system

Abstract

The system according to this embodiment aims to achieve efficient shift management and optimization of personnel allocation. [Solution] The system according to the embodiment comprises a visualization unit, an adjustment unit, and a suggestion unit. The visualization unit visualizes the skills and work status of employees. The adjustment unit automatically adjusts shifts based on the information visualized by the visualization unit, taking into account weather and events. The suggestion unit suggests part-time jobs based on the shifts adjusted by the adjustment unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, efficient shift management is difficult, and there is room for improvement in optimizing personnel allocation.

[0005] The system according to the embodiment aims to perform efficient shift management and optimize personnel allocation.

Means for Solving the Problems

[0006] The system according to the embodiment includes a visualization unit, an adjustment unit, and a proposal unit. The visualization unit visualizes the skills and working conditions of employees. The adjustment unit automatically adjusts the shift in consideration of the weather and events based on the information visualized by the visualization unit. The proposal unit proposes part-time jobs based on the shift adjusted by the adjustment unit.

Effects of the Invention

[0007] The system according to this embodiment can perform efficient shift management and optimize staffing. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The shift management system according to an embodiment of the present invention is a system that achieves efficient shift management using AI. This shift management system enables efficient operation by visualizing employee skills and work status, automatically adjusting shifts considering external factors such as weather and events, and suggesting part-time work during downtime. The shift management system visualizes employee skills and work status, allowing companies to accurately understand the skills and abilities of their employees. Next, the shift management system automatically adjusts shifts considering external factors such as weather and events to create an optimal shift schedule. Furthermore, the shift management system suggests part-time work during downtime, enabling flexible placement. This ensures that employees are placed in the right place at the right time, improving productivity. For example, the shift management system collects data such as employee skills and abilities and past work history, and the AI ​​analyzes this data. If employee A possesses a specific skill, an appropriate shift can be suggested based on that skill. This allows companies to accurately understand employee skills and abilities and improve operational efficiency. Next, the shift management system automatically adjusts shifts considering external factors such as weather and events. For example, on days with bad weather, fewer customers are expected, so the number of shifts can be reduced. Furthermore, on days when large-scale events are held, an increase in customers is expected, allowing for increased shifts. This enables efficient staffing and improves customer satisfaction. In addition, the shift management system suggests part-time workers to enable flexible staffing. Part-time workers are a method of solving sudden labor shortages by utilizing employees who can complete tasks in a short amount of time. For example, if a sudden absence occurs, the shift management system can suggest part-time workers and place them in the right place at the right time. This improves productivity and reduces employee stress. Through this system, the shift management system automatically adjusts the optimal shifts and suggests part-time workers, optimizing staffing and enabling efficient operations. For example, managers can efficiently perform tasks such as sales, inventory management, employee scheduling, and customer service.Furthermore, HR personnel can efficiently handle HR tasks such as employee recruitment, hiring, training, evaluation, compensation, and benefits. This allows the shift management system to visualize employee skills and workload, automatically adjust shifts considering weather and events, and suggest part-time work options, enabling efficient shift management.

[0029] The shift management system according to this embodiment comprises a visualization unit, an adjustment unit, and a proposal unit. The visualization unit visualizes the skills and work status of employees. The visualization unit collects data such as employees' skills and abilities and past work history, and AI analyzes this data. For example, if employee A has a specific skill, the system can propose an appropriate shift based on that skill. This allows companies to accurately understand the skills and abilities of their employees and improve operational efficiency. The adjustment unit automatically adjusts shifts based on the information visualized by the visualization unit, taking into account weather and events. For example, the adjustment unit can reduce shifts on days with bad weather, when fewer customers are expected. Conversely, it can increase shifts on days when large-scale events are held, when more customers are expected. This enables efficient staff allocation and improves customer satisfaction. The proposal unit proposes part-time jobs based on the shifts adjusted by the adjustment unit. For example, if a sudden absence occurs, the proposal unit can propose part-time jobs and assign them to the right place at the right time. This improves productivity and reduces employee stress. As a result, the shift management system according to this embodiment enables efficient shift management by visualizing employee skills and work status, automatically adjusting shifts considering weather and events, and suggesting part-time jobs that fit into gaps in the work schedule.

[0030] The visualization unit visualizes employee skills and work performance. Specifically, it collects data such as employee skills, abilities, and past work history, and AI analyzes this data. For example, if employee A possesses a specific skill, the system can suggest appropriate shifts based on that skill. The AI ​​analyzes employee skill sets in detail and evaluates which tasks each employee is best suited for. Furthermore, it understands employee performance and attendance trends based on past work history and makes optimal shift assignments. This allows companies to accurately understand employee skills and abilities and improve operational efficiency. The visualization unit displays this information in a graphical interface, making it intuitive for managers to understand. For example, it can display employee skill matrices and work performance heatmaps on a dashboard, allowing managers to see at a glance which employees are most effective at which times of day. The visualization unit also helps in planning employee skill development and career paths. When an employee acquires a new skill, that information can be immediately updated and reflected in the next shift adjustment. This can increase employee motivation and support long-term growth. In addition, the visualization unit can collect employee feedback and continuously improve the accuracy of the system. For example, employees can provide feedback on their skill evaluations, and this feedback is used to improve the AI's analysis algorithms. This allows the visualization unit to always provide the latest and most accurate information, improving the efficiency and precision of shift management.

[0031] The adjustment unit automatically adjusts shifts based on information visualized by the visualization unit, taking into account weather and events. Specifically, on days with bad weather, fewer customers are expected, so shifts can be reduced. Conversely, on days when large-scale events are held, more customers are expected, so shifts can be increased. The adjustment unit uses AI to analyze these factors in real time and calculate the optimal shift allocation. For example, it takes in weather forecast data and adjusts the number of staff on rainy or snowy days to decrease, and increases on sunny days. It also refers to local event calendars and adjusts shifts according to the expected number of customers on specific days. This enables efficient staff allocation and improves customer satisfaction. Furthermore, the adjustment unit can build predictive models based on past data to forecast future shift demands. For example, it analyzes past sales data and customer count data to understand trends in customer numbers on specific days of the week and time slots. This allows for shift adjustments in advance to avoid congestion during peak hours. The adjustment unit can also flexibly adjust shifts, taking into account employees' preferred shifts and vacation requests. When employees input their desired working hours and vacation schedules into the system, the scheduling unit automatically generates the optimal shifts based on that information. This allows for efficient shift management while respecting employees' work-life balance.

