Learning and Optimization of Personalized Behavioral Patterns
RunRabbit's memory system uses a federated learning framework to build user profiles on the local device without uploading private data. It records three types of key information: high-frequency commands (e.g., "open mailbox" corresponds to a specific web address), paths (preferences for the order in which forms should be filled out), and temporal patterns (frequently used functions at specific times). This data is modeled by a graph neural network to form a personalized prediction model.
In practice, the system guides users through the complete steps the first time they say "order your regular coffee"; the third time they use it, it automatically populates the store, category, and payment method. This incremental learning reduces weekly processing time for repetitive tasks by an average of 62%.
The system uses differential privacy technology to protect user data, and all memory entries can be viewed or deleted at any time through the Settings Center, in compliance with GDPR compliance requirements.
This answer comes from the articleRunRabbit: Using Voice and Text to Operate Intelligent Bodies to Complete Computer Operations》
