System

Abstract

An object of a system according to an embodiment is to realize efficient schedule management by automating labor prediction and shift adjustment.SOLUTION: A system includes an analysis unit, a generation unit, and an adjustment unit. The analysis unit analyzes past labor data or demand forecast. The generation unit automatically generates a shift based on the analysis result obtained by the analysis unit. The adjustment unit adjusts the work shift generated by the generation unit in accordance with an actual work situation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, workforce forecasts and shift adjustments were often done manually, making efficient schedule management difficult.

[0005] The system according to the embodiment aims to automate workforce forecasting and shift adjustments, thereby realizing efficient schedule management. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, and an adjustment unit. The analysis unit analyzes past labor force data or demand forecasts. The generation unit automatically generates shifts based on the analysis results obtained by the analysis unit. The adjustment unit adjusts the shifts generated by the generation unit to match the actual work situation. [Effects of the Invention]

[0007] The system according to the embodiment automates workforce forecasting and shift adjustment, enabling efficient schedule management. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A labor force forecasting and schedule adjustment system according to an embodiment of the present invention analyzes past labor force data and demand forecasts, and a generation AI automatically generates optimal shifts and adjusts them to match actual work conditions. The labor force forecasting and schedule adjustment system analyzes past labor force data and demand forecasts to propose optimal schedules and the required labor force. For example, the labor force forecasting and schedule adjustment system analyzes data such as employee work history, job type, and working hours to predict labor demand for a specific period. Next, the generation AI automatically generates optimized shifts taking into account goals and constraints. For example, the optimal shifts are generated while taking into account constraints such as employee skills, qualifications, and available working hours. Furthermore, the labor force forecasting and schedule adjustment system compares the proposed schedule with the actual work conditions and adjusts the schedule as necessary. For example, if the predicted demand differs from the actual demand, the generation AI automatically readjusts the schedule to maintain optimal labor allocation. This enables efficient labor allocation and improves work productivity. The labor force forecasting and schedule adjustment system thus realizes efficient labor allocation and improves work productivity. For example, by securing the necessary labor force during busy periods and reducing the labor force during slow periods, it is possible to improve business efficiency. It also reduces the burden on employees and provides a comfortable working environment.

[0029] A labor force prediction and schedule adjustment system according to an embodiment includes an analysis unit, a generation unit, and an adjustment unit. The analysis unit analyzes past labor force data or demand forecasts. Past labor force data includes, but is not limited to, employee working hours, job content, and number of work days. For example, the analysis unit predicts labor demand for a specific period from past data. The analysis unit can also perform demand forecasting using statistical models or machine learning algorithms. The generation unit automatically generates shifts based on the analysis results obtained by the analysis unit. The generation unit generates optimized shifts, for example, taking into account goals and constraints. The generation unit uses a generation AI to generate shifts while taking into account constraints such as employee skills, qualifications, and available work hours. For example, the generation AI proposes optimal shifts based on employee skill sets and qualification information. The generation unit can also use the generation AI to generate shifts while taking into account employees' available work hours. The adjustment unit adjusts the shifts generated by the generation unit to suit actual business conditions. For example, the adjustment unit compares the proposed schedule with the actual work situation and readjusts the schedule as necessary. The adjustment unit can also use the generative AI to readjust the schedule when the predicted demand differs from the actual demand. For example, the adjustment unit monitors the actual work situation in real time and readjusts shifts as necessary. As a result, the workforce prediction and schedule adjustment system according to the embodiment can realize efficient workforce allocation and improve work productivity.

[0030] The workforce forecasting and schedule adjustment system includes a constraint unit that considers constraints such as employee skills or qualifications and available working hours. The constraint unit considers constraints such as employee skills, qualifications, and available working hours. Examples of skills include, but are not limited to, technical skills and communication skills. The constraint unit generates optimal shifts based on employee skill sets. For example, employees with skills required for specific tasks are preferentially assigned. Examples of qualifications include, but are not limited to, professional qualifications and business qualifications. The constraint unit generates optimal shifts based on employee qualification information. For example, employees with specific qualifications are assigned to specific tasks. Examples of available working hours include, but are not limited to, full-time, part-time, and shift work. The constraint unit generates optimal shifts based on the available working hours of employees. For example, shifts are generated while taking into account the available working hours of employees. In this way, optimal shifts can be generated by taking into account constraints such as employee skills, qualifications, and available working hours.

[0031] The workforce forecasting and schedule adjustment system includes a comparison unit that compares a proposed schedule with an actual work situation. The comparison unit compares the proposed schedule with the actual work situation. The proposed schedule includes, for example, but is not limited to, shift time periods and assigned tasks. The comparison unit compares the proposed schedule with the actual work situation to improve the accuracy of the schedule. The actual work situation includes, for example, but is not limited to, the progress of work and employee attendance status. The comparison unit evaluates the suitability of the proposed schedule based on the actual work situation. For example, the comparison unit monitors the progress of work in real time and compares it with the proposed schedule. The comparison unit can also evaluate the suitability of the proposed schedule based on employee attendance status. In this way, the accuracy of the schedule can be improved by comparing the proposed schedule with the actual work situation.

[0032] The workforce forecasting and schedule adjustment system includes a readjustment unit that readjusts the schedule when predicted demand differs from actual demand. The readjustment unit readjusts the schedule when predicted demand differs from actual demand. Examples of predicted demand include, but are not limited to, sales forecasts and customer number forecasts. The readjustment unit compares the predicted demand with actual demand and readjusts the schedule. Examples of actual demand include, but are not limited to, actual sales and the actual number of customers. The readjustment unit readjusts the schedule based on the actual demand. For example, if actual sales differ from predicted sales, the readjustment unit readjusts the schedule. The readjustment unit can also readjust the schedule when the actual number of customers differs from the predicted number of customers. In this way, by readjusting the schedule when predicted demand differs from actual demand, optimal workforce allocation can be maintained.

[0033] The analysis unit can analyze past labor force data or demand forecasts. The analysis unit analyzes past labor force data and demand forecasts. Past labor force data includes, for example, employee working hours, job content, number of working days, etc., but is not limited to these examples. The analysis unit predicts labor demand for a specific period from past data. For example, labor demand for a specific period is predicted based on past data. The analysis unit can also perform demand forecasts using statistical models or machine learning algorithms. For example, a statistical model is used to analyze demand trends from past data. A machine learning algorithm can also be used to learn demand patterns from past data and perform demand forecasts. In this way, by analyzing past labor force data and demand forecasts, labor demand can be accurately predicted.

[0034] The generation unit can automatically generate shifts taking into account goals or constraints. The generation unit automatically generates an optimized shift taking into account goals and constraints. Goals include, for example, sales targets and business targets, but are not limited to these examples. The generation unit generates an optimal shift based on the goals. For example, a shift is generated while taking into account the labor force required to achieve sales targets. The generation unit can also generate a shift while taking into account the labor force required to achieve business targets. Constraints include, for example, but are not limited to these examples. The generation unit generates an optimal shift based on constraints. For example, a shift is generated while taking into account legal constraints. The generation unit can also generate a shift while taking into account business policies. This enables efficient shift management by automatically generating an optimized shift taking into account goals and constraints.

[0035] The analysis unit can extract specific patterns from past labor force data to improve the accuracy of the analysis. The analysis unit extracts specific patterns from past labor force data to improve the accuracy of the analysis. Specific patterns include, but are not limited to, patterns of busy and slow seasons, periods when specific skill sets are needed, and labor demand patterns on specific days of the week or time periods. The analysis unit extracts patterns of busy and slow seasons from past data to improve the accuracy of the demand forecast. For example, the analysis unit can identify patterns of busy and slow seasons based on past data and reflect them in the demand forecast. It can also identify periods when specific skill sets are needed from employee work histories and reflect them in the analysis. For example, it can identify periods when specific skill sets are needed and predict the labor required during those periods. It can also extract labor demand patterns on specific days of the week or time periods from past data and reflect them in the analysis. For example, it can identify labor demand patterns on specific days of the week or time periods and perform demand forecasts based on those patterns. By extracting specific patterns from past labor force data, the accuracy of the analysis can be improved.

[0036] The analysis unit can adjust the analysis results during analysis, taking into account the employee's health condition and fatigue level. The analysis unit adjusts the analysis results during analysis, taking into account the employee's health condition and fatigue level. Health conditions include, for example, health checkup results and self-reporting, but are not limited to these examples. The analysis unit adjusts the allocation of labor if the employee's fatigue level is high based on the employee's health data. For example, the analysis unit adjusts the shifts of employees with high fatigue levels based on health checkup results. The analysis unit can also adjust the shifts of employees with high fatigue levels based on self-reporting. Furthermore, the analysis unit can prioritize the allocation of employees in good health and reflect this in the analysis results. For example, prioritizing the allocation of employees in good health achieves efficient allocation of labor. In this way, more appropriate analysis results can be obtained by taking into account the employee's health condition and fatigue level.

[0037] The analysis unit can optimize the analysis algorithm during analysis by taking into account the type of work and seasonal fluctuations. The analysis unit optimizes the analysis algorithm during analysis by taking into account the type of work and seasonal fluctuations. Examples of types of work include, but are not limited to, desk work and fieldwork. The analysis unit adjusts the analysis algorithm by taking into account the required skill set depending on the type of work. For example, the analysis algorithm is adjusted by taking into account the skill set required for desk work. The analysis unit can also adjust the analysis algorithm by taking into account the skill set required for fieldwork. Examples of seasonal fluctuations include, but are not limited to, busy seasons and slow seasons. The analysis unit applies an analysis algorithm to improve the accuracy of demand forecasting based on seasonal fluctuations. For example, the analysis unit applies an analysis algorithm to improve the accuracy of demand forecasting during busy seasons. The analysis unit can also apply an analysis algorithm to improve the accuracy of demand forecasting during slow seasons. In this way, the accuracy of the analysis algorithm is improved by taking into account the type of work and seasonal fluctuations.

