# mPAPA: A Fully Local Automatic Patent Agency Workflow System

> mPAPA is a fully locally-run nine-step AI workflow system that can independently complete the entire process from prior art search, novelty analysis to full patent document drafting, providing professional-level patent services for inventors and small innovation teams.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-28T21:45:11.000Z
- 最近活动: 2026-04-29T01:38:38.920Z
- 热度: 147.1
- 关键词: 专利代理, 现有技术检索, 新颖性分析, 专利撰写, 本地 AI, 工作流自动化, 知识产权, 发明评估
- 页面链接: https://www.zingnex.cn/en/forum/thread/mpapa
- Canonical: https://www.zingnex.cn/forum/thread/mpapa
- Markdown 来源: floors_fallback

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## mPAPA: Guide to the Fully Local Automatic Patent Agency Workflow System

mPAPA (my Personal Artificial Patent Agent) is a fully locally-run nine-step AI workflow system that can independently complete the entire process from prior art search, novelty analysis to full patent document drafting. Its core value lies in using local AI technology to provide professional-level patent services for individual inventors and small innovation teams while protecting data privacy, lowering the cost threshold for patent applications.

## Project Background and Core Value

Patent application is a key link in protecting innovative achievements, but the high cost of professional services often discourages individual inventors and small teams. mPAPA attempts to "democratize" core patent agency services through local AI workflows—providing end-to-end automated support from search to drafting while ensuring data privacy is not compromised.

## Panoramic Analysis of the Nine-Step Workflow

mPAPA breaks down patent agency work into nine consecutive steps:
1. Invention Disclosure Understanding: Extract key information such as technical field and core innovation points;
2. Technical Field Analysis: Determine IPC/CPC classification codes;
3. Prior Art Search: Automatically generate search queries and retrieve relevant documents in patent databases;
4. Prior Art Screening and Analysis: Relevance ranking and comparison document identification;
5. Novelty Assessment: Judge the novelty of claims according to patent examination standards;
6. Inventiveness Assessment: Analyze the inspiration from prior art combinations and judge non-obviousness;
7. Market Potential Analysis: Evaluate business prospects such as target markets and competitive landscape;
8. Patent Strategy Recommendations: Strategies for application type, geographical layout, etc.;
9. Full Patent Document Generation: Output standardized documents such as specifications and claims.

## Advantages of Local-First Architecture

mPAPA adheres to the design concept of fully local operation, with advantages including:
- **Data Privacy Protection**: Sensitive technical details and business information do not leave the user's device, eliminating leakage risks;
- **Controllable Cost**: No need for paid APIs or subscription services, only consumes local computing resources;
- **Customizability**: The open-source architecture allows users to adjust workflows, such as optimizing search strategies or modifying document formats.

## Usage Scenarios and Target Users

mPAPA is suitable for the following scenarios:
- **Individual Inventors**: Quickly understand the patentability and application prospects of their inventions;
- **Small R&D Teams**: Complete preliminary evaluation and document preparation before formally entrusting an agent;
- **Patent Education**: Help understand the patent agency process and technical key points.

## Limitations and Usage Recommendations

The content generated by mPAPA should be used as a starting point rather than a replacement for professional agency work:
1. Patent applications involve complex legal judgments, and the final documents need to be reviewed by professional agents;
2. The search scope may be limited by local data sources; it is recommended to supplement with professional database searches for important applications.

## Open-Source Significance and Industry Value

mPAPA represents the trend of AI democratization in the professional services field. Through open-source sharing, it not only provides practical tools but also demonstrates how to break down complex professional workflows into automatable AI task sequences, providing a reference paradigm for the development of automated agents in other fields.