Systems and apparatuses include one or more processing circuits comprising one or more memory devices coupled to one or more processors, the one or more memory devices configured to store instructions thereon that, when executed by the one or more processors, cause the one or more processors to: receive vehicle information including operating conditions and historical vehicle information, develop a vehicle model using a machine learning engine that receives the vehicle information, determine a fleet failure probability based on the vehicle model and fleet information including fleet usage and fleet vehicle types, and determine a predictive maintenance schedule based on the fleet failure probability.

A sensor system for a structure comprises a sensor node in force transmitting contact with an impact receiving surface of a structure envelope of the structure. The sensor node is configured to generate first sensor data associated with the structure envelope of the structure and perform a first set of operations to filter out unwanted data from the first sensor data to form a first filtered dataset. The sensor system includes a sensor hub in communication with the sensor node. The sensor hub is configured to receive the first filtered dataset from the sensor node and perform a second set of operations on the first filtered dataset to identify an event experienced by the structure envelope that caused the sensor node to produce the first sensor data.

Artificial intelligence (Al) based technologies for improving patient appointment scheduling and inventory management are disclosed herein. An example method includes receiving, at a server including a natural language processing (NLP) model, an appointment request from a user. The example method further includes initiating, based on the appointment request, a patient appointment data stream including verbal responses from the user regarding an appointment of the user. The example method further includes applying, while simultaneously receiving the patient appointment data stream, the NLP model to the verbal responses from the user to output (i) textual transcriptions and (ii) intent interpretations. The example method further includes querying a scheduling database to determine a matching appointment that satisfies a distance threshold, a date threshold, a service threshold, and an inventory threshold. The example method further includes causing a user device of the user to convey the matching appointment to the user.

A life management system receives data from a client device worn by a user, the data comprising biotelemetry data and activity data collected about a user wearing the client device. The life management system generates snapshot information using information from a group consisting of: the biotelemetry data, activity data, social data associated with the user, and user profile information associated with the user. The life management system generates a recommendation using portions of the snapshot information, and updates the snapshot information with the recommendation. The life management system executes a recommendation associated with the snapshot information in accordance with the user controls associated with the user.

According to another aspect, a computer system is provided. The computer system includes a memory; at least one processor in data communication with the memory; and a reporting component executable by the at least processor and configured to execute a user interface component configured to receive schedule information, the schedule information including a company-wide schedule specifying an expense report generation date and at least one reminder; store the schedule information in a data store; send the at least one reminder to a plurality of users prior to the expense report generation date; and generate, on the expense report generation date, a plurality of expense reports respectively corresponding to the plurality of users.

Systems/techniques that facilitate automated training of machine learning classification for patient missed care opportunities or late arrivals are provided. In various embodiments, a system can access a set of annotated data candidates defined by two or more feature categories. In various aspects, the system can train a machine learning classifier on the set of annotated data candidates, thereby causing internal parameters of the machine learning classifier to become iteratively updated. In various instances, the system rank the two or more feature categories in order of classification importance, based on the iteratively updated internal parameters of the machine learning classifier. In various cases, the system can perform one or more electronic actions based on the two or more feature categories being ranked in order of classification importance.

An information processing apparatus includes a processor configured to: receive an instruction to produce a meeting notice; acquire a schedule of a participant of a meeting, the schedule arranged within a meeting time of the meeting; if the participant is occupied during the meeting time, acquire log information on a conversation that the participant has made using a messenger application; and by analyzing the log information on the conversation, display information indicating whether holding of the meeting is possible during the meeting time.

Systems, methods, and program products for providing services to a user by a mobile device based on the user's daily routine of movement. The mobile device determines whether a location cluster indicates a significant location for the user based on one or more hints that indicate an interest of the user in locations in the cluster. The mobile device can perform adaptive clustering to determine a size of area of the significant location based on how multiple locations converge in the location cluster. The mobile device can provide location-based services for calendar items, including predicting a time of arrival at an estimated location of a calendar item. The mobile device can provide various services related to a location of the mobile device or a significant location of the user through an application programming interface (API).

A method for controlling a charge transfer of an electric vehicle using an electric vehicle charging station, a mobile device, and a cloud server is disclosed. The method includes: receiving, at a mobile device, a message for an electric vehicle of a user from the electric vehicle charging station, wherein a user of the mobile device is associated with the electric vehicle to be charged; sending, from the mobile device, the message for the electric vehicle of the user to the cloud server, wherein the charge transfer request relayed from the mobile device includes identification information; in response to a charging control signal being authorized using identification information received from the mobile device, receiving the charging control signal from the cloud server at the mobile device to be forwarded to the electric vehicle charging station, wherein the charging control signal is configured to adjust a charging parameter at the electric vehicle charging station.

A method for controlling a charge transfer of an electric vehicle using an electric vehicle charging station, a mobile device, and a cloud server is disclosed. The method includes: receiving, at a mobile device, a message for an electric vehicle of a user from the electric vehicle charging station, wherein a user of the mobile device is associated with the electric vehicle to be charged; sending, from the mobile device, the message for the electric vehicle of the user to the cloud server, wherein the charge transfer request relayed from the mobile device includes identification information; in response to a charging control signal being authorized using identification information received from the mobile device, receiving the charging control signal from the cloud server at the mobile device to be forwarded to the electric vehicle charging station, wherein the charging control signal is configured to adjust a charging parameter at the electric vehicle charging station.