[0038] The analysis unit can take into account the geographical location information of employees when performing the analysis. The analysis unit can take into account the geographical location information of employees when performing the analysis. Geographical location information includes, but is not limited to, GPS data and address information, for example. The analysis unit analyzes optimal labor allocation based on the current location of employees. For example, the analysis unit analyzes labor allocation taking into account commuting time based on the current location of employees. The analysis unit can also analyze labor demand in a specific area based on the geographical location information. For example, the analysis unit analyzes labor demand in a specific area and proposes labor allocation appropriate for that area. In this way, by taking into account the geographical location information of employees, more appropriate labor allocation can be achieved.

[0039] The analysis unit can analyze the social media activities of employees during the analysis and reflect related data in the analysis. The analysis unit can analyze the social media activities of employees during the analysis and reflect related data in the analysis. Social media activities include, for example, but are not limited to, the content of posts and the number of likes. The analysis unit estimates the stress level of employees from their social media activities and reflects the data in the analysis. For example, the analysis unit can estimate the stress level of employees based on the content of their social media posts and reflect the data in the analysis. The analysis unit can also reflect the interests and concerns of employees based on the content of their social media posts. For example, the analysis unit can identify the interests and concerns of employees based on the content of their social media posts and reflect the data in the analysis. Furthermore, the analysis can also reflect the effectiveness of teamwork based on friendships on social media. For example, the analysis unit can identify the effectiveness of teamwork based on friendships on social media and reflect the data in the analysis. In this way, more detailed analysis results can be obtained by analyzing employees' social media activities.

[0040] The analysis unit can customize the analysis method by reflecting employees' past feedback during analysis. The analysis unit customizes the analysis method by reflecting employees' past feedback during analysis. Past feedback includes, for example, survey results and evaluation comments, but is not limited to these examples. The analysis unit adjusts the analysis algorithm based on the past feedback. For example, the analysis algorithm can be adjusted based on the past feedback to improve employee satisfaction. The feedback content can also be reflected in the analysis to improve employee satisfaction. For example, the analysis method for a specific task can be customized based on the feedback content. In this way, the analysis method can be customized by reflecting employees' past feedback.

[0041] The generation unit can generate an optimal shift taking into consideration the skill matching of employees when generating shifts. The generation unit generates an optimal shift taking into consideration the skill matching of employees when generating shifts. Skill matching includes, for example, skill sets and job requirements, but is not limited to these examples. The generation unit generates an optimal shift based on the skill sets of employees. For example, employees with the skills required for specific jobs can be preferentially assigned. It is also possible to assign employees suitable for specific jobs by taking skill matching into consideration. Furthermore, it is possible to adjust the content of the shift according to skill level. For example, employees with high skill levels can be assigned to important jobs, and employees with low skill levels can be assigned to auxiliary jobs. In this way, it is possible to generate an optimal shift by taking employee skill matching into consideration.

[0042] When generating shifts, the generation unit can improve the accuracy of generation by referring to employees' past work history. When generating shifts, the generation unit improves the accuracy of generation by referring to employees' past work history. Past work history includes, for example, working hours and assigned tasks, but is not limited to these examples. The generation unit generates an optimal shift based on past work history. For example, based on past work history, employees suitable for specific tasks are assigned. The shift content can also be adjusted by referring to work history. For example, based on past work history, employees suitable for specific tasks are assigned and the shift content is adjusted. In this way, by referring to employees' past work history, the accuracy of generation is improved.

[0043] The generation unit can determine the priority of shifts based on the importance of tasks when generating shifts. The generation unit determines the priority of shifts based on the importance of tasks when generating shifts. Task importance includes, but is not limited to, the impact and urgency of tasks, for example. The generation unit generates shifts on a priority basis based on the importance of tasks. For example, tasks with a high impact on tasks are prioritized in shifts. Tasks with a high urgency can also be prioritized in shifts. Furthermore, the shift content can be adjusted according to the importance of tasks. For example, appropriate employees are assigned to important tasks, and the shift content is adjusted. In this way, by determining the priority of shifts based on the importance of tasks, important tasks can be processed with priority.

[0044] The generation unit can customize shifts based on the available working hours of employees when generating shifts. The generation unit customizes shifts based on the available working hours of employees when generating shifts. Available working hours include, but are not limited to, full-time, part-time, and shift work. The generation unit generates optimal shifts based on the available working hours of employees. For example, long shifts are provided to full-time employees and short shifts are provided to part-time employees. Shifts based on the available working hours can also be provided to employees working on shift work. Furthermore, the content of the shift can be adjusted based on the available working hours. For example, the priority of shifts is determined taking into account the available working hours. This enables efficient shift management by customizing shifts based on the available working hours of employees.

[0045] The generation unit can adjust the content of the shift according to the expertise level of the employees when generating the shift. The generation unit adjusts the content of the shift according to the expertise level of the employees when generating the shift. Expertise level includes, but is not limited to, for example, qualifications and years of experience. The generation unit generates an optimal shift based on the expertise level of the employees. For example, qualified employees are assigned to specific tasks. Employees with many years of experience can also be assigned to important tasks. Furthermore, the shift content can be adjusted according to the expertise level. For example, employees with high expertise levels are assigned to important tasks, and employees with low expertise levels are assigned to supporting tasks. In this way, by adjusting the shift content according to the expertise level of the employees, appropriate labor allocation is possible.

[0046] The generation unit can optimize shifts by taking into account the geographical location information of employees when generating shifts. The generation unit optimizes shifts by taking into account the geographical location information of employees when generating shifts. Geographical location information includes, but is not limited to, GPS data and address information, for example. The generation unit generates optimal shifts based on the geographical location information of employees. For example, the generation unit provides shifts that take commute times into account based on the current location of employees. The generation unit can also optimize labor allocation in a specific area based on the geographical location information. For example, the generation unit analyzes labor demand in a specific area and provides shifts appropriate for that area. In this way, by taking into account the geographical location information of employees, commute times can be reduced and efficient shift management becomes possible.

[0047] The adjustment unit can make adjustments taking into account the employee's health condition and fatigue level when making adjustments. The adjustment unit makes adjustments taking into account the employee's health condition and fatigue level when making adjustments. Health conditions include, for example, health checkup results and self-reporting, but are not limited to these examples. The adjustment unit adjusts labor allocation when the employee's fatigue level is high based on the employee's health data. For example, the adjustment unit adjusts the shift of an employee with a high fatigue level based on the health checkup results. The adjustment unit can also adjust the shift of an employee with a high fatigue level based on self-reporting. Furthermore, it can prioritize employees in good health and reflect this in the adjustment results. For example, prioritizing employees in good health can achieve efficient labor allocation. This makes it possible to make appropriate labor allocations by taking into account the employee's health condition and fatigue level.

[0048] The adjustment unit can monitor the progress of work in real time during adjustment and make adjustments as necessary. The adjustment unit can monitor the progress of work in real time during adjustment and make adjustments as necessary. The progress of work includes, for example, progress reports and real-time data, but is not limited to these examples. The adjustment unit monitors the progress of work in real time and adjusts shifts as necessary. For example, additional workers are assigned based on the progress of work. The content of shifts can also be adjusted based on the progress. Furthermore, shift priorities can be determined based on the progress. For example, if progress is delayed, additional workers are assigned and the content of shifts is adjusted. In this way, by monitoring the progress of work in real time, appropriate labor allocation is possible.

[0049] The adjustment department can make adjustments taking into account the skills and qualifications of employees when making adjustments. The adjustment department makes adjustments taking into account the skills and qualifications of employees when making adjustments. Skills include, for example, technical skills and communication skills, but are not limited to these examples. The adjustment department adjusts the optimal shift based on the employee's skill set. For example, employees with skills required for specific tasks are preferentially assigned. Qualifications include, for example, professional qualifications and business qualifications, but are not limited to these examples. The adjustment department adjusts the optimal shift based on employee qualification information. For example, employees with specific qualifications are assigned to specific tasks. This makes it possible to make appropriate workforce assignments by taking into account the skills and qualifications of employees.

[0050] The adjustment unit can take into account the geographical location information of employees when making adjustments. The adjustment unit can take into account the geographical location information of employees when making adjustments. Geographical location information includes, but is not limited to, GPS data and address information, for example. The adjustment unit adjusts optimal labor allocation based on the geographical location information of employees. For example, the adjustment unit adjusts labor allocation taking into account commuting time based on the current location of employees. The adjustment unit can also optimize labor allocation in a specific area based on the geographical location information. For example, the adjustment unit analyzes labor demand in a specific area and proposes labor allocation appropriate for that area. In this way, by taking into account the geographical location information of employees, commuting time can be reduced and efficient labor allocation can be achieved.

[0051] The adjustment unit can analyze the employee's social media activity during adjustment and reflect related information in the adjustment. The adjustment unit analyzes the employee's social media activity during adjustment and reflects related information in the adjustment. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The adjustment unit estimates the employee's stress level from social media activity and reflects the information in the adjustment. For example, the adjustment unit estimates the employee's stress level based on the content of social media posts and reflects the information in the adjustment. The adjustment unit can also reflect the employee's interests and concerns based on the content of social media posts. For example, the adjustment unit identifies the employee's interests and concerns based on the content of social media posts and reflects the information in the adjustment. Furthermore, the adjustment unit can also reflect the effectiveness of teamwork based on friendships on social media in the adjustment. For example, the adjustment unit identifies the effectiveness of teamwork based on friendships on social media and reflects the information in the adjustment. This makes it possible to make more detailed adjustments by analyzing the employee's social media activity.

