System

Abstract

An object of a system according to an embodiment is to improve the accuracy of prediction of absence from work or retirement and to formulate an operation plan of a call center.SOLUTION: A system according to an embodiment includes a data collection unit, a data accumulation unit, a data analysis unit, and an operating plan formulation unit. The data collection unit collects attribute data, attendance data, and external information of a crew. The data storage part stores the data collected by the data collection part. The data analysis unit analyzes the data accumulated by the data accumulation unit. The operating plan formulation unit formulates an operating plan based on a result analyzed by the data analysis 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 had low accuracy in predicting absenteeism and resignations when planning call center operations, posing challenges to stable operations.

[0005] The system according to the embodiment aims to improve the accuracy of prediction of absences and resignations and to formulate operation plans for call centers. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data accumulation unit, a data analysis unit, and an operation plan formulation unit. The data collection unit collects crew attribute data, attendance data, and external information. The data accumulation unit accumulates the data collected by the data collection unit. The data analysis unit analyzes the data accumulated by the data accumulation unit. The operation plan formulation unit formulates an operation plan based on the results of the analysis by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the accuracy of prediction of absences and resignations and formulate operation plans for call centers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A call center operation system according to an embodiment of the present invention is a system that collects crew attribute data, attendance data, and external information, analyzes them using AI, and formulates operation plans. As a result, the call center operation system can improve the accuracy of predictions of absenteeism and resignation, prevent excess or shortage of operating personnel, and achieve stable operation.

[0029] A call center operation system according to an embodiment includes a data collection unit, a data storage unit, a data analysis unit, and an operation planning unit. The data collection unit collects crew attribute data, attendance data, and external information. For example, the data collection unit collects crew attribute data such as age, gender, and work history. The data collection unit can also collect attendance data such as the number of days the crew has worked, the number of days they have been absent, and the number of times they have been late. The data collection unit can also collect external information such as job market trends and the hiring status of competitors. For example, the data collection unit acquires crew attribute data from a human resources system, attendance data from an attendance management system, and external information from the Internet. The data storage unit stores the data collected by the data collection unit. For example, the data storage unit stores the collected data in a database. The data storage unit can also perform data cleansing to maintain data integrity. The data storage unit can also perform encryption to ensure data security. For example, the data storage unit stores data using a database management system, maintains data integrity using a data cleansing tool, and ensures data security using encryption technology. The data analysis unit analyzes the data stored by the data storage unit. For example, the data analysis unit uses AI to predict absenteeism and resignation. The data analysis unit can also analyze past data to learn patterns of absenteeism and resignation. The data analysis unit can also analyze crew attribute data and attendance data to identify crew members at high risk of resignation. For example, the data analysis unit predicts absenteeism and resignation using a machine learning algorithm, analyzes past data using data mining technology, and analyzes crew attribute data and attendance data using statistical analysis. The operation planning unit formulates an operation plan based on the results of the analysis by the data analysis unit. For example, the operation planning unit formulates a plan to deploy backup crew members during periods of predicted high absenteeism. The operation planning unit can also formulate a plan to take early measures for crew members at high risk of resignation. The operation planning unit can also propose measures to improve crew motivation and satisfaction.For example, the operation plan formulation unit adjusts shift schedules, optimizes resource allocation, and proposes incentive programs. This enables the call center operation system according to the embodiment to improve the accuracy of predictions of absenteeism and resignation, prevent excess or shortage of working personnel, and achieve stable operation. For example, the output unit provides the operation plan to the manager via a web application or a mobile application. If feedback is desired in paper form, the plan is printed using a printer. Sending the plan via email provides prompt feedback by sending it directly to the manager.

[0030] The data collection unit collects crew health data and analyzes the relationship between health status and risk of absenteeism. For example, to collect crew health data, the data collection unit distributes wearable devices and regularly collects heart rate and sleep data. This allows for understanding fluctuations in health status and analyzing absenteeism risk. The data collection unit also conducts regular health checks to collect crew health data and accumulates the results in a database. This allows for analyzing the relationship between health status and risk of absenteeism. The data collection unit also conducts self-reported health questionnaires to collect crew health data and accumulates the results in a database. This allows for analyzing the relationship between health status and risk of absenteeism. In this way, by collecting crew health data and analyzing the relationship between health status and risk of absenteeism, the accuracy of absenteeism predictions is improved.

