47 results about "Staffing" patented technology

A method, system, and article of manufacture for modifying a queue assignment for a contact center are provided. At least one computing device determines, by solving for constraints, at least one staffing change for at least one of the first queue or the second queue, using as inputs (a) first time-based service metrics, (b) second time-based service metrics, (c) data describing eligible staffing changes, and (d) at least one predetermined constraint. The at least one staffing change is a modification to the queue assignment of a subset of the computing device instances that is predicted to improve net staffing relating to service targets selected from (a) an average speed of answer (ASA) specifying an average time for one of the computing device instances to initiate handling of a particular task after the particular task has been assigned to the relevant queue and (b) a service level specifying a percentage of tasks for which handling is initiated within a specified time frame after the tasks are assigned to the queue. The at least one staffing change can be implemented by modifying the queue assignment of the subset of the computing device instances.

The invention discloses an intelligent medical management system based on artificial intelligence, and relates to the field of data processing, and the system comprises a sensing module which is used for sensing the position information of medical personnel in a hospital and an emergency transportation sickbed in real time; the labeling module is used for labeling available operating rooms on the in-hospital path three-dimensional topology in real time; by sensing the positions of medical workers and emergency transport sickbeds in real time, marking available operating rooms on a three-dimensional topology, intelligently deciding medical worker configuration and sickbed moving paths, visualizing the paths and directions through a public area display, and playing early warning audio to prompt evacuation, the first-aid efficiency is effectively improved, precious time is won for patients, and the medical workers and the emergency transport sickbeds are protected. And the first-aid channel is ensured to be smooth, so that the in-hospital multi-source resource scheduling arrangement of the medical first-aid scene is more scientific, reasonable and orderly.

A system for generating alternative shifts for contact center agent scheduling according to an embodiment includes a system that receives a staffing requirement forecast indicative of a number of agents required to handle a workload forecast. Agent data is received for a plurality of agents. The system receives a service level override value and performs column generation to identify shifts for the agents based on the staffing requirement forecast, agent working rules, and work plan constraints. A subset of the shifts is selected to generate an optimized agent shift schedule. A request to replace an individual shift of the optimized shift schedule with an alternative shift is received. In response, the system identifies the alternative shift from the plurality of shifts based on the service level override value. The individual shift is replaced with the identified alternative shift on the optimized shift schedule.

A system is provided.SOLUTION: A generative AI model means for analyzing the optimal matching between the crew and the shops by using the integrated database, a means for generating an optimal manpower allocation scheme based on the analysis result, a means for notifying the staff of the manpower allocation scheme based on the characteristics of the crew and the characteristics of the shops, and a means for collecting feedback information on the manpower allocation scheme; Means for updating the generative AI model to reflect the collected feedback in a next placement proposal.SELECTED DRAWING: Figure 1

The invention provides a post recommendation method and device, a storage medium and a product, and relates to the technical field of software, and the method comprises the steps: receiving a post recommendation request message sent by a client; acquiring employee data of the to-be-recommended employees from a database based on the employee identifiers; determining an adaptation index of at least one candidate post based on the employee data and the post demand data; generating matching scores of the candidate posts based on the adaptation indexes of the candidate posts; determining a target post from the at least one candidate post based on the matching score of each candidate post; and sending a post recommendation response message to the client. According to the method, on the premise of not additionally increasing the data acquisition cost and being compatible with the existing human resource system architecture of the enterprise, efficient adaptive recommendation of flexible employment posts and employees in the enterprise is realized, and the recommendation accuracy and employment configuration efficiency are remarkably improved.

A method for incentivizing contact center agents with scheduling perks that includes: receiving a workload forecast and staffing plan for a current shift; receiving a service level target; receiving types of scheduling perks; performing a selection routine for determining a select scheduling perk for offering to a select agent; and sending a scheduling perk offer via electronic communication to the select agent. The selection routine may include: selecting a proposed scheduling perks and a proposed agent; modifying the staffing plan to create a proposed staffing plan that reflects the proposed scheduling perk; predicting a proposed forecasted target adherence using the modified staffing plan; determining whether the proposed forecasted target adherence satisfies a threshold defined by an acceptable forecasted target adherence and, if so, deeming the proposed scheduling perk as select scheduling perk and proposed agent as select agent.

A system is provided.SOLUTION: A system comprising: means for collecting information on skills and career vision of employees; database means for storing and analyzing the information; generation and AI means for proposing optimum staffing based on the analyzed result; means for periodically performing condition check of employees and analyzing the result; and means for providing feedback based on the result of the condition check.SELECTED DRAWING: Figure 1

We provide the system. [Solution] A data analysis means for collecting and preprocessing surveys, emails, chats, and meeting records, A personality trait evaluation means that uses a generative AI model to perform a process that objectively evaluates the user's personality traits and skills, A personnel placement recommendation tool that calculates the optimal team structure and staffing based on evaluation results and creates a specific placement plan, A feedback analysis tool that analyzes user-provided feedback to evaluate actual performance and team harmony, A database update mechanism that updates personality profiles and skill maps based on feedback and newly collected data, A system that includes this.

