System

Abstract

An object of the system according to the embodiment is to perform demand prediction based on past labor data and propose an optimal schedule and necessary labor.SOLUTION: A system according to an embodiment includes a labor data collection unit, a demand prediction unit, a schedule proposal unit, and a labor proposal unit. The labor data collection part collects past labor data. The demand prediction unit performs demand prediction based on the data collected by the labor data collection unit. The schedule proposal unit proposes an optimum schedule on the basis of the demand predicted by the demand prediction unit. The labor force proposal unit proposes necessary labor force based on the schedule proposed by the schedule proposal unit.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] Conventional technology does not adequately forecast demand or adjust schedules based on past labor data, leaving room for improvement.

[0005] The system according to the embodiment aims to predict demand based on past labor data and propose an optimal schedule and required labor force. [Means for solving the problem]

[0006] The system according to the embodiment includes a labor force data collection unit, a demand forecasting unit, a schedule proposal unit, and a labor force proposal unit. The labor force data collection unit collects past labor force data. The demand forecasting unit performs demand forecasting based on the data collected by the labor force data collection unit. The schedule proposal unit proposes an optimal schedule based on the demand predicted by the demand forecasting unit. The labor force proposal unit proposes the required labor force based on the schedule proposed by the schedule proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict demand based on past labor data and propose optimal schedules and required labor. [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) The labor force forecasting and schedule adjustment optimization system according to an embodiment of the present invention is a system that uses generative AI to propose optimal schedules and required labor based on past labor force data and demand forecasts. As a result, the labor force forecasting and schedule adjustment optimization system can achieve efficient and flexible labor management.

[0029] A labor force forecasting and schedule adjustment optimization system according to an embodiment includes a labor force data collection unit, a demand forecasting unit, a schedule proposal unit, and a labor force proposal unit. The labor force data collection unit collects past labor force data. For example, it collects employee working hours, work content, and productivity data. The labor force data collection unit can also digitally store employee work records. The demand forecasting unit performs demand forecasting based on the data collected by the labor force data collection unit. For example, it analyzes past sales data and seasonal demand fluctuations to predict future demand. The demand forecasting unit can also analyze market trends and reflect them in the demand forecast. The schedule proposal unit proposes an optimal schedule based on the demand predicted by the demand forecasting unit. For example, it generates a schedule that assigns more employees to times of high demand and fewer employees to times of low demand. The schedule proposal unit can also propose a schedule that takes into account employees' work preferences and vacation requests. The labor force proposal unit proposes the required labor force based on the schedule proposed by the schedule proposal unit. For example, it proposes the number of people and skill sets required to complete a specific project. The labor force proposal department can also propose the optimal labor force, taking into account achievement goals and constraints. This allows the labor force forecasting and schedule adjustment optimization system to achieve efficient and flexible labor management.

[0030] The workforce data collection unit can analyze employee productivity and stress levels and reflect them in workforce data. For example, the workforce data collection unit uses generation AI to analyze employee productivity data and quantify each employee's work efficiency. For example, it calculates a productivity score based on the time required to complete tasks and their quality, and reflects this in the workforce data. In addition, to analyze employees' stress levels, the workforce data collection unit uses generation AI to collect biometric data (heart rate, electrodermal activity, etc.) and calculates a stress score. This allows it to identify tasks and time periods that are highly stressful and reflect this in the workforce data. In addition, the workforce data collection unit uses generation AI to analyze the correlation between employee productivity and stress levels and propose optimal work assignments. For example, it can prioritize tasks that are highly productive and low stress. This makes it possible to collect workforce data that takes employee productivity and stress levels into account.

[0031] The workforce data collection unit can collect employee skill sets and qualification information and analyze compatibility with job content. For example, the workforce data collection unit uses generative AI to register employee skill sets and qualification information in a database and analyze compatibility with each job. For example, employees with specific qualifications are preferentially assigned to specific jobs. The workforce data collection unit also analyzes employee skill sets and identifies skill gaps. For example, if there is a shortage of employees with the skills required for a specific job, it makes a proposal to hire employees with those skills. The workforce data collection unit also uses generative AI to score the compatibility between employee skill sets and job content and propose optimal job assignments. For example, employees with high skill scores are assigned to important jobs. This makes it possible to collect workforce data that takes into account employee skill sets and qualification information.

[0032] The workforce data collection unit can collect employee working hours and work content in real time using IoT devices. The workforce data collection unit, for example, uses IoT devices to collect employee working hours and work content in real time. For example, it monitors employee movements using smartwatches and sensors. The workforce data collection unit also uses IoT devices to collect employee biometric data (heart rate, body temperature, etc.) in real time and reflects this in workforce data. For example, it analyzes stress levels and fatigue levels. The workforce data collection unit also uses IoT devices to collect employee location information in real time and monitor the progress of work. For example, it issues an alert if work in a specific area is delayed. This makes it possible to collect data in real time using IoT devices.

[0033] The labor force data collection unit can collect labor force data from different industries and perform cross-industry analysis. For example, the labor force data collection unit collects labor force data from different industries and performs cross-industry analysis using generative AI. For example, it compares and analyzes labor force data from the manufacturing and service industries. The labor force data collection unit also integrates labor force data from different industries and extracts common issues and success stories. For example, it shares best practices for labor optimization in different industries. The labor force data collection unit also performs cross-industry analysis and proposes new labor force management methods based on labor force data from different industries. For example, it applies efficiency methods from the manufacturing industry to the service industry. This makes it possible to perform cross-industry analysis based on labor force data from different industries.

[0034] The demand forecasting unit can analyze social media trend data and reflect it in the demand forecast. The demand forecasting unit, for example, uses generation AI to collect social media trend data and reflect it in the demand forecast. For example, it analyzes the number of posts and engagement regarding a specific product or service. The demand forecasting unit also analyzes social media trend data and predicts sudden increases or decreases in demand. For example, it predicts demand fluctuations related to a specific event or campaign. The demand forecasting unit also uses generation AI to integrate social media trend data with past sales data to improve the accuracy of the demand forecast. For example, it predicts peaks in demand based on trend data. This makes it possible to perform demand forecasting based on social media trend data.