[0031] The data collection department analyzes the crew's internal communication data to evaluate stress levels and the workplace atmosphere. For example, to collect the crew's internal communication data, the data collection department analyzes the content of emails and chats to evaluate stress levels. For example, the frequency of negative words is analyzed. In addition, to collect the crew's internal communication data, the data collection department conducts regular surveys to evaluate the workplace atmosphere. For example, it investigates workplace satisfaction and stress levels. In addition, to collect the crew's internal communication data, the data collection department analyzes the content of posts on the internal SNS to evaluate stress levels and the workplace atmosphere. For example, it analyzes the frequency of positive posts. In this way, by analyzing the crew's internal communication data and evaluating stress levels and the workplace atmosphere, the accuracy of predicting absenteeism and resignation is improved.

[0032] The data collection unit collects crew member home environment data and analyzes the impact of the home environment on absenteeism and resignation. For example, to collect crew member home environment data, the data collection unit conducts regular questionnaires to investigate family composition and commuting time. This allows for analysis of the impact of the home environment on absenteeism and resignation. In addition, to collect crew member home environment data, the data collection unit analyzes the usage status of the company's employee benefits program and evaluates the impact of the home environment on absenteeism and resignation. In addition, to collect crew member home environment data, the data collection unit obtains commuting time from GPS data and analyzes the impact of the home environment on absenteeism and resignation. In this way, by collecting crew member home environment data and analyzing the impact of the home environment on absenteeism and resignation, the accuracy of predicting absenteeism and resignation can be improved.

[0033] The data collection department collects data on the hobbies and interests of crew members and proposes measures to increase individual motivation. For example, the data collection department conducts regular surveys to collect data on the hobbies and interests of crew members and stores the results in a database. This allows the department to propose measures to increase individual motivation. The data collection department also analyzes the content of posts on the in-house SNS to collect data on the hobbies and interests of crew members and proposes measures to increase individual motivation. The data collection department also analyzes participation in in-house events to collect data on the hobbies and interests of crew members and proposes measures to increase individual motivation. In this way, by collecting data on the hobbies and interests of crew members and proposing measures to increase individual motivation, crew satisfaction is improved.

[0034] The data analysis unit combines and analyzes the crew's health data and attendance data to predict the impact of the crew's health condition on the risk of absence. For example, the data analysis unit combines and analyzes the crew's health data and attendance data, and compares and analyzes past health examination results and the absence data to predict the impact of the crew's health condition on the risk of absence. The data analysis unit also combines and analyzes the crew's health data and attendance data, and trains a machine learning model on fluctuation patterns in the health data to predict the impact of the crew's health condition on the risk of absence. The data analysis unit also combines and analyzes the crew's health data and attendance data, and combines and analyzes the health data with other attribute data to predict the impact of the crew's health condition on the risk of absence. In this way, the accuracy of absenteeism predictions is improved by combining and analyzing the crew's health data and attendance data to predict the impact of the crew's health condition on the risk of absence.

[0035] The data analysis unit analyzes the crew's internal communication data and predicts the impact of stress levels and workplace atmosphere on absenteeism and resignation. For example, the data analysis unit analyzes the crew's internal communication data and analyzes the content of emails and chats to evaluate the frequency of negative words in order to predict the impact of stress levels and workplace atmosphere on absenteeism and resignation. The data analysis unit also analyzes the crew's internal communication data and analyzes the results of regular surveys to evaluate workplace satisfaction and stress levels in order to predict the impact of stress levels and workplace atmosphere on absenteeism and resignation. The data analysis unit also analyzes the crew's internal communication data and analyzes the content of posts on the internal SNS and evaluates the frequency of positive posts in order to predict the impact of stress levels and workplace atmosphere on absenteeism and resignation. In this way, the data analysis unit analyzes the crew's internal communication data and predicts the impact of stress levels and workplace atmosphere on absenteeism and resignation, thereby improving the accuracy of absenteeism and resignation predictions.

[0036] The data analysis unit analyzes the crew's home environment data and predicts the impact of the home environment on absenteeism and resignation. For example, the data analysis unit analyzes the crew's home environment data and analyzes family composition and commuting time to predict the impact of the home environment on absenteeism and resignation, and evaluates the risk of absenteeism and resignation. The data analysis unit also analyzes the crew's home environment data and analyzes the usage status of in-house employee benefit programs to predict the impact of the home environment on absenteeism and resignation, and evaluates the impact of the home environment on absenteeism and resignation. The data analysis unit also analyzes the crew's home environment data and obtains commuting time from GPS data to analyze the impact of the home environment on absenteeism and resignation. In this way, by analyzing the crew's home environment data and predicting the impact of the home environment on absenteeism and resignation, the accuracy of predictions of absenteeism and resignation is improved.

