Abstract: A system for training and deploying an artificial conversational entity using an artificial intelligence (AI) based communications system is disclosed. The system may comprise a memory storing machine readable instructions. The system may also comprise a processor to execute the machine readable instructions to receive a request via an artificial conversational entity. The processor may also transmit a response to the request based on a dialog tree generated from at least a model-based action generator and a memory-based action generator. The processor may further provide a training option to a user in the event the response is suboptimal. The processor may additionally receive a selection from the user via the training option. The selection may be associated with an optimal response.

Abstract: A system for training and deploying an artificial conversational entity using an artificial intelligence (AI) based communications system is disclosed. The system may comprise a memory storing machine readable instructions. The system may also comprise a processor to execute the machine readable instructions to receive a request via an artificial conversational entity. The processor may also transmit a response to the request based on a dialog tree generated from at least a model-based action generator and a memory-based action generator. The processor may further provide a training option to a user in the event the response is suboptimal. The processor may additionally receive a selection from the user via the training option. The selection may be associated with an optimal response.

Abstract: A method for presenting health notifications begins with creating a plurality of different health notifications, each conveying the same type of information. Each of the different health notifications is provided to a plurality of different users, each user categorized with user health metrics. Post-health notification user activity is tracked for each of the different users. A machine-learning classification machine is trained with tracked user activity, along with corresponding user health metrics, for each of the different health notifications. When provided with user health metrics received from a health-monitoring computing device associated with a user, the machine-learning classification machine chooses a selected health notification for the user from among the different health notifications, the selected notification determined to be more likely than any of the other health notifications to elicit a healthy response from the user. The selected health notification is then sent to the user.