[0052] The adjustment unit can customize the adjustment method by reflecting the employee's past feedback when making adjustments. The adjustment unit customizes the adjustment method by reflecting the employee's past feedback when making adjustments. Past feedback includes, but is not limited to, for example, survey results and evaluation comments. The adjustment unit adjusts the adjustment algorithm based on the past feedback. For example, the adjustment algorithm can be adjusted based on the past feedback to improve employee satisfaction. The feedback content can also be reflected in the adjustment to improve employee satisfaction. For example, the adjustment method for a specific task can be customized based on the feedback content. In this way, the adjustment method can be customized by reflecting the employee's past feedback.

[0053] The constraint unit can set constraints taking into account the employee's health condition and fatigue level when setting constraint conditions. The constraint unit sets constraints taking into account the employee's health condition and fatigue level when setting constraint conditions. Health conditions include, but are not limited to, health checkup results and self-reporting, for example. The constraint unit sets constraint conditions when the employee's fatigue level is high based on the employee's health data. For example, the constraint unit sets constraint conditions for employees with high fatigue levels based on health checkup results. Constraint conditions for employees with high fatigue levels can also be set based on self-reporting. Furthermore, constraint conditions can be set to prioritize the allocation of employees in good health. For example, constraint conditions can be set to prioritize the allocation of employees in good health, thereby achieving efficient allocation of labor. In this way, appropriate constraint conditions can be set by taking into account the employee's health condition and fatigue level.

[0054] The constraint unit can optimize the constraints by taking into consideration the type of work and seasonal fluctuations when setting the constraints. The constraint unit optimizes the constraints by taking into consideration the type of work and seasonal fluctuations when setting the constraints. The type of work includes, for example, desk work and fieldwork, but is not limited to these examples. The constraint unit sets constraints that take into consideration the required skill set depending on the type of work. For example, the constraint unit sets constraints that take into consideration the skill set required for desk work. It can also set constraints that take into consideration the skill set required for fieldwork. The seasonal fluctuations include, for example, busy seasons and slow seasons, but are not limited to these examples. The constraint unit sets constraints to improve the accuracy of demand forecasting based on seasonal fluctuations. For example, the constraint unit sets constraints to improve the accuracy of demand forecasting during busy seasons. It can also set constraints to improve the accuracy of demand forecasting during slow seasons. In this way, the accuracy of the constraints is improved by taking into consideration the type of work and seasonal fluctuations.

[0055] The constraint unit can set constraints taking into account the geographical location information of employees when setting constraint conditions. The constraint unit sets constraints taking into account the geographical location information of employees when setting constraint conditions. Geographical location information includes, but is not limited to, GPS data and address information, for example. The constraint unit sets optimal labor allocation as a constraint based on the geographical location information of employees. For example, a labor allocation that takes commuting time into account based on the current location of employees can be set as a constraint. Constraints that optimize labor allocation in a specific area can also be set based on geographical location information. For example, labor demand in a specific area can be analyzed, and a labor allocation appropriate for that area can be set as a constraint. In this way, appropriate constraint conditions can be set by taking into account the geographical location information of employees.

[0056] The constraint unit can analyze the employee's social media activity when setting the constraint conditions and reflect related information in the constraints. The constraint unit analyzes the employee's social media activity when setting the constraint conditions and reflect related information in the constraints. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The constraint unit estimates the employee's stress level from the social media activity and reflects it in the constraint conditions. For example, the constraint unit estimates the employee's stress level based on the content of social media posts and reflects it in the constraint conditions. The constraint unit can also reflect the employee's interests and concerns based on the content of social media posts. For example, the constraint unit identifies the employee's interests and concerns based on the content of social media posts and reflects it in the constraint conditions. Furthermore, the constraint unit can also reflect the effectiveness of teamwork based on friendships on social media in the constraint conditions. For example, the constraint unit identifies the effectiveness of teamwork based on friendships on social media and reflects it in the constraint conditions. In this way, more detailed constraint conditions can be set by analyzing the employee's social media activity.

[0057] The comparison unit can take into account the employee's health condition and fatigue level when making the comparison. The comparison unit can take into account the employee's health condition and fatigue level when making the comparison. Health conditions include, but are not limited to, health checkup results and self-reporting, for example. The comparison unit adjusts labor allocation when the employee's fatigue level is high based on the employee's health data. For example, the comparison unit adjusts the shifts of employees with high fatigue levels based on health checkup results. The comparison unit can also adjust the shifts of employees with high fatigue levels based on self-reporting. Furthermore, the comparison unit can prioritize the allocation of employees in good health and reflect this in the comparison results. For example, prioritizing the allocation of employees in good health achieves efficient labor allocation. This enables appropriate comparison by taking into account the employee's health condition and fatigue level.

[0058] The comparison unit can monitor the progress of work in real time during the comparison and make comparisons as necessary. The comparison unit can monitor the progress of work in real time during the comparison and make comparisons as necessary. The progress of work includes, for example, progress reports and real-time data, but is not limited to these examples. The comparison unit monitors the progress of work in real time and compares shifts as necessary. For example, additional workers are assigned based on the progress of work. The shift contents can also be compared based on the progress. Furthermore, shift priorities can be determined based on the progress. For example, if progress is delayed, additional workers are assigned and shift contents are adjusted. In this way, by monitoring the progress of work in real time, appropriate comparisons can be made.

[0059] The comparison unit may take into account the geographical location information of employees when making the comparison. The comparison unit may take into account the geographical location information of employees when making the comparison. Examples of geographical location information include, but are not limited to, GPS data and address information. The comparison unit compares optimal labor allocations based on the geographical location information of employees. For example, the comparison unit may compare labor allocations that take commute times into account based on the current locations of employees. The comparison unit may also set comparison criteria that optimize labor allocation in a specific area based on the geographical location information. For example, the comparison unit may analyze labor demand in a specific area and compare labor allocations that are suitable for that area. In this way, appropriate labor allocation can be achieved by taking into account the geographical location information of employees.

[0060] The comparison unit can analyze the social media activities of employees during the comparison and reflect related information in the comparison. The comparison unit can analyze the social media activities of employees during the comparison and reflect related information in the comparison. Social media activities include, but are not limited to, the content of posts and the number of likes. The comparison unit estimates the stress levels of employees from their social media activities and reflects them in the comparison. For example, the stress levels of employees can be estimated based on the content of posts on social media and reflected in the comparison. The comparison unit can also reflect the interests and concerns of employees based on the content of posts on social media. For example, the interests and concerns of employees can be identified based on the content of posts on social media and reflected in the comparison. Furthermore, the effectiveness of teamwork can be reflected in the comparison based on friendships on social media. For example, the effectiveness of teamwork can be identified based on friendships on social media and reflected in the comparison. This makes it possible to perform a more detailed comparison by analyzing employees' social media activities.

[0061] The readjustment unit can perform readjustment taking into account the employee's health condition and fatigue level when performing readjustment. The readjustment unit performs readjustment taking into account the employee's health condition and fatigue level when performing readjustment. Health conditions include, but are not limited to, health checkup results and self-reporting, for example. The readjustment unit adjusts labor allocation when the employee's fatigue level is high based on the employee's health data. For example, the readjustment unit adjusts the shift of an employee with a high fatigue level based on health checkup results. The readjustment unit can also adjust the shift of an employee with a high fatigue level based on self-reporting. Furthermore, it can prioritize employees in good health and reflect this in the readjustment results. For example, prioritizing employees in good health can achieve efficient labor allocation. This enables appropriate readjustment by taking into account the employee's health condition and fatigue level.

[0062] The readjustment unit can monitor the progress of work in real time at the time of readjustment and make readjustment as necessary. The readjustment unit can monitor the progress of work in real time at the time of readjustment and make readjustment as necessary. The progress of work includes, for example, progress reports and real-time data, but is not limited to these examples. The readjustment unit monitors the progress of work in real time and readjusts shifts as necessary. For example, additional workers are assigned based on the progress of work. The shift contents can also be readjusted based on the progress. Furthermore, the shift priorities can be determined based on the progress. For example, if the progress is delayed, additional workers are assigned and the shift contents are adjusted. In this way, appropriate readjustment is possible by monitoring the progress of work in real time.

[0063] The readjustment unit can perform readjustment taking into account the geographical location information of employees when performing readjustment. The readjustment unit performs readjustment taking into account the geographical location information of employees when performing readjustment. Geographical location information includes, but is not limited to, GPS data and address information, for example. The readjustment unit readjusts optimal labor allocation based on the geographical location information of employees. For example, the readjustment unit readjusts labor allocation taking into account commuting time based on the current location of employees. The readjustment unit can also perform readjustment to optimize labor allocation in a specific area based on the geographical location information. For example, the labor demand in a specific area is analyzed and the labor allocation is readjusted to suit that area. In this way, by taking into account the geographical location information of employees, commuting time can be reduced and efficient labor allocation can be achieved.