[0037] The data analysis department analyzes data related to the crew's hobbies and interests and proposes measures to increase each individual's motivation. For example, the data analysis department analyzes data related to the crew's hobbies and interests, analyzes the results of regular surveys, and evaluates each individual's motivation in order to propose measures to increase each individual's motivation. The data analysis department also analyzes data related to the crew's hobbies and interests, analyzes posts on the in-house SNS, and evaluates each individual's motivation in order to propose measures to increase each individual's motivation. The data analysis department also analyzes data related to the crew's hobbies and interests, analyzes participation in in-house events, and evaluates each individual's motivation in order to propose measures to increase each individual's motivation. In this way, by analyzing data related to the crew's hobbies and interests and proposing measures to increase each individual's motivation, crew satisfaction is improved.

[0038] The operation planning department, based on the crew's health data, plans to assign backup crew members during periods when their health conditions are likely to deteriorate. For example, the operation planning department analyzes past health examination results and absence data to identify patterns of fluctuations in health conditions in order to plan to assign backup crew members during periods when their health conditions are likely to deteriorate based on the crew's health data. Furthermore, the operation planning department combines and analyzes the health data with other attribute data to evaluate the impact of fluctuations in health conditions on absences in order to plan to assign backup crew members during periods when their health conditions are likely to deteriorate based on the crew's health data. Furthermore, the operation planning department monitors the health data in real time in order to plan to assign backup crew members during periods when their health conditions are likely to deteriorate based on the crew's health data, and responds immediately if a fluctuation in health conditions is detected. In this way, the risk of absenteeism is reduced by planning to assign backup crew members during periods when their health conditions are likely to deteriorate based on the crew's health data.

[0039] The operation planning department makes plans to deploy backup crew members during periods when stress levels are likely to be high based on the crew members' internal communication data. For example, the operation planning department analyzes the content of emails and chats and evaluates the frequency of negative words in order to make plans to deploy backup crew members during periods when stress levels are likely to be high based on the crew members' internal communication data. The operation planning department also analyzes the results of regular surveys and evaluates workplace satisfaction and stress levels in order to make plans to deploy backup crew members during periods when stress levels are likely to be high based on the crew members' internal communication data. The operation planning department also analyzes the content of posts on internal SNS and evaluates the frequency of positive posts in order to make plans to deploy backup crew members during periods when stress levels are likely to be high based on the crew members' internal communication data. In this way, by making plans to deploy backup crew members during periods when stress levels are likely to be high based on the crew members' internal communication data, the risk of absenteeism and resignation is reduced.

[0040] The operation planning department, based on the crew's home environment data, makes a plan to assign a reserve crew member during a period when the crew's home environment is likely to change. For example, the operation planning department analyzes family composition and commuting time to evaluate the risk of absenteeism and resignation in order to make a plan to assign a reserve crew member during a period when the crew's home environment is likely to change based on the crew's home environment data. The operation planning department also analyzes the usage status of the company's employee benefits program and evaluates the impact of the home environment on absenteeism and resignation in order to make a plan to assign a reserve crew member during a period when the crew's home environment is likely to change based on the crew's home environment data. The operation planning department also acquires commuting time from GPS data and analyzes the impact of the home environment on absenteeism and resignation in order to make a plan to assign a reserve crew member during a period when the crew's home environment is likely to change based on the crew's home environment data. In this way, the risk of absenteeism and resignation is reduced by making a plan to assign a reserve crew member during a period when the crew's home environment is likely to change based on the crew's home environment data.

[0041] The operation planning department proposes measures to increase individual motivation based on data on the crew's hobbies and interests. For example, the operation planning department analyzes the results of regular surveys and evaluates the motivation of each individual in order to propose measures to increase individual motivation based on the data on the crew's hobbies and interests. The operation planning department also analyzes the content of posts on in-house SNS and evaluates the motivation of each individual in order to propose measures to increase individual motivation based on the data on the crew's hobbies and interests. The operation planning department also analyzes the participation status of in-house events and evaluates the motivation of each individual in order to propose measures to increase individual motivation based on the data on the crew's hobbies and interests. In this way, by proposing measures to increase individual motivation based on the data on the crew's hobbies and interests, crew satisfaction is improved.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The Data Collection Department can collect data on crew members' hobbies and interests and propose measures to increase their individual motivation. For example, it can conduct regular surveys and store the results in a database. It can also analyze posts on the company's internal social media platforms and propose measures to increase individual motivation. It can also analyze participation in company events and propose measures to increase individual motivation. In this way, by collecting data on crew members' hobbies and interests and proposing measures to increase their individual motivation, it is possible to improve crew satisfaction.