[0032] The Proposal Department proposes part-time jobs based on shifts adjusted by the Coordination Department. Specifically, in the event of a sudden absence, part-time jobs can be proposed and assigned to the right place at the right time. The Proposal Department uses AI to quickly identify part-time job candidates and propose the most suitable personnel. For example, it lists employees who can immediately respond to sudden absences based on their past work history and skill sets. In addition, part-time job candidates are automatically notified by the system, enabling a quick response. This improves productivity and reduces employee stress. Furthermore, the Proposal Department collects employee feedback to continuously improve the system's algorithms in order to improve the accuracy of part-time job matching. For example, based on feedback from employees who have participated in part-time jobs, the accuracy and suitability of the proposals can be evaluated and reflected in future proposals. The Proposal Department can also flexibly set compensation and working conditions for part-time jobs to increase employee motivation. For example, by setting higher compensation than regular work for part-time jobs to cover sudden absences, a quick response can be encouraged. In this way, the Proposal Department can achieve efficient and flexible shift management, improving overall company productivity and employee satisfaction.

[0033] The data collection unit collects data on employees' skills and work status. For example, the data collection unit can collect data such as employees' skills and abilities, and their past work history. For example, the data collection unit can collect employees' past work history and skill test results to evaluate their skills and abilities. This allows the data collection unit to collect employee skill and work status data, enabling more accurate shift management. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input employee skill and work status data into an AI, which can then analyze and collect the data.

[0034] The adjustment unit can automatically adjust shifts by taking into account external factors such as weather and events. For example, on days with bad weather, the adjustment unit can reduce the number of shifts because fewer customers are expected. Conversely, on days when large-scale events are held, the adjustment unit can increase the number of shifts because more customers are expected. The adjustment unit can reduce the number of shifts because fewer customers are expected on days with bad weather. Conversely, on days when large-scale events are held, the adjustment unit can increase the number of shifts because more customers are expected. In this way, the adjustment unit enables efficient shift management by automatically adjusting shifts by taking into account external factors such as weather and events. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input external factors such as weather and events into the AI, and the AI ​​can automatically adjust the shifts.

[0035] The proposal department can propose part-time jobs. For example, if a sudden absence occurs, the proposal department can propose part-time jobs and assign them to the right place at the right time. The proposal department can propose part-time jobs and assign them to the right place at the right time. The proposal department can propose part-time jobs and assign them to the right place at the right time. In this way, the proposal department can respond to sudden labor shortages by proposing part-time jobs. Some or all of the above processing in the proposal department may be performed using AI or not. For example, if a sudden absence occurs, the proposal department can input part-time jobs into the AI, and the AI ​​can propose part-time jobs.

[0036] The data collection unit can collect data such as employees' skills and abilities and past work history. For example, the data collection unit can collect employees' past work history and skill test results in order to evaluate employees' skills and abilities. The data collection unit can collect employees' past work history and skill test results in order to evaluate employees' skills and abilities. For example, the data collection unit can collect employees' past work history and skill test results in order to evaluate employees' skills and abilities. This allows the data collection unit to perform more accurate shift management by collecting data such as employees' skills and abilities and past work history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data such as employees' skills and abilities and past work history into AI, and the AI ​​can analyze and collect the data.

[0037] The adjustment unit may include a provisioning unit that provides the results of the shift adjustments. The adjustment unit may include a provisioning unit for, for example, providing the results of the shift adjustments. The adjustment unit may include a provisioning unit for, for example, providing the results of the shift adjustments. This makes it easier for employees to check shift changes by providing the shift adjustment results. Some or all of the above processing in the provisioning unit may be performed using AI or not. For example, the provisioning unit can input the shift adjustment results into the AI, and the AI ​​can provide the shift adjustment results.

[0038] The visualization unit can analyze an employee's past performance data and visualize their skill growth trends. For example, the visualization unit can display a skill growth curve based on an employee's past evaluation data. The visualization unit can also analyze an employee's past project participation history and create a graph showing skill improvement. The visualization unit can also visualize the employee's skill acquisition status based on their training history. This makes it easier to understand an employee's growth by visualizing their skill growth trends. Some or all of the above-described processes in the visualization unit may be performed using a generative AI, or they may not. For example, the visualization unit can input an employee's past performance data into a generative AI, which can then analyze the data and visualize the skill growth trends.

[0039] The visualization unit can update employee skills and work status in real time, providing the latest information. For example, the visualization unit updates skill information in real time when an employee acquires a new skill. For example, the visualization unit can immediately reflect changes in an employee's work status in the system. For example, the visualization unit can also update work status in real time when an employee's shift changes. In this way, the visualization unit can provide the latest information by updating employee skills and work status in real time. Some or all of the above processing in the visualization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the visualization unit can input employee skill and work status data into a generation AI, and the generation AI can update the data in real time.

[0040] The visualization unit can display employee skills and workload while considering the overall team balance. For example, the visualization unit can display a team-wide skill map and compare the skill levels of each member. The visualization unit can also display the overall team workload and identify overloaded members. For example, the visualization unit can display the overall team skills and workload in a graph and suggest a balanced allocation. In this way, the visualization unit enables efficient team management by displaying information while considering the overall team balance. Some or all of the above processing in the visualization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the visualization unit can input team-wide skill and workload data into a generation AI, which can analyze the data and display it while considering balance.

[0041] The visualization unit can visualize an employee's skills and work status by comparing them with their past work history. For example, the visualization unit can compare and display the employee's current skill level based on their past work history. The visualization unit can also compare and display the employee's current work status based on their past work performance. For example, the visualization unit can compare and display an employee's past work history and current skill level in a graph. This makes it easier to understand an employee's growth by comparing them with their past work history. Some or all of the above processing in the visualization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the visualization unit can input data on an employee's past work history and current skill level into a generation AI, which can then compare and display the data.

[0042] The adjustment unit can make adjustments to shifts while taking into account the health status and fatigue level of employees. For example, based on employee health data, the adjustment unit can reduce shifts if an employee's health is poor. For example, the adjustment unit can monitor employee fatigue levels and increase breaks if fatigue is accumulating. For example, the adjustment unit can evenly distribute the shift burden while taking into account the health status and fatigue level of employees. In this way, the adjustment unit can maintain employee health by making adjustments while taking into account the health status and fatigue level of employees. Some or all of the above processing in the adjustment unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the adjustment unit can input employee health status and fatigue level data into a generation AI, and the generation AI can analyze the data and make shift adjustments.

[0043] The adjustment unit can apply different adjustment algorithms to employees' skill levels when adjusting shifts. For example, the adjustment unit can assign important tasks to highly skilled employees. For example, the adjustment unit can assign tasks to improve the skills of medium-skilled employees. For example, the adjustment unit can assign tasks requiring support to low-skilled employees. This allows the adjustment unit to make appropriate shift adjustments by applying different adjustment algorithms according to employees' skill levels. Some or all of the above processing in the adjustment unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the adjustment unit can input employee skill level data into a generative AI, which can then analyze the data and apply an adjustment algorithm.

[0044] The adjustment unit can propose the optimal shifts when adjusting shifts, taking into account employees' commuting times. For example, the adjustment unit may prioritize assigning employees to nearby workplaces to reduce their commuting time. The adjustment unit may also propose shifts that avoid rush hour, taking into account employees' commuting times. The adjustment unit may also propose shifts that minimize commuting burden, based on employees' commuting times. In this way, the adjustment unit can reduce the commuting burden by proposing the optimal shifts, taking employees' commuting times into consideration. Some or all of the above processing in the adjustment unit may be performed using a generation AI, or not. For example, the adjustment unit can input employee commuting time data into a generation AI, which can then analyze the data and propose the optimal shifts.

