Self-learning home system and framework for autonomous home operation

A system for automated operation within a home communication network, the system comprising:

• a plurality of actuators within the home communication network, each actuator of the plurality of actuators configured to change a state of at least one network device within the home communication network;
• a plurality of sensors within the home communication network, the plurality of sensors configured to generate sensor data including one or more sensor values;
• a memory storing instructions;
• and
• at least one processor in communication with a wireless connectivity circuitry, the memory, the plurality of actuators, and the plurality of sensors, the at least one processor configured, upon execution of the instructions, to perform the following steps:
• decoding first information received from the plurality of actuators using the wireless connectivity circuitry, the first information indicating a plurality of actuator actions performed by the plurality of actuators within the home communication network based on user interaction;
• decoding second information received from the plurality of sensors using the wireless connectivity circuitry, the second information indicating the one or more sensor values;
• retrieving a machine learning (ML) model from the memory, the ML model being trained with a training data set of the home communication network to correlate the first information and the second information;
• generating a correlation between the plurality of actuator actions and the one or more sensor values based on applying the ML model to the first information and the second information;
• generating configuration data based on the correlation, the configuration data comprising:
• a trigger graph with one or more trigger conditions, the one or more trigger conditions specifying at least one of the plurality of actuator actions;
• and
• an action graph corresponding to the trigger graph, the action graph indicating one or more automatic actuator actions associated with at least one actuator of the plurality of actuators;
• detecting a trigger condition of the one or more trigger conditions specified by the trigger graph;
• and
• causing the at least one actuator of the plurality of actuators to perform the one or more automatic actuator actions indicated by the action graph based on the detecting the one or more automatic actuator actions performed without the user interaction.

A non-transitory computer-readable media storing instructions for automated operation of network devices within a home communication network, that configure at least one processor, upon execution of the instructions, to perform the following steps:

• decoding first information received from a plurality of actuators within the home communication network using a wireless connectivity circuitry of the computing device, the first information indicating a plurality of actuator actions performed by the plurality of actuators within the home communication network based on user interaction, each actuator of the plurality of actuators configured to change a state of at least one network device;
• decoding second information received from a plurality of sensors in the home communication network using the wireless connectivity circuitry, the second information indicating one or more sensor values generated by the plurality of sensors;
• retrieving a machine learning (ML) model from storage of the computing device, the ML model being trained with a training data set of the home communication network to correlate the first information and the second information;
• generating a correlation between the plurality of actuator actions and the one or more sensor values based on applying the ML model to the first information and the second information;
• generating configuration data based on the correlation, the configuration data comprising:
• a trigger graph with one or more trigger conditions, the one or more trigger conditions specifying at least one of the plurality of actuator actions;
• and
• an action graph corresponding to the trigger graph, the action graph indicating one or more automatic actuator actions associated with at least one actuator of the plurality of actuators;
• detecting a trigger condition of the one or more trigger conditions specified by the trigger graph;
• and
• causing the at least one actuator of the plurality of actuators to perform the one or more automatic actuator actions indicated by the action graph based on the detecting the one or more automatic actuator actions performed without the user interaction.