[0035] The demand forecasting unit can analyze weather data and event information and incorporate it into the demand forecast. The demand forecasting unit, for example, uses generative AI to analyze weather data and reflect it in the demand forecast. For example, it analyzes the impact of weather fluctuations on demand and incorporates it into the forecast model. The demand forecasting unit also analyzes event information and reflects it in the demand forecast. For example, it predicts demand fluctuations related to specific events or holidays. The demand forecasting unit also integrates weather data and event information to improve the accuracy of the demand forecast. For example, it predicts demand fluctuations when weather and events occur simultaneously. This makes it possible to perform demand forecasts based on weather data and event information.

[0036] The demand forecasting unit can incorporate data from different regions and countries to make forecasts from a global perspective. For example, the demand forecasting unit uses generative AI to collect sales data from different regions and countries to make demand forecasts from a global perspective. For example, it analyzes demand fluctuations by region. The demand forecasting unit also analyzes market trends in different regions and countries and reflects this in the demand forecast. For example, it predicts demand based on trends and consumer behavior in specific regions. The demand forecasting unit also makes demand forecasts from a global perspective and visualizes demand fluctuations by region. For example, it can focus resources on areas where demand is increasing. This makes it possible to make demand forecasts from a global perspective based on data from different regions and countries.

[0037] The demand forecasting unit can display the results of the demand forecast on a visual dashboard, allowing for intuitive understanding. The demand forecasting unit, for example, uses a generative AI to build a system that displays the results of the demand forecast on a visual dashboard. For example, it visualizes demand fluctuations using graphs and charts. The demand forecasting unit also updates the results of the demand forecast in real time using the visual dashboard. For example, it dynamically displays the demand forecast based on the latest data. The demand forecasting unit also displays the results of the demand forecast on a visual dashboard, allowing for intuitive understanding by the user. For example, it highlights demand peaks and trends. This makes it possible to display the results of the demand forecast in a way that is intuitively understandable.

[0038] The schedule proposal unit can analyze individual employee performance data and propose optimal schedules. The schedule proposal unit, for example, uses generation AI to analyze employee performance data and propose optimal schedules for each employee. For example, it calculates a performance score based on the time required to complete tasks and their quality. The schedule proposal unit also analyzes employee performance data and proposes a schedule that allocates important tasks to time periods when performance is high. For example, it allocates important meetings and projects to time periods when performance is high. The schedule proposal unit also uses generation AI to analyze the compatibility between employee performance data and work content and proposes optimal schedules. For example, it prioritizes employees who perform well in specific tasks. This makes it possible to propose optimal schedules based on individual employee performance data.

[0039] The schedule proposal unit can propose schedules that take into consideration employees' health conditions and vacation requests. The schedule proposal unit, for example, uses generation AI to analyze employees' health condition data and propose an optimal schedule based on their health conditions. For example, it assigns important tasks to employees in good health. The schedule proposal unit also proposes a schedule that takes into consideration employees' vacation requests. For example, it gives priority to desired vacation days and coordinates with other employees. The schedule proposal unit also uses generation AI to comprehensively analyze employees' health conditions and vacation requests and propose an optimal schedule. For example, it assigns lighter tasks to employees in poor health. This makes it possible to propose schedules that take into consideration employees' health conditions and vacation requests.

[0040] The schedule proposal unit can reflect the progress of the project in real time. For example, the schedule proposal unit uses generation AI to monitor the progress of the project in real time and reflect it in the schedule. For example, additional resources may be allocated to projects that are behind schedule. The schedule proposal unit also analyzes project progress data and proposes a schedule based on the progress. For example, resources may be reduced for projects that are progressing smoothly and reallocated to other projects. The schedule proposal unit also uses generation AI to optimize the project progress and resource allocation in real time. For example, additional resources may be allocated to projects that are behind schedule. This makes it possible to propose a schedule that reflects the progress of the project in real time.

[0041] The schedule proposal unit can automate schedule adjustments between different departments and teams. For example, the schedule proposal unit uses generative AI to build a system that automates schedule adjustments between different departments and teams. For example, it automates resource sharing and schedule adjustments between departments. The schedule proposal unit also integrates schedule data from different departments and teams to propose optimal schedules. For example, it adjusts schedules to resolve resource conflicts between departments. The schedule proposal unit also uses generative AI to adjust schedules between different departments and teams in real time. For example, it dynamically adjusts resources between departments depending on the progress of the project. This makes it possible to automate schedule adjustments between different departments and teams.

[0042] The labor force proposal unit can match employee skills and propose the optimal labor force. For example, the labor force proposal unit uses generative AI to analyze employee skill sets and perform optimal skill matching for each job. For example, employees with specific skills are preferentially assigned to specific jobs. The labor force proposal unit also analyzes the compatibility of employee skill sets with job content and proposes the optimal labor force. For example, employees with high skill scores are assigned to important jobs. The labor force proposal unit also uses generative AI to score the compatibility of employee skill sets with job content and propose the optimal labor force. For example, employees with high skill scores for specific jobs are preferentially assigned. This makes it possible to propose the optimal labor force based on employee skill matching.

[0043] The labor force proposal department can propose labor taking into consideration employees' career paths and growth goals. The labor force proposal department, for example, uses generative AI to analyze employees' career paths and growth goals and proposes the optimal labor force based on that. For example, it assigns tasks that align with their career paths. The labor force proposal department also makes labor proposals taking into consideration employees' growth goals. For example, it prioritizes the allocation of tasks that match their growth goals. The labor force proposal department also uses generative AI to comprehensively analyze employees' career paths and growth goals and proposes the optimal labor force. For example, it assigns tasks that align with their career paths. This makes it possible to propose labor that takes into consideration employees' career paths and growth goals.