[0045] The adjustment unit can make adjustments to shifts while taking into account employees' family circumstances and personal schedules. For example, the adjustment unit can propose shifts that reduce the burden on employees' families by taking into account their family circumstances. The adjustment unit can also propose shifts that fit employees' personal schedules by taking into account their personal schedules. The adjustment unit can also make flexible shift adjustments based on employees' family circumstances and personal schedules. In this way, the adjustment unit can reduce the burden on employees by making adjustments while taking into account their family circumstances and personal schedules. Some or all of the above processing in the adjustment unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the adjustment unit can input data on employees' family circumstances and personal schedules into a generation AI, and the generation AI can analyze the data and make shift adjustments.

[0046] The suggestion department can make suggestions for part-time jobs by considering the employee's past part-time job history. For example, the suggestion department can suggest similar part-time jobs based on the employee's past successes. The suggestion department can also analyze the employee's past part-time job history and suggest suitable part-time jobs. For example, the suggestion department can suggest part-time jobs that will lead to skill improvement based on the employee's past part-time job history. In this way, the suggestion department can suggest appropriate part-time jobs by considering the employee's past part-time job history. Some or all of the above processes in the suggestion department may be performed using a generation AI, or they may not be performed using a generation AI. For example, the suggestion department can input data on the employee's past part-time job history into a generation AI, which can then analyze the data and make suggestions.

[0047] The proposal department can suggest the most suitable part-time jobs based on the employee's skill set when proposing part-time work. For example, the proposal department can suggest part-time jobs that utilize the employee's skills based on their skill set. For example, the proposal department can analyze the employee's skill set and suggest part-time jobs that will lead to skill improvement. For example, the proposal department can consider the employee's skill set and suggest suitable part-time jobs. In this way, the proposal department can maximize the use of employees' skills by suggesting the most suitable part-time jobs based on their skill set. Some or all of the above processes in the proposal department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the proposal department can input employee skill set data into a generative AI, and the generative AI can analyze the data and suggest the most suitable part-time jobs.

[0048] The suggestion department can propose the most suitable part-time jobs when suggesting part-time work, taking into account the employee's geographical location. For example, the suggestion department can prioritize suggesting part-time jobs close to the employee's current location. The suggestion department can also propose part-time jobs with a low commute burden, taking into account the employee's commute time. The suggestion department can also propose part-time jobs with good access based on the employee's geographical location. In this way, the suggestion department can reduce the commute burden by suggesting the most suitable part-time jobs, taking into account the employee's geographical location. Some or all of the above processing in the suggestion department may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion department can input the employee's geographical location data into a generation AI, which can then analyze the data and propose the most suitable part-time jobs.

[0049] The suggestion department can analyze an employee's social media activity and suggest relevant part-time jobs when proposing part-time work. For example, the suggestion department can suggest relevant part-time jobs based on the employee's interests on social media. The suggestion department can also analyze an employee's social media activity history and suggest suitable part-time jobs. The suggestion department can also consider the number of followers an employee has on social media and suggest influential part-time jobs. In this way, the suggestion department can suggest part-time jobs that match the employee's interests by analyzing their social media activity and suggesting relevant part-time jobs. Some or all of the above processing in the suggestion department may be performed using or without a generative AI. For example, the suggestion department can input data on an employee's social media activity into a generative AI, which can then analyze the data and suggest relevant part-time jobs.

[0050] The data collection unit can collect data including past employee performance data during data collection. For example, the data collection unit can collect past employee evaluation data and compare it to current performance. For example, the data collection unit can collect an employee's past project participation history and analyze skill improvement. For example, the data collection unit can collect an employee's past training history and evaluate their skill acquisition status. This makes it easier for the data collection unit to understand employee growth by including past employee performance data in the collection. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input past employee performance data into a generative AI, which can then analyze and collect the data.

[0051] The data collection unit can collect relevant data while considering the geographical location information of employees. For example, the data collection unit can collect relevant data based on the employee's current location. The data collection unit can also prioritize data that can be collected during the employee's commute, for example, by considering the employee's commute route. The data collection unit can also improve operational efficiency by collecting data related to the employee's workplace, for example. In this way, the data collection unit improves operational efficiency by collecting relevant data while considering the geographical location information of employees. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input employee geographical location data into a generative AI, and the generative AI can analyze the data and collect relevant data.

[0052] The service provider can select the optimal display method by referring to the employee's past shift history when providing shift adjustment results. For example, the service provider can compare and display the current shift adjustment results based on the employee's past shift history. The service provider can also, for example, analyze the employee's past shift history and propose the optimal display method. The service provider can also, for example, refer to the employee's past shift history and select a display method with high visibility. This makes it easier for employees to understand the information by allowing the service provider to select the optimal display method by referring to the employee's past shift history. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input data of the employee's past shift history into a generation AI, and the generation AI can analyze the data and select the optimal display method.

[0053] The service provider can select the optimal display method when providing shift adjustment results, taking into account the employee's device information. For example, if an employee is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if an employee is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if an employee is using a smartwatch, the service provider can also provide a concise and highly visible display method. This makes it easier for employees to understand the information by allowing the service provider to select the optimal display method considering the employee's device information. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input employee device information data into a generation AI, which can then analyze the data and select the optimal display method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] A shift management system can monitor employees' health and adjust shifts accordingly. For example, if an employee's health check reveals an abnormality, their shifts can be reduced. Similarly, if an employee catches a cold, their shifts can be reduced and another employee can be offered as a replacement. Furthermore, if an employee's health is good, their shifts can be increased. This allows for efficient shift management while maintaining employee health.

[0056] A shift management system can adjust shifts to take employee commute times into consideration. For example, it can adjust shifts for employees with long commutes to avoid rush hour. It can also suggest early morning or late-night shifts for employees with short commutes. Furthermore, it can prioritize assigning employees to nearby workplaces to reduce their commute time. This reduces the burden of commuting for employees and enables efficient shift management.

[0057] A shift management system can suggest training programs to support employee skill development. For example, it can suggest training programs to improve specific skills for employees who lack those skills. Furthermore, it can suggest more advanced training programs as the next step for employees who have shown improvement. It can also monitor the progress of training programs and adjust shifts as needed. This supports employee skill development and enables efficient shift management.

[0058] A shift management system can adjust shifts to accommodate employees' family circumstances. For example, it can adjust shifts for employees with young children to fit their childcare schedules. It can also adjust shifts for employees with family members requiring care to fit their caregiving needs. Furthermore, if an employee needs sudden time off due to family reasons, the system can suggest a replacement employee. This enables flexible shift management that takes employees' family situations into consideration.

