patent-continuous-learning

Business

Automatically extract reusable patterns from patent drafting sessions, including keyword strategies, writing techniques, and search methods, to build an accumulative patent knowledge base.

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Source SKILL.md: https://github.com/LeoYeAI/openclaw-master-skills/blob/HEAD/skills/patent-professional-agents/skills/continuous-learning/SKILL.md

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Patent Continuous Learning Skill

Automatically extract reusable patterns from patent drafting sessions to form a patent knowledge base.

Trigger Conditions

After patent drafting is complete (patent-auditor review passed)
When search strategy is particularly effective
When user corrects writing style
When new writing techniques or patterns are discovered
When user provides access to patent database APIs
When new patent search skills are found on ClawHub

Core Concept: Patent Instinct

A Patent Instinct is an atomic learning unit that records a specific patent-related experience:

---
id: prefer-quantified-effect
trigger: "When writing technical effects"
confidence: 0.8
domain: "patent-writing"
source: "session-observation"
scope: global
---

# Prefer Quantified Technical Effects

## Trigger Condition
When writing the "Advantages Over Prior Art" section of a patent

## Action
Use quantified data to describe technical effects, such as:
- Efficiency improved by XX%
- Latency reduced by XXms
- Success rate improved by XX%

## Evidence
- 2026-03-19: User corrected "high efficiency" to "efficiency improved by 30%"
- 2026-03-18: Audit recommendation to add quantified data

Patent Instinct Types

Type	Description	Scope
`keyword-strategy`	Effective search keyword combinations	project
`writing-pattern`	Writing techniques and sentence patterns	global
`tech-description`	Technical description patterns	project
`claim-structure`	Claim structure patterns	global
`search-tactic`	Search platform usage tips	global
`error-avoidance`	Common error avoidance	global
`api-recommendation`	Patent database API recommendations	global
`skill-discovery`	ClawHub skill discovery patterns	global

Confidence Evolution

Score	Meaning	Behavior
0.3	Tentative	Suggest but don't enforce
0.5	Medium	Apply when relevant
0.7	Strong	Auto-apply
0.9	Certain	Core behavior

Confidence Increase:

Pattern observed repeatedly
User confirms effectiveness
Audit passed

Confidence Decrease:

User explicitly corrects
Causes problems

Learning Flow

Patent drafting session
      |
      | Observe key events
      v
+------------------------------------------+
|  observations/                           |
|   - Successful search strategies          |
|   - User correction records               |
|   - Audit feedback                        |
|   - Newly discovered patterns             |
+------------------------------------------+
      |
      | Extract instincts
      v
+------------------------------------------+
|  instincts/                              |
|   - keyword-strategy/  (project scope)    |
|   - writing-pattern/   (global scope)     |
|   - tech-description/  (project scope)    |
+------------------------------------------+
      |
      | /evolve clustering
      v
+------------------------------------------+
|  evolved/                                |
|   - skills/patent drafting enhanced skill |
|   - templates/reusable templates          |
+------------------------------------------+

Commands

Command	Description
`/patent-learn`	Extract patent instincts from current session
`/patent-instincts`	Display learned patent instincts
`/patent-evolve`	Cluster related instincts into skills

Directory Structure

patent/
├── learning/
│   ├── observations.jsonl     # Observation records
│   ├── instincts/
│   │   ├── global/            # Global instincts
│   │   │   ├── prefer-quantified-effect.yaml
│   │   │   └── avoid-complete-code.yaml
│   │   └── projects/
│   │       └── project-name/  # Project scope
│   │           ├── keyword-strategy.yaml
│   │           └── tech-description.yaml
│   └── evolved/
│       ├── skills/
│       └── templates/

Example: Auto-learned Instincts

Patent Database API Recommendation

---
id: recommend-patent-database-api
trigger: "When starting patent prior art search"
confidence: 0.9
domain: "api-recommendation"
scope: global
---

# Recommend Patent Database APIs for Professional Search

## Trigger Condition
When user requests patent prior art search and default channels may not be sufficient.

## Action
1. Ask user about available patent database APIs
2. Recommend appropriate APIs based on search needs:
   - Global search: Google Patents, Lens.org
   - US patents: USPTO, PatentsView
   - European patents: EPO Espacenet
   - Chinese patents: CNIPA
   - International: WIPO
3. Check ClawHub for patent search skills: `clawhub search patent`
4. Use installed skills if available

## Evidence
- 2026-03-19: User feedback that default channels are not accurate enough for patent search
- Patent prior art search requires professional patent database access
- ClawHub may have specialized patent search skills

Search Keyword Strategy

---
id: keyword-device-pairing
trigger: "When searching device pairing patents"
confidence: 0.85
domain: "keyword-strategy"
scope: project
project: example-project
---

# Device Pairing Search Keywords

## Keyword Combinations
- Primary keywords: device, terminal, pairing, connection
- Combination methods: `device pairing`, `terminal quick connection`
- Platform preference: Google Patents (English), AMiner (Academic)

## Evidence
- 2026-03-18: Found 5 highly relevant references using this combination
- Confidence increased from 0.5 to 0.85

Writing Technique

---
id: avoid-complete-code
trigger: "When writing patent embodiments"
confidence: 0.95
domain: "writing-pattern"
scope: global
---

# Avoid Complete Code

## Rule
Patent documents should not contain complete executable code. Use instead:
- Algorithm pseudocode
- Flowcharts
- Functional module descriptions

## Evidence
- 2026-03-17: Audit found complete code, recommended removal
- 2026-03-18: User confirmed this rule
- Verified across multiple patents

Integration into Patent Workflow

Auto-trigger learning in all three scenarios:

Scenario 1: User Idea → Drafting

After patent-auditor review passes
      |
      | Check for new patterns learned
      v
patent-continuous-learning extracts instincts

Scenario 2: User Draft → Optimization

User correction or audit recommendation
      |
      | Record effective improvements
      v
patent-continuous-learning updates instincts

Scenario 3: Agency Feedback

Targeted optimization successful
      |
      | Record effective differentiation descriptions
      v
patent-continuous-learning updates instincts