[0044] The data collection department can also collect crew health data and analyze the relationship between health status and absenteeism risk. For example, wearable devices can be distributed to crew members to regularly collect heart rate and sleep data. Regular health checkups can also be conducted and the results can be stored in a database. Self-reported health questionnaires can also be conducted and the results can be stored in a database. In this way, by collecting crew health data and analyzing the relationship between health status and absenteeism risk, the accuracy of absenteeism prediction can be improved.

[0045] The Data Collection Department can also collect data on crew members' home environments and analyze the impact of home environments on absenteeism and resignation. For example, it can conduct regular surveys to investigate family composition and commuting time. It can also analyze the usage of company employee benefits programs to evaluate the impact of home environments on absenteeism and resignation. It can also obtain commuting time from GPS data and analyze the impact of home environments on absenteeism and resignation. In this way, by collecting data on crew members' home environments and analyzing the impact of home environments on absenteeism and resignation, it is possible to improve the accuracy of predicting absenteeism and resignation.

[0046] The data collection department can also analyze crew internal communication data to evaluate stress levels and the workplace atmosphere. For example, they can analyze the content of emails and chats to evaluate the frequency of negative language. They can also conduct regular surveys to evaluate workplace satisfaction and stress levels. They can also analyze the content of posts on the company's internal social media accounts to evaluate the frequency of positive posts. This allows them to analyze crew internal communication data and evaluate stress levels and the workplace atmosphere, thereby improving the accuracy of predicting absenteeism and resignation.

[0047] The data analysis department can also combine and analyze crew health data and attendance data to predict the impact of health status on the risk of absence. For example, it can compare and analyze past health checkup results with absence data. It can also train a machine learning model on fluctuation patterns in health data. It can also combine and analyze health data with other attribute data. This allows for the combined analysis of crew health data and attendance data to predict the impact of health status on the risk of absence, thereby improving the accuracy of absenteeism predictions.

[0048] The operation planning department can also use the crew's health data to create plans to deploy backup crew during periods when their health is likely to deteriorate. For example, it can analyze past health checkup results and absenteeism data to identify patterns of fluctuations in health status. It can also combine health data with other attribute data for analysis to evaluate the impact of fluctuations in health status on absenteeism. It can also monitor health data in real time and respond immediately if a change in health status is detected. This makes it possible to reduce the risk of absenteeism by creating plans to deploy backup crew during periods when their health is likely to deteriorate based on the crew's health data.

[0049] The operation planning department can use the crew's home environment data to create plans to deploy backup crew members during periods when their home environment is likely to change. For example, they can analyze family composition and commuting time to assess the risk of absenteeism and resignation. They can also analyze the usage of the company's employee benefits program to assess the impact of home environment on absenteeism and resignation. Furthermore, they can obtain commuting time from GPS data and analyze the impact of home environment on absenteeism and resignation. This makes it possible to reduce the risk of absenteeism and resignation by creating plans to deploy backup crew members during periods when their home environment is likely to change, based on the crew's home environment data.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The data collection department collects crew attribute data, attendance data, and external information. For example, attribute data such as crew age, gender, and work history, attendance data such as number of days worked, number of days absent, and number of times late, and external information such as job market trends and the hiring situation of competitors are collected. This data is obtained from sources such as human resources systems, attendance management systems, and the Internet. Step 2: The data storage unit stores the data collected by the data collection unit. For example, it stores the data in a database, cleanses the data to maintain its integrity, and encrypts it to ensure its security. Step 3: The data analysis unit analyzes the data accumulated by the data accumulation unit. For example, it uses AI to predict absenteeism and resignation, analyzes past data to learn patterns of absenteeism and resignation, and analyzes crew attribute data and attendance data to identify crew members at high risk of resigning. Step 4: The Operation Planning Department formulates an operation plan based on the results of the analysis by the Data Analysis Department. For example, it may plan to deploy backup crew members during periods when there are predicted to be many absentees, plan to take early action for crew members at high risk of quitting, and propose measures to improve crew motivation and satisfaction.

[0052] (Example 2) A call center operation system according to an embodiment of the present invention is a system that collects crew attribute data, attendance data, and external information, analyzes them using AI, and formulates operation plans. As a result, the call center operation system can improve the accuracy of predictions of absenteeism and resignation, prevent excess or shortage of operating personnel, and achieve stable operation.