[0059] A shift management system can analyze an employee's past work history and suggest the most suitable shifts. For example, it can suggest similar shifts to employees who have performed well on a particular shift in the past. It can also suggest different shifts to employees who have had problems on a particular shift in the past. Furthermore, it can evaluate an employee's skills and abilities based on their past work history and suggest the most suitable shifts. This enables efficient shift management that utilizes past work history.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The visualization unit visualizes employee skills and work performance. For example, it collects data such as employee skills, abilities, and past work history, and the AI ​​analyzes this data. This allows companies to accurately understand employee skills and abilities and improve operational efficiency. Step 2: The adjustment unit automatically adjusts shifts based on the information visualized by the visualization unit, taking into account weather and events. For example, on days with bad weather, fewer customers are expected, so the number of shifts can be reduced. Conversely, on days when large-scale events are held, more customers are expected, so the number of shifts can be increased. This enables efficient staff allocation and improves customer satisfaction. Step 3: The proposal department proposes part-time workers based on the shifts adjusted by the adjustment department. For example, if a sudden absence occurs, a part-time worker can be proposed and placed in the right place at the right time. This improves productivity and reduces employee stress.

[0062] (Example of form 2) The shift management system according to an embodiment of the present invention is a system that achieves efficient shift management using AI. This shift management system enables efficient operation by visualizing employee skills and work status, automatically adjusting shifts considering external factors such as weather and events, and suggesting part-time work during downtime. The shift management system visualizes employee skills and work status, allowing companies to accurately understand the skills and abilities of their employees. Next, the shift management system automatically adjusts shifts considering external factors such as weather and events to create an optimal shift schedule. Furthermore, the shift management system suggests part-time work during downtime, enabling flexible placement. This ensures that employees are placed in the right place at the right time, improving productivity. For example, the shift management system collects data such as employee skills and abilities and past work history, and the AI ​​analyzes this data. If employee A possesses a specific skill, an appropriate shift can be suggested based on that skill. This allows companies to accurately understand employee skills and abilities and improve operational efficiency. Next, the shift management system automatically adjusts shifts considering external factors such as weather and events. For example, on days with bad weather, fewer customers are expected, so the number of shifts can be reduced. Furthermore, on days when large-scale events are held, an increase in customers is expected, allowing for increased shifts. This enables efficient staffing and improves customer satisfaction. In addition, the shift management system suggests part-time workers to enable flexible staffing. Part-time workers are a method of solving sudden labor shortages by utilizing employees who can complete tasks in a short amount of time. For example, if a sudden absence occurs, the shift management system can suggest part-time workers and place them in the right place at the right time. This improves productivity and reduces employee stress. Through this system, the shift management system automatically adjusts the optimal shifts and suggests part-time workers, optimizing staffing and enabling efficient operations. For example, managers can efficiently perform tasks such as sales, inventory management, employee scheduling, and customer service.Furthermore, HR personnel can efficiently handle HR tasks such as employee recruitment, hiring, training, evaluation, compensation, and benefits. This allows the shift management system to visualize employee skills and workload, automatically adjust shifts considering weather and events, and suggest part-time work options, enabling efficient shift management.

[0063] The shift management system according to this embodiment comprises a visualization unit, an adjustment unit, and a proposal unit. The visualization unit visualizes the skills and work status of employees. The visualization unit collects data such as employees' skills and abilities and past work history, and AI analyzes this data. For example, if employee A has a specific skill, the system can propose an appropriate shift based on that skill. This allows companies to accurately understand the skills and abilities of their employees and improve operational efficiency. The adjustment unit automatically adjusts shifts based on the information visualized by the visualization unit, taking into account weather and events. For example, the adjustment unit can reduce shifts on days with bad weather, when fewer customers are expected. Conversely, it can increase shifts on days when large-scale events are held, when more customers are expected. This enables efficient staff allocation and improves customer satisfaction. The proposal unit proposes part-time jobs based on the shifts adjusted by the adjustment unit. For example, if a sudden absence occurs, the proposal unit can propose part-time jobs and assign them to the right place at the right time. This improves productivity and reduces employee stress. As a result, the shift management system according to this embodiment enables efficient shift management by visualizing employee skills and work status, automatically adjusting shifts considering weather and events, and suggesting part-time jobs that fit into gaps in the work schedule.

[0064] The visualization unit visualizes employee skills and work performance. Specifically, it collects data such as employee skills, abilities, and past work history, and AI analyzes this data. For example, if employee A possesses a specific skill, the system can suggest appropriate shifts based on that skill. The AI ​​analyzes employee skill sets in detail and evaluates which tasks each employee is best suited for. Furthermore, it understands employee performance and attendance trends based on past work history and makes optimal shift assignments. This allows companies to accurately understand employee skills and abilities and improve operational efficiency. The visualization unit displays this information in a graphical interface, making it intuitive for managers to understand. For example, it can display employee skill matrices and work performance heatmaps on a dashboard, allowing managers to see at a glance which employees are most effective at which times of day. The visualization unit also helps in planning employee skill development and career paths. When an employee acquires a new skill, that information can be immediately updated and reflected in the next shift adjustment. This can increase employee motivation and support long-term growth. In addition, the visualization unit can collect employee feedback and continuously improve the accuracy of the system. For example, employees can provide feedback on their skill evaluations, and this feedback is used to improve the AI's analysis algorithms. This allows the visualization unit to always provide the latest and most accurate information, improving the efficiency and precision of shift management.

[0065] The adjustment unit automatically adjusts shifts based on information visualized by the visualization unit, taking into account weather and events. Specifically, on days with bad weather, fewer customers are expected, so shifts can be reduced. Conversely, on days when large-scale events are held, more customers are expected, so shifts can be increased. The adjustment unit uses AI to analyze these factors in real time and calculate the optimal shift allocation. For example, it takes in weather forecast data and adjusts the number of staff on rainy or snowy days to decrease, and increases on sunny days. It also refers to local event calendars and adjusts shifts according to the expected number of customers on specific days. This enables efficient staff allocation and improves customer satisfaction. Furthermore, the adjustment unit can build predictive models based on past data to forecast future shift demands. For example, it analyzes past sales data and customer count data to understand trends in customer numbers on specific days of the week and time slots. This allows for shift adjustments in advance to avoid congestion during peak hours. The adjustment unit can also flexibly adjust shifts, taking into account employees' preferred shifts and vacation requests. When employees input their desired working hours and vacation schedules into the system, the scheduling unit automatically generates the optimal shifts based on that information. This allows for efficient shift management while respecting employees' work-life balance.

