Source: https://github.com/aipoch/medical-research-skills

When to Use

Use this skill in the following scenarios:

1. Drafting a patent technical disclosure from an inventor’s informal or incomplete technical description.
2. Structuring an invention description into standard patent-style sections (field, background, summary, embodiments, drawings).
3. Preparing for a novelty search by extracting keywords, synonyms, and IPC suggestions from a technical solution.
4. Finding related patents and producing a similarity comparison against the user’s key technical features.
5. Improving patent readiness by identifying missing technical details and proposing claim-writing directions (non-legal, for drafting support).

Key Features

• Converts colloquial technical descriptions into a structured patent technical disclosure document.
• Uses a guided information-collection checklist to fill gaps (problem, prior art defects, core solution, features, effects).
• Generates a disclosure with a consistent section template (Title, Field, Background, Summary, Detailed Description, Drawings, Keywords).
• Performs multi-platform patent search orchestration via a CLI script and supports optional similarity analysis.
• Produces novelty-oriented analysis: similarity ranking, key-feature comparison, and preliminary novelty judgment.
• Provides post-draft optimization suggestions (claim directions, expansion ideas, missing details to supplement).

Dependencies

• Python >= 3.9
• (Optional, if enabled by the project) Common Python packages for HTTP parsing and analysis, such as:
  • requests >= 2.28
  • beautifulsoup4 >= 4.11
  • lxml >= 4.9

Note: Exact runtime dependencies may vary depending on how scripts/patent_search.py is implemented in your repository.

Example Usage

1) Generate a Patent Technical Disclosure Document

User prompt

Help me write a patent disclosure document:
I designed a system that uses an on-device model to detect abnormal vibration patterns in industrial motors.
It compresses sensor streams, runs anomaly detection locally, and only uploads event snippets to reduce bandwidth.

Expected output (Markdown disclosure template)

# Patent Technical Disclosure Document

## I. Title of Invention
A method and system for on-device vibration anomaly detection and event-based uploading for industrial motors

## II. Technical Field
The present invention relates to the technical field of industrial equipment monitoring, and specifically relates to on-device vibration signal processing and anomaly detection.

## III. Background Art
### 3.1 Description of Existing Technology
Existing solutions typically stream high-frequency vibration data to a cloud platform for centralized analysis, or use threshold-based alarms on edge devices.

### 3.2 Defects of Existing Technology
The existing technology has the following problems:
1. High bandwidth and storage costs due to continuous raw data uploading.
2. High latency for cloud-based detection, which may delay fault response.
3. Threshold-based edge alarms have poor adaptability across different motor types and operating conditions.

## IV. Summary of Invention
### 4.1 Technical Problem to be Solved
The technical problem to be solved by the present invention is reducing bandwidth and latency while maintaining reliable anomaly detection for motor vibration monitoring.

### 4.2 Technical Solution
To solve the above technical problem, the present invention adopts the following technical solution:
- Collect vibration sensor data from an industrial motor using an edge device.
- Perform stream compression/feature extraction on the edge device to obtain compact representations.
- Execute an on-device anomaly detection model to identify abnormal vibration patterns.
- When an anomaly is detected, generate an event snippet containing a time window of raw/feature data and metadata (timestamp, motor ID, operating state).
- Upload only the event snippet (and optionally periodic summaries) to a remote server for storage, visualization, and further diagnosis.

### 4.3 Beneficial Effects
By adopting the technical solution of the present invention, the following beneficial effects are achieved:
1. Significantly reduced network bandwidth usage by avoiding continuous raw data uploads.
2. Faster anomaly response due to local inference and event-triggered reporting.
3. Improved detection robustness compared with fixed thresholds by using a learned model.

## V. Detailed Description
### 5.1 Embodiment 1
An edge device connected to an accelerometer samples vibration at a preset rate, computes spectral features, and runs an anomaly model. Upon detection, it uploads a 5-second window around the event plus operating metadata.

### 5.2 Embodiment 2 (Optional)
The anomaly model is periodically updated using federated or scheduled offline training, while inference remains on-device.

## VI. Description of Drawings
Suggestions for drawing the following figures:
- Figure 1: System architecture (sensor, edge device, network, server).
- Figure 2: On-device processing pipeline (sampling → compression/features → anomaly detection → event packaging → upload).

## VII. Keywords
vibration monitoring; anomaly detection; edge computing; event-based upload; industrial motor; signal compression

2) Run a Patent Search (CLI)

Basic search (default platform: Google Patents)

python scripts/patent_search.py "vibration anomaly detection edge event-based upload" --limit 20

Parallel search across all supported platforms (recommended)

python scripts/patent_search.py "vibration anomaly detection edge event-based upload" -s all -p

Search specific platforms

python scripts/patent_search.py "vibration anomaly detection edge event-based upload" -s google,cnipa,innojoy

Search with similarity analysis

python scripts/patent_search.py "vibration anomaly detection edge event-based upload" -s all -p -a

Expected search output (conceptual)

• Related patents list (patent number, title, abstract)
• Similarity ranking and key-feature overlap
• Preliminary novelty judgment (non-binding)

Implementation Details

1) Disclosure Document Generation Workflow

1. Information collection (ask if missing)

   • What technical problem is solved?
   • What are the defects of existing solutions (prior art)?
   • What is the core idea of the solution?
   • What are the key technical features (modules/steps/parameters)?
   • What beneficial effects are achieved and why?

2. Document synthesis

   • Produce a disclosure using the fixed section template:
     • Title of Invention
     • Technical Field
     • Background Art (existing tech + defects)
     • Summary (problem, solution, effects)
     • Detailed Description (embodiments/variants)
     • Drawings suggestions
     • Keywords

3. Optimization suggestions

   • Claim-writing directions (e.g., independent claim scope + dependent claim fallbacks)
   • Expansion directions (alternative embodiments, parameter ranges, optional modules)
   • Missing technical details to supplement (interfaces, data formats, thresholds, model training/inference constraints)

2) Patent Search Workflow

1. Keyword extraction

   • Core technical terms (components, steps, objectives)
   • Synonyms/near-synonyms (e.g., “edge” vs “on-device”, “anomaly” vs “fault detection”)
   • IPC suggestions (high-level guidance based on domain)

2. Search execution

   • Use scripts/patent_search.py to query one or multiple platforms.
   • Supported platform parameters:
     • google, lens, innojoy, baidu, espacenet, cnipa, all

3. Result analysis

   • Rank results by technical similarity (based on title/abstract/claims when available)
   • Compare key features against the user’s solution (feature-by-feature mapping)
   • Provide a preliminary novelty judgment and highlight the closest references

3) Common IPC Suggestions (Reference)

4) Usage Notes / Constraints

• Generated disclosures are drafting aids and should be reviewed and completed by the inventor.
• Automated search results do not replace a formal novelty search by professional institutions.
• Claims drafting is specialized; consider review by a qualified patent attorney.
• Confirm confidentiality and avoid premature public disclosure before filing.