[0044] The labor proposal department can incorporate data on freelancers and contract employees to make flexible labor proposals. For example, the labor proposal department uses generative AI to collect data on freelancers and contract employees to make flexible labor proposals. For example, it proposes freelancers with the skills required for a specific project. The labor proposal department also analyzes the skill sets of freelancers and contract employees to propose the optimal labor force. For example, it prioritizes the allocation of freelancers with high skill scores for specific tasks. The labor proposal department also uses generative AI to integrate data on freelancers and contract employees to make flexible labor proposals. For example, it dynamically allocates freelancers and contract employees depending on the progress of a project. This makes it possible to make flexible labor proposals based on data on freelancers and contract employees.

[0045] The labor force proposal unit can display the results of the labor force proposal on a visual dashboard to enable intuitive understanding. The labor force proposal unit, for example, uses generative AI to build a system that displays the results of the labor force proposal on a visual dashboard. For example, it visualizes labor allocation using graphs and charts. The labor force proposal unit also updates the results of the labor force proposal in real time using the visual dashboard. For example, it dynamically displays the labor force proposal based on the latest data. The labor force proposal unit also displays the results of the labor force proposal on a visual dashboard to enable intuitive understanding by the user. For example, it highlights labor peaks and trends. This makes it possible to display the results of the labor force proposal in a way that is intuitively understandable.

[0046] The schedule proposal unit can analyze project progress data and propose a schedule based on achievement goals. The schedule proposal unit, for example, uses generation AI to analyze project progress data in real time and propose a schedule based on achievement goals. For example, it allocates additional resources to tasks where progress is behind schedule. The schedule proposal unit also analyzes project progress data and proposes a schedule according to achievement goals. For example, it concentrates resources toward important milestones. The schedule proposal unit also uses generation AI to comprehensively analyze project progress data and achievement goals and propose an optimal schedule. For example, it reduces resources for tasks that are progressing smoothly and reallocates resources to other tasks. This makes it possible to propose a schedule based on achievement goals based on project progress data.

[0047] The schedule proposal unit can reflect budget and resource constraints in the constraint conditions in real time. For example, the schedule proposal unit uses generation AI to analyze budget and resource constraints in real time and reflect them in the schedule. For example, it optimizes resource allocation within the budget. The schedule proposal unit also proposes a schedule that takes budget and resource constraints into consideration. For example, when the budget is limited, it prioritizes allocating cost-effective resources. The schedule proposal unit also uses generation AI to monitor budget and resource constraints in real time and reflect them in the schedule. For example, it adjusts resource allocation according to the budget consumption situation. This makes it possible to propose a schedule that reflects budget and resource constraints in real time.

[0048] The schedule proposal unit can share goals and constraints between different projects and departments to achieve overall optimization. The schedule proposal unit, for example, uses generation AI to share goals and constraints between different projects and departments, building a system to achieve overall optimization. For example, it automates resource sharing and schedule adjustment between departments. The schedule proposal unit also integrates the goals and constraints of different projects and departments to achieve overall optimization. For example, it adjusts schedules to resolve resource conflicts between departments. The schedule proposal unit also uses generation AI to share goals and constraints between different projects and departments in real time to achieve overall optimization. For example, it dynamically adjusts resources between departments depending on the progress of the project. This makes it possible to share goals and constraints between different projects and departments to achieve overall optimization.

[0049] The schedule proposal unit can display the results of the goals and constraints on a visual dashboard to enable intuitive understanding. The schedule proposal unit, for example, uses generative AI to build a system that displays the results of the goals and constraints on a visual dashboard. For example, the goals and constraints are visualized using graphs and charts. The schedule proposal unit also updates the results of the goals and constraints in real time using the visual dashboard. For example, the goals and constraints are dynamically displayed based on the latest data. The schedule proposal unit also displays the results of the goals and constraints on a visual dashboard to enable intuitive understanding by the user. For example, the progress of the goals and the impact of the constraints are highlighted. This makes it possible to display the results of the goals and constraints in a way that allows intuitive understanding.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The workforce data collection unit can monitor employee health status and reflect it in data. For example, it can collect biometric data such as employees' heart rate and body temperature to evaluate their health status. It can also make suggestions for adjusting work loads based on employees' health status. It can also analyze the correlation between employee health status and work performance and assign important tasks to employees in good health. This makes it possible to collect workforce data and assign work that takes employee health status into account.

[0052] The workforce data collection unit can analyze employee communication patterns and optimize team collaboration. For example, it can analyze email and chat exchanges between employees to evaluate the frequency and quality of communication. It can also make suggestions for adjusting the division of roles and work assignments within a team based on communication patterns. It can also analyze the correlation between communication patterns and work performance and assign important projects to teams with effective communication. This makes it possible to collect workforce data and assign work that takes team collaboration into account.

[0053] The workforce data collection unit can collect employee skill sets and qualification information and analyze their compatibility with job content. For example, generative AI can be used to register employee skill sets and qualification information in a database and analyze their compatibility with each job. Employees with specific qualifications can be preferentially assigned to specific jobs. It is also possible to analyze employee skill sets and identify skill gaps. Furthermore, it can score the compatibility between employee skill sets and job content and propose optimal job assignments. This makes it possible to collect workforce data that takes employee skill sets and qualification information into account.

[0054] The workforce data collection unit can analyze employee stress levels and reflect them in workforce data. For example, generative AI can be used to collect employees' biometric data (heart rate, electrodermal activity, etc.) and calculate a stress score. High-stress tasks and time periods can be identified and reflected in workforce data. It is also possible to analyze the correlation between employees' stress levels and work performance and prioritize the allocation of less stressful tasks. Furthermore, it is possible to monitor employees' stress levels and adjust work assignments in real time. This makes it possible to collect workforce data and assign tasks that take employee stress levels into account.

[0055] The workforce data collection unit can monitor employee health status and reflect it in data. For example, it can collect biometric data such as employees' heart rate and body temperature to evaluate their health status. It can also make suggestions for adjusting work loads based on employees' health status. It can also analyze the correlation between employee health status and work performance and assign important tasks to employees in good health. This makes it possible to collect workforce data and assign work that takes employee health status into account.

[0056] The workforce data collection unit can analyze employee communication patterns and optimize team collaboration. For example, it can analyze email and chat exchanges between employees to evaluate the frequency and quality of communication. It can also make suggestions for adjusting the division of roles and work assignments within a team based on communication patterns. It can also analyze the correlation between communication patterns and work performance and assign important projects to teams with effective communication. This makes it possible to collect workforce data and assign work that takes team collaboration into account.