[0066] The Proposal Department proposes part-time jobs based on shifts adjusted by the Coordination Department. Specifically, in the event of a sudden absence, part-time jobs can be proposed and assigned to the right place at the right time. The Proposal Department uses AI to quickly identify part-time job candidates and propose the most suitable personnel. For example, it lists employees who can immediately respond to sudden absences based on their past work history and skill sets. In addition, part-time job candidates are automatically notified by the system, enabling a quick response. This improves productivity and reduces employee stress. Furthermore, the Proposal Department collects employee feedback to continuously improve the system's algorithms in order to improve the accuracy of part-time job matching. For example, based on feedback from employees who have participated in part-time jobs, the accuracy and suitability of the proposals can be evaluated and reflected in future proposals. The Proposal Department can also flexibly set compensation and working conditions for part-time jobs to increase employee motivation. For example, by setting higher compensation than regular work for part-time jobs to cover sudden absences, a quick response can be encouraged. In this way, the Proposal Department can achieve efficient and flexible shift management, improving overall company productivity and employee satisfaction.

[0067] The data collection unit collects data on employees' skills and work status. For example, the data collection unit can collect data such as employees' skills and abilities, and their past work history. For example, the data collection unit can collect employees' past work history and skill test results to evaluate their skills and abilities. This allows the data collection unit to collect employee skill and work status data, enabling more accurate shift management. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input employee skill and work status data into an AI, which can then analyze and collect the data.

[0068] The adjustment unit can automatically adjust shifts by taking into account external factors such as weather and events. For example, on days with bad weather, the adjustment unit can reduce the number of shifts because fewer customers are expected. Conversely, on days when large-scale events are held, the adjustment unit can increase the number of shifts because more customers are expected. The adjustment unit can reduce the number of shifts because fewer customers are expected on days with bad weather. Conversely, on days when large-scale events are held, the adjustment unit can increase the number of shifts because more customers are expected. In this way, the adjustment unit enables efficient shift management by automatically adjusting shifts by taking into account external factors such as weather and events. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input external factors such as weather and events into the AI, and the AI ​​can automatically adjust the shifts.

[0069] The proposal department can propose part-time jobs. For example, if a sudden absence occurs, the proposal department can propose part-time jobs and assign them to the right place at the right time. The proposal department can propose part-time jobs and assign them to the right place at the right time. The proposal department can propose part-time jobs and assign them to the right place at the right time. In this way, the proposal department can respond to sudden labor shortages by proposing part-time jobs. Some or all of the above processing in the proposal department may be performed using AI or not. For example, if a sudden absence occurs, the proposal department can input part-time jobs into the AI, and the AI ​​can propose part-time jobs.

[0070] The data collection unit can collect data such as employees' skills and abilities and past work history. For example, the data collection unit can collect employees' past work history and skill test results in order to evaluate employees' skills and abilities. The data collection unit can collect employees' past work history and skill test results in order to evaluate employees' skills and abilities. For example, the data collection unit can collect employees' past work history and skill test results in order to evaluate employees' skills and abilities. This allows the data collection unit to perform more accurate shift management by collecting data such as employees' skills and abilities and past work history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data such as employees' skills and abilities and past work history into AI, and the AI ​​can analyze and collect the data.

[0071] The adjustment unit may include a provisioning unit that provides the results of the shift adjustments. The adjustment unit may include a provisioning unit for, for example, providing the results of the shift adjustments. The adjustment unit may include a provisioning unit for, for example, providing the results of the shift adjustments. This makes it easier for employees to check shift changes by providing the shift adjustment results. Some or all of the above processing in the provisioning unit may be performed using AI or not. For example, the provisioning unit can input the shift adjustment results into the AI, and the AI ​​can provide the shift adjustment results.

[0072] The visualization unit can estimate an employee's emotions and adjust the display method for skills and work performance based on the estimated emotions. For example, if an employee is stressed, the visualization unit provides a simple and highly visible display method. For example, if an employee is relaxed, the visualization unit can also provide a display method that includes detailed information. For example, if an employee is tired, the visualization unit can highlight and display only important information. In this way, the visualization unit makes it easier for employees to understand the information by adjusting the display method based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input employee emotion data into a generative AI, which can estimate emotions and adjust the display method.

[0073] The visualization unit can analyze an employee's past performance data and visualize their skill growth trends. For example, the visualization unit can display a skill growth curve based on an employee's past evaluation data. The visualization unit can also analyze an employee's past project participation history and create a graph showing skill improvement. The visualization unit can also visualize the employee's skill acquisition status based on their training history. This makes it easier to understand an employee's growth by visualizing their skill growth trends. Some or all of the above-described processes in the visualization unit may be performed using a generative AI, or they may not. For example, the visualization unit can input an employee's past performance data into a generative AI, which can then analyze the data and visualize the skill growth trends.

[0074] The visualization unit can update employee skills and work status in real time, providing the latest information. For example, the visualization unit updates skill information in real time when an employee acquires a new skill. For example, the visualization unit can immediately reflect changes in an employee's work status in the system. For example, the visualization unit can also update work status in real time when an employee's shift changes. In this way, the visualization unit can provide the latest information by updating employee skills and work status in real time. Some or all of the above processing in the visualization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the visualization unit can input employee skill and work status data into a generation AI, and the generation AI can update the data in real time.

[0075] The visualization unit can estimate employees' emotions and prioritize skills and work performance based on the estimated emotions. For example, if an employee is stressed, the visualization unit can set lower-priority tasks. For example, if an employee is relaxed, the visualization unit can set higher-priority tasks. For example, if an employee is tired, the visualization unit can set higher-priority tasks. In this way, the visualization unit can reduce the burden on employees by determining priorities based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input employee emotion data into a generative AI, which can estimate emotions and determine priorities.

[0076] The visualization unit can display employee skills and workload while considering the overall team balance. For example, the visualization unit can display a team-wide skill map and compare the skill levels of each member. The visualization unit can also display the overall team workload and identify overloaded members. For example, the visualization unit can display the overall team skills and workload in a graph and suggest a balanced allocation. In this way, the visualization unit enables efficient team management by displaying information while considering the overall team balance. Some or all of the above processing in the visualization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the visualization unit can input team-wide skill and workload data into a generation AI, which can analyze the data and display it while considering balance.

[0077] The visualization unit can visualize an employee's skills and work status by comparing them with their past work history. For example, the visualization unit can compare and display the employee's current skill level based on their past work history. The visualization unit can also compare and display the employee's current work status based on their past work performance. For example, the visualization unit can compare and display an employee's past work history and current skill level in a graph. This makes it easier to understand an employee's growth by comparing them with their past work history. Some or all of the above processing in the visualization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the visualization unit can input data on an employee's past work history and current skill level into a generation AI, which can then compare and display the data.

[0078] The adjustment unit can estimate an employee's emotions and change the shift scheduling method based on the estimated emotions. For example, if an employee is stressed, the adjustment unit can make adjustments to reduce the shift burden. For example, if an employee is relaxed, the adjustment unit can also make adjustments to increase the shift burden. For example, if an employee is tired, the adjustment unit can also make adjustments to increase break times. In this way, the adjustment unit can reduce the burden on employees by changing the shift scheduling method based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input employee emotion data into a generative AI, which can estimate emotions and change the shift scheduling method.