[0053] A call center operation system according to an embodiment includes a data collection unit, a data storage unit, a data analysis unit, and an operation planning unit. The data collection unit collects crew attribute data, attendance data, and external information. For example, the data collection unit collects crew attribute data such as age, gender, and work history. The data collection unit can also collect attendance data such as the number of days the crew has worked, the number of days they have been absent, and the number of times they have been late. The data collection unit can also collect external information such as job market trends and the hiring status of competitors. For example, the data collection unit acquires crew attribute data from a human resources system, attendance data from an attendance management system, and external information from the Internet. The data storage unit stores the data collected by the data collection unit. For example, the data storage unit stores the collected data in a database. The data storage unit can also perform data cleansing to maintain data integrity. The data storage unit can also perform encryption to ensure data security. For example, the data storage unit stores data using a database management system, maintains data integrity using a data cleansing tool, and ensures data security using encryption technology. The data analysis unit analyzes the data stored by the data storage unit. For example, the data analysis unit uses AI to predict absenteeism and resignation. The data analysis unit can also analyze past data to learn patterns of absenteeism and resignation. The data analysis unit can also analyze crew attribute data and attendance data to identify crew members at high risk of resignation. For example, the data analysis unit predicts absenteeism and resignation using a machine learning algorithm, analyzes past data using data mining technology, and analyzes crew attribute data and attendance data using statistical analysis. The operation planning unit formulates an operation plan based on the results of the analysis by the data analysis unit. For example, the operation planning unit formulates a plan to deploy backup crew members during periods of predicted high absenteeism. The operation planning unit can also formulate a plan to take early measures for crew members at high risk of resignation. The operation planning unit can also propose measures to improve crew motivation and satisfaction.For example, the operation plan formulation unit adjusts shift schedules, optimizes resource allocation, and proposes incentive programs. This enables the call center operation system according to the embodiment to improve the accuracy of predictions of absenteeism and resignation, prevent excess or shortage of working personnel, and achieve stable operation. For example, the output unit provides the operation plan to the manager via a web application or a mobile application. If feedback is desired in paper form, the plan is printed using a printer. Sending the plan via email provides prompt feedback by sending it directly to the manager.

[0054] The data collection unit monitors the crew's emotional state in real time and collects emotional data using an emotion estimation function. For example, the data collection unit uses a wearable device to measure heart rate and electrodermal activity to monitor the crew's emotional state in real time. This detects signs of stress and fatigue and collects emotional data. In addition, to monitor the crew's emotional state, the data collection unit analyzes facial expressions using a camera installed in the office and estimates emotions. This allows for real-time understanding of fluctuations in the crew's emotions. In addition, to monitor the crew's emotional state, the data collection unit uses voice analysis technology to analyze the tone and pitch of voice during calls and collects emotional data. This detects fluctuations in the crew's emotions. In this way, by monitoring the crew's emotional state in real time and collecting emotional data, the accuracy of predicting absenteeism and resignation is improved.

[0055] The data collection unit collects crew health data and analyzes the relationship between health status and risk of absenteeism. For example, to collect crew health data, the data collection unit distributes wearable devices and regularly collects heart rate and sleep data. This allows for understanding fluctuations in health status and analyzing absenteeism risk. The data collection unit also conducts regular health checks to collect crew health data and accumulates the results in a database. This allows for analyzing the relationship between health status and risk of absenteeism. The data collection unit also conducts self-reported health questionnaires to collect crew health data and accumulates the results in a database. This allows for analyzing the relationship between health status and risk of absenteeism. In this way, by collecting crew health data and analyzing the relationship between health status and risk of absenteeism, the accuracy of absenteeism predictions is improved.

[0056] The data collection department analyzes the crew's internal communication data to evaluate stress levels and the workplace atmosphere. For example, to collect the crew's internal communication data, the data collection department analyzes the content of emails and chats to evaluate stress levels. For example, the frequency of negative words is analyzed. In addition, to collect the crew's internal communication data, the data collection department conducts regular surveys to evaluate the workplace atmosphere. For example, it investigates workplace satisfaction and stress levels. In addition, to collect the crew's internal communication data, the data collection department analyzes the content of posts on the internal SNS to evaluate stress levels and the workplace atmosphere. For example, it analyzes the frequency of positive posts. In this way, by analyzing the crew's internal communication data and evaluating stress levels and the workplace atmosphere, the accuracy of predicting absenteeism and resignation is improved.