[0057] The workforce data collection unit can analyze employee stress levels and reflect them in workforce data. For example, generative AI can be used to collect employees' biometric data (heart rate, electrodermal activity, etc.) and calculate a stress score. High-stress tasks and time periods can be identified and reflected in workforce data. It is also possible to analyze the correlation between employees' stress levels and work performance and prioritize the allocation of less stressful tasks. Furthermore, it is possible to monitor employees' stress levels and adjust work assignments in real time. This makes it possible to collect workforce data and assign tasks that take employee stress levels into account.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The Workforce Data Collection Department collects historical workforce data, such as employee working hours, work content, and productivity data, and stores this data in digital format. Step 2: The demand forecasting unit makes a demand forecast based on the data collected by the labor data collection unit. For example, it analyzes past sales data and seasonal demand fluctuations to predict future demand. It can also analyze market trends and reflect them in the demand forecast. Step 3: The schedule proposal unit proposes an optimal schedule based on the demand predicted by the demand forecast unit. For example, it generates a schedule that assigns more employees to times when demand is high and fewer employees to times when demand is low. It can also propose a schedule that takes into account employees' work preferences and vacation requests. Step 4: The Workforce Proposal Unit proposes the required workforce based on the schedule proposed by the Schedule Proposal Unit. For example, it proposes the number of people and skill sets required to complete a specific project. It can also propose the optimal workforce based on the goals and constraints.

[0060] (Example 2) The labor force forecasting and schedule adjustment optimization system according to an embodiment of the present invention is a system that uses generative AI to propose optimal schedules and required labor based on past labor force data and demand forecasts. As a result, the labor force forecasting and schedule adjustment optimization system can achieve efficient and flexible labor management.

[0061] A labor force forecasting and schedule adjustment optimization system according to an embodiment includes a labor force data collection unit, a demand forecasting unit, a schedule proposal unit, and a labor force proposal unit. The labor force data collection unit collects past labor force data. For example, it collects employee working hours, work content, and productivity data. The labor force data collection unit can also digitally store employee work records. The demand forecasting unit performs demand forecasting based on the data collected by the labor force data collection unit. For example, it analyzes past sales data and seasonal demand fluctuations to predict future demand. The demand forecasting unit can also analyze market trends and reflect them in the demand forecast. The schedule proposal unit proposes an optimal schedule based on the demand predicted by the demand forecasting unit. For example, it generates a schedule that assigns more employees to times of high demand and fewer employees to times of low demand. The schedule proposal unit can also propose a schedule that takes into account employees' work preferences and vacation requests. The labor force proposal unit proposes the required labor force based on the schedule proposed by the schedule proposal unit. For example, it proposes the number of people and skill sets required to complete a specific project. The labor force proposal department can also propose the optimal labor force, taking into account achievement goals and constraints. This allows the labor force forecasting and schedule adjustment optimization system to achieve efficient and flexible labor management.

[0062] The workforce data collection unit can analyze employee productivity and stress levels and reflect them in workforce data. For example, the workforce data collection unit uses generation AI to analyze employee productivity data and quantify each employee's work efficiency. For example, it calculates a productivity score based on the time required to complete tasks and their quality, and reflects this in the workforce data. In addition, to analyze employees' stress levels, the workforce data collection unit uses generation AI to collect biometric data (heart rate, electrodermal activity, etc.) and calculates a stress score. This allows it to identify tasks and time periods that are highly stressful and reflect this in the workforce data. In addition, the workforce data collection unit uses generation AI to analyze the correlation between employee productivity and stress levels and propose optimal work assignments. For example, it can prioritize tasks that are highly productive and low stress. This makes it possible to collect workforce data that takes employee productivity and stress levels into account.

[0063] The workforce data collection unit can collect employee skill sets and qualification information and analyze compatibility with job content. For example, the workforce data collection unit uses generative AI to register employee skill sets and qualification information in a database and analyze compatibility with each job. For example, employees with specific qualifications are preferentially assigned to specific jobs. The workforce data collection unit also analyzes employee skill sets and identifies skill gaps. For example, if there is a shortage of employees with the skills required for a specific job, it makes a proposal to hire employees with those skills. The workforce data collection unit also uses generative AI to score the compatibility between employee skill sets and job content and propose optimal job assignments. For example, employees with high skill scores are assigned to important jobs. This makes it possible to collect workforce data that takes into account employee skill sets and qualification information.

[0064] The workforce data collection unit can use the emotion estimation function to analyze the emotional state of employees and propose emotionally optimal work assignments. For example, the workforce data collection unit uses the emotion estimation function to analyze the emotional state of employees in real time and calculate an emotion score. For example, employees with strong positive emotions are assigned to important tasks. The workforce data collection unit also uses generative AI to analyze the correlation between employees' emotional state and work performance and propose emotionally optimal work assignments. For example, tasks with high emotion scores are assigned preferentially. The workforce data collection unit also monitors employees' emotional state using the emotion estimation function and adjusts emotionally optimal work assignments in real time. For example, employees with strong negative emotions are assigned to tasks with less stress. This makes it possible to assign tasks taking into account the emotional state of employees.

[0065] The workforce data collection unit can collect employee working hours and work content in real time using IoT devices. The workforce data collection unit, for example, uses IoT devices to collect employee working hours and work content in real time. For example, it monitors employee movements using smartwatches and sensors. The workforce data collection unit also uses IoT devices to collect employee biometric data (heart rate, body temperature, etc.) in real time and reflects this in workforce data. For example, it analyzes stress levels and fatigue levels. The workforce data collection unit also uses IoT devices to collect employee location information in real time and monitor the progress of work. For example, it issues an alert if work in a specific area is delayed. This makes it possible to collect data in real time using IoT devices.