[0079] The adjustment unit can make adjustments to shifts while taking into account the health status and fatigue level of employees. For example, based on employee health data, the adjustment unit can reduce shifts if an employee's health is poor. For example, the adjustment unit can monitor employee fatigue levels and increase breaks if fatigue is accumulating. For example, the adjustment unit can evenly distribute the shift burden while taking into account the health status and fatigue level of employees. In this way, the adjustment unit can maintain employee health by making adjustments while taking into account the health status and fatigue level of employees. Some or all of the above processing in the adjustment unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the adjustment unit can input employee health status and fatigue level data into a generation AI, and the generation AI can analyze the data and make shift adjustments.

[0080] The adjustment unit can apply different adjustment algorithms to employees' skill levels when adjusting shifts. For example, the adjustment unit can assign important tasks to highly skilled employees. For example, the adjustment unit can assign tasks to improve the skills of medium-skilled employees. For example, the adjustment unit can assign tasks requiring support to low-skilled employees. This allows the adjustment unit to make appropriate shift adjustments by applying different adjustment algorithms according to employees' skill levels. Some or all of the above processing in the adjustment unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the adjustment unit can input employee skill level data into a generative AI, which can then analyze the data and apply an adjustment algorithm.

[0081] The adjustment unit can estimate an employee's emotions and determine the frequency of shift adjustments based on the estimated emotions. For example, if an employee is stressed, the adjustment unit may reduce the frequency of shift adjustments. For example, if an employee is relaxed, the adjustment unit may increase the frequency of shift adjustments. For example, if an employee is tired, the adjustment unit may reduce the frequency of shift adjustments and increase breaks. In this way, the adjustment unit can reduce the burden on employees by determining the frequency of shift adjustments based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input employee emotion data into a generative AI, which can estimate emotions and determine the frequency of shift adjustments.

[0082] The adjustment unit can propose the optimal shifts when adjusting shifts, taking into account employees' commuting times. For example, the adjustment unit may prioritize assigning employees to nearby workplaces to reduce their commuting time. The adjustment unit may also propose shifts that avoid rush hour, taking into account employees' commuting times. The adjustment unit may also propose shifts that minimize commuting burden, based on employees' commuting times. In this way, the adjustment unit can reduce the commuting burden by proposing the optimal shifts, taking employees' commuting times into consideration. Some or all of the above processing in the adjustment unit may be performed using a generation AI, or not. For example, the adjustment unit can input employee commuting time data into a generation AI, which can then analyze the data and propose the optimal shifts.

[0083] The adjustment unit can make adjustments to shifts while taking into account employees' family circumstances and personal schedules. For example, the adjustment unit can propose shifts that reduce the burden on employees' families by taking into account their family circumstances. The adjustment unit can also propose shifts that fit employees' personal schedules by taking into account their personal schedules. The adjustment unit can also make flexible shift adjustments based on employees' family circumstances and personal schedules. In this way, the adjustment unit can reduce the burden on employees by making adjustments while taking into account their family circumstances and personal schedules. Some or all of the above processing in the adjustment unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the adjustment unit can input data on employees' family circumstances and personal schedules into a generation AI, and the generation AI can analyze the data and make shift adjustments.

[0084] The suggestion department can estimate an employee's emotions and adjust its suggestion method for part-time jobs based on the estimated emotions. For example, if an employee is stressed, the suggestion department can suggest a less demanding part-time job. If an employee is relaxed, the suggestion department can suggest a more challenging part-time job. If an employee is tired, the suggestion department can suggest a short-term part-time job. In this way, the suggestion department can reduce the burden on employees by adjusting its suggestion method for part-time jobs based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department can input employee emotion data into a generative AI, which can estimate the emotions and adjust its suggestion method for part-time jobs.

[0085] The suggestion department can make suggestions for part-time jobs by considering the employee's past part-time job history. For example, the suggestion department can suggest similar part-time jobs based on the employee's past successes. The suggestion department can also analyze the employee's past part-time job history and suggest suitable part-time jobs. For example, the suggestion department can suggest part-time jobs that will lead to skill improvement based on the employee's past part-time job history. In this way, the suggestion department can suggest appropriate part-time jobs by considering the employee's past part-time job history. Some or all of the above processes in the suggestion department may be performed using a generation AI, or they may not be performed using a generation AI. For example, the suggestion department can input data on the employee's past part-time job history into a generation AI, which can then analyze the data and make suggestions.

[0086] The proposal department can suggest the most suitable part-time jobs based on the employee's skill set when proposing part-time work. For example, the proposal department can suggest part-time jobs that utilize the employee's skills based on their skill set. For example, the proposal department can analyze the employee's skill set and suggest part-time jobs that will lead to skill improvement. For example, the proposal department can consider the employee's skill set and suggest suitable part-time jobs. In this way, the proposal department can maximize the use of employees' skills by suggesting the most suitable part-time jobs based on their skill set. Some or all of the above processes in the proposal department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the proposal department can input employee skill set data into a generative AI, and the generative AI can analyze the data and suggest the most suitable part-time jobs.

[0087] The suggestion unit can estimate an employee's emotions and adjust the frequency of part-time job suggestions based on the estimated emotions. For example, if an employee is stressed, the suggestion unit can reduce the frequency of part-time job suggestions. For example, if an employee is relaxed, the suggestion unit can increase the frequency of part-time job suggestions. For example, if an employee is tired, the suggestion unit can reduce the frequency of part-time job suggestions and prioritize breaks. In this way, the suggestion unit can reduce the burden on employees by adjusting the frequency of part-time job suggestions based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input employee emotion data into a generative AI, which can estimate emotions and adjust the frequency of part-time job suggestions.

[0088] The suggestion department can propose the most suitable part-time jobs when suggesting part-time work, taking into account the employee's geographical location. For example, the suggestion department can prioritize suggesting part-time jobs close to the employee's current location. The suggestion department can also propose part-time jobs with a low commute burden, taking into account the employee's commute time. The suggestion department can also propose part-time jobs with good access based on the employee's geographical location. In this way, the suggestion department can reduce the commute burden by suggesting the most suitable part-time jobs, taking into account the employee's geographical location. Some or all of the above processing in the suggestion department may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion department can input the employee's geographical location data into a generation AI, which can then analyze the data and propose the most suitable part-time jobs.

[0089] The suggestion department can analyze an employee's social media activity and suggest relevant part-time jobs when proposing part-time work. For example, the suggestion department can suggest relevant part-time jobs based on the employee's interests on social media. The suggestion department can also analyze an employee's social media activity history and suggest suitable part-time jobs. The suggestion department can also consider the number of followers an employee has on social media and suggest influential part-time jobs. In this way, the suggestion department can suggest part-time jobs that match the employee's interests by analyzing their social media activity and suggesting relevant part-time jobs. Some or all of the above processing in the suggestion department may be performed using or without a generative AI. For example, the suggestion department can input data on an employee's social media activity into a generative AI, which can then analyze the data and suggest relevant part-time jobs.

