py, dp_train.py | D6 AI Safety (Privacy) |
config.yaml, pipeline.py + langchain | D2 RAG / Agent |
Also detect 6 industry contexts: medical, financial, autonomous driving, industrial, smart city, education.
B.2 Technical Points Extraction
Priority: model definition -> training/inference -> domain-specific core -> papers/design docs -> README.
Output: Key Points List (innovations, scheme skeleton, key params, distinctions, quantifiable effects, domain classification).
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. Each result: source ID, scheme summary, limitations.
CPC suggestions by domain:
- D1 3D Vision: G06T 7/50, G06T 17/00
- D2 NLP/RAG: G06F 40/30, G06N 3/08
- D3 Generative AI: G06N 3/045, G06T 13/00
- D4 Embodied: G05B 19/00, B25J 9/16
- D5 AI Engineering: G06N 3/084
- D6 AI Safety: G06F 21/60
- D7 Industry: varies by sector
- D8 Big Data: G06F 16/245, G06F 16/903
C1.2 Claims (11 Templates)
Structure: Method (1 independent + 3-8 dependent) + System (1 independent + 3-8 dependent) + Storage Medium (1 independent).
Template selection by domain:
| Template | Domain | Independent claim skeleton |
|---|
| T1 Model Architecture | D1/D2/D5 | Predefined network -> layer composition -> feature extraction -> output |
| T2 3D Vision | D1 3D | Capture -> sparse reconstruction -> dense optimization -> rendering (expand formula) |
| T3 Training Strategy | D5 | Data construction -> model initialization -> loss design -> optimization -> convergence |
| T4 Multimodal Fusion | D1/D2 | Multi-modal input -> modality-specific encoding -> cross-modal alignment -> fused output |
| T5 RAG Pipeline | D2 | Parse -> retrieve -> rerank -> reconstruct -> generate |
| T6 Diffusion Model | D3 | Noise scheduling -> condition injection (specify: cross-attention/adapter/ControlNet) -> denoising -> decode |
| T7 Agent | D2/D4 | Environment perception -> task decomposition -> tool selection -> execution -> feedback |
| T8 Embodied Intelligence | D4 | Sensor input -> perception -> planning -> actuator output + safety constraint (dependent) |
| T9 Inference Optimization | D5 | Model loading -> computation graph optimization -> kernel fusion -> output |
| T10 Big Data Processing | D8 | Data ingestion -> distributed processing (specify: Spark/Flink/MapReduce) -> aggregation -> storage/output |
| T11 Data Engineering & Quality | D8 | Data collection -> quality assessment -> anomaly detection -> cleaning -> feature extraction -> storage |
Drafting rules (all domains):
- Method + System claims in pairs
- Independent: preamble (prior art) + "characterized by" (essential features)
- Dependent: "according to claim X..." with further limitation
- Every step must link to system component
- Avoid functional limitation; prefer structural/step-based description
- Quantify effects where possible ("improves accuracy by X%", "reduces latency to Y ms")
C1.3 Specification
5-chapter: Tech Field -> Background (prior art + defects) -> Invention Content (problem + scheme + effects, quantified) -> Figure Description -> Specific Embodiments.
Desensitization:
- Dataset name -> "preset dataset"
- Parameter count -> "preset-scale model"
- Hardware -> "graphics processor" / "distributed computing node"
- Training duration -> "preset period"
- Framework -> "DL framework" / "distributed computing framework"
- API -> "remote interface"
- Company -> "institution"
- Specific values -> ranges
Figures (mermaid flowchart TB/LR): System architecture + method flow + domain-specific pipeline (training/rendering/data pipeline/stream topology/etc.).
C1.4 Abstract
<=300 chars. Tech domain + core scheme + main effect. No commercial terms.
C1.5 Self-Check
Domain-specific self-check:
| Domain | Extra checks |
|---|
| D1 3D Vision | Rendering formula in claim? 4-stage pipeline? |
| D2 NLP/RAG | Full 5-stage RAG chain? Specific embedding model? |
| D3 Generative AI | Condition injection method specified? Not pure content gen? |
| D4 Embodied | Sensor + actuator bound in every step? Safety dependent claim? |
| D5 AI Engineering | Specific model architecture? Hardware binding for inference? |
| D6 AI Safety | Concrete technical measure? Not policy-level? |
| D7 Financial/Medical | Data processing means bound? Not pure business method? |
| D8 Big Data | Specific application scenario bound? Not pure platform? Distributed topology described? |
Phase C2: Software Copyright
C2.1 Software Manual (10-15 pages, >=6 screenshots)
Structure: Introduction (env + capability) -> Installation (env + weights + config) -> Functions (core + data + API + monitoring) -> Non-functional -> FAQ.
Templates by domain:
- General AI: standard template
- 3D Vision: add rendering/visualization section
- Generative AI: add sampling/inference section
- Embodied AI: add sensor/hardware integration section
- Big Data: add data pipeline/deployment section (distributed topology, cluster config, streaming topology diagram)
C2.2 Source Code Document (front 30 + back 30 pages, >=50 lines/page)
File priority by domain:
| Domain | Required files | Domain-specific required |
|---|
| D1 3D Vision | model.py, train.py, inference.py, render.py | render.py |
| D2 NLP/RAG | model.py, train.py, inference.py, retriever.py | retriever.py |
| D3 Generative AI | model.py, train.py, inference.py, generate.py | generate.py |
| D4 Embodied | model.py, train.py, inference.py, control.py, env.py | control.py |
| D5 AI Engineering | model.py, finetune.py, export.py, deploy.py | finetune.py |
| D6 AI Safety | model.py, watermark.py, adv_train.py | watermark.py |
| D8 Big Data | pipeline.py, etl.py, stream.py, config.yaml | pipeline.py |
<3000 lines: submit all; >3000: front 1500 + back 1500 by priority.