[0064] The readjustment unit can analyze the employee's social media activity at the time of readjustment and reflect related information in the readjustment. The readjustment unit analyzes the employee's social media activity at the time of readjustment and reflects related information in the readjustment. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The readjustment unit estimates the employee's stress level from social media activity and reflects the information in the readjustment. For example, the readjustment unit estimates the employee's stress level based on the content of social media posts and reflects the information in the readjustment. The readjustment unit can also reflect the employee's interests and concerns based on the content of social media posts. For example, the employee's interests and concerns can be identified based on the content of social media posts and reflect the information in the readjustment. Furthermore, the effectiveness of teamwork can be reflected in the readjustment based on friendships on social media. For example, the effectiveness of teamwork can be identified based on friendships on social media and reflect the information in the readjustment. In this way, more detailed readjustment can be performed by analyzing the employee's social media activity.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The analysis unit can also monitor employee health and adjust labor demand forecasts based on their health status. For example, based on the results of employee health checks, employees in good health can be prioritized for shifts. It can also adjust the shifts of employees who are highly fatigued, taking into account employees' self-reported health status. Furthermore, it can assign employees who are suitable for specific tasks based on their health status. This allows for optimal labor allocation that takes employee health status into account.

[0067] The generation unit can also analyze work patterns based on employees' past work history and generate optimal shifts. For example, it can identify the tasks that a particular employee excels at from their past work history and generate shifts suited to those tasks. It can also analyze employees' preferred work hours from their past work history and provide shifts that meet their preferences. It can also identify the time periods when employees perform best based on their past work history and schedule shifts around those times. This allows for efficient shift generation that utilizes employees' past work history.

[0068] The coordination department can also adjust shifts to minimize commuting time by taking into account employees' geographical location information. For example, based on the distance from an employee's home to their workplace, it can prioritize employees with short commute times. It can also analyze labor demand in a specific area and prioritize employees who live in that area. Furthermore, it can adjust the shifts of employees with long commute times based on employees' geographical location information to reduce their burden. This allows for efficient shift adjustment that takes into account employees' geographical location information.

[0069] The readjustment unit can also monitor the progress of work in real time and readjust shifts according to the progress. For example, if work progress is delayed, additional workers can be deployed and shifts can be readjusted. Conversely, if work progress is going smoothly, shifts can be readjusted by reducing the number of workers. Furthermore, it is possible to change the priority of shifts for specific work based on the progress of work. This allows for flexible shift readjustment according to the progress of work.

[0070] The analysis unit can also analyze employees' social media activity, estimate their stress levels, and reflect this in the analysis results. For example, it can estimate employees' stress levels based on the content of their social media posts and adjust the shifts of employees with high stress levels. It can also identify employees' interests and concerns from their social media activity and assign them to work that is aligned with their interests. Furthermore, it can also reflect the effectiveness of teamwork in the analysis based on friendships made on social media. This makes it possible to perform detailed analysis using employees' social media activity.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The analysis unit analyzes past labor data or demand forecasts. Past labor data includes employee working hours, job duties, and number of work days. The analysis unit predicts labor demand for a specific period from past data and can also perform demand forecasts using statistical models and machine learning algorithms. Step 2: The generation unit automatically generates shifts based on the analysis results obtained by the analysis unit. The generation unit generates optimized shifts taking into account the goals and constraints, and uses generation AI to generate shifts while taking into account constraints such as employee skills, qualifications, and available working hours. Step 3: The adjustment unit adjusts the shifts generated by the generation unit to match the actual work situation. The adjustment unit compares the proposed schedule with the actual work situation and readjusts the schedule as necessary. The adjustment unit readjusts the schedule if the demand predicted using the generation AI differs from the actual demand, and readjusts the shifts while monitoring the actual work situation in real time.

[0073] (Example 2) A labor force forecasting and schedule adjustment system according to an embodiment of the present invention analyzes past labor force data and demand forecasts, and a generation AI automatically generates optimal shifts and adjusts them to match actual work conditions. The labor force forecasting and schedule adjustment system analyzes past labor force data and demand forecasts to propose optimal schedules and the required labor force. For example, the labor force forecasting and schedule adjustment system analyzes data such as employee work history, job type, and working hours to predict labor demand for a specific period. Next, the generation AI automatically generates optimized shifts taking into account goals and constraints. For example, the optimal shifts are generated while taking into account constraints such as employee skills, qualifications, and available working hours. Furthermore, the labor force forecasting and schedule adjustment system compares the proposed schedule with the actual work conditions and adjusts the schedule as necessary. For example, if the predicted demand differs from the actual demand, the generation AI automatically readjusts the schedule to maintain optimal labor allocation. This enables efficient labor allocation and improves work productivity. The labor force forecasting and schedule adjustment system thus realizes efficient labor allocation and improves work productivity. For example, by securing the necessary labor force during busy periods and reducing the labor force during slow periods, it is possible to improve business efficiency. It also reduces the burden on employees and provides a comfortable working environment.

[0074] A labor force prediction and schedule adjustment system according to an embodiment includes an analysis unit, a generation unit, and an adjustment unit. The analysis unit analyzes past labor force data or demand forecasts. Past labor force data includes, but is not limited to, employee working hours, job content, and number of work days. For example, the analysis unit predicts labor demand for a specific period from past data. The analysis unit can also perform demand forecasting using statistical models or machine learning algorithms. The generation unit automatically generates shifts based on the analysis results obtained by the analysis unit. The generation unit generates optimized shifts, for example, taking into account goals and constraints. The generation unit uses a generation AI to generate shifts while taking into account constraints such as employee skills, qualifications, and available work hours. For example, the generation AI proposes optimal shifts based on employee skill sets and qualification information. The generation unit can also use the generation AI to generate shifts while taking into account employees' available work hours. The adjustment unit adjusts the shifts generated by the generation unit to suit actual business conditions. For example, the adjustment unit compares the proposed schedule with the actual work situation and readjusts the schedule as necessary. The adjustment unit can also use the generative AI to readjust the schedule when the predicted demand differs from the actual demand. For example, the adjustment unit monitors the actual work situation in real time and readjusts shifts as necessary. As a result, the workforce prediction and schedule adjustment system according to the embodiment can realize efficient workforce allocation and improve work productivity.

[0075] The workforce forecasting and schedule adjustment system includes a constraint unit that considers constraints such as employee skills or qualifications and available working hours. The constraint unit considers constraints such as employee skills, qualifications, and available working hours. Examples of skills include, but are not limited to, technical skills and communication skills. The constraint unit generates optimal shifts based on employee skill sets. For example, employees with skills required for specific tasks are preferentially assigned. Examples of qualifications include, but are not limited to, professional qualifications and business qualifications. The constraint unit generates optimal shifts based on employee qualification information. For example, employees with specific qualifications are assigned to specific tasks. Examples of available working hours include, but are not limited to, full-time, part-time, and shift work. The constraint unit generates optimal shifts based on the available working hours of employees. For example, shifts are generated while taking into account the available working hours of employees. In this way, optimal shifts can be generated by taking into account constraints such as employee skills, qualifications, and available working hours.

[0076] The workforce forecasting and schedule adjustment system includes a comparison unit that compares a proposed schedule with an actual work situation. The comparison unit compares the proposed schedule with the actual work situation. The proposed schedule includes, for example, but is not limited to, shift time periods and assigned tasks. The comparison unit compares the proposed schedule with the actual work situation to improve the accuracy of the schedule. The actual work situation includes, for example, but is not limited to, the progress of work and employee attendance status. The comparison unit evaluates the suitability of the proposed schedule based on the actual work situation. For example, the comparison unit monitors the progress of work in real time and compares it with the proposed schedule. The comparison unit can also evaluate the suitability of the proposed schedule based on employee attendance status. In this way, the accuracy of the schedule can be improved by comparing the proposed schedule with the actual work situation.

[0077] The workforce forecasting and schedule adjustment system includes a readjustment unit that readjusts the schedule when predicted demand differs from actual demand. The readjustment unit readjusts the schedule when predicted demand differs from actual demand. Examples of predicted demand include, but are not limited to, sales forecasts and customer number forecasts. The readjustment unit compares the predicted demand with actual demand and readjusts the schedule. Examples of actual demand include, but are not limited to, actual sales and the actual number of customers. The readjustment unit readjusts the schedule based on the actual demand. For example, if actual sales differ from predicted sales, the readjustment unit readjusts the schedule. The readjustment unit can also readjust the schedule when the actual number of customers differs from the predicted number of customers. In this way, by readjusting the schedule when predicted demand differs from actual demand, optimal workforce allocation can be maintained.

[0078] The analysis unit can analyze past labor force data or demand forecasts. The analysis unit analyzes past labor force data and demand forecasts. Past labor force data includes, for example, employee working hours, job content, number of working days, etc., but is not limited to these examples. The analysis unit predicts labor demand for a specific period from past data. For example, labor demand for a specific period is predicted based on past data. The analysis unit can also perform demand forecasts using statistical models or machine learning algorithms. For example, a statistical model is used to analyze demand trends from past data. A machine learning algorithm can also be used to learn demand patterns from past data and perform demand forecasts. In this way, by analyzing past labor force data and demand forecasts, labor demand can be accurately predicted.

[0079] The generation unit can automatically generate shifts taking into account goals or constraints. The generation unit automatically generates an optimized shift taking into account goals and constraints. Goals include, for example, sales targets and business targets, but are not limited to these examples. The generation unit generates an optimal shift based on the goals. For example, a shift is generated while taking into account the labor force required to achieve sales targets. The generation unit can also generate a shift while taking into account the labor force required to achieve business targets. Constraints include, for example, but are not limited to these examples. The generation unit generates an optimal shift based on constraints. For example, a shift is generated while taking into account legal constraints. The generation unit can also generate a shift while taking into account business policies. This enables efficient shift management by automatically generating an optimized shift taking into account goals and constraints.