[0090] The data collection unit can estimate employees' emotions and adjust the timing of data collection based on the estimated emotions. For example, if an employee is stressed, the data collection unit can reduce the frequency of data collection. For example, if an employee is relaxed, the data collection unit can increase the frequency of data collection. For example, if an employee is tired, the data collection unit can adjust the timing of data collection to coincide with the employee's break time. In this way, the data collection unit can reduce the burden on employees by adjusting the timing of data collection based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee emotion data into a generative AI, which can estimate emotions and adjust the timing of data collection.

[0091] The data collection unit can collect data including past employee performance data during data collection. For example, the data collection unit can collect past employee evaluation data and compare it to current performance. For example, the data collection unit can collect an employee's past project participation history and analyze skill improvement. For example, the data collection unit can collect an employee's past training history and evaluate their skill acquisition status. This makes it easier for the data collection unit to understand employee growth by including past employee performance data in the collection. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input past employee performance data into a generative AI, which can then analyze and collect the data.

[0092] The data collection unit can estimate employees' emotions and prioritize the data to collect based on those estimated emotions. For example, if an employee is stressed, the data collection unit may postpone the collection of less important data. For example, if an employee is relaxed, the data collection unit may prioritize the collection of detailed data. For example, if an employee is tired, the data collection unit may prioritize the collection of only important data. This allows the data collection unit to reduce the burden on employees by prioritizing the data to collect based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee emotion data into a generative AI, which can estimate emotions and determine the priority of the data to collect.

[0093] The data collection unit can collect relevant data while considering the geographical location information of employees. For example, the data collection unit can collect relevant data based on the employee's current location. The data collection unit can also prioritize data that can be collected during the employee's commute, for example, by considering the employee's commute route. The data collection unit can also improve operational efficiency by collecting data related to the employee's workplace, for example. In this way, the data collection unit improves operational efficiency by collecting relevant data while considering the geographical location information of employees. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input employee geographical location data into a generative AI, and the generative AI can analyze the data and collect relevant data.

[0094] The service provider can estimate an employee's emotions and adjust the display method of shift adjustment results based on the estimated emotions. For example, if an employee is stressed, the service provider can provide a simple and highly visible display method. For example, if an employee is relaxed, the service provider can also provide a display method that includes detailed information. For example, if an employee is tired, the service provider can highlight and display only the important information. This makes it easier for employees to understand the information by adjusting the display method of shift adjustment results based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input employee emotion data into a generative AI, which can estimate emotions and adjust the display method.

[0095] The service provider can select the optimal display method by referring to the employee's past shift history when providing shift adjustment results. For example, the service provider can compare and display the current shift adjustment results based on the employee's past shift history. The service provider can also, for example, analyze the employee's past shift history and propose the optimal display method. The service provider can also, for example, refer to the employee's past shift history and select a display method with high visibility. This makes it easier for employees to understand the information by allowing the service provider to select the optimal display method by referring to the employee's past shift history. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input data of the employee's past shift history into a generation AI, and the generation AI can analyze the data and select the optimal display method.

[0096] The service provider can estimate an employee's emotions and prioritize shift adjustments based on those emotions. For example, if an employee is stressed, the service provider might postpone less important shift adjustments. If an employee is relaxed, the service provider might prioritize more detailed shift adjustments. If an employee is tired, the service provider might prioritize displaying only important shift adjustments. This allows the service provider to reduce the burden on employees by prioritizing shift adjustments based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input employee emotion data into a generative AI, which can estimate emotions and determine the priority of shift adjustments.

[0097] The service provider can select the optimal display method when providing shift adjustment results, taking into account the employee's device information. For example, if an employee is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if an employee is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if an employee is using a smartwatch, the service provider can also provide a concise and highly visible display method. This makes it easier for employees to understand the information by allowing the service provider to select the optimal display method considering the employee's device information. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input employee device information data into a generation AI, which can then analyze the data and select the optimal display method.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] A shift management system can monitor employees' health and adjust shifts accordingly. For example, if an employee's health check reveals an abnormality, their shifts can be reduced. Similarly, if an employee catches a cold, their shifts can be reduced and another employee can be offered as a replacement. Furthermore, if an employee's health is good, their shifts can be increased. This allows for efficient shift management while maintaining employee health.

[0100] A shift management system can adjust shifts to take employee commute times into consideration. For example, it can adjust shifts for employees with long commutes to avoid rush hour. It can also suggest early morning or late-night shifts for employees with short commutes. Furthermore, it can prioritize assigning employees to nearby workplaces to reduce their commute time. This reduces the burden of commuting for employees and enables efficient shift management.

[0101] A shift management system can suggest training programs to support employee skill development. For example, it can suggest training programs to improve specific skills for employees who lack those skills. Furthermore, it can suggest more advanced training programs as the next step for employees who have shown improvement. It can also monitor the progress of training programs and adjust shifts as needed. This supports employee skill development and enables efficient shift management.

[0102] A shift management system can adjust shifts to accommodate employees' family circumstances. For example, it can adjust shifts for employees with young children to fit their childcare schedules. It can also adjust shifts for employees with family members requiring care to fit their caregiving needs. Furthermore, if an employee needs sudden time off due to family reasons, the system can suggest a replacement employee. This enables flexible shift management that takes employees' family situations into consideration.

[0103] A shift management system can analyze an employee's past work history and suggest the most suitable shifts. For example, it can suggest similar shifts to employees who have performed well on a particular shift in the past. It can also suggest different shifts to employees who have had problems on a particular shift in the past. Furthermore, it can evaluate an employee's skills and abilities based on their past work history and suggest the most suitable shifts. This enables efficient shift management that utilizes past work history.

[0104] A shift management system can estimate employees' emotions and adjust shifts based on those estimates. For example, if an employee is stressed, their shifts can be reduced. Conversely, if an employee is relaxed, their shifts can be increased. Furthermore, if an employee is tired, their break time can be increased. This enables flexible shift management based on employee emotions.

[0105] The shift management system can estimate employees' emotions and suggest part-time jobs based on those estimates. For example, if an employee is feeling stressed, it can suggest a less demanding part-time job. Conversely, if an employee is relaxed, it can suggest a more challenging part-time job. Furthermore, if an employee is tired, it can suggest a short-duration part-time job. This allows for flexible part-time job suggestions based on employees' emotions.

[0106] A shift management system can estimate employees' emotions and prioritize shifts based on those emotions. For example, if an employee is stressed, less important tasks can be postponed. Conversely, if an employee is relaxed, challenging tasks can be prioritized. Furthermore, if an employee is tired, less demanding tasks can be prioritized. This enables flexible shift management based on employee emotions.

[0107] A shift management system can estimate employees' emotions and determine the frequency of shift adjustments based on those estimates. For example, if an employee is stressed, the frequency of shift adjustments can be reduced. Conversely, if an employee is relaxed, the frequency of shift adjustments can be increased. Furthermore, if an employee is tired, the frequency of shift adjustments can be reduced and break times increased. This enables flexible shift management based on employee emotions.

