Abstract: The present disclosure describes a conversation facilitation system for facilitating conversation-based social interactions to improve senior health, one or more operations and functions being efficiently achieved via this system comprise: receiving a dialog act of a conversation, applying natural language understanding (NLU) processing on the dialog act, computing a conversation metric, and generating a result of the conversation to conclude the conversation based on the conversation metric.

Abstract: The in-home care of seniors is augmented using Internet of Things (IOT) technologies. In-home sensors monitor a senior and their caregiver. Physical conditions and psychological conditions may be monitored. In some implementations, a machine learning system has a classifier trained to detect a specified condition, such as depression. The system may perform various transformations of raw sensor data into a format indicative of a particular condition. In one implementation, a psychological or medical condition has symptoms in which each symptom has one or more measurable events. Mappings between symptoms, events, sensor data, and sensor transformation functions may be supported.

Abstract: The present disclosure describes a system to use conversation data of patients to detect dangerous mental or physical conditions, such as suicidal thoughts, physical abuse, recent falls, and viral infection. A machine learning system may be trained to identify a dangerous mental or physical condition from conversations based on examples of patients evaluated to have a specific mental or physical condition. Conversations of patients may be monitored, natural language understanding (NLU) processing performed, and a machine learning system used to detect dangerous mental or physical conditions.

Abstract: A machine learning system passively monitors sensor data from the living space of a patient. The sensor data may include audio data. Audio features are generated. A trained machine learning model is used to detect a change in condition. In some implementations, the machine learning model is trained in a learning phase based on training data that includes questionnaires completed by caregivers and identified audio features.

Abstract: The present disclosure describes a conversation facilitation system for facilitating conversation-based social interactions to improve senior health, one or more operations and functions being efficiently achieved via this system comprise: receiving a dialog act of a conversation, applying natural language understanding (NLU) processing on the dialog act, computing a conversation metric, and generating a result of the conversation to conclude the conversation based on the conversation metric.