[0066] The labor force data collection unit can collect labor force data from different industries and perform cross-industry analysis. For example, the labor force data collection unit collects labor force data from different industries and performs cross-industry analysis using generative AI. For example, it compares and analyzes labor force data from the manufacturing and service industries. The labor force data collection unit also integrates labor force data from different industries and extracts common issues and success stories. For example, it shares best practices for labor optimization in different industries. The labor force data collection unit also performs cross-industry analysis and proposes new labor force management methods based on labor force data from different industries. For example, it applies efficiency methods from the manufacturing industry to the service industry. This makes it possible to perform cross-industry analysis based on labor force data from different industries.

[0067] The workforce data collection unit can use the emotion estimation function to monitor the emotional state of employees in real time and reflect it in data collection. The workforce data collection unit, for example, uses the emotion estimation function to monitor the emotional state of employees in real time and reflects the data in workforce data. For example, it adjusts work assignments based on emotion scores. The workforce data collection unit also uses generative AI to analyze the correlation between employees' emotional states and work performance in real time and reflects the results in workforce data. For example, it prioritizes the allocation of tasks with high emotion scores. The workforce data collection unit also uses the emotion estimation function to monitor employees' emotional states and adjust emotionally optimal work assignments in real time. For example, it assigns employees with strong negative emotions to tasks with less stress. This makes it possible to monitor employees' emotional states in real time and reflect them in data collection.

[0068] The demand forecasting unit can analyze social media trend data and reflect it in the demand forecast. The demand forecasting unit, for example, uses generation AI to collect social media trend data and reflect it in the demand forecast. For example, it analyzes the number of posts and engagement regarding a specific product or service. The demand forecasting unit also analyzes social media trend data and predicts sudden increases or decreases in demand. For example, it predicts demand fluctuations related to a specific event or campaign. The demand forecasting unit also uses generation AI to integrate social media trend data with past sales data to improve the accuracy of the demand forecast. For example, it predicts peaks in demand based on trend data. This makes it possible to perform demand forecasting based on social media trend data.

[0069] The demand forecasting unit can analyze weather data and event information and incorporate it into the demand forecast. The demand forecasting unit, for example, uses generative AI to analyze weather data and reflect it in the demand forecast. For example, it analyzes the impact of weather fluctuations on demand and incorporates it into the forecast model. The demand forecasting unit also analyzes event information and reflects it in the demand forecast. For example, it predicts demand fluctuations related to specific events or holidays. The demand forecasting unit also integrates weather data and event information to improve the accuracy of the demand forecast. For example, it predicts demand fluctuations when weather and events occur simultaneously. This makes it possible to perform demand forecasts based on weather data and event information.

[0070] The demand forecasting unit can use the emotion estimation function to analyze the emotional state of customers and reflect it in the demand forecast. For example, the demand forecasting unit uses the emotion estimation function to analyze the emotional state of customers and reflect it in the demand forecast. For example, it identifies products and services for which demand increases during periods of strong positive emotions. The demand forecasting unit also uses generative AI to integrate the emotional state of customers with past sales data to improve the accuracy of the demand forecast. For example, it predicts products for which demand will increase sharply during periods of high emotion scores. The demand forecasting unit also uses the emotion estimation function to monitor the emotional state of customers in real time and reflect it in the demand forecast. For example, it identifies products for which demand decreases during periods of strong negative emotions. This makes it possible to make demand forecasts based on the emotional state of customers.

[0071] The demand forecasting unit can incorporate data from different regions and countries to make forecasts from a global perspective. For example, the demand forecasting unit uses generative AI to collect sales data from different regions and countries to make demand forecasts from a global perspective. For example, it analyzes demand fluctuations by region. The demand forecasting unit also analyzes market trends in different regions and countries and reflects this in the demand forecast. For example, it predicts demand based on trends and consumer behavior in specific regions. The demand forecasting unit also makes demand forecasts from a global perspective and visualizes demand fluctuations by region. For example, it can focus resources on areas where demand is increasing. This makes it possible to make demand forecasts from a global perspective based on data from different regions and countries.

[0072] The demand forecasting unit can display the results of the demand forecast on a visual dashboard, allowing for intuitive understanding. The demand forecasting unit, for example, uses a generative AI to build a system that displays the results of the demand forecast on a visual dashboard. For example, it visualizes demand fluctuations using graphs and charts. The demand forecasting unit also updates the results of the demand forecast in real time using the visual dashboard. For example, it dynamically displays the demand forecast based on the latest data. The demand forecasting unit also displays the results of the demand forecast on a visual dashboard, allowing for intuitive understanding by the user. For example, it highlights demand peaks and trends. This makes it possible to display the results of the demand forecast in a way that is intuitively understandable.

[0073] The demand forecasting unit can use the emotion estimation function to monitor the emotional state of customers in real time and reflect the data in the demand forecast. For example, the demand forecasting unit uses the emotion estimation function to monitor the emotional state of customers in real time and reflect the data in the demand forecast. For example, it identifies products whose demand will increase sharply when the emotion score is high. The demand forecasting unit also uses generative AI to integrate the emotional state of customers with past sales data to improve the accuracy of the demand forecast. For example, it predicts products whose demand will increase sharply when the emotion score is high. The demand forecasting unit also uses the emotion estimation function to monitor the emotional state of customers in real time and reflect the data in the demand forecast. For example, it identifies products whose demand will decrease when negative emotions are strong. This makes it possible to monitor the emotional state of customers in real time and reflect the data in the demand forecast.

[0074] The schedule proposal unit can analyze individual employee performance data and propose optimal schedules. The schedule proposal unit, for example, uses generation AI to analyze employee performance data and propose optimal schedules for each employee. For example, it calculates a performance score based on the time required to complete tasks and their quality. The schedule proposal unit also analyzes employee performance data and proposes a schedule that allocates important tasks to time periods when performance is high. For example, it allocates important meetings and projects to time periods when performance is high. The schedule proposal unit also uses generation AI to analyze the compatibility between employee performance data and work content and proposes optimal schedules. For example, it prioritizes employees who perform well in specific tasks. This makes it possible to propose optimal schedules based on individual employee performance data.