[0080] The analysis unit can estimate the user's emotions and adjust the timing of analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the timing of analysis based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The analysis unit uses an emotion analysis algorithm to estimate the user's emotions. For example, the analysis unit can analyze the user's facial expressions and voice to estimate the emotions. The analysis unit can also estimate the user's emotions based on survey results. The analysis unit can adjust the timing of analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can reduce the frequency of analysis to reduce the burden. Furthermore, if the user is relaxed, the analysis unit can increase the frequency of analysis to collect more detailed data. Furthermore, if the user is in a hurry, the analysis can be performed quickly and the results can be provided immediately. In this way, the burden on the user can be reduced by adjusting the timing of analysis based on the user's emotions.

[0081] The analysis unit can extract specific patterns from past labor force data to improve the accuracy of the analysis. The analysis unit extracts specific patterns from past labor force data to improve the accuracy of the analysis. Specific patterns include, but are not limited to, patterns of busy and slow seasons, periods when specific skill sets are needed, and labor demand patterns on specific days of the week or time periods. The analysis unit extracts patterns of busy and slow seasons from past data to improve the accuracy of the demand forecast. For example, the analysis unit can identify patterns of busy and slow seasons based on past data and reflect them in the demand forecast. It can also identify periods when specific skill sets are needed from employee work histories and reflect them in the analysis. For example, it can identify periods when specific skill sets are needed and predict the labor required during those periods. It can also extract labor demand patterns on specific days of the week or time periods from past data and reflect them in the analysis. For example, it can identify labor demand patterns on specific days of the week or time periods and perform demand forecasts based on those patterns. By extracting specific patterns from past labor force data, the accuracy of the analysis can be improved.

[0082] The analysis unit can adjust the analysis results during analysis, taking into account the employee's health condition and fatigue level. The analysis unit adjusts the analysis results during analysis, taking into account the employee's health condition and fatigue level. Health conditions include, for example, health checkup results and self-reporting, but are not limited to these examples. The analysis unit adjusts the allocation of labor if the employee's fatigue level is high based on the employee's health data. For example, the analysis unit adjusts the shifts of employees with high fatigue levels based on health checkup results. The analysis unit can also adjust the shifts of employees with high fatigue levels based on self-reporting. Furthermore, the analysis unit can prioritize the allocation of employees in good health and reflect this in the analysis results. For example, prioritizing the allocation of employees in good health achieves efficient allocation of labor. In this way, more appropriate analysis results can be obtained by taking into account the employee's health condition and fatigue level.

[0083] The analysis unit can optimize the analysis algorithm during analysis by taking into account the type of work and seasonal fluctuations. The analysis unit optimizes the analysis algorithm during analysis by taking into account the type of work and seasonal fluctuations. Examples of types of work include, but are not limited to, desk work and fieldwork. The analysis unit adjusts the analysis algorithm by taking into account the required skill set depending on the type of work. For example, the analysis algorithm is adjusted by taking into account the skill set required for desk work. The analysis unit can also adjust the analysis algorithm by taking into account the skill set required for fieldwork. Examples of seasonal fluctuations include, but are not limited to, busy seasons and slow seasons. The analysis unit applies an analysis algorithm to improve the accuracy of demand forecasting based on seasonal fluctuations. For example, the analysis unit applies an analysis algorithm to improve the accuracy of demand forecasting during busy seasons. The analysis unit can also apply an analysis algorithm to improve the accuracy of demand forecasting during slow seasons. In this way, the accuracy of the analysis algorithm is improved by taking into account the type of work and seasonal fluctuations.

[0084] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The analysis unit uses an emotion analysis algorithm to estimate the user's emotions. For example, the analysis unit can analyze the user's facial expressions and voice to estimate the emotions. The analysis unit can also estimate the user's emotions based on survey results. The analysis unit can prioritize the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, important analysis results can be displayed with priority. Furthermore, if the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is in a hurry, important analysis results can be displayed quickly. In this way, by prioritizing the analysis results based on the user's emotions, important information can be provided with priority.

[0085] The analysis unit can take into account the geographical location information of employees when performing the analysis. The analysis unit can take into account the geographical location information of employees when performing the analysis. Geographical location information includes, but is not limited to, GPS data and address information, for example. The analysis unit analyzes optimal labor allocation based on the current location of employees. For example, the analysis unit analyzes labor allocation taking into account commuting time based on the current location of employees. The analysis unit can also analyze labor demand in a specific area based on the geographical location information. For example, the analysis unit analyzes labor demand in a specific area and proposes labor allocation appropriate for that area. In this way, by taking into account the geographical location information of employees, more appropriate labor allocation can be achieved.

[0086] The analysis unit can analyze the social media activities of employees during the analysis and reflect related data in the analysis. The analysis unit can analyze the social media activities of employees during the analysis and reflect related data in the analysis. Social media activities include, for example, but are not limited to, the content of posts and the number of likes. The analysis unit estimates the stress level of employees from their social media activities and reflects the data in the analysis. For example, the analysis unit can estimate the stress level of employees based on the content of their social media posts and reflect the data in the analysis. The analysis unit can also reflect the interests and concerns of employees based on the content of their social media posts. For example, the analysis unit can identify the interests and concerns of employees based on the content of their social media posts and reflect the data in the analysis. Furthermore, the analysis can also reflect the effectiveness of teamwork based on friendships on social media. For example, the analysis unit can identify the effectiveness of teamwork based on friendships on social media and reflect the data in the analysis. In this way, more detailed analysis results can be obtained by analyzing employees' social media activities.

[0087] The analysis unit can customize the analysis method by reflecting employees' past feedback during analysis. The analysis unit customizes the analysis method by reflecting employees' past feedback during analysis. Past feedback includes, for example, survey results and evaluation comments, but is not limited to these examples. The analysis unit adjusts the analysis algorithm based on the past feedback. For example, the analysis algorithm can be adjusted based on the past feedback to improve employee satisfaction. The feedback content can also be reflected in the analysis to improve employee satisfaction. For example, the analysis method for a specific task can be customized based on the feedback content. In this way, the analysis method can be customized by reflecting employees' past feedback.

[0088] The generation unit can estimate the user's emotions and adjust the shift generation method based on the estimated user's emotions. The generation unit can estimate the user's emotions and adjust the shift generation method based on the estimated user's emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The generation unit uses an emotion analysis algorithm to estimate the user's emotions. For example, the generation unit can analyze the user's facial expressions and voice to estimate the emotions. The generation unit can also estimate the user's emotions based on survey results. The generation unit can adjust the shift generation method based on the estimated user's emotions. For example, if the user is stressed, a simple shift generation method can be provided. Furthermore, if the user is relaxed, a detailed shift generation method can be provided. Furthermore, if the user is in a hurry, a shift can be quickly generated. In this way, by adjusting the shift generation method based on the user's emotions, the burden on the user can be reduced.

[0089] The generation unit can generate an optimal shift taking into consideration the skill matching of employees when generating shifts. The generation unit generates an optimal shift taking into consideration the skill matching of employees when generating shifts. Skill matching includes, for example, skill sets and job requirements, but is not limited to these examples. The generation unit generates an optimal shift based on the skill sets of employees. For example, employees with the skills required for specific jobs can be preferentially assigned. It is also possible to assign employees suitable for specific jobs by taking skill matching into consideration. Furthermore, it is possible to adjust the content of the shift according to skill level. For example, employees with high skill levels can be assigned to important jobs, and employees with low skill levels can be assigned to auxiliary jobs. In this way, it is possible to generate an optimal shift by taking employee skill matching into consideration.

[0090] When generating shifts, the generation unit can improve the accuracy of generation by referring to employees' past work history. When generating shifts, the generation unit improves the accuracy of generation by referring to employees' past work history. Past work history includes, for example, working hours and assigned tasks, but is not limited to these examples. The generation unit generates an optimal shift based on past work history. For example, based on past work history, employees suitable for specific tasks are assigned. The shift content can also be adjusted by referring to work history. For example, based on past work history, employees suitable for specific tasks are assigned and the shift content is adjusted. In this way, by referring to employees' past work history, the accuracy of generation is improved.

[0091] The generation unit can determine the priority of shifts based on the importance of tasks when generating shifts. The generation unit determines the priority of shifts based on the importance of tasks when generating shifts. Task importance includes, but is not limited to, the impact and urgency of tasks, for example. The generation unit generates shifts on a priority basis based on the importance of tasks. For example, tasks with a high impact on tasks are prioritized in shifts. Tasks with a high urgency can also be prioritized in shifts. Furthermore, the shift content can be adjusted according to the importance of tasks. For example, appropriate employees are assigned to important tasks, and the shift content is adjusted. In this way, by determining the priority of shifts based on the importance of tasks, important tasks can be processed with priority.

[0092] The generation unit can estimate the user's emotion and adjust the length of the shift based on the estimated user's emotion. The generation unit can estimate the user's emotion and adjust the length of the shift based on the estimated user's emotion. Examples of user's emotion include, but are not limited to, stress, relaxation, and hurry. The generation unit uses an emotion analysis algorithm to estimate the user's emotion. For example, the generation unit can analyze the user's facial expression or voice to estimate the emotion. The generation unit can also estimate the user's emotion based on survey results. The generation unit can adjust the length of the shift based on the estimated user's emotion. For example, if the user is stressed, a shorter shift can be provided. Also, if the user is relaxed, a longer shift can be provided. Furthermore, if the user is in a hurry, a shift can be generated quickly. In this way, by adjusting the shift length based on the user's emotion, the burden on the user can be reduced.

