A learning management system may be configured to retrieve roster data from a roster database and determine from the roster data whether a pilot has a scheduled downtime during a flight or a layover time before the flight. The system may further retrieve flight data associated with the flight from the learning management system and determine a training concept associated with the flight. The system may also select a training exercise from multiple training exercises, where the training exercise is associated with the training concept. A notification may be sent to an electronic device associated with the pilot, where the notification includes an offer to perform the training exercise.

Provided are a device and a method which enable easy analysis and evaluation of work efficiency of a worker without burdensome tasks and easy determination of a worker's skill level by comparing the worker's work efficiency status with that of another worker or a past record of the same worker, wherein analytical information is produced by estimating the worker's joint positions based on a video; acquiring time series data on joint positions; determining work efficiency based on the time series data; acquiring a target range (part of a work process with low work efficiency); output an image of the target range overlaid on a graph of the time series data; and output a posture image of the worker overlaid on the video. The analytical information may include information on a working activity to be analyzed and information on a chosen model working activity with high work efficiency.

Embodiments of the present disclosure provide methods and systems for providing management of customer gratitude feedbacks received in respect of a customer service catered by a first user to a second user. A plurality of user devices executes, via a server, a gratitude feedback management application that displays predefined categories of the customer gratitude feedbacks. The second user is prompted to select at least one predefined category of the customer gratitude feedbacks and send it to the first user, via the respective user devices. The received customer gratitude feedbacks are analyzed and subsequently, at least one award badge is assigned to the first user and the second user. The assigned award badge may be added to the respective user profile of the first user for future use in the career growth of the first user.

Artificial intelligence-based systems and methods for learning management are described.

Embodiments of the invention are directed to a computer-implemented system and method of performing quality assurance. The method may include receiving, by a processor, a quality plan comprising an expected number of evaluation tasks, wherein each evaluation task is distributed to an evaluator of a plurality of evaluators to be completed. For each evaluator, the processor computes an expected number of evaluation tasks to be completed per period, wherein the expected number of evaluation tasks completed per period is based on the expected number of evaluation tasks averaged over a set time period. The processor receives, for each evaluator, a number of actual evaluation tasks completed during the set time period and reassigns one or more evaluation tasks from the evaluator if the actual evaluation tasks completed during the set time period for said evaluator does not meet a predetermined threshold.

Described embodiments provide systems, methods, non-transitory computer-readable medium for generating a workspace score. The workspace score may be used to help a user make better use of applications on a remote workspace. The workspace score may be based on factors of user behavior. These factors may include such things as the immersion dimension, mature dimension, vendor preference dimension, discovery dimension, and others. These factors may monitor such things as the amount of features used in the remote workspace compared with the available features, the amount of applications used on a remote desktop workspace when compared with the amount of applications used on the local client device, and the amount of applications used by a preferred vendor, among others.

To provide an action evaluation system capable of more efficiently planning a new action based on an execution result of a test for limited action conditions. A condition change tracking unit determines, based on execution result information obtained by executing a test related to an action for an action target having a predetermined action condition, a condition change which is a change in the action condition before and after the test. A result verification unit calculates, based on the execution result information, an evaluation value obtained by evaluating an effect of the action. A reward distribution unit calculates a change contribution degree which is a degree of contribution of the condition change to the evaluation value.

A customer who is contacting customer care via a support session regarding a problem is classified into a customer category of multiple customer categories based at least on customer account information of the customer. A customer care topic in a predetermined set of multiple customer care topics that correspond to the problem is then identified via machine learning. A topic script that corresponds to the customer category of the customer for the customer care topic in the predetermined set of customer care topics is further retrieved or generated, in which the topic script includes one or more topic issues related to the customer care topics. The topic script is provided for presentation to a customer service representative (CSR) to prompt the CSR to discuss the one or more topic issues related to the customer care topic with the customer.

Techniques described herein relate to computer-implemented systems and methods for generating an insurance business plan. The method includes receiving an identification of an agent associated with the insurance business plan and a selection of a future time period for the insurance business plan. A business plan objective graphical user interface (GUI) is displayed, presenting for selection a plurality of insurance business plan objectives, wherein one of the objectives is to grow or maintain insurance policy counts for one or more insurance lines of business. The business plan objective GUI receives a selection of the objective to grow or maintain insurance policy counts for one or more lines of business. Based on a determination that the objective to grow or maintain insurance policy counts for one or more lines of business has been selected, a plurality of insurance lines of business is displayed. The business plan objective GUI receives a selection of an insurance line of business of the plurality of insurance lines of business. Insurance policy information for insurance policies assigned to the agent in the selected line of business is retrieved from an insurance policy database over a network. Additionally, displayed is a goal setting GUI for the selected line of business, the goal setting GUI being populated based on the retrieved insurance policy information and including a policy count chart showing a number of insurance policies assigned to the agent having a selected status, among a plurality of statuses, during a historical period of time through the present and showing a number of polices projected to have the selected status during the selected future time period of the business plan.

An apparatus, method and computer program is described comprising: receiving interaction information via at least one user device, wherein the interaction information is related to at least one user using at least one user device in relation to a first content creation task; receiving sensor data relating to the at least one user from one or more sensors; and determining data, using a first model, the data comprising content creation performance data and user state data, wherein: the content creation performance data indicates performance of the at least one user in relation to the first content creation task, based, at least in part, on the interaction information and a first content created when the at least one user performs the first content creation task; and the user state data is based, at least in part, on the received sensor data in relation to the first content creation task.