Provided are an index value calculator to calculate an index value obtained by quantifying a degree of status for evaluation items, a teamwork evaluator to evaluate a teamwork based on the index value, a support content determiner to select target persons to be supported including the members of the team based on the evaluation result of the teamwork and determine support contents in accordance with the target persons to be supported, and a presentation information generator to generate presentation information for presenting the determined support contents for each of the output devices corresponding to the selected target persons to be supported, and the teamwork evaluator evaluates the teamwork using an evaluation logic configured by combining logical formulas for comparing a threshold value set for evaluation items with the calculated index value.

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 a work sequence capable of suppressing reduction of an evaluation value within an allowable range. A work sequence generation apparatus including a processor that executes a program and a storage device that stores therein the program, and generating a work sequence specifying an order of working on a processing object group performs a perturbation process of generating a second work sequence by perturbating a first work sequence, and a learning process of generating a learning model for generating a specific work sequence so that a difference from an evaluation value regarding a work in an input work sequence is within an allowable range by learning that a difference between a first evaluation value regarding a work in the first work sequence and a second evaluation value regarding a work in a second work sequence generated in the perturbation process should be within the allowable range.

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 business plan generation device including a business plan definition unit configured to define a first business plan for a first business and a set of target business outcomes, a business information acquisition unit configured to acquire a set of business state information that includes at least a set of internal business information or a set of competitor business information, and a business plan generation unit configured to use a machine learning model to generate a set of output data that includes at least a set of revised business plans for achieving the set of target business outcomes, and output the set of output data.

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.

Machine data of an operating environment is conveyed by a network to a data intake and query system (DIQS) which reflects the machine data as timestamped entries of a field-searchable datastore. Monitoring functionality may search the machine data to identify notable event instances. A notable event processing system correlates the notable event instance to one or more triaging models which are executed against the notable event to produce a modeled result. Information of the received notable event and the modeled results are combined into an enhanced representation of a notable event instance. The enhanced representation conditions downstream processing to automatically perform or assist triaging of notable event instances to optimize application of computing resources to highest priority conditions in the operating environment.

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.

Devices, methods, and systems for airside performance analysis are described herein. One system includes a computing device to: receive key performance indicator (KPI) data from a number of different system monitors, correlate the KPI data to generate a number of graphical representations of the correlated KPI data, link the number of widgets to update the corresponding report based on a received selection, and display a number of widgets that generate a corresponding report of the correlated KPI data.

A method includes receiving uncompilable code from a candidate. The method further includes extracting features from the uncompilable code. The method further includes outputting, with a coding machine-learning model, compilable code based on the uncompilable code and the extracted features. The method further includes generating a coding score based on the uncompilable code and the compilable code. The method further includes receiving first media of one or more answers to questions provided by the candidate during an interview. The method further includes outputting, with a media machine-learning model, one or more corresponding ratings for the one or more answers. The method further includes generating a media score based on the one or more corresponding ratings. The method further includes generating a total score based on the coding score and the media score.