[0093] The generation unit can customize shifts based on the available working hours of employees when generating shifts. The generation unit customizes shifts based on the available working hours of employees when generating shifts. Available working hours include, but are not limited to, full-time, part-time, and shift work. The generation unit generates optimal shifts based on the available working hours of employees. For example, long shifts are provided to full-time employees and short shifts are provided to part-time employees. Shifts based on the available working hours can also be provided to employees working on shift work. Furthermore, the content of the shift can be adjusted based on the available working hours. For example, the priority of shifts is determined taking into account the available working hours. This enables efficient shift management by customizing shifts based on the available working hours of employees.

[0094] The generation unit can adjust the content of the shift according to the expertise level of the employees when generating the shift. The generation unit adjusts the content of the shift according to the expertise level of the employees when generating the shift. Expertise level includes, but is not limited to, for example, qualifications and years of experience. The generation unit generates an optimal shift based on the expertise level of the employees. For example, qualified employees are assigned to specific tasks. Employees with many years of experience can also be assigned to important tasks. Furthermore, the shift content can be adjusted according to the expertise level. For example, employees with high expertise levels are assigned to important tasks, and employees with low expertise levels are assigned to supporting tasks. In this way, by adjusting the shift content according to the expertise level of the employees, appropriate labor allocation is possible.

[0095] The generation unit can optimize shifts by taking into account the geographical location information of employees when generating shifts. The generation unit optimizes shifts by taking into account the geographical location information of employees when generating shifts. Geographical location information includes, but is not limited to, GPS data and address information, for example. The generation unit generates optimal shifts based on the geographical location information of employees. For example, the generation unit provides shifts that take commute times into account based on the current location of employees. The generation unit can also optimize labor allocation in a specific area based on the geographical location information. For example, the generation unit analyzes labor demand in a specific area and provides shifts appropriate for that area. In this way, by taking into account the geographical location information of employees, commute times can be reduced and efficient shift management becomes possible.

[0096] The adjustment unit can estimate the user's emotion and determine an adjustment method based on the estimated user's emotion. The adjustment unit can estimate the user's emotion and determine an adjustment method based on the estimated user's emotion. Examples of user's emotion include, but are not limited to, stress, relaxation, and hurry. The adjustment unit uses an emotion analysis algorithm to estimate the user's emotion. For example, the adjustment unit can analyze the user's facial expression or voice to estimate the emotion. The adjustment unit can also estimate the user's emotion based on survey results. The adjustment unit can determine an adjustment method based on the estimated user's emotion. For example, if the user is stressed, a simple adjustment method can be provided. Furthermore, if the user is relaxed, a detailed adjustment method can be provided. Furthermore, if the user is in a hurry, a quick adjustment can be performed. In this way, by determining an adjustment method based on the user's emotion, the burden on the user can be reduced.

[0097] The adjustment unit can make adjustments taking into account the employee's health condition and fatigue level when making adjustments. The adjustment unit makes adjustments taking into account the employee's health condition and fatigue level when making adjustments. Health conditions include, for example, health checkup results and self-reporting, but are not limited to these examples. The adjustment unit adjusts labor allocation when the employee's fatigue level is high based on the employee's health data. For example, the adjustment unit adjusts the shift of an employee with a high fatigue level based on the health checkup results. The adjustment unit can also adjust the shift of an employee with a high fatigue level based on self-reporting. Furthermore, it can prioritize employees in good health and reflect this in the adjustment results. For example, prioritizing employees in good health can achieve efficient labor allocation. This makes it possible to make appropriate labor allocations by taking into account the employee's health condition and fatigue level.

[0098] The adjustment unit can monitor the progress of work in real time during adjustment and make adjustments as necessary. The adjustment unit can monitor the progress of work in real time during adjustment and make adjustments as necessary. The progress of work includes, for example, progress reports and real-time data, but is not limited to these examples. The adjustment unit monitors the progress of work in real time and adjusts shifts as necessary. For example, additional workers are assigned based on the progress of work. The content of shifts can also be adjusted based on the progress. Furthermore, shift priorities can be determined based on the progress. For example, if progress is delayed, additional workers are assigned and the content of shifts is adjusted. In this way, by monitoring the progress of work in real time, appropriate labor allocation is possible.

[0099] The adjustment department can make adjustments taking into account the skills and qualifications of employees when making adjustments. The adjustment department makes adjustments taking into account the skills and qualifications of employees when making adjustments. Skills include, for example, technical skills and communication skills, but are not limited to these examples. The adjustment department adjusts the optimal shift based on the employee's skill set. For example, employees with skills required for specific tasks are preferentially assigned. Qualifications include, for example, professional qualifications and business qualifications, but are not limited to these examples. The adjustment department adjusts the optimal shift based on employee qualification information. For example, employees with specific qualifications are assigned to specific tasks. This makes it possible to make appropriate workforce assignments by taking into account the skills and qualifications of employees.

[0100] The adjustment unit can estimate the user's emotion and determine the priority of adjustments based on the estimated user's emotion. The adjustment unit can estimate the user's emotion and determine the priority of adjustments based on the estimated user's emotion. Examples of user's emotion include, but are not limited to, stress, relaxation, and hurry. The adjustment unit uses an emotion analysis algorithm to estimate the user's emotion. For example, the adjustment unit can analyze the user's facial expression or voice to estimate the emotion. The adjustment unit can also estimate the user's emotion based on survey results. The adjustment unit can determine the priority of adjustments based on the estimated user's emotion. For example, if the user is stressed, important adjustments can be prioritized. Furthermore, if the user is relaxed, detailed adjustments can be made. Furthermore, if the user is in a hurry, important adjustments can be made quickly. In this way, by determining the priority of adjustments based on the user's emotion, important adjustments can be made with priority.

[0101] The adjustment unit can take into account the geographical location information of employees when making adjustments. The adjustment unit can take into account the geographical location information of employees when making adjustments. Geographical location information includes, but is not limited to, GPS data and address information, for example. The adjustment unit adjusts optimal labor allocation based on the geographical location information of employees. For example, the adjustment unit adjusts labor allocation taking into account commuting time based on the current location of employees. The adjustment unit can also optimize labor allocation in a specific area based on the geographical location information. For example, the adjustment unit analyzes labor demand in a specific area and proposes labor allocation appropriate for that area. In this way, by taking into account the geographical location information of employees, commuting time can be reduced and efficient labor allocation can be achieved.

[0102] The adjustment unit can analyze the employee's social media activity during adjustment and reflect related information in the adjustment. The adjustment unit analyzes the employee's social media activity during adjustment and reflects related information in the adjustment. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The adjustment unit estimates the employee's stress level from social media activity and reflects the information in the adjustment. For example, the adjustment unit estimates the employee's stress level based on the content of social media posts and reflects the information in the adjustment. The adjustment unit can also reflect the employee's interests and concerns based on the content of social media posts. For example, the adjustment unit identifies the employee's interests and concerns based on the content of social media posts and reflects the information in the adjustment. Furthermore, the adjustment unit can also reflect the effectiveness of teamwork based on friendships on social media in the adjustment. For example, the adjustment unit identifies the effectiveness of teamwork based on friendships on social media and reflects the information in the adjustment. This makes it possible to make more detailed adjustments by analyzing the employee's social media activity.

[0103] The adjustment unit can customize the adjustment method by reflecting the employee's past feedback when making adjustments. The adjustment unit customizes the adjustment method by reflecting the employee's past feedback when making adjustments. Past feedback includes, but is not limited to, for example, survey results and evaluation comments. The adjustment unit adjusts the adjustment algorithm based on the past feedback. For example, the adjustment algorithm can be adjusted based on the past feedback to improve employee satisfaction. The feedback content can also be reflected in the adjustment to improve employee satisfaction. For example, the adjustment method for a specific task can be customized based on the feedback content. In this way, the adjustment method can be customized by reflecting the employee's past feedback.

[0104] The constraint unit can estimate the user's emotion and determine the priority of constraints based on the estimated user's emotion. The constraint unit can estimate the user's emotion and determine the priority of constraints based on the estimated user's emotion. Examples of user's emotion include, but are not limited to, stress, relaxation, and hurry. The constraint unit uses an emotion analysis algorithm to estimate the user's emotion. For example, the constraint unit can analyze the user's facial expression or voice to estimate the emotion. The constraint unit can also estimate the user's emotion based on survey results. The constraint unit determines the priority of constraints based on the estimated user's emotion. For example, if the user is stressed, important constraints can be set with priority. Furthermore, if the user is relaxed, detailed constraints can be set. Furthermore, if the user is in a hurry, important constraints can be set quickly. In this way, by determining the priority of constraints based on the user's emotion, important constraints can be set with priority.

[0105] The constraint unit can set constraints taking into account the employee's health condition and fatigue level when setting constraint conditions. The constraint unit sets constraints taking into account the employee's health condition and fatigue level when setting constraint conditions. Health conditions include, but are not limited to, health checkup results and self-reporting, for example. The constraint unit sets constraint conditions when the employee's fatigue level is high based on the employee's health data. For example, the constraint unit sets constraint conditions for employees with high fatigue levels based on health checkup results. Constraint conditions for employees with high fatigue levels can also be set based on self-reporting. Furthermore, constraint conditions can be set to prioritize the allocation of employees in good health. For example, constraint conditions can be set to prioritize the allocation of employees in good health, thereby achieving efficient allocation of labor. In this way, appropriate constraint conditions can be set by taking into account the employee's health condition and fatigue level.