Desensitization: Remove API keys, absolute paths, internal addresses, personal info, hardware models, cloud URLs, DB passwords. Retain algorithm comments.
C2.3 Self-Check
Knowledge Index
Deep-dive reference files for domain-specific patent writing rules, claim templates, and software copyright guides.
| File | Sections | Key Content |
|---|
| | |
| references/ai-patent-claims-guide.md | 11 claim templates (T1-T14) | Full legal claim text per template: method/system/medium triples with dependent claims; Big Data T10-T14 included |
| | |
| references/ai-patent-special.md | Patentability framework, 8 risk domains, CPC codes, desensitization rules | AI+Big Data patentability risk assessment; domain mapping; figure requirements; industry desensitization; CPC classification (7.1-7.7); 9-domain quick reference |
| | |
| references/ai-software-copyright-guide.md | Type detection, source file priority, 5 domain templates, FAQ | Decision tree for 10+ project types; source code priority by domain; Big Data dedicated template (section 3.5); desensitization checklist; common pitfalls |
3DGS Patentable Innovation Examples (CVPR 2026)
| Innovation | Method | Patentable Aspect | Claim Template |
|---|
| 3DGS-Physics Engine Bridge | RAF (Representation Abstraction Framework) | Bidirectional mapping between Gaussian primitives and physics simulation state; claim the abstraction layer + synchronization protocol | T2 (3D Vision) + T8 (Embodied) |
| Elastic Eigenmode Deformation | FreeForm | Eigenmode-based elastic deformation for Gaussians; claim the modal analysis pipeline + real-time deformation update | T2 (3D Vision) |
CVPR 2026 accepted 116 3DGS-related papers, creating a surge of patentable innovations. When filing patents for 3DGS methods:
- The 4D reconstruction wave (D4RT and followers) creates IP opportunities in temporal Gaussian representations
- Physics-integrated rendering (FieryGS, RAF) opens claims for simulation-rendering bridges
- Articulated 3DGS methods generate IP around interaction primitives and joint representations
- File early -- the dense publication cohort means similar innovations may appear concurrently
Knowledge base: 690+ methods across 25 categories (updated for v0.3.3 cycle).
Phase D: Iterative Correction
Identify -> Locate -> Targeted fix -> Save as v{N} -> Re-run affected self-check items only. Do NOT re-run full pipeline.
Output
outputs/{case-id}/
patent/ claims.md + specification.md + abstract.md + full.md
software-copyright/ manual.md + source_code.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.
Quick Reference: 8 High-Risk Rejection Patterns
| Pattern | Why rejected | Fix |
|---|
| Pure content generation (no condition injection) | "Intellectual activity rules" | Specify cross-attention/adapter/ControlNet in claims |
| Financial AI without data processing means | "Business method" | Bind to specific feature engineering + model architecture |
| Embodied AI without sensor/actuator binding | "Pure algorithm" | Add "executed via LiDAR module" + "motor controller" |
| RAG without full pipeline | "Insufficient disclosure" | Show all 5 stages in method claim |
| Big Data platform without application | "Abstract idea" | Bind to specific scenario (e.g., real-time traffic analytics) |
| RL without reward function | "Insufficient disclosure" | Include reward computation formula |
| AI watermark without robustness test | "Insufficient technical effect" | Add adversarial/noise/compression robustness claim |
| Medical AI without clinical validation | "Insufficient enablement" | Add evaluation on specific dataset with clinical metrics |
Red Lines
The following are categorical prohibitions. Violating any of these invalidates the output:
- No invented data: Never fabricate patent claims, prior art references, or legal requirements not in the loaded reference files. If a value is not found, write "data not available" or "N/A".
- No hallucinated citations: Never invent paper titles, authors, DOIs, arXiv IDs, patent numbers, or venue names. Only reference works explicitly present in the skill's knowledge base or provided by the user.
- No silent speculation: If you are uncertain about a technical or legal detail, explicitly flag it with "[UNCERTAIN]" rather than presenting it as fact.
- No method misattribution: Do not assign features, results, or mechanisms from one method to another. Each method's data is specific to that method.
- No legal advice: Never provide definitive legal advice. All patent and copyright output is draft material requiring review by a qualified attorney.
Related Skills
- 3dgs-method-compare — Method comparison (use for prior art analysis and novelty assessment)
- 3dgs-paper-reader — Paper analysis (use for extracting patentable contributions)
- 3dgs-code-reviewer — Code review (use for identifying technical innovations in code)
- 3dgs-engineering-guide — Deployment guidance (use for documenting industrial applications)
Guardrail: Do Not Apply From Memory
Do NOT try to apply the logic, method data, bug patterns, or technical details described in this skill from memory. Always read the SKILL.md and referenced files from disk before producing any output. The knowledge base is updated frequently; stale memory may produce outdated, inaccurate, or fabricated results.
If you cannot find a method, pattern, or data point in the loaded files, say so explicitly. Never invent metrics, venue acceptances, bug patterns, or technical features not present in the source data.
