A non-transitory computer-readable recording medium stores a program for causing a computer to execute a process, the process includes identifying a working status of each of plural members based on a work volume of each of the plural members, determining a priority for each of the plural members as an assistance request destination based on the identified working status of each of the plural members, and displaying the plural members as candidates of the assistance request destination based on the determined priority for each of the plural members.

A communication terminal includes circuitry configured to: perform first transmission of connection request information indicating a connection request to a first explainer terminal that preferentially responds to the connection request from the communication terminal provided in an unmanned store; and receive response information indicating a response of connection availability from the first explainer terminal. The circuitry is further configured to perform second transmission of the connection request information to a second explainer terminal that does not preferentially respond to the connection request from the communication terminal provided in the unmanned store, when the circuitry receives the response information indicating that connection is unavailable from the first explainer terminal, or when the circuitry receives from the first explainer terminal no response information indicating whether the connection is available within a certain time period after the first transmission of the connection request information.

A device receives interviewer data, associated with interviewers conducting interviews with interviewees, that includes data identifying avatars presented to the interviewers. The device receives interviewee data, associated with the interviewees, that includes data identifying genders of the interviewees. The device processes the interviewer data and the interviewee data, with a model, to generate unbiased training data, and trains a machine learning model, with the unbiased training data, to generate a trained machine learning model. The device receives particular interviewer data identifying a particular role, location, and/or gender of a particular interviewer, and receives particular interviewee data identifying a gender of a particular interviewee. The device processes the particular interviewer data and the particular interviewee data, with the trained machine learning model, to determine one or more anonymized avatars to present to the particular interviewer, and performs one or more actions based on the one or more anonymized avatars.

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform: determining one or more work orders for a service provider; determining an optimal service route for the one or more work orders; updating an available time slot in a work schedule of the service provider; and transmitting the work schedule updated with the optimal service route to be displayed on a user interface executed on a device of the service provider. Other embodiments are also provided.

The present disclosure describes a talent-identification system that can be used by companies to assist in the recruitment process for new employees. Additionally, the system can be used by job seekers to determine ideal career fields and industries. The system employs an array of neuroscience-based tests to assess a user's career propensities, after which the system can provide career recommendations to the user or report on employment suitability of the user to a company.

An exemplary embodiment of the present application is a system and method for work allocation optimization. In the present disclosure, analytics are applied to work items while the work items are waiting in a work queue in order to optimize the routing and allocation of work items to agents in the most efficient manner possible, while optimizing the utilization of agents. By performing a look ahead at more than the initial work item, the system assesses the agent skills required by imminent work items in the work queue. This is then compared to a skillset of each available and/or soon to be available agent in order to achieve the optimal allocation of the work items to maximize utilization of agents. The work items are then routed to the agents accordingly.

The present disclosure relates to a system for a training method for enhancing a quality by design of a medicine that is capable of effectively training a trainee on the training method for enhancing the quality by design by the medicine.

The system for a training method for enhancing a quality by design (QbD) of a medicine according to one aspect of the present disclosure may include a server that generates content and data for training a trainee on the method for enhancing the quality by design of the medicine, and performs evaluation and certification based on data provided from the trainee and a user terminal that receives data from the server through a network to provide the content and the data to the trainee, and transmits information entered by the trainee to the server.

Embodiments include systems and methods for routing a request by a customer to an agent to handle the request. The method includes receiving a request from a customer, analyzing a digital footprint of the customer to determine one or more clout indicators of the customer, analyzing a digital footprint of a plurality of agents to determine one or more clout indicators for at least one of the plurality of agents, and routing the request to a selected one of the plurality of agents based at least in part on the one or more clout indicators of the customer and the one or more clout indicators for at least one of the plurality of agents.

A plan evaluation apparatus, which evaluates a schedule planned by combining a plurality of plans, includes: a feature conversion unit that divides the schedule into plan components based on a predetermined conversion rule, and convert the divided plan components into features; a model learning unit that uses the features as an input and creates a machine learning model having a key performance indicator (KPI) of the schedule as an objective variable; a contribution rate calculation unit that calculates a contribution rate of each of the features with respect to the machine learning model; and an influence degree calculation unit that calculates an influence degree of influence, on the KPI of the schedule, of the plan component which is a conversion source of the feature, based on the contribution rate of the feature.

A resource configuration and project management system identifies sandboxed task data and task parameters including project skill sets and project tools. An online community is provided of autonomous or semiautonomous artificial agents (digital workers), examples being chatbots for customer service, technical support, and advisory services. The digital workers are matched to projects based on skills and past performance metrics. Digital workers may be trained (using well-known supervised, unsupervised, or semi-supervised approaches) for specific tasks, such as parsing, analysis, filling, and/or characterization of particular types of digital document.