Personal Daemon Accelerated Training via Pre-trained Models

The patent applies preliminary action by pre-training personal daemon models offline before deployment. Multiple personal daemon models are trained in advance using diverse user data, creating a library of pre-trained models that can be quickly assigned to users without requiring lengthy on-device training. This resolves the contradiction by performing the time-consuming training operation before the service is actually needed.

The patent uses copying by creating multiple personal daemon model instances that can be replicated and assigned to different users. Instead of training a unique model for each user from scratch, the system copies pre-trained model architectures and adapts them to individual users through lighter-weight fine-tuning or configuration, dramatically reducing training time while maintaining personalization accuracy.

The patent applies segmentation by dividing the personal information processing into separate functional components: data collection, model training, and service delivery. User data remains on-device and is used only to train local personal daemon models, which then operate autonomously without requiring continuous data sharing. This segmentation allows the service to be self-sufficient and reduces the need to share personal information with third parties for monetization.

The patent implements self-service through on-device machine learning models that perform personalization locally without requiring external services. The personal daemon models run autonomously on user devices, processing and acting on user information independently. This self-service capability reduces dependency on third-party data processing services, thereby improving personal security while maintaining service functionality.