A computerized-method for determining critical-time-slots and scheduling focus-activity therefore during WFM scheduling. The computerized-method includes while generating a schedule: a) for each time-slot: (i) retrieving forecast-data of forecasted interaction volumes of traditional-interactions and forecasted active-counts for digital-interactions; (ii) retrieving historic-data of adherence and calculating predicted out-of-adherence percentage for each agent; (iii) determining the time-slot as one of: traditional-interactions-time; and digital-interactions-time, based on the forecasted interaction volumes of traditional-interactions and the forecasted active-counts for digital-interactions; b) for each agent, for each time-slot that has been determined as traditional-interactions-time determining critical-traditional-interactions-time and for each time-slot that has been determined as digital-interactions-time, determining critical-digital-interactions-time based on the retrieved forecasted-data, AHT and maximum staffing; and c) for each agent having in-adherence above adherence-threshold, scheduling focus-activity for digital skills as the activity-type during time-slots that were determined as critical-digital-interactions-time, and focus-activity for traditional skills as the activity-type during the time-slots that were determined as critical-traditional-interactions-time.

The system according to the embodiment aims to automate the process of evaluating the validity of estimates and enable flexible staffing. [Solution] A system according to an embodiment includes a reception unit, a collection unit, an evaluation unit, and a provision unit. The reception unit receives input of quotation data. The collection unit analyzes the quotation data received by the reception unit and collects data on similar past cases or market prices. The evaluation unit evaluates the validity of the quotation based on the data collected by the collection unit. The provision unit provides the evaluation results obtained by the evaluation unit.

A system and method of automated case costing for surgical procedures can include the operations of tracking a real time location of a plurality of staff within a surgery center using wearable tracking devices that monitor the real time location of staff members among the plurality of staff within the surgery center to provide tracked time information, and allocating staffing costs based on actual time spent in an operating room and in other areas of the surgery center including one or more among a waiting room, a Pre-Op area, a recovery area, and a post-recovery area using one or more processors. Such a system can use and integrate with human resources clocking and surgery scheduling software modules.

The system according to the embodiment aims to predict future demand and propose appropriate staffing arrangements. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a proposal unit. The collection unit collects past event data and shift data. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts future demand based on the analysis results obtained by the analysis unit. The proposal unit proposes staffing arrangements based on the demand predicted by the prediction unit.

A system and methods for optimizing labor scheduling in retail environments by incorporating shrink factors alongside traditional considerations are provided. A machine learning model (MLM) receives traffic data, hourly labor data, overall shrink data, and proven shrink incidents as inputs. The MLM learns the labor-shrink causality across stores, connecting staffing levels to shrink impact. The MLM outputs a recommended labor scheduling plan for both assisted and self-checkout lanes, optimized for overall store margins rather than just labor costs. This approach balances labor efficiency with shrink prevention, potentially improving store profitability. In an embodiment, an application programming interface (API) is provided for consuming recommendations from the MLM as a service to integrate insights into business intelligence dashboards, providing a comprehensive solution for retail labor management.

This invention provides a method and system for evaluating organizational structure and staffing based on a large language model. The method includes: collecting industry standard data and evaluation reference data related to organizational structure and staffing; constructing scoring rules for multiple preset dimensions based on the industry standard data and evaluation reference data; obtaining a level judgment threshold for each preset dimension; constructing an evaluation standard knowledge base based on the scoring rules and the level judgment thresholds; collecting data from multiple set data sources to obtain multi-source data of the target organization; using a large language model to perform semantic extraction on the multi-source data to obtain the organizational entities within the target organization and the relationships between them, as well as the operational feedback data of the target organization; and using the evaluation standard database to evaluate the operational feedback data to obtain the evaluation result of the organizational structure and staffing system of the target organization. The technical solution of this invention can improve the accuracy and efficiency of organizational structure and staffing evaluation.

The application discloses a personnel configuration information security storage method and system based on a blockchain, relates to the technical field of blockchain storage, and comprises the following steps: constructing a personnel configuration graph, using an encrypted public key of an employee, and storing the personnel configuration graph into a personnel configuration blockchain; dividing the employee into K employee clustering clusters; setting a minimum data access permission set for each employee clustering cluster according to a historical access data subset of the employee contained in the employee clustering cluster; searching for an employee clustering cluster in which an inquiring employee is located as a target clustering cluster; receiving a knowledge graph query statement input by the inquiring employee; obtaining corresponding query data from the personnel configuration blockchain; and using a decryption private key corresponding to the inquiring employee to decrypt the query data.

