jaccen/awesome-gaussian-skills/patent-software-ip

Name: patent-software-ip
Author: jaccen

Claude Code and agent skill by jaccen/awesome-gaussian-skills

Trust tier 2

community

LLM review pending

Source check pending

Checking that the SKILL.md file is still in the repository.

'Generate CN patent docs (claims, specification, abstract) and software copyright materials from AI/big-data

Install

npx vskill@latest install jaccen/awesome-gaussian-skills/patent-software-ip

SKILL.md

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

[ ] Independent claim contains all essential features
[ ] Dependent claims correctly reference
[ ] Method + System + Medium triple complete
[ ] Specification sufficiently disclosed (enabling)
[ ] Embodiments cover all claim features
[ ] Beneficial effects quantified
[ ] Terminology consistent throughout
[ ] Abstract corresponds to claim 1
[ ] Desensitization complete (no company/person/business name leak)
[ ] Figure numbering consistent
[ ] Domain-specific checks passed (see below)

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

[ ] Pages >= 15
[ ] Screenshots >= 6
[ ] Feature coverage complete
[ ] Non-technical description for reviewers
[ ] Code pages with >= 50 lines/page
[ ] Name consistency
[ ] No secret leaks

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)

This is the start of the file. Read the full SKILL.md on GitHub.

Security

Security scanPassed, 100 of 100Pattern checks on the skill's files.
Model reviewNot runA model review is scheduled for the next rescan.

See the full security report

Quality evaluation

Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

See evaluation results

Source

SKILL.md in the repositoryCheckingskills/patent-software-ip/SKILL.md

Popularity

Repository stars: 114
Repository forks: 9
7-day trend score: 24

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

Works with

Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.

Claude Code
Cursor
GitHub Copilot
Windsurf
Codex
Gemini CLI
Cline
Amp
Roo Code
Goose
Continue
Kilo Code
Junie
Trae
OpenHands
Qwen Code
AI Agent
Tabnine Chat
Sourcegraph Cody
Amazon Q
Firebase Genkit
Sweep
Devin
Phind
Mentat
Claude Desktop
Aider
GPT Pilot
Plandex
bolt.new
v0
Replit Agent
Anse
TypingMind
BetterChatGPT
LibreChat
Warp AI
Pieces
Fig AI

Embed badge

[![vskill VERIFIED](https://verified-skill.com/api/v1/skills/jaccen/awesome-gaussian-skills/patent-software-ip/badge)](https://verified-skill.com/skills/jaccen/awesome-gaussian-skills/patent-software-ip)

Something wrong with this skill or its page? Tell us.