There is disclosed, in an embodiment, video-based requests for proposals and video-based bids used to create a contract through a set of terms of service, to provide a contract that dictates the release schedule of escrow funds. Embodiments use video and terms of service inside the function of a mobile application program (app) and digitize an escrow account for construction purposes. Embodiments integrate, via the app that is also used for video-based bid solicitation, video-based bidding and video-based milestone reporting and acceptance, to prove project video-based bidding, escrow and milestone payments. Other embodiments are also disclosed.

A method implements a worksite information management system. A selection that includes a task block is received. A task corresponding to a worksite is updated to include the task block. The task is presented to one of a worker interface, a trade interface, and a builder interface. An update to the task block is received. The task is updated using the update to generate an updated task. The updated task is presented to one of the trade interface and the builder interface.

In an approach to predictive maintenance explanations based on a user profile, one or more computer processors receive a failure prediction associated with a physical asset of an organization. One or more computer processors receive a work order associated with the failure prediction and an assignment of a first user to the work order. One or more computer processors retrieve a profile associated with the first user. One or more computer processors determine a best match between a taxonomy node of a taxonomy of user expertise associated with the work order and the retrieved profile associated with the first user. Based on the determined best match, one or more computer processors generate an explanation of the failure prediction. One or more computer processors display the explanation to the first user.

A method implements a worksite information management system. A selection that includes a task block is received. A task corresponding to a worksite is updated to include the task block. The task is presented to one of a worker interface, a trade interface, and a builder interface. An update to the task block is received. The task is updated using the update to generate an updated task. The updated task is presented to one of the trade interface and the builder interface.

A method according to one embodiment includes receiving baseline health data of a user, receiving activity-based health data of the user collected by a sensor device worn by the user while participating in physical activity and receiving environmental-based data of the user collected by the sensor device worn by the user. An alert is output for display on a user device in response to a determination that a core body temperature of the user is greater than a predetermined threshold temperature. A personalized schedule is generated for the user to follow while participating in the physical activity. The personalized schedule includes at least one instruction of when to start participating in the physical activity and at least one instruction of when to stop participating in the physical activity. The method further includes outputting the personalized schedule for display on the user device.

The present invention provides a virtual foreman dispatch planning system installed in a host in a factory, including: a knowledge graph unit, a matching unit and a recommendation unit. The knowledge graph unit has a first memory and a second memory which are connected with each other, and constructs and stores structural information including checking nodes, maintenance nodes and edges. The matching unit includes a neural network classifier that adopts semi-supervised learning method to retain original structural information, and downgrades the dimension of a continuous lantent space so that the continuous lantent space becomes a vector space, making nodes with more similar structures be closer in distance in the vector space. Through the K-Nearest Neighbor algorithm, the recommendation unit calculates the node of the maintenance record nearest to the vector space which is used as the dispatched manpower required for recommendation, so as to achieve the optimal dispatching effect.

A decision support tool is provided for predicting a patient's length of stay within a healthcare unit, such as the emergency department. The predicted length of stay may be determined using patient information and facility information, which includes information about the department and the facility in which the department is located. The patient information and the facility information may be used to determine feature values input into a plurality of machine learning models. The models may be used sequentially such that initial models are used to predict variables relating to the patient's length of stay and output of such models is used with a length-of-stay model to determine the predicted length of stay. One or more response actions may be automatically initiated based on the predicted length of stay. Such response actions may include resource management actions to allocate resources within the healthcare unit.

A central system, or server, may store information for determining the schedules of drivers. For example, the information may include loads assigned to the drivers, training, lunch breaks, time off, and/or other commitments. The central system may centrally store the information to efficiently and conveniently determine and update schedules. The central system may allow drivers and/or carriers to manage driver onboarding, training, compliance, scheduling, work assignment, and reporting, as well as build monthly, weekly, and daily schedules. The central system may also automate matching drivers to loads, balances work distribution, reduce human errors, and assist in audits.

This invention refers to a method for maintaining machinery, comprising the steps of: a) determining, by a computer, when the machinery will need to be maintained, b) acquiring, by a computer, tasks to be executed by workers, c) generating, by a computer, a list of tasks for at least one of the workers, the list is generated including a task for maintaining the machinery as determined in step a) and including the tasks acquired in step b), d) maintaining the machinery based on the list of tasks for the one of the workers, wherein steps a) to c) are executed independently from each other.

There is a need for more accurate and more efficient optimized scheduling operations. This need can be addressed by, for example, techniques for performing one or more optimized scheduling operations. In one example, a method includes: determining, using an optimal event time prediction learning machine model, a predicted interactivity measure for an event data object; determining, based at least in part on the predicted interactivity measure and using an optimal event time prediction machine learning model, an optimal event time modification value for the event data object; and determining, by one or more processors, an optimized appointment prediction based at least in part on optimal event time modification value.