Computerized systems and methods are provided for automatically detecting an indication that a task has been completed and associated user interface functionality. These systems and methods improve existing technologies by automatically detecting indications that tasks have been completed via new logic or rules and improving the functionality and computing resource consumption relative to existing machine learning models. These systems also improve the way computers operate by reducing computing resource consumption, such as memory, network latency, I/O, and the like.

A system and a method include at least one control unit that determines a de-icing time for an aircraft within a de-icing area of an airport, predicts a de-icing time for an aircraft within a de-icing area of an airport, schedules de-icing times for a plurality of aircraft within a de-icing area of an airport, and/or predicts demand for future de-icing operations of aircraft within a de-icing area of an airport.

A system for managing a local area shopping network is disclosed. The plurality of subsystems includes an order request subsystem, configured to receive a request of a product order from registered/unregistered (a customer can be guest customer) customers. The plurality of subsystems includes a parameter determination subsystem configured to determine a delivery area, a delivery schedule for sellers and determine product pickup area for the customers/buyers based on their preference and location. A buyer may decide for delivery only, in that case there is no pickup area/perimeter. The plurality of subsystems includes a product transaction subsystem. The product transaction subsystem is configured to identify (all sellers. If there are none that meet criteria then it is blank) one seller from the list of the registered sellers based on received request of the product order and establish a direct communication link with the customer. The product transaction subsystem is configured to complete the delivery of the order to the registered customers upon accepting the received request. (sellers do delivery and not the application) Customers and sellers are connected in a single platform, and transaction is facilitated.

Disclosed are systems, methods, and non-transitory computer-readable storage media for setting a reminder triggered by a target device. A requesting device sends a request to a server to set a notification triggered by a target device. The request includes parameters, such as a location and a condition that define when the notification is triggered. The server sends instruction to the target device to set the notification based on the parameters. When the condition such as arrival is met by the target device in relation to the location the target device sends a message to the server that the notification has been triggered. The target device can set a geo-fence to determine the position of the target device in relation to the location, and the requesting user can dictate the size of the geo-fence. The server sends a message to the requesting device that the notification has been triggered.

The embodiments herein provide a common/universal multi-platform-based cloud solution/system for tracking and monitoring documents of different categories such as Subscriptions, Warranties, Insurances, Mortgages, Rent, IDs and Visas, Permits and Certificates, etc. several cards corresponding to every category is provided. Every category is a DECK of cards. The user create/review the CARDS representing each document to be tracked and monitored. The user inputs/enters all the information and set a desired time for alerting and reminding about payments and other deadlines/important information. A statistic page displays the total amount spent on each category with averages and sort by/filter by. A Calendar is provided for selecting a date for exhibiting the cards having a payment due on the selected date and the amount due. The due amount for each category such as subscriptions such as premiums, EMIs, etc., are also displayed separately.

In order to facilitate automated electronic calendar task management with automatic task scheduling, systems and methods are described including receiving and electronic meeting request to schedule a meeting. Work task data items identifying work tasks associated with the attendees are determined. A task estimation machine learning model predicts work parameters of meeting task objects based on the work task data items, and a work history data identifying work history of each attendee. A meeting scheduling machine learning model predicts parameters of unavailability period objects representing unavailability periods required to complete the work tasks based on the meeting task object, schedule information and location information. An indication of the at least one unavailability period is displayed on a screen of a computing device associated with each attendee. Selections of the unavailability period is received from the attendees, and the unavailability period is dynamically secured prior to the meeting.

An information processing apparatus includes a processor configured to: display a process reception screen in which plural processes included in a workflow are displayed in order of execution and that receives selection of at least one of the plural displayed processes; receive, for the at least one process received by the process reception screen, disclosure information indicating whether to disclose workflow-related information relating to the workflow; and set whether to disclose the workflow-related information in accordance with the received disclosure information.

A method of configuring a smart booklet to encourage adherence to a treatment and/or educational program is disclosed, wherein the smart booklet is “off the grid” (e.g., not connected to an electrical or networking grid) and includes an integrated behavior incentivization feature. Based on an activation of a detection feature of the smart booklet, it is detected whether a state of one or more pages of the smart booklet has changed. Based on the determination that the state of the one or more pages has changed, a set of data items pertaining to the adherence to the treatment and/or educational program is created or updated. Based on an identification that the set of data items is indicative of a failure or a success with respect to the adherence to the treatment program, a message pertaining to the failure or the success is communicated via an output component of the smart booklet (e.g., to encourage the adherence to the treatment and/or educational program).

A landing pad receives and stores packages delivered from an aerial vehicle and awaiting pickup from an aerial vehicle. The landing pad can be placed outside of a window and can contain a transmitter for sending out an identification signal via radio frequency to aid aerial vehicles in finding the landing pad. The landing pad contains a landing platform with a trapdoor that leads to a storage compartment. The trapdoor can be configured to only open when it receives a signal from an authorized aerial vehicle. The storage compartment can be accessed via a storage compartment door which can contain a locking mechanism. The storage compartment can be climate controlled. The landing pad can also have a transmitter that emits sounds to discourage animals from nesting on or near the landing pad. The landing pad can also include a solar power generator as a source of electrical energy.

Examples of the present disclosure describe systems and methods for predicting the reading time and/or reading complexity of a breast image. In aspects, a first set of data relating to the reading time of breast images may be collected from one or more data sources, such as image acquisition workstations, image review workstations, and healthcare professional profile data. The first set of data may be used to train a predictive model to predict/estimate an expected reading time and/or an expected reading complexity for various breast images. Subsequently, a second set of data comprising at least one breast image may be provided as input to the trained predictive model. The trained predictive model may output an estimated reading time and/or reading complexity for the breast image. The output of the trained predictive model may be used to prioritize mammographic studies or optimize the utilization of available time for radiologists.