An intelligent office system includes an application layer, an engine layer and a system layer. The application layer may be configured to create work cards for work tasks acquired from application systems, store each work card into the system layer by calling the engine layer; acquire changing information of each work card, and update each work card that has been stored in the system layer by calling the engine layer. In response to being called by the application layer, the engine layer sends the created work cards and the changing information of each work card to the system layer for storage and update. The system layer stores information of each work card.

Techniques are provided that may include receiving sensor data from one or more machine sensors, the sensor data indicating machine failure. Informational data about current repair technician backlog, shop availability backlog, and parts availability are retrieved and inputted to a machine learning model for computing an estimated time to when the failed machine will be repaired. Customer data and dealer site data are retrieved and an estimated financial impact from a potential loaner machine is computed. The various computed and retrieved data are inputted to a decision tree algorithm, which computes an optimal solution for maintaining customer productivity. The network-accessible dealer site is notified of the optimal solution, which is intended for the customer.

Provided herein are a method and device of crew replanning for high-speed railway typical scenarios, and an electronic device and a computer-readable storage medium. The method includes: building a high-speed railway crew connection network; establishing a model for high-speed railway crew replanning; decomposing the model into a first sub-model for team leaders and a second sub-model for general crew members; and designing a Lagrangian relaxation algorithm to solve the first sub-model and the second sub-model.

A method, system, apparatus, and/or device for moving or scrolling a virtual object in a virtual or augmented reality environment. The method, system, apparatus, and/or device may include: detecting, by a first sensor, a first gesture associated with selecting a first virtual object in an augmented reality environment displayed by a head-mounted display; displaying, by the head-mounted display, a first indicator indicating a selection of the first virtual object by a user; detecting, using the first sensor or a second sensor, a first movement of the head-mounted display associated with a first movement command; and in response to detecting the first movement of the head-mounted display, executing the first movement command, where the first movement command is a scrolling function to scroll text or a graphical object of the first virtual object or a movement function to move the text or the graphical object of the first virtual object.

A System, Method, and Apparatus for Predicting Medical No-Shows and for Scheduling is provided. The system is based on a patient appointment schedule and indicators including office type, practice area, appointment type, engagement with the office leading up to appointment, and a variety of other characteristics of each patient and appointment. The indicators can be selected, weighted proportionately, and input into an algorithm for analysis and processing. The algorithm can then predict the likelihood of a no-show for the respective patient and appointment. The likelihood can then be used for scheduling to minimize the negative impact on the medical professional of the no-show. The algorithm can be used in conjunction with machine learning to improve its accuracy as it informs itself with experience and additional data points. The likelihood of a no-show can be incorporated into scheduling programs and made accessible on mobile devices, computers, and other monitoring devices.

A method, apparatus, system, and computer program product for scheduling access to a set of locations. Workers assigned to the set of locations are identified by a set of processor unit. A collaboration network is identified by the set of processor units in which the collaboration network comprises nodes representing the workers and edges having weights representing interactions between the workers in which the interactions are based on communications information sent between the workers in a computer system. A schedule is generated by the set of processor units for the workers to work at the set of locations using the collaboration network.

A method, computer program product, and system includes a processor(s) determining, based on obtaining data from the one or more sensors over the wireless communications connection, that individuals are within a given vicinity. The processor(s) queries personal computing devices associated with the individuals to determine an identity of each individual in the given vicinity. The processor(s) assigns a service provider(s) to a portion of the identified individuals. The processor(s) obtains preference information related to the portion and determines environmental information related to an environment experienced by the portion by locating at least one sensor within a predetermined distance of the portion, and communicating, over the wireless communications connection, with the at least one sensor. The processor(s) generate simulations for providing services to the portion that include temporal parameters and selects an optimal simulation. The processor(s) communicates instructions to achieve aspects of the optimal simulation to the service provider(s).

The present application includes a method and system for real-time scheduling reallocation. The system receives a schedule which allocates customer service representatives (CSRs) to channels and queues based on analytics rules as well as the forecast work patterns and information on CSR skills and shift patterns. The system then monitors adherence to the schedule by comparing the difference between forecast and current volume and type of work to a reallocation threshold at predetermined intervals. If the difference extends beyond the reallocation threshold, the system reallocates CSRs to update the schedule.

A vehicle ride-sharing assist system includes a terminal including a user interface and a terminal processing unit; an action information managing server; and a ride-sharing managing server configured to create an operation schedule based on a desired ride-sharing condition, which includes at least one of a departure place, a departure time, an arrival place, and an arrival time of each user, transmitted from the terminal processing unit. The ride-sharing managing server is configured to set a recommended ride-sharing condition, which includes at least one of the departure place, the departure time, the arrival place, and the arrival time suitable for each user, based on the action information and transmit the recommended ride-sharing condition to the terminal processing unit. The terminal processing unit is configured to cause the user interface to display the recommended ride-sharing condition on receiving the recommended ride-sharing condition from the ride-sharing managing server.

A web-based system solution, including process logic and data model, that allocates available resources to project tasks in a manner that ensures availability of specific resources with required skill sets to perform specific tasks at specific time periods in future when those tasks become due. The solution automates the creation and maintenance of realistic project plans linked at all times to resource availability, as an outcome of such resource allocations. Resource allocations are done to the entire set of future tasks across all projects in the organization in a coordinated manner. The system continually monitors changes to critical input parameters that could impact the demand or the availability of specific resources at specific time periods. The resource allocations are performed at a minimum at least once a day to incorporate the impact of changes in real time on resource availability and resulting project plans and resource loads, thus “dynamic”.