[0057] The data collection unit collects crew member home environment data and analyzes the impact of the home environment on absenteeism and resignation. For example, to collect crew member home environment data, the data collection unit conducts regular questionnaires to investigate family composition and commuting time. This allows for analysis of the impact of the home environment on absenteeism and resignation. In addition, to collect crew member home environment data, the data collection unit analyzes the usage status of the company's employee benefits program and evaluates the impact of the home environment on absenteeism and resignation. In addition, to collect crew member home environment data, the data collection unit obtains commuting time from GPS data and analyzes the impact of the home environment on absenteeism and resignation. In this way, by collecting crew member home environment data and analyzing the impact of the home environment on absenteeism and resignation, the accuracy of predicting absenteeism and resignation can be improved.

[0058] The data collection department collects data on the hobbies and interests of crew members and proposes measures to increase individual motivation. For example, the data collection department conducts regular surveys to collect data on the hobbies and interests of crew members and stores the results in a database. This allows the department to propose measures to increase individual motivation. The data collection department also analyzes the content of posts on the in-house SNS to collect data on the hobbies and interests of crew members and proposes measures to increase individual motivation. The data collection department also analyzes participation in in-house events to collect data on the hobbies and interests of crew members and proposes measures to increase individual motivation. In this way, by collecting data on the hobbies and interests of crew members and proposing measures to increase individual motivation, crew satisfaction is improved.

[0059] The data collection unit uses the emotion estimation function to collect crew emotional data and analyze the impact of emotional fluctuations on absenteeism and resignation. For example, the data collection unit uses the emotion estimation function to analyze facial expressions using a camera installed in the office to collect crew emotional data and grasp emotional fluctuations in real time. In addition, the data collection unit uses the emotion estimation function to analyze the tone and pitch of voice during calls and collect emotional data. In addition, the data collection unit uses the emotion estimation function to use a wearable device to measure heart rate and electrodermal activity to collect crew emotional data. In this way, signs of stress and fatigue are detected and emotional data is collected. In this way, the emotion estimation function is used to collect crew emotional data and the impact of emotional fluctuations on absenteeism and resignation is analyzed, thereby improving the accuracy of predicting absenteeism and resignation.

[0060] The data analysis unit uses the emotion estimation function to analyze the crew's emotion data and predict the impact of emotional fluctuations on absenteeism and resignation. The data analysis unit, for example, uses the emotion estimation function to analyze the crew's emotion data and compares and analyzes past emotion data with absenteeism and resignation data to predict the impact of emotional fluctuations on absenteeism and resignation. The data analysis unit also uses the emotion estimation function to analyze the crew's emotion data and trains a machine learning model on emotion fluctuation patterns to predict the impact of emotional fluctuations on absenteeism and resignation. The data analysis unit also uses the emotion estimation function to analyze the crew's emotion data and analyzes the emotion data in combination with other attribute data to predict the impact of emotional fluctuations on absenteeism and resignation. In this way, the emotion estimation function is used to analyze the crew's emotion data and predict the impact of emotional fluctuations on absenteeism and resignation, thereby improving the accuracy of absenteeism and resignation predictions.

[0061] The data analysis unit combines and analyzes the crew's health data and attendance data to predict the impact of the crew's health condition on the risk of absence. For example, the data analysis unit combines and analyzes the crew's health data and attendance data, and compares and analyzes past health examination results and the absence data to predict the impact of the crew's health condition on the risk of absence. The data analysis unit also combines and analyzes the crew's health data and attendance data, and trains a machine learning model on fluctuation patterns in the health data to predict the impact of the crew's health condition on the risk of absence. The data analysis unit also combines and analyzes the crew's health data and attendance data, and combines and analyzes the health data with other attribute data to predict the impact of the crew's health condition on the risk of absence. In this way, the accuracy of absenteeism predictions is improved by combining and analyzing the crew's health data and attendance data to predict the impact of the crew's health condition on the risk of absence.

[0062] The data analysis unit analyzes the crew's internal communication data and predicts the impact of stress levels and workplace atmosphere on absenteeism and resignation. For example, the data analysis unit analyzes the crew's internal communication data and analyzes the content of emails and chats to evaluate the frequency of negative words in order to predict the impact of stress levels and workplace atmosphere on absenteeism and resignation. The data analysis unit also analyzes the crew's internal communication data and analyzes the results of regular surveys to evaluate workplace satisfaction and stress levels in order to predict the impact of stress levels and workplace atmosphere on absenteeism and resignation. The data analysis unit also analyzes the crew's internal communication data and analyzes the content of posts on the internal SNS and evaluates the frequency of positive posts in order to predict the impact of stress levels and workplace atmosphere on absenteeism and resignation. In this way, the data analysis unit analyzes the crew's internal communication data and predicts the impact of stress levels and workplace atmosphere on absenteeism and resignation, thereby improving the accuracy of absenteeism and resignation predictions.

