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AI-related copyright disputes are surging, as seen in the three major Korean broadcasters' lawsuit against OpenAI. When small law firms (3-10 attorneys) take on AI copyright cases, they must collect and preserve evidence that AI service outputs are similar to original copyrighted works. However, AI service outputs vary by time of query, and simple webpage screenshots lack sufficient legal evidentiary weight. Evidence collection and preservation takes 20-40 hours per case.
An automation tool that inputs specific prompts into AI services and preserves output results with timestamps and hash values via blockchain anchoring. Generates one-click legal evidence packages with similarity analysis reports comparing outputs to original works. Includes recurring monitoring to track output changes over time.
| N Novelty | 1-5 | How uncommon the service is in market context. |
| U Urgency | 1-5 | How urgently users need this problem solved now. |
| M Market | 1-5 | Market size and growth potential from proxy indicators. |
| R Realizability | 1-5 | Buildability for a small team with realistic constraints. |
| V Validation | 1-5 | Validation signal quality from competition and demand data. |
| Tech Complexity | / 40 | Difficulty of core implementation stack. |
| Data Availability | / 25 | Practical availability and cost of required data. |
| MVP Timeline | / 20 | Expected time to ship a usable MVP. |
| API Bonus | / 15 | Bonus for viable public API leverage. |
| Competition | / 20 | Signal quality from competitor landscape. |
| Market Demand | / 20 | Demand proxies from search and mention patterns. |
| Timing | / 20 | Fit with current shifts in tech, behavior, and regulation. |
| Revenue Signals | / 15 | Reference evidence for monetization viability. |
| Pick-Axe Fit | / 15 | How well the concept serves participants in a trend. |
| Solo Buildability | / 10 | Practicality for lean-team implementation. |