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).

Methods and systems for resource allocation using data from a variety of systems are described herein. A plurality of periods of time for using one or more resources may be identified based on a user request. Data indicative of locations of one or more users over one or more time periods may be received. Such data may be from an application other than a calendar application. Anticipated locations of the one or more users may be determined based on the data received. A resource of the one or more resources may be selected based on a distance between the resource and the anticipated locations of the one or more users. Based on detecting a change to the data, a new resource may be selected.

Method, apparatus and computer program product for generating a member calendar in a group-based communication system are described herein.

Systems, methods, and computer program products for managing and networking schedules and calendars of events are provided herein. A method of a managing, networking, and presenting calendars, schedules and events can include uploading from one or more computing devices of one or more users at least one of calendars of events, schedules of events or individual events to a scheduling event networking system. The method can also include grouping two or more calendars of events, schedules of events or individual events into one or more umbrella groupings based on instructions received from one or more computing devices of one or more users. The method can further include displaying on one or more computing devices of one or more users a layered network of one or more of the calendars of events, schedules of events, individual events, or umbrella groupings on the scheduling event networking system based on user preferences.

An appointment scheduling system configured to predict the likelihood of a missed appointment during the scheduling process, thereby minimizing missed appointments and improving access to care is discussed. In particular, artificial intelligence/machine-learning techniques are used to more efficiently utilize a provider's time and a patient's needs and to help optimize scheduling at healthcare facilities. A predictive model can aid in prioritizing the design and implementation of interventions that may improve efficiency towards more timely access to care at healthcare facilities. In particular, patients can be provided with an earliest appointment, closer to his or her home. In addition, patient and provider satisfaction with scheduling services can be improved.

Disclosed herein are apparatuses and methods for verifying a level of trust in a zone having multiple persons. One or more persons of the multiple persons within the zone can be identified and, based on identifying the one or more persons, it can be determined whether the one or more persons are associated with a level of trust. An observable indicator can be set to specify whether the multiple persons in the zone, including the one or more persons, are associated with the level of trust.

Disclosed are various approaches for detecting user availability. A work pattern can be generated based upon user activity data taken from various sources. A work pattern can be provided to an email client or another requesting service for predicted availability of a user.

A schedule management apparatus is provided with a storage device that stores a schedule management program and a processor that reads out and executes the schedule management program. The schedule management program is provided with a first day-schedule suggestion means, a second day-schedule suggestion means, and a day-schedule registration means. The first day-schedule suggestion means determines, based on a registered schedule of a set participant, a suggestion day-schedule that is a day-schedule to be suggested to the participant, and suggests the determined suggestion day-schedule to a user included in the participant. The second day-schedule suggestion means confirms, based on an answer from the user, the determined suggestion day-schedule with the participant except the user. The day -schedule registration means generates, based on an answer from the participant, a day-schedule notification signal that notifies the participant of the suggestion day-schedule, and registers the suggestion day-schedule in the registered schedule of the participant.