[0075] The schedule proposal unit can propose schedules that take into consideration employees' health conditions and vacation requests. The schedule proposal unit, for example, uses generation AI to analyze employees' health condition data and propose an optimal schedule based on their health conditions. For example, it assigns important tasks to employees in good health. The schedule proposal unit also proposes a schedule that takes into consideration employees' vacation requests. For example, it gives priority to desired vacation days and coordinates with other employees. The schedule proposal unit also uses generation AI to comprehensively analyze employees' health conditions and vacation requests and propose an optimal schedule. For example, it assigns lighter tasks to employees in poor health. This makes it possible to propose schedules that take into consideration employees' health conditions and vacation requests.

[0076] The schedule proposal unit can analyze the emotional state of employees using the emotion estimation function and propose an emotionally optimal schedule. For example, the schedule proposal unit uses the emotion estimation function to analyze the emotional state of employees and propose an optimal schedule based on their emotion score. For example, it assigns important tasks to employees with strong positive emotions. The schedule proposal unit also uses generative AI to analyze the correlation between employees' emotional state and work performance and proposes an emotionally optimal schedule. For example, it allocates important tasks to time periods with high emotion scores. The schedule proposal unit also monitors employees' emotional state using the emotion estimation function and adjusts the emotionally optimal schedule in real time. For example, it assigns less stressful tasks to employees with strong negative emotions. This makes it possible to propose schedules that take employees' emotional states into consideration.

[0077] The schedule proposal unit can reflect the progress of the project in real time. For example, the schedule proposal unit uses generation AI to monitor the progress of the project in real time and reflect it in the schedule. For example, additional resources may be allocated to projects that are behind schedule. The schedule proposal unit also analyzes project progress data and proposes a schedule based on the progress. For example, resources may be reduced for projects that are progressing smoothly and reallocated to other projects. The schedule proposal unit also uses generation AI to optimize the project progress and resource allocation in real time. For example, additional resources may be allocated to projects that are behind schedule. This makes it possible to propose a schedule that reflects the progress of the project in real time.

[0078] The schedule proposal unit can automate schedule adjustments between different departments and teams. For example, the schedule proposal unit uses generative AI to build a system that automates schedule adjustments between different departments and teams. For example, it automates resource sharing and schedule adjustments between departments. The schedule proposal unit also integrates schedule data from different departments and teams to propose optimal schedules. For example, it adjusts schedules to resolve resource conflicts between departments. The schedule proposal unit also uses generative AI to adjust schedules between different departments and teams in real time. For example, it dynamically adjusts resources between departments depending on the progress of the project. This makes it possible to automate schedule adjustments between different departments and teams.

[0079] The schedule proposal unit can use the emotion estimation function to monitor the emotional state of employees in real time and reflect it in the schedule. The schedule proposal unit, for example, uses the emotion estimation function to monitor the emotional state of employees in real time and reflects the data in the schedule. For example, it allocates important tasks to time periods with high emotion scores. The schedule proposal unit also uses generative AI to analyze the correlation between employees' emotional states and work performance in real time and reflects the results in the schedule. For example, it allocates important tasks to time periods with high emotion scores. The schedule proposal unit also uses the emotion estimation function to monitor employees' emotional states and adjust the emotionally optimal schedule in real time. For example, it assigns less stressful tasks to employees with strong negative emotions. This makes it possible to monitor employees' emotional states in real time and reflect them in the schedule.

[0080] The labor force proposal unit can match employee skills and propose the optimal labor force. For example, the labor force proposal unit uses generative AI to analyze employee skill sets and perform optimal skill matching for each job. For example, employees with specific skills are preferentially assigned to specific jobs. The labor force proposal unit also analyzes the compatibility of employee skill sets with job content and proposes the optimal labor force. For example, employees with high skill scores are assigned to important jobs. The labor force proposal unit also uses generative AI to score the compatibility of employee skill sets with job content and propose the optimal labor force. For example, employees with high skill scores for specific jobs are preferentially assigned. This makes it possible to propose the optimal labor force based on employee skill matching.

[0081] The labor force proposal department can propose labor taking into consideration employees' career paths and growth goals. The labor force proposal department, for example, uses generative AI to analyze employees' career paths and growth goals and proposes the optimal labor force based on that. For example, it assigns tasks that align with their career paths. The labor force proposal department also makes labor proposals taking into consideration employees' growth goals. For example, it prioritizes the allocation of tasks that match their growth goals. The labor force proposal department also uses generative AI to comprehensively analyze employees' career paths and growth goals and proposes the optimal labor force. For example, it assigns tasks that align with their career paths. This makes it possible to propose labor that takes into consideration employees' career paths and growth goals.

[0082] The labor force proposal unit can analyze the emotional state of employees using the emotion estimation function and propose the emotionally optimal labor force. For example, the labor force proposal unit uses the emotion estimation function to analyze the emotional state of employees and propose the optimal labor force based on their emotion score. For example, employees with strong positive emotions are assigned to important tasks. The labor force proposal unit also uses generative AI to analyze the correlation between employees' emotional state and work performance and propose the emotionally optimal labor force. For example, tasks with high emotion scores are assigned preferentially. The labor force proposal unit also uses the emotion estimation function to monitor employees' emotional state and adjust the emotionally optimal labor force in real time. For example, employees with strong negative emotions are assigned to tasks with less stress. This makes it possible to propose labor force that takes employees' emotional state into consideration.

[0083] The labor proposal department can incorporate data on freelancers and contract employees to make flexible labor proposals. For example, the labor proposal department uses generative AI to collect data on freelancers and contract employees to make flexible labor proposals. For example, it proposes freelancers with the skills required for a specific project. The labor proposal department also analyzes the skill sets of freelancers and contract employees to propose the optimal labor force. For example, it prioritizes the allocation of freelancers with high skill scores for specific tasks. The labor proposal department also uses generative AI to integrate data on freelancers and contract employees to make flexible labor proposals. For example, it dynamically allocates freelancers and contract employees depending on the progress of a project. This makes it possible to make flexible labor proposals based on data on freelancers and contract employees.

