System and method for adaptively executing user routines based on user interactions

A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations of executing routines associated with a user, the operations comprising:

• maintaining, at a server, one or more workflows for managing daily routines of a user located at a premise, each workflow having one or more workflow stages, each workflow stage being associated with a subroutine configured to be executed at a particular time of a day;
• for each of the workflows, iteratively performing
• determining a current time of a day,
• identifying a workflow stage corresponding to the current time of the day,
• executing a subroutine corresponding to the workflow stage, including
• retrieving a content item corresponding to the workflow stage,
• transmitting the content item to one or more preconfigured user devices of the user to be played thereon,
• receiving a response from at least one of the preconfigured user devices representing a user response to the content item, and
• logging the user response and timing of the user response in an event log storing user interaction history of the routine;
• invoking a machine-learning engine to analyze the user interaction history in the event log to determine a user behavior of the user for each workflow;
• and
• modifying at least timing of at least one subroutine of at least one workflow based on the user behavior.

A data processing system, comprising:

• a processor;
• and
• a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations of executing routines associated with a user, the operations comprising:
• maintaining, at a server, one or more workflows for managing daily routines of a user located at a premise, each workflow having one or more workflow stages, each workflow stage being associated with a subroutine configured to be executed at a particular time of a day;
• for each of the workflows, iteratively performing
• determining a current time of a day,
• identifying a workflow stage corresponding to the current time of the day,
• executing a subroutine corresponding to the workflow stage, including retrieving a content item corresponding to the workflow stage,
• transmitting the content item to one or more preconfigured user devices of the user to be played thereon,
• receiving a response from at least one of the preconfigured user devices representing a user response to the content item, and
• logging the user response and timing of the user response in an event log storing user interaction history of the routine;
• invoking a machine-learning engine to analyze the user interaction history in the event log to determine a user behavior of the user for each workflow;
• and
• modifying at least timing of at least one subroutine of at least one workflow based on the user behavior.