[0108] The shift management system can estimate employee emotions and adjust shift schedules based on those estimates. For example, if an employee is stressed, the system can adjust their shift workload to reduce it. Conversely, if an employee is relaxed, the system can adjust their shift workload to increase it. Furthermore, if an employee is tired, the system can adjust their break times to increase them. This enables flexible shift management based on employee emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The visualization unit visualizes employee skills and work performance. For example, it collects data such as employee skills, abilities, and past work history, and the AI ​​analyzes this data. This allows companies to accurately understand employee skills and abilities and improve operational efficiency. Step 2: The adjustment unit automatically adjusts shifts based on the information visualized by the visualization unit, taking into account weather and events. For example, on days with bad weather, fewer customers are expected, so the number of shifts can be reduced. Conversely, on days when large-scale events are held, more customers are expected, so the number of shifts can be increased. This enables efficient staff allocation and improves customer satisfaction. Step 3: The proposal department proposes part-time workers based on the shifts adjusted by the adjustment department. For example, if a sudden absence occurs, a part-time worker can be proposed and placed in the right place at the right time. This improves productivity and reduces employee stress.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the visualization unit, adjustment unit, proposal unit, and collection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the visualization unit is implemented by the control unit 46A of the smart device 14 and visualizes employee skills and work status. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically adjusts shifts considering weather and events. The proposal unit is implemented by the control unit 46A of the smart device 14 and proposes gaps in work time. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects data on employee skills and work status. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the visualization unit, adjustment unit, suggestion unit, and collection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the visualization unit is implemented by the control unit 46A of the smart glasses 214 and visualizes the skills and work status of employees. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically adjusts shifts considering weather and events. The suggestion unit is implemented by the control unit 46A of the smart glasses 214 and suggests gaps in work. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects data on the skills and work status of employees. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the visualization unit, adjustment unit, suggestion unit, and collection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the visualization unit is implemented by the control unit 46A of the headset terminal 314 and visualizes employee skills and work status. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically adjusts shifts considering weather and events. The suggestion unit is implemented by the control unit 46A of the headset terminal 314 and suggests gaps in work time. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects data on employee skills and work status. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the visualization unit, adjustment unit, suggestion unit, and collection unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the visualization unit is implemented by the control unit 46A of the robot 414 and visualizes the skills and working status of employees. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically adjusts shifts considering weather and events. The suggestion unit is implemented by the control unit 46A of the robot 414 and suggests gaps in the work schedule. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects data on the skills and working status of employees. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A visualization unit that visualizes employee skills and work status, An adjustment unit that automatically adjusts the shift based on the information visualized by the visualization unit, taking into account weather and events, A suggestion unit proposes a gap bit based on the shift adjusted by the adjustment unit, Equipped with A system characterized by the following features. (Note 2) It includes a data collection unit that collects data on employee skills and work performance. The system described in Appendix 1, characterized by the features described herein. (Note 3) The adjustment unit is, The system automatically adjusts shifts to account for external factors such as weather and events. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, I propose a part-time job. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect data such as employees' skills and abilities, and past work history. The system described in Appendix 2, characterized by the features described herein. (Note 6) The adjustment unit is, It includes a unit that provides the results of the shift adjustments. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned visualization unit, The system estimates employee sentiment and adjusts how skills and performance are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned visualization unit, Analyze employees' past performance data to visualize their skill growth trends. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned visualization unit, We update employee skills and work status in real time, providing the latest information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned visualization unit, The system estimates employee sentiment and prioritizes skills and performance based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned visualization unit, When visualizing employee skills and workload, display them while considering the overall balance of the team. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned visualization unit, When visualizing employee skills and work performance, display them in comparison to past work history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The adjustment unit is, Estimate employee emotions and change shift scheduling based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The adjustment unit is, When adjusting shifts, we take into consideration the health and fatigue levels of our employees. The system described in Appendix 1, characterized by the features described herein. (Note 15) The adjustment unit is, When adjusting shifts, different adjustment algorithms are applied depending on the employee's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 16) The adjustment unit is, The system estimates employee emotions and determines the frequency of shift adjustments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The adjustment unit is, When adjusting shifts, we propose the optimal shift schedule considering employees' commute times. The system described in Appendix 1, characterized by the features described herein. (Note 18) The adjustment unit is, When adjusting work shifts, we take into consideration employees' family circumstances and personal schedules. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, We estimate the emotions of our employees and adjust how we suggest part-time jobs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When proposing part-time jobs, consider the employee's past part-time job history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When proposing part-time jobs, we suggest the most suitable jobs based on the employee's skill set. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, The system estimates employee sentiment and adjusts the frequency of suggesting part-time jobs based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When proposing part-time jobs, we take into account the geographical location of employees to suggest the most suitable jobs. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When suggesting part-time jobs, we analyze employees' social media activity and suggest relevant jobs. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned collection unit is We estimate employee sentiment and adjust the timing of data collection based on the estimated employee sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned collection unit is When collecting data, include historical performance data of employees. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned collection unit is We estimate employee sentiment and prioritize the data to collect based on the estimated employee sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned collection unit is When collecting data, relevant data is collected while considering the geographical location of employees. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned supply unit is, The system estimates employee emotions and adjusts how shift adjustment results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing shift adjustment results, the system will refer to the employee's past shift history to select the most suitable display method. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, The system estimates employee sentiment and prioritizes shift adjustments based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing shift adjustment results, the optimal display method will be selected considering the employee's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A visualization unit that visualizes employee skills and work status, An adjustment unit that automatically adjusts the shift based on the information visualized by the visualization unit, taking into account weather and events, A suggestion unit proposes a gap bit based on the shift adjusted by the adjustment unit, Equipped with A system characterized by the following features.

2. It includes a data collection unit that collects data on employee skills and work performance. The system according to feature 1.

3. The adjustment unit is, The system automatically adjusts shifts to account for external factors such as weather and events. The system according to feature 1.

4. The aforementioned proposal section is, I suggest part-time jobs. The system according to feature 1.

5. The aforementioned collection unit is Collect data such as employees' skills and abilities, and their past work history. The system according to feature 2.

6. The adjustment unit is, It includes a provisioning unit that provides the results of the shift adjustments. The system according to feature 1.

7. The aforementioned visualization unit, The system estimates employee sentiment and adjusts how skills and performance are displayed based on that estimated sentiment. The system according to feature 1.

8. The aforementioned visualization unit, Analyze employees' past performance data to visualize their skill growth trends. The system according to feature 1.

9. The aforementioned visualization unit, We update employee skills and work status in real time, providing the latest information. The system according to feature 1.

10. The aforementioned visualization unit, The system estimates employee sentiment and prioritizes skills and performance based on the estimated sentiment. The system according to feature 1.

Images