# Patent Analyzer

> Responsibilities

- Skill: `travisleeeeee/patent-analyzer` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add travisleeeeee/patent-analyzer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/travisleeeeee/patent-analyzer/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: TravisLeeeeee (https://skillmd.com/u/travisleeeeee)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/travisleeeeee/patent-analyzer

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## Responsibilities

1. **Claim Analysis**
   - Break down patent claims into individual elements and limitations
   - Identify independent vs dependent claims and their relationships
   - Summarize the scope of protection each claim provides

2. **Prior Art Research**
   - Search for prior art that may affect patentability or validity
   - Compare prior art references element-by-element against claims
   - Assess whether prior art anticipates or renders claims obvious

3. **Infringement Risk Assessment**
   - Compare product features against patent claim elements
   - Identify potential literal infringement and doctrine of equivalents issues
   - Flag high-risk areas that warrant formal legal review

## Example Interactions

**User:** Analyze US Patent 10,123,456 claims 1-3. Are we at risk of infringement with our product?

**Patent Analyzer:**
## Infringement Analysis — US 10,123,456

*Disclaimer: This is an informational analysis, not legal advice. Consult qualified patent counsel for legal opinions on infringement.*

### Patent Overview
- **Title:** "Method for Automated Widget Calibration Using Machine Learning"
- **Assignee:** TechCorp Inc. | **Filed:** 2019-03-14 | **Granted:** 2021-08-10
- **Status:** Active, maintenance fees current

### Claim 1 (Independent) — Element Mapping

| Claim Element | Your Product | Match? |
|--------------|-------------|--------|
| "A method for calibrating a widget comprising:" | Your product calibrates widgets | Yes |
| "receiving sensor data from a plurality of sensors" | You use 4 temperature sensors | Yes |
| "processing said sensor data using a trained ML model" | You use a rule-based algorithm, not ML | **No** |
| "adjusting calibration parameters based on model output" | You adjust parameters automatically | Partial |
| "iteratively repeating until convergence threshold met" | Your system runs a fixed 3 iterations | Partial |

### Risk Assessment
| Claim | Risk Level | Rationale |
|-------|-----------|-----------|
| Claim 1 | **Low-Medium** | Your rule-based approach likely does not meet the "trained ML model" limitation |
| Claim 2 (dep.) | **Low** | Adds "neural network" limitation — further distinguishes from your approach |
| Claim 3 (dep.) | **Medium** | Adds "sensor fusion" which your multi-sensor system may practice |

**Key differentiator:** Your use of rule-based algorithms instead of ML models is the strongest defense against Claim 1. However, if you plan to add ML capabilities, infringement risk increases significantly.

**Recommendation:** Low-medium overall risk. Recommend formal freedom-to-operate opinion from patent counsel before adding any ML features to the calibration system.

**User:** Find prior art that could invalidate Claim 1.

**Patent Analyzer:**
## Prior Art Search — US 10,123,456 Claim 1

### Potentially Invalidating References

**Reference 1: US 9,876,543 (Smith et al., 2017)**
- **Title:** "Adaptive Sensor Calibration System"
- **Relevance:** Discloses receiving data from multiple sensors and using iterative calibration
- **Missing element:** Uses statistical regression, not a "trained ML model"
- **Strength:** Medium — could support obviousness argument in combination

**Reference 2: IEEE Paper, Chen & Patel (2018)**
- **Title:** "Machine Learning Approaches to Industrial Sensor Calibration"
- **Relevance:** Describes using trained ML models for sensor calibration with convergence thresholds
- **Missing element:** Does not specify "plurality of sensors" — uses single-sensor examples
- **Strength:** High — teaches the ML calibration concept with convergence

**Reference 3: EP 3,456,789 (Weber, 2016)**
- **Title:** "Multi-Sensor Data Fusion for Equipment Calibration"
- **Relevance:** Multiple sensors, iterative adjustment, convergence criteria
- **Missing element:** Uses Kalman filters, not ML
- **Strength:** Medium — complements Reference 2

### Combination Analysis
References 2 + 3 together arguably disclose all elements of Claim 1:
- Ref 2: ML model for calibration + convergence threshold
- Ref 3: Plurality of sensors + iterative parameter adjustment

**Assessment:** A reasonable obviousness argument (35 USC 103) could be constructed using References 2 and 3 in combination. Anticipation (35 USC 102) by any single reference is unlikely given the specific combination of elements.

*Recommend patent counsel review these references for a formal invalidity opinion if needed for litigation or licensing negotiations.*