An inspection system and method of operation may include receiving sensor data for a component of a powered system, and determining an expected failure time of the component at which the component is expected to fail based at least in part on the sensor data. The expected failure time occurring on a timeline. A window start time may be determined on the timeline that is after a current time but is prior to the expected failure time. The window start time and the expected failure time may define a window time range that extends between the window start time and the expected failure time on the timeline. One or both of a repair action or a maintenance action of the powered system may be scheduled at a scheduled time occurring during the window time range and at a time prior to the expected failure time on the timeline.

A method of managing a plurality of washrooms in a facility for servicing by service personnel and, more particularly, for establishing servicing operations which provide for service of at least selected of the dispensers before their consumable product supply falls below a pre-selected refill value.

Unmanned monitoring devices, such as unmanned aerial vehicles (UAV), drones or rovers may inspect system components within an area of interest (AOI) such an electric power distribution system including generation, transmission, and distribution elements for autonomous detection of damage to the components. Work orders for repairing the damage are autonomously generated and resources identified within the work orders are autonomously provisioned.

In some aspects, an edge computing system may receive, a plurality of images. An image in the plurality of images may be associated with products in a retail store. The edge computing system may select a subset of images in the plurality of images based on spatial contextual data associated with each image in the plurality of images, a level of redundancy between images in the plurality of images, and temporal contextual data associated with each image in the plurality of images. The edge computing system may transmit, to a cloud computing system, the subset of images for image analysis.

Systems and methods for validating telematics device installations are provided. The systems and methods involve operating at least one processor to: receive, from an installer, a device identifier corresponding to a telematics device; determine whether telematics data was received from the telematics device based at least on the device identifier; in response to determining that telematics data was received from the telematics device: present an indication to the installer that the telematics device was correctly installed in the vehicle; receive, from the installer, vehicle information associated with the vehicle; and store the vehicle information in association with the device identifier; and in response to determining that telematics data was not received from the telematics device: present an indication to the installer that the telematics device was not correctly installed in the vehicle; and store installation information in association with the device identifier.

One embodiment includes a computer-implemented data labeling platform. The platform provides a confidence-driven workflow (CDW) executable to receive and process labeling requests to label data items. The CDW comprises a set of executable labelers, each labeler in having a dynamically modeled confidence range. The execution path for processing a labeling request to label a data item is dynamically determined. Dynamically determining the execution path comprises dynamically determining a bounded number of candidate paths through the set of labelers using dynamically calculated cost and confidence metrics for the labelers in the set of labelers to estimate a probability of each candidate path to satisfy a set of constraints on cost and final result confidence, selecting a candidate path that minimizes cost for a specified confidence from the candidate paths as a selected path, executing a next labeler consultation according to the selected path to label the data item, and dynamically re-determining the remaining execution path using calculated results arising from executing the completed path steps.

The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of remote scheduling of calendar and other means based queues and reminders to a user of a presentation device. The user of the presentation device may be suffering from a progressive cognitive disorder, and the queues and other care provided may be provided remotely by a loved one or other administrator.

One embodiment provides a method, including: receiving, from a healthcare enterprise system, a notification indicating a need for at least one medical resource during a predetermined time frame at a predetermined facility within the healthcare enterprise system; determining that the indicated need is unavailable during at least one of: the predetermined time frame and the predetermined facility; identifying, based upon the indicated need being unavailable, at least one action to be performed to supply the at least one medical resource; and performing the at least one action. Other aspects are described and claimed.

Generally discussed herein are devices, systems, and methods for task classification. A method can include modifying a representation of a source sentence of a source sample from a source corpus to more closely resemble a representation of target sentences of target samples from a target corpus, operating, using a machine learning model trained using the modified representation of the source sentence, with the target sample to generate a task label, the task label indicating whether the target sample includes a task, and causing a personal information manager (PIM) to generate a reminder, based on whether the target sample includes the task.

The method, system and a computer program and a computer product for managing workers and documents is provided. The method includes storing industry representations and a list of workers with data related to the workers, linking the industry representations to the workers and selecting at least one worker from based on the industry representations for that worker. The method also includes scheduling workers to a job based on whether they have all of the required industry representations. In addition, the method includes uploading industry representations and bar code scanning industry representations into the database. The method further includes dispatching said industry representations to other users and automatically mapping fields of the dispatched data in the destination database. In addition, the method includes selectively encrypting only sensitive fields in data transmission between two entities.