The application discloses a kind of medical skill examination intelligent reservation and resource scheduling system based on rule engine, it is related to medical information technology field, including data acquisition module, rule engine module, intelligent reservation module, the dynamic analysis and matching of medical business rules are realized by introducing rule engine module, and resource scheduling strategy and reservation priority sequence are generated;Intelligent reservation module provides personalized recommendation using reservation priority sequence and rehearses detection conflict, realizes zero conflict reservation;Resource scheduling module optimizes equipment, clinic and personnel configuration according to strategy in real time, improves resource utilization;Process control module carries out closed loop tracking and synchronization to reservation and scheduling result.Each module cooperates and operates, it is jointly relieved that traditional medical skill examination is uneven, reservation process rigid, multiple system coordination difficult and the problem that rule adaptation is not flexible, finally reach shorten patient waiting time, reduce hospital operation cost, improve the effect of overall efficiency and intelligent level of medical service.

An object of a system according to an embodiment is to appropriately grasp desires and needs of employees and propose an optimal career plan and an appropriate arrangement of materials.SOLUTION: A system includes a collection unit, an analysis unit, an evaluation unit, and a proposal unit. The collection unit collects requests and needs of employees. The analysis unit analyzes the information collected by the collection unit and proposes a career plan. The evaluation part analyzes the resume of the applicant and evaluates the aptitude and skill. The proposal unit proposes an appropriate material arrangement based on the information obtained by the evaluation unit.SELECTED DRAWING: Figure 1

The invention provides a computer-implemented method and system for developing optimized work schedules in workplace environments. Employees are classified into performance categories based on metrics such as sales figures, customer feedback, task completion rates, and adherence to schedules, using an unbiased AI system that excludes factors like gender, race, and age. Availability data, including shift preferences and real-time location, is integrated with anticipated sales targets to determine an optimal staffing mix. Work schedules are generated in compliance with labor laws and workplace policies, ensuring operational efficiency while preventing overtime. Employees can access real-time rankings and performance evaluations, fostering transparency and accountability. The system also integrates with payroll to ensure fair task allocation and evaluates anomalies in sales and profitability, updating policies and schedules to enhance outcomes.

The invention discloses a staff and quota optimization system and method based on power grid enterprise operation performance analysis, and belongs to the technical field of power enterprise management. Comprising a data acquisition module used for acquiring instantaneous state data of each factor; the index calculation module is used for calculating a real-time business fluctuation index; the mode judgment module is used for dynamically judging whether a current operation mode should be in a standard operation mode or an elastic regulation and control mode according to a comparison result; the interval calculation module is used for calculating and outputting an elastic fixed personnel interval; and the parameter correction module is used for carrying out posteriori correction on weight parameters of various impact factors for calculating the real-time business fluctuation index. By constructing the multi-source business fluctuation factor set, calculating the real-time business fluctuation index and outputting the elastic fixed-staff interval, the effect of accurate matching of human resource configuration and dynamic business requirements is achieved, and the problem of insufficient business fluctuation adaptability caused by a static prediction model in the prior art is solved.

A system is provided.SOLUTION: This system is provided with a means for acquiring the ability and desire of an employee by using interactive artificial intelligence, a means for proposing personnel arrangement corresponding to the ability and desire of the employee based on the acquired data, a means for evaluating the effect of the proposed personnel arrangement, and a means for continuously improving the precision of the personnel arrangement based on the evaluated result.SELECTED DRAWING: Figure 1

A method for synchronizing agent staffing and customer call volume in a contact center estimates the number of agents to staff the contact center during a future time period and uses historical data to estimate the number of customer calls expected during the future time period. The method performs a first comparison of the number of agents to the expected customer call volume for the future time period and then determines at least one preferred contact center callback window. The method communicates with the customer to request at least one preferred customer-selected callback time and performs a second comparison to provide the customer with a suggested callback time selected to synchronize the customer call volume with the number of agents, the preferred contact center callback window, and the preferred customer-selected callback time, thereby improving the customer experience and contact center efficiency.

A method and apparatus for integrating multiple disjunct processes into a cohesive workflow can involve receiving proposal development inputs through a system linked to a back-end processor and user interface, and collecting with the proposal development, data for staffing and cost estimation, customizable per case. The data can be processed by a baseline development component to confirm realistic paths, establish target dates, and maintain cost and schedule metrics within tolerances. The system can update tasks, displays critical paths, and monitor performance metrics while integrating with a portfolio workforce planning component, staffing data across proposal, baseline development, and program execution components. This integration of staffing data can allow for strategic decision making and / or program prioritization.