[0106] The constraint unit can optimize the constraints by taking into consideration the type of work and seasonal fluctuations when setting the constraints. The constraint unit optimizes the constraints by taking into consideration the type of work and seasonal fluctuations when setting the constraints. The type of work includes, for example, desk work and fieldwork, but is not limited to these examples. The constraint unit sets constraints that take into consideration the required skill set depending on the type of work. For example, the constraint unit sets constraints that take into consideration the skill set required for desk work. It can also set constraints that take into consideration the skill set required for fieldwork. The seasonal fluctuations include, for example, busy seasons and slow seasons, but are not limited to these examples. The constraint unit sets constraints to improve the accuracy of demand forecasting based on seasonal fluctuations. For example, the constraint unit sets constraints to improve the accuracy of demand forecasting during busy seasons. It can also set constraints to improve the accuracy of demand forecasting during slow seasons. In this way, the accuracy of the constraints is improved by taking into consideration the type of work and seasonal fluctuations.

[0107] The constraint unit can estimate the user's emotion and adjust the display method of the constraints based on the estimated user's emotion. The constraint unit can estimate the user's emotion and adjust the display method of the constraints based on the estimated user's emotion. Examples of user's emotion include, but are not limited to, stress, relaxation, and hurry. The constraint unit uses an emotion analysis algorithm to estimate the user's emotion. For example, the constraint unit can analyze the user's facial expression or voice to estimate the emotion. The constraint unit can also estimate the user's emotion based on the results of a survey. The constraint unit can adjust the display method of the constraints based on the estimated user's emotion. For example, if the user is stressed, a simple display method can be provided. On the other hand, if the user is relaxed, a detailed display method can be provided. Furthermore, if the user is in a hurry, important constraints can be quickly displayed. In this way, by adjusting the display method of the constraints based on the user's emotion, the burden on the user can be reduced.

[0108] The constraint unit can set constraints taking into account the geographical location information of employees when setting constraint conditions. The constraint unit sets constraints taking into account the geographical location information of employees when setting constraint conditions. Geographical location information includes, but is not limited to, GPS data and address information, for example. The constraint unit sets optimal labor allocation as a constraint based on the geographical location information of employees. For example, a labor allocation that takes commuting time into account based on the current location of employees can be set as a constraint. Constraints that optimize labor allocation in a specific area can also be set based on geographical location information. For example, labor demand in a specific area can be analyzed, and a labor allocation appropriate for that area can be set as a constraint. In this way, appropriate constraint conditions can be set by taking into account the geographical location information of employees.

[0109] The constraint unit can analyze the employee's social media activity when setting the constraint conditions and reflect related information in the constraints. The constraint unit analyzes the employee's social media activity when setting the constraint conditions and reflect related information in the constraints. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The constraint unit estimates the employee's stress level from the social media activity and reflects it in the constraint conditions. For example, the constraint unit estimates the employee's stress level based on the content of social media posts and reflects it in the constraint conditions. The constraint unit can also reflect the employee's interests and concerns based on the content of social media posts. For example, the constraint unit identifies the employee's interests and concerns based on the content of social media posts and reflects it in the constraint conditions. Furthermore, the constraint unit can also reflect the effectiveness of teamwork based on friendships on social media in the constraint conditions. For example, the constraint unit identifies the effectiveness of teamwork based on friendships on social media and reflects it in the constraint conditions. In this way, more detailed constraint conditions can be set by analyzing the employee's social media activity.

[0110] The comparison unit can estimate the user's emotion and adjust the comparison criteria based on the estimated user's emotion. The comparison unit can estimate the user's emotion and adjust the comparison criteria based on the estimated user's emotion. Examples of user's emotion include, but are not limited to, stress, relaxation, and hurry. The comparison unit uses an emotion analysis algorithm to estimate the user's emotion. For example, the comparison unit can analyze the user's facial expression or voice to estimate the emotion. The comparison unit can also estimate the user's emotion based on survey results. The comparison unit adjusts the comparison criteria based on the estimated user's emotion. For example, if the user is stressed, important comparison criteria can be prioritized. Furthermore, if the user is relaxed, detailed comparison criteria can be set. Furthermore, if the user is in a hurry, important comparison criteria can be quickly set. In this way, by adjusting the comparison criteria based on the user's emotion, important information can be prioritized for comparison.

[0111] The comparison unit can take into account the employee's health condition and fatigue level when making the comparison. The comparison unit can take into account the employee's health condition and fatigue level when making the comparison. Health conditions include, but are not limited to, health checkup results and self-reporting, for example. The comparison unit adjusts labor allocation when the employee's fatigue level is high based on the employee's health data. For example, the comparison unit adjusts the shifts of employees with high fatigue levels based on health checkup results. The comparison unit can also adjust the shifts of employees with high fatigue levels based on self-reporting. Furthermore, the comparison unit can prioritize the allocation of employees in good health and reflect this in the comparison results. For example, prioritizing the allocation of employees in good health achieves efficient labor allocation. This enables appropriate comparison by taking into account the employee's health condition and fatigue level.

[0112] The comparison unit can monitor the progress of work in real time during the comparison and make comparisons as necessary. The comparison unit can monitor the progress of work in real time during the comparison and make comparisons as necessary. The progress of work includes, for example, progress reports and real-time data, but is not limited to these examples. The comparison unit monitors the progress of work in real time and compares shifts as necessary. For example, additional workers are assigned based on the progress of work. The shift contents can also be compared based on the progress. Furthermore, shift priorities can be determined based on the progress. For example, if progress is delayed, additional workers are assigned and shift contents are adjusted. In this way, by monitoring the progress of work in real time, appropriate comparisons can be made.

[0113] The comparison unit can estimate the user's emotion and adjust the display method of the comparison results based on the estimated user's emotion. The comparison unit can estimate the user's emotion and adjust the display method of the comparison results based on the estimated user's emotion. Examples of user's emotion include, but are not limited to, stress, relaxation, and hurry. The comparison unit uses an emotion analysis algorithm to estimate the user's emotion. For example, the comparison unit can analyze the user's facial expression or voice to estimate the emotion. The comparison unit can also estimate the user's emotion based on survey results. The comparison unit can adjust the display method of the comparison results based on the estimated user's emotion. For example, if the user is stressed, a simple display method can be provided. If the user is relaxed, a detailed display method can be provided. Furthermore, if the user is in a hurry, important comparison results can be quickly displayed. In this way, by adjusting the display method of the comparison results based on the user's emotion, the burden on the user can be reduced.

[0114] The comparison unit may take into account the geographical location information of employees when making the comparison. The comparison unit may take into account the geographical location information of employees when making the comparison. Examples of geographical location information include, but are not limited to, GPS data and address information. The comparison unit compares optimal labor allocations based on the geographical location information of employees. For example, the comparison unit may compare labor allocations that take commute times into account based on the current locations of employees. The comparison unit may also set comparison criteria that optimize labor allocation in a specific area based on the geographical location information. For example, the comparison unit may analyze labor demand in a specific area and compare labor allocations that are suitable for that area. In this way, appropriate labor allocation can be achieved by taking into account the geographical location information of employees.

[0115] The comparison unit can analyze the social media activities of employees during the comparison and reflect related information in the comparison. The comparison unit can analyze the social media activities of employees during the comparison and reflect related information in the comparison. Social media activities include, but are not limited to, the content of posts and the number of likes. The comparison unit estimates the stress levels of employees from their social media activities and reflects them in the comparison. For example, the stress levels of employees can be estimated based on the content of posts on social media and reflected in the comparison. The comparison unit can also reflect the interests and concerns of employees based on the content of posts on social media. For example, the interests and concerns of employees can be identified based on the content of posts on social media and reflected in the comparison. Furthermore, the effectiveness of teamwork can be reflected in the comparison based on friendships on social media. For example, the effectiveness of teamwork can be identified based on friendships on social media and reflected in the comparison. This makes it possible to perform a more detailed comparison by analyzing employees' social media activities.

[0116] The readjustment unit can estimate the user's emotion and determine a readjustment method based on the estimated user's emotion. The readjustment unit can estimate the user's emotion and determine a readjustment method based on the estimated user's emotion. Examples of user's emotion include, but are not limited to, stress, relaxation, and hurry. The readjustment unit uses an emotion analysis algorithm to estimate the user's emotion. For example, the readjustment unit can analyze the user's facial expression or voice to estimate the emotion. The user's emotion can also be estimated based on survey results. The readjustment unit can determine a readjustment method based on the estimated user's emotion. For example, if the user is stressed, a simple readjustment method can be provided. Furthermore, if the user is relaxed, a detailed readjustment method can be provided. Furthermore, if the user is in a hurry, a quick readjustment can be performed. In this way, by determining a readjustment method based on the user's emotion, the burden on the user can be reduced.

[0117] The readjustment unit can perform readjustment taking into account the employee's health condition and fatigue level when performing readjustment. The readjustment unit performs readjustment taking into account the employee's health condition and fatigue level when performing readjustment. Health conditions include, but are not limited to, health checkup results and self-reporting, for example. The readjustment unit adjusts labor allocation when the employee's fatigue level is high based on the employee's health data. For example, the readjustment unit adjusts the shift of an employee with a high fatigue level based on health checkup results. The readjustment unit can also adjust the shift of an employee with a high fatigue level based on self-reporting. Furthermore, it can prioritize employees in good health and reflect this in the readjustment results. For example, prioritizing employees in good health can achieve efficient labor allocation. This enables appropriate readjustment by taking into account the employee's health condition and fatigue level.

