Gaming

Technical Deep Dive

Our patent-pending loop runs as a closed-form control system: multimodal biometric ingest (PPG/ECG for HRV, EDA, pupillometry, facial EMG/vision, EEG/SSVEP) feeds a deviation calculator against an intended physiological response, which then conditions a generative policy to synthesize new audiovisual/game-state segments rather than selecting from pre-authored assets. This constraint—“not pre-chosen, not pre-made, not assembled from preexisting media assets”—is what differentiates the pipeline from branching/DAG-based prior art (e.g., Everett/Chappell style recombination).

In practice, we fuse biometric features using a transformer-based state encoder and drive world/physics parameters (lighting, camera trajectory, NPC affect, audio timbre) via low-latency control heads. Target end-to-end latency from biometric sample to rendered frame is <50–80 ms to preserve presence and avoid motion-sickness in VR. Reinforcement learning or bandit-style policies can optimize for session-level reward (engagement, retention, arousal control) while respecting comfort constraints (e.g., cybersickness thresholds).

Multi-user weighting is supported for co-op and esports spectatorship: per-user embeddings are weighted and aggregated to drive shared scene synthesis, enabling group-responsive lighting cues, pacing adjustments, or dynamic camera work. The system can also attach auxiliary actuators (haptics, spatial audio arrays) with the same control signal, ensuring cross-modal coherence. All outputs are logged to a user record for continual fine-tuning of personalization models while maintaining privacy budgets.