[0063] The data analysis unit analyzes the crew's home environment data and predicts the impact of the home environment on absenteeism and resignation. For example, the data analysis unit analyzes the crew's home environment data and analyzes family composition and commuting time to predict the impact of the home environment on absenteeism and resignation, and evaluates the risk of absenteeism and resignation. The data analysis unit also analyzes the crew's home environment data and analyzes the usage status of in-house employee benefit programs to predict the impact of the home environment on absenteeism and resignation, and evaluates the impact of the home environment on absenteeism and resignation. The data analysis unit also analyzes the crew's home environment data and obtains commuting time from GPS data to analyze the impact of the home environment on absenteeism and resignation. In this way, by analyzing the crew's home environment data and predicting the impact of the home environment on absenteeism and resignation, the accuracy of predictions of absenteeism and resignation is improved.

[0064] The data analysis department analyzes data related to the crew's hobbies and interests and proposes measures to increase each individual's motivation. For example, the data analysis department analyzes data related to the crew's hobbies and interests, analyzes the results of regular surveys, and evaluates each individual's motivation in order to propose measures to increase each individual's motivation. The data analysis department also analyzes data related to the crew's hobbies and interests, analyzes posts on the in-house SNS, and evaluates each individual's motivation in order to propose measures to increase each individual's motivation. The data analysis department also analyzes data related to the crew's hobbies and interests, analyzes participation in in-house events, and evaluates each individual's motivation in order to propose measures to increase each individual's motivation. In this way, by analyzing data related to the crew's hobbies and interests and proposing measures to increase each individual's motivation, crew satisfaction is improved.

[0065] The operation planning unit uses the emotion estimation function to plan the allocation of backup crew members for periods when emotional fluctuations are predicted based on the crew's emotion data. For example, the operation planning unit uses the emotion estimation function to analyze past emotion data and absence data and identify patterns of emotional fluctuations in order to plan the allocation of backup crew members for periods when emotional fluctuations are predicted based on the crew's emotion data. The operation planning unit also uses the emotion estimation function to analyze the emotion data in combination with other attribute data and evaluate the impact of emotional fluctuations on absences in order to plan the allocation of backup crew members for periods when emotional fluctuations are predicted based on the crew's emotion data. The operation planning unit also uses the emotion estimation function to monitor the emotion data in real time in order to plan the allocation of backup crew members for periods when emotional fluctuations are predicted based on the crew's emotion data, and responds immediately if an emotional fluctuation is detected. This reduces the risk of absenteeism and resignation by using the emotion estimation function to plan the allocation of backup crew members for periods when emotional fluctuations are predicted based on the crew's emotion data.

[0066] The operation planning department, based on the crew's health data, plans to assign backup crew members during periods when their health conditions are likely to deteriorate. For example, the operation planning department analyzes past health examination results and absence data to identify patterns of fluctuations in health conditions in order to plan to assign backup crew members during periods when their health conditions are likely to deteriorate based on the crew's health data. Furthermore, the operation planning department combines and analyzes the health data with other attribute data to evaluate the impact of fluctuations in health conditions on absences in order to plan to assign backup crew members during periods when their health conditions are likely to deteriorate based on the crew's health data. Furthermore, the operation planning department monitors the health data in real time in order to plan to assign backup crew members during periods when their health conditions are likely to deteriorate based on the crew's health data, and responds immediately if a fluctuation in health conditions is detected. In this way, the risk of absenteeism is reduced by planning to assign backup crew members during periods when their health conditions are likely to deteriorate based on the crew's health data.

[0067] The operation planning department makes plans to deploy backup crew members during periods when stress levels are likely to be high based on the crew members' internal communication data. For example, the operation planning department analyzes the content of emails and chats and evaluates the frequency of negative words in order to make plans to deploy backup crew members during periods when stress levels are likely to be high based on the crew members' internal communication data. The operation planning department also analyzes the results of regular surveys and evaluates workplace satisfaction and stress levels in order to make plans to deploy backup crew members during periods when stress levels are likely to be high based on the crew members' internal communication data. The operation planning department also analyzes the content of posts on internal SNS and evaluates the frequency of positive posts in order to make plans to deploy backup crew members during periods when stress levels are likely to be high based on the crew members' internal communication data. In this way, by making plans to deploy backup crew members during periods when stress levels are likely to be high based on the crew members' internal communication data, the risk of absenteeism and resignation is reduced.