[0118] The readjustment unit can monitor the progress of work in real time at the time of readjustment and make readjustment as necessary. The readjustment unit can monitor the progress of work in real time at the time of readjustment and make readjustment as necessary. The progress of work includes, for example, progress reports and real-time data, but is not limited to these examples. The readjustment unit monitors the progress of work in real time and readjusts shifts as necessary. For example, additional workers are assigned based on the progress of work. The shift contents can also be readjusted based on the progress. Furthermore, the shift priorities can be determined based on the progress. For example, if the progress is delayed, additional workers are assigned and the shift contents are adjusted. In this way, appropriate readjustment is possible by monitoring the progress of work in real time.

[0119] The readjustment unit can estimate the user's emotions and determine the priority of readjustment based on the estimated user's emotions. The readjustment unit can estimate the user's emotions and determine the priority of readjustment based on the estimated user's emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The readjustment unit uses an emotion analysis algorithm to estimate the user's emotions. For example, the readjustment unit can analyze the user's facial expressions and voice to estimate the emotions. The user's emotions can also be estimated based on survey results. The readjustment unit determines the priority of readjustment based on the estimated user's emotions. For example, if the user is feeling stressed, important readjustment can be prioritized. Furthermore, if the user is relaxed, detailed readjustment can be performed. Furthermore, if the user is in a hurry, important readjustment can be performed quickly. In this way, by determining the priority of readjustment based on the user's emotions, important readjustment can be prioritized.

[0120] The readjustment unit can perform readjustment taking into account the geographical location information of employees when performing readjustment. The readjustment unit performs readjustment taking into account the geographical location information of employees when performing readjustment. Geographical location information includes, but is not limited to, GPS data and address information, for example. The readjustment unit readjusts optimal labor allocation based on the geographical location information of employees. For example, the readjustment unit readjusts labor allocation taking into account commuting time based on the current location of employees. The readjustment unit can also perform readjustment to optimize labor allocation in a specific area based on the geographical location information. For example, the labor demand in a specific area is analyzed and the labor allocation is readjusted to suit that area. In this way, by taking into account the geographical location information of employees, commuting time can be reduced and efficient labor allocation can be achieved.

[0121] The readjustment unit can analyze the employee's social media activity at the time of readjustment and reflect related information in the readjustment. The readjustment unit analyzes the employee's social media activity at the time of readjustment and reflects related information in the readjustment. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The readjustment unit estimates the employee's stress level from social media activity and reflects the information in the readjustment. For example, the readjustment unit estimates the employee's stress level based on the content of social media posts and reflects the information in the readjustment. The readjustment unit can also reflect the employee's interests and concerns based on the content of social media posts. For example, the employee's interests and concerns can be identified based on the content of social media posts and reflect the information in the readjustment. Furthermore, the effectiveness of teamwork can be reflected in the readjustment based on friendships on social media. For example, the effectiveness of teamwork can be identified based on friendships on social media and reflect the information in the readjustment. In this way, more detailed readjustment can be performed by analyzing the employee's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, adjustment unit, constraint unit, comparison unit, and readjustment unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the adjustment unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the constraint unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the comparison unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the readjustment unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, adjustment unit, constraint unit, comparison unit, and readjustment unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the adjustment unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the constraint unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the comparison unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the readjustment unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, adjustment unit, constraint unit, comparison unit, and readjustment unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the adjustment unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the constraint unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the comparison unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the readjustment unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, adjustment unit, constraint unit, comparison unit, and readjustment unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the adjustment unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the constraint unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the comparison unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the readjustment unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The analysis unit can also monitor employee health and adjust labor demand forecasts based on their health status. For example, based on the results of employee health checks, employees in good health can be prioritized for shifts. It can also adjust the shifts of employees who are highly fatigued, taking into account employees' self-reported health status. Furthermore, it can assign employees who are suitable for specific tasks based on their health status. This allows for optimal labor allocation that takes employee health status into account.

[0124] The generation unit can also analyze work patterns based on employees' past work history and generate optimal shifts. For example, it can identify the tasks that a particular employee excels at from their past work history and generate shifts suited to those tasks. It can also analyze employees' preferred work hours from their past work history and provide shifts that meet their preferences. It can also identify the time periods when employees perform best based on their past work history and schedule shifts around those times. This allows for efficient shift generation that utilizes employees' past work history.

[0125] The coordination department can also adjust shifts to minimize commuting time by taking into account employees' geographical location information. For example, based on the distance from an employee's home to their workplace, it can prioritize employees with short commute times. It can also analyze labor demand in a specific area and prioritize employees who live in that area. Furthermore, it can adjust the shifts of employees with long commute times based on employees' geographical location information to reduce their burden. This allows for efficient shift adjustment that takes into account employees' geographical location information.

[0126] The readjustment unit can also monitor the progress of work in real time and readjust shifts according to the progress. For example, if work progress is delayed, additional workers can be deployed and shifts can be readjusted. Conversely, if work progress is going smoothly, shifts can be readjusted by reducing the number of workers. Furthermore, it is possible to change the priority of shifts for specific work based on the progress of work. This allows for flexible shift readjustment according to the progress of work.

[0127] The analysis unit can also analyze employees' social media activity, estimate their stress levels, and reflect this in the analysis results. For example, it can estimate employees' stress levels based on the content of their social media posts and adjust the shifts of employees with high stress levels. It can also identify employees' interests and concerns from their social media activity and assign them to work that is aligned with their interests. Furthermore, it can also reflect the effectiveness of teamwork in the analysis based on friendships made on social media. This makes it possible to perform detailed analysis using employees' social media activity.

[0128] The analysis unit can also estimate the user's emotions and adjust the timing of analysis based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of analysis can be reduced to reduce the burden. Also, if the user is relaxed, the frequency of analysis can be increased to collect more detailed data. Furthermore, if the user is in a hurry, it is possible to perform analysis quickly and provide results immediately. This allows for flexible adjustment of the analysis timing based on the user's emotions.

[0129] The generation unit can also estimate the user's emotions and adjust the shift generation method based on the estimated user emotions. For example, if the user is feeling stressed, a simple shift generation method can be provided. Alternatively, if the user is relaxed, a detailed shift generation method can be provided. Furthermore, if the user is in a hurry, a shift can be generated quickly. This allows for flexible adjustment of the shift generation method based on the user's emotions.

[0130] The adjustment unit can also estimate the user's emotions and determine the adjustment method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple adjustment method can be provided. Alternatively, if the user is relaxed, a detailed adjustment method can be provided. Furthermore, if the user is in a hurry, it is also possible to make a quick adjustment. This allows for flexible determination of the adjustment method based on the user's emotions.

[0131] The constraint unit can also estimate the user's emotions and determine the priority of constraints based on the estimated user's emotions. For example, if the user is feeling stressed, important constraints can be set with priority. Also, if the user is relaxed, detailed constraints can be set. Furthermore, if the user is in a hurry, important constraints can be set quickly. This allows for flexible determination of the priority of constraints based on the user's emotions.

[0132] The comparison unit can also estimate the user's emotions and adjust the comparison criteria based on the estimated user's emotions. For example, if the user is feeling stressed, important comparison criteria can be set with priority. Also, if the user is relaxed, detailed comparison criteria can be set. Furthermore, if the user is in a hurry, important comparison criteria can be set quickly. This allows for flexible adjustment of comparison criteria based on the user's emotions.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The analysis unit analyzes past labor data or demand forecasts. Past labor data includes employee working hours, job duties, and number of work days. The analysis unit predicts labor demand for a specific period from past data and can also perform demand forecasts using statistical models and machine learning algorithms. Step 2: The generation unit automatically generates shifts based on the analysis results obtained by the analysis unit. The generation unit generates optimized shifts taking into account the goals and constraints, and uses generation AI to generate shifts while taking into account constraints such as employee skills, qualifications, and available working hours. Step 3: The adjustment unit adjusts the shifts generated by the generation unit to match the actual work situation. The adjustment unit compares the proposed schedule with the actual work situation and readjusts the schedule as necessary. The adjustment unit readjusts the schedule if the demand predicted using the generation AI differs from the actual demand, and readjusts the shifts while monitoring the actual work situation in real time.

[0135] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0136] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0157] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0174] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0175] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0177] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0178] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0179] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0180] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0181] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0182] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0183] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0184] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0185] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0186] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0189] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0190] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0191] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0192] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0193] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0194] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0195] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0196] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0197] 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.

[0198] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0199] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0200] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0201] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0202] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0203] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0204] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0205] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0206] [Explanation of symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes past labor force data or demand forecasts; a generation unit that automatically generates a shift based on the analysis result obtained by the analysis unit; an adjustment unit that adjusts the shift generated by the generation unit to match an actual work situation; Equipped with A system characterized by:

2. Constraints section that considers employee skills or qualifications and available working hours 2. The system of claim 1.

3. A comparison section is provided to compare the proposed schedule with the actual work situation.

2. The system of claim 1.

4. A readjustment unit is provided to readjust the schedule when the predicted demand differs from the actual demand.

2. The system of claim 1.

5. The analysis unit Analyze historical labor data or demand forecasts 2. The system of claim 1.

6. The generation unit Automatically generate shifts taking into account goals or constraints 2. The system of claim 1.

7. The analysis unit Estimate the user's emotions and adjust the timing of analysis based on the estimated user emotions.

2. The system of claim 1.

8. The analysis unit Extract specific patterns from past workforce data to improve analysis accuracy 2. The system of claim 1.

9. The analysis unit During analysis, adjust the analysis results taking into account the health status and fatigue level of employees 2. The system of claim 1.

Images