[0084] The labor force proposal unit can display the results of the labor force proposal on a visual dashboard to enable intuitive understanding. The labor force proposal unit, for example, uses generative AI to build a system that displays the results of the labor force proposal on a visual dashboard. For example, it visualizes labor allocation using graphs and charts. The labor force proposal unit also updates the results of the labor force proposal in real time using the visual dashboard. For example, it dynamically displays the labor force proposal based on the latest data. The labor force proposal unit also displays the results of the labor force proposal on a visual dashboard to enable intuitive understanding by the user. For example, it highlights labor peaks and trends. This makes it possible to display the results of the labor force proposal in a way that is intuitively understandable.

[0085] The labor force proposal unit can use the emotion estimation function to monitor the emotional state of employees in real time and reflect the data in labor proposals. For example, the labor force proposal unit uses the emotion estimation function to monitor the emotional state of employees in real time and reflect the data in labor proposals. For example, employees with high emotion scores are assigned to important tasks. The labor force proposal unit also uses generative AI to analyze the correlation between employees' emotional states and work performance in real time and reflect the data in labor proposals. For example, tasks with high emotion scores are assigned preferentially. The labor force proposal unit also uses the emotion estimation function to monitor employees' emotional states and adjust the emotionally optimal labor force in real time. For example, employees with strong negative emotions are assigned to tasks with less stress. This makes it possible to monitor employees' emotional states in real time and reflect the data in labor proposals.

[0086] The schedule proposal unit can analyze project progress data and propose a schedule based on achievement goals. The schedule proposal unit, for example, uses generation AI to analyze project progress data in real time and propose a schedule based on achievement goals. For example, it allocates additional resources to tasks where progress is behind schedule. The schedule proposal unit also analyzes project progress data and proposes a schedule according to achievement goals. For example, it concentrates resources toward important milestones. The schedule proposal unit also uses generation AI to comprehensively analyze project progress data and achievement goals and propose an optimal schedule. For example, it reduces resources for tasks that are progressing smoothly and reallocates resources to other tasks. This makes it possible to propose a schedule based on achievement goals based on project progress data.

[0087] The schedule proposal unit can reflect budget and resource constraints in the constraint conditions in real time. For example, the schedule proposal unit uses generation AI to analyze budget and resource constraints in real time and reflect them in the schedule. For example, it optimizes resource allocation within the budget. The schedule proposal unit also proposes a schedule that takes budget and resource constraints into consideration. For example, when the budget is limited, it prioritizes allocating cost-effective resources. The schedule proposal unit also uses generation AI to monitor budget and resource constraints in real time and reflect them in the schedule. For example, it adjusts resource allocation according to the budget consumption situation. This makes it possible to propose a schedule that reflects budget and resource constraints in real time.

[0088] The schedule proposal unit can use the emotion estimation function to analyze the emotional state of employees and propose emotionally optimal goals and constraints. For example, the schedule proposal unit can use the emotion estimation function to analyze the emotional state of employees and propose optimal goals and constraints based on their emotion scores. For example, it can assign important tasks to employees with strong positive emotions. The schedule proposal unit can also use generative AI to analyze the correlation between employees' emotional states and work performance and propose emotionally optimal goals and constraints. For example, it can prioritize the allocation of tasks with high emotion scores. The schedule proposal unit can also monitor employees' emotional states using the emotion estimation function and adjust emotionally optimal goals and constraints in real time. For example, it can assign less stressful tasks to employees with strong negative emotions. This makes it possible to propose goals and constraints that take employees' emotional states into account.

[0089] The schedule proposal unit can share goals and constraints between different projects and departments to achieve overall optimization. The schedule proposal unit, for example, uses generation AI to share goals and constraints between different projects and departments, building a system to achieve overall optimization. For example, it automates resource sharing and schedule adjustment between departments. The schedule proposal unit also integrates the goals and constraints of different projects and departments to achieve overall optimization. For example, it adjusts schedules to resolve resource conflicts between departments. The schedule proposal unit also uses generation AI to share goals and constraints between different projects and departments in real time to achieve overall optimization. For example, it dynamically adjusts resources between departments depending on the progress of the project. This makes it possible to share goals and constraints between different projects and departments to achieve overall optimization.

[0090] The schedule proposal unit can display the results of the goals and constraints on a visual dashboard to enable intuitive understanding. The schedule proposal unit, for example, uses generative AI to build a system that displays the results of the goals and constraints on a visual dashboard. For example, the goals and constraints are visualized using graphs and charts. The schedule proposal unit also updates the results of the goals and constraints in real time using the visual dashboard. For example, the goals and constraints are dynamically displayed based on the latest data. The schedule proposal unit also displays the results of the goals and constraints on a visual dashboard to enable intuitive understanding by the user. For example, the progress of the goals and the impact of the constraints are highlighted. This makes it possible to display the results of the goals and constraints in a way that allows intuitive understanding.

[0091] The schedule proposal unit can use the emotion estimation function to monitor employees' emotional states in real time and reflect the data in the goals and constraints. For example, the schedule proposal unit can use the emotion estimation function to monitor employees' emotional states in real time and reflect the data in the goals and constraints. For example, employees with high emotion scores can be assigned to important tasks. The schedule proposal unit can also use generative AI to analyze the correlation between employees' emotional states and work performance in real time and reflect the data in the goals and constraints. For example, tasks with high emotion scores can be assigned preferentially. The schedule proposal unit can also monitor employees' emotional states using the emotion estimation function and adjust the emotionally optimal goals and constraints in real time. For example, employees with strong negative emotions can be assigned tasks that are less stressful. This makes it possible to monitor employees' emotional states in real time and reflect the data in the goals and constraints.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The workforce data collection unit can monitor employee health status and reflect it in data. For example, it can collect biometric data such as employees' heart rate and body temperature to evaluate their health status. It can also make suggestions for adjusting work loads based on employees' health status. It can also analyze the correlation between employee health status and work performance and assign important tasks to employees in good health. This makes it possible to collect workforce data and assign work that takes employee health status into account.