A computerized-method for distributing workload of a working-shift of a source-location to working-shifts in target-locations, via a WFM-application in a multiple-locations contact center. The computerized-method comprising: (i) receiving a rebalancing-request and workload-information of the working-shift of the source-location; (ii) parsing the workload-information to extract affected-SUs, target-SUs and critical-skills; (iii) for each parallel time-interval in a parallel working-shift of each target-location and for each critical-skill: retrieving staffing-plans of the target-SUs, and marking the parallel time-interval as overstaffed for the critical-skill based on a net-staffing calculation; (iv) operating agents-distribution for each parallel time-interval and for each critical-skill based on the parallel time-intervals marked as overstaffed; and (v) configuring WFM-application to: update staffing-plans of parallel working-shift of each target-location, based on the operated agents-distribution; generate new-schedules for agents in the target-SUs based on the updated staffing plans of parallel working-shift of each target-location; and remove existing schedules of the affected-SUs of parallel working-shift.

Disclosed is a platform that manages worker users in a temporary staffing environment via an artificial machine learning model. The temporary staffing platform matches available workers to available shifts / gigs. Additional features include generating provisional or near-miss matches and informing workers how to turn those near-misses into full matches, plotting a gig-career path to develop additional skills, gamify development, and automatically generate resumes. The platform generates a set of skill tags associated with each shift / gig performed by the user. Designing of resume text files by the artificial machine learning model includes procedurally generated descriptions of experience the user has based on the recording of each shift / gig performed by the user and the skill tags associated with each recorded shift / gig, wherein a format of the resume text file is formulated by the artificial machine learning model evaluating a mix of skill tags and employers amassed by the user.

A method for automatedly determining hours of operation (HOO) is provided. A system for work allocation is also provided. A method for generating staffing forecasts is also provided. The method for automatedly determining hours of operation includes performing historical data analysis on historical data comprising past data for a plurality of skills, queue reports for the plurality of skills, or a combination thereof; identifying trends in skills demand for each of the plurality of skills based on the historical data analysis; performing analysis on forecasted reports for skills allocation to generate future demand patterns for each of the plurality of skills; performing HOO analysis for each of the plurality of skills based on the generated future demand patterns and identified skills demand trends; generating a report of the HOO analysis for the plurality of skills; and providing the analysis report to a user.

The present application belongs to the technical field of safety responsibility examination, and particularly relates to a safety responsibility examination system. The system comprises a multi-terminal cooperative examination function module, which comprises a WEB terminal function module. The WEB terminal function module comprises a configuration management module, four list modules, a full staff examination module and a responsibility book management module. The configuration management module is used for displaying scoring rules, personal examination data, examination type configuration, post personnel configuration and business relationship configuration. The four list modules are used for managing the responsibilities of main posts. The full staff examination module is used for displaying personal examination list information of individuals and staff under their management. The responsibility book management module is used for automatically generating an annual full staff responsibility list according to post responsibility lists and post indexes.

A system is provided.SOLUTION: The system includes a means for collecting data on job history, results, strengths / weaknesses, characteristics, desires, and human relationships of employees, a means for preprocessing and analyzing the collected data, a means for learning a generation AI model for predicting appropriateness and allocation of employees based on an analysis result, a means for generating an optimal personnel allocation plan using the learned generation AI model, a means for suggesting appropriate project members, and a means for monitoring an organization result after allocation and improving accuracy of the generation AI model.SELECTED DRAWING: Figure 1

Methods and a system for determining understaffed periods in retail stores leverages self-checkout (SCO) usage patterns and machine learning. The methods and system select stores with medium to high SCO adoption rates, identifies “busy” hours based on SCO and point-of-sale (POS) idle times, and trains a classification machine learning model using various transactional features. This model then predicts understaffed hours for all stores, including those without SCOs. The methods and system provide real-time alerts and reports, enabling retailers to make informed staffing decisions, improve customer satisfaction, and optimize labor costs. By analyzing existing transactional data, the methods and system offer a novel approach to staffing management in modern retail environments.

Earnings visibility systems and methods, and non-transitory computer readable media, include receiving a request for extra time or time off, wherein the extra time or time off corresponds to a first time interval; displaying the first time interval via a graphical user interface (GUI); receiving a net staffing value for the first time interval; receiving a payroll rule that specifies a percentage increase or decrease in pay based on the net staffing value for the first time interval; assigning a color to a particular percentage increase or decrease in pay for the first time interval; determining the percentage increase or decrease in pay for the first time interval based on the net staffing value for the first time interval; and coloring the first time interval on the GUI with the assigned color based on the percentage increase or decrease in pay for the first time interval.

Apparatuses, systems, and methods described herein include receiving by a quantum hybrid branch simulation engine, a request for a next task by a branch personnel or worker. The branch simulation engine may receive data representing a simulated branch environment and apply a deep reinforcement learning (DRL) staffing operations model to predict a next action or task. The model may weigh impacts of a reward to substantially maximize the reward in light of a policy to determine the next action and update a database to store / represent a next state of the simulated or real-life branch environment after the next action is performed.