Binary Kinetics filed 1 patent this quarter in the AI & Machine Learning category.
The application covers a stateless AI Game Master architecture designed for interactive fiction systems. This architecture employs transaction-based context reconstruction to reduce per-session memory requirements by 99% while incorporating multi-provider LLM failover capabilities for improved scalability.
The AI & Machine Learning patent tackles the challenge of managing conversational context in LLM-powered game systems by borrowing from event sourcing architecture. Instead of maintaining full conversation histories that quickly exhaust context windows, the system stores compact action logs and reconstructs only the semantic context needed for each prompt. This approach pairs with provider-agnostic orchestration that can switch between LLM services and enforces game mechanics without requiring persistent state between sessions. The architecture treats each interaction as a transaction, rebuilding context on demand rather than carrying forward accumulated conversational baggage.
All data sourced from USPTO patent filings. Google Patents may take several weeks to index recent publications. If a link is unavailable, search for the patent number at USPTO Patent Public Search.