[0094] The workforce data collection unit can analyze employee communication patterns and optimize team collaboration. For example, it can analyze email and chat exchanges between employees to evaluate the frequency and quality of communication. It can also make suggestions for adjusting the division of roles and work assignments within a team based on communication patterns. It can also analyze the correlation between communication patterns and work performance and assign important projects to teams with effective communication. This makes it possible to collect workforce data and assign work that takes team collaboration into account.

[0095] The workforce data collection unit can analyze the emotional state of employees and propose emotionally optimal work assignments. For example, the emotional state of employees can be analyzed in real time using the emotion estimation function to calculate an emotion score. Employees with strong positive emotions can be assigned to important tasks. It is also possible to analyze the correlation between an employee's emotional state and work performance and propose emotionally optimal work assignments. Furthermore, the emotion estimation function can be used to monitor an employee's emotional state and adjust emotionally optimal work assignments in real time. This makes it possible to assign tasks that take the emotional state of employees into consideration.

[0096] The workforce data collection unit can collect employee skill sets and qualification information and analyze their compatibility with job content. For example, generative AI can be used to register employee skill sets and qualification information in a database and analyze their compatibility with each job. Employees with specific qualifications can be preferentially assigned to specific jobs. It is also possible to analyze employee skill sets and identify skill gaps. Furthermore, it can score the compatibility between employee skill sets and job content and propose optimal job assignments. This makes it possible to collect workforce data that takes employee skill sets and qualification information into account.

[0097] The workforce data collection unit can analyze employee stress levels and reflect them in workforce data. For example, generative AI can be used to collect employees' biometric data (heart rate, electrodermal activity, etc.) and calculate a stress score. High-stress tasks and time periods can be identified and reflected in workforce data. It is also possible to analyze the correlation between employees' stress levels and work performance and prioritize the allocation of less stressful tasks. Furthermore, it is possible to monitor employees' stress levels and adjust work assignments in real time. This makes it possible to collect workforce data and assign tasks that take employee stress levels into account.

[0098] The workforce data collection unit can analyze the emotional state of employees and propose emotionally optimal work assignments. For example, the emotional state of employees can be analyzed in real time using the emotion estimation function to calculate an emotion score. Employees with strong positive emotions can be assigned to important tasks. It is also possible to analyze the correlation between an employee's emotional state and work performance and propose emotionally optimal work assignments. Furthermore, the emotion estimation function can be used to monitor an employee's emotional state and adjust emotionally optimal work assignments in real time. This makes it possible to assign tasks that take the emotional state of employees into consideration.

[0099] The workforce data collection unit can monitor employee health status and reflect it in data. For example, it can collect biometric data such as employees' heart rate and body temperature to evaluate their health status. It can also make suggestions for adjusting work loads based on employees' health status. It can also analyze the correlation between employee health status and work performance and assign important tasks to employees in good health. This makes it possible to collect workforce data and assign work that takes employee health status into account.

[0100] The workforce data collection unit can analyze employee communication patterns and optimize team collaboration. For example, it can analyze email and chat exchanges between employees to evaluate the frequency and quality of communication. It can also make suggestions for adjusting the division of roles and work assignments within a team based on communication patterns. It can also analyze the correlation between communication patterns and work performance and assign important projects to teams with effective communication. This makes it possible to collect workforce data and assign work that takes team collaboration into account.

[0101] The workforce data collection unit can analyze the emotional state of employees and propose emotionally optimal work assignments. For example, the emotional state of employees can be analyzed in real time using the emotion estimation function to calculate an emotion score. Employees with strong positive emotions can be assigned to important tasks. It is also possible to analyze the correlation between an employee's emotional state and work performance and propose emotionally optimal work assignments. Furthermore, the emotion estimation function can be used to monitor an employee's emotional state and adjust emotionally optimal work assignments in real time. This makes it possible to assign tasks that take the emotional state of employees into consideration.

[0102] The workforce data collection unit can analyze employee stress levels and reflect them in workforce data. For example, generative AI can be used to collect employees' biometric data (heart rate, electrodermal activity, etc.) and calculate a stress score. High-stress tasks and time periods can be identified and reflected in workforce data. It is also possible to analyze the correlation between employees' stress levels and work performance and prioritize the allocation of less stressful tasks. Furthermore, it is possible to monitor employees' stress levels and adjust work assignments in real time. This makes it possible to collect workforce data and assign tasks that take employee stress levels into account.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The Workforce Data Collection Department collects historical workforce data, such as employee working hours, work content, and productivity data, and stores this data in digital format. Step 2: The demand forecasting unit makes a demand forecast based on the data collected by the labor data collection unit. For example, it analyzes past sales data and seasonal demand fluctuations to predict future demand. It can also analyze market trends and reflect them in the demand forecast. Step 3: The schedule proposal unit proposes an optimal schedule based on the demand predicted by the demand forecast unit. For example, it generates a schedule that assigns more employees to times when demand is high and fewer employees to times when demand is low. It can also propose a schedule that takes into account employees' work preferences and vacation requests. Step 4: The Workforce Proposal Unit proposes the required workforce based on the schedule proposed by the Schedule Proposal Unit. For example, it proposes the number of people and skill sets required to complete a specific project. It can also propose the optimal workforce based on the goals and constraints.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[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 robot 414, 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 robot 414 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.

[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 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.

[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 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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. [Explanation of symbols]

[0172] 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. a labor force data collection unit that collects historical labor force data; a demand forecasting unit that forecasts demand based on the data collected by the labor force data collecting unit; a schedule proposal unit that proposes an optimal schedule based on the demand predicted by the demand prediction unit; a labor force proposal unit that proposes a required labor force based on the schedule proposed by the schedule proposal unit. A system characterized by:

2. The workforce data collection unit Analyze employee productivity and stress levels and reflect them in workforce data 2. The system of claim 1.

3. The workforce data collection unit Collect employee skill sets and qualifications and analyze job fit 2. The system of claim 1.

4. The workforce data collection unit Analyze employees' emotional state and suggest optimal work placements 2. The system of claim 1.

5. The workforce data collection unit The IoT device will be used to collect employee working hours and work details in real time.

2. The system of claim 1.

6. The workforce data collection unit Collect labor force data from the different industries and conduct cross-industry analysis 2. The system of claim 1.

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