[0068] The operation planning department, based on the crew's home environment data, makes a plan to assign a reserve crew member during a period when the crew's home environment is likely to change. For example, the operation planning department analyzes family composition and commuting time to evaluate the risk of absenteeism and resignation in order to make a plan to assign a reserve crew member during a period when the crew's home environment is likely to change based on the crew's home environment data. The operation planning department also analyzes the usage status of the company's employee benefits program and evaluates the impact of the home environment on absenteeism and resignation in order to make a plan to assign a reserve crew member during a period when the crew's home environment is likely to change based on the crew's home environment data. The operation planning department also acquires commuting time from GPS data and analyzes the impact of the home environment on absenteeism and resignation in order to make a plan to assign a reserve crew member during a period when the crew's home environment is likely to change based on the crew's home environment data. In this way, the risk of absenteeism and resignation is reduced by making a plan to assign a reserve crew member during a period when the crew's home environment is likely to change based on the crew's home environment data.

[0069] The operation planning department proposes measures to increase individual motivation based on data on the crew's hobbies and interests. For example, the operation planning department analyzes the results of regular surveys and evaluates the motivation of each individual in order to propose measures to increase individual motivation based on the data on the crew's hobbies and interests. The operation planning department also analyzes the content of posts on in-house SNS and evaluates the motivation of each individual in order to propose measures to increase individual motivation based on the data on the crew's hobbies and interests. The operation planning department also analyzes the participation status of in-house events and evaluates the motivation of each individual in order to propose measures to increase individual motivation based on the data on the crew's hobbies and interests. In this way, by proposing measures to increase individual motivation based on the data on the crew's hobbies and interests, crew satisfaction is improved.

[0070] The operation planning unit uses the emotion estimation function to plan the allocation of backup crew members for periods when emotional fluctuations are predicted based on the crew's emotion data. For example, the operation planning unit uses the emotion estimation function to analyze past emotion data and absence data and identify patterns of emotional fluctuations in order to plan the allocation of backup crew members for periods when emotional fluctuations are predicted based on the crew's emotion data. The operation planning unit also uses the emotion estimation function to analyze the emotion data in combination with other attribute data and evaluate the impact of emotional fluctuations on absences in order to plan the allocation of backup crew members for periods when emotional fluctuations are predicted based on the crew's emotion data. The operation planning unit also uses the emotion estimation function to monitor the emotion data in real time in order to plan the allocation of backup crew members for periods when emotional fluctuations are predicted based on the crew's emotion data, and responds immediately if an emotional fluctuation is detected. This reduces the risk of absenteeism and resignation by using the emotion estimation function to plan the allocation of backup crew members for periods when emotional fluctuations are predicted based on the crew's emotion data.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The Data Collection Department can collect data on crew members' hobbies and interests and propose measures to increase their individual motivation. For example, it can conduct regular surveys and store the results in a database. It can also analyze posts on the company's internal social media platforms and propose measures to increase individual motivation. It can also analyze participation in company events and propose measures to increase individual motivation. In this way, by collecting data on crew members' hobbies and interests and proposing measures to increase their individual motivation, it is possible to improve crew satisfaction.

[0073] The data collection department can also collect crew health data and analyze the relationship between health status and absenteeism risk. For example, wearable devices can be distributed to crew members to regularly collect heart rate and sleep data. Regular health checkups can also be conducted and the results can be stored in a database. Self-reported health questionnaires can also be conducted and the results can be stored in a database. In this way, by collecting crew health data and analyzing the relationship between health status and absenteeism risk, the accuracy of absenteeism prediction can be improved.

[0074] The Data Collection Department can also collect data on crew members' home environments and analyze the impact of home environments on absenteeism and resignation. For example, it can conduct regular surveys to investigate family composition and commuting time. It can also analyze the usage of company employee benefits programs to evaluate the impact of home environments on absenteeism and resignation. It can also obtain commuting time from GPS data and analyze the impact of home environments on absenteeism and resignation. In this way, by collecting data on crew members' home environments and analyzing the impact of home environments on absenteeism and resignation, it is possible to improve the accuracy of predicting absenteeism and resignation.

[0075] The data collection department can also analyze crew internal communication data to evaluate stress levels and the workplace atmosphere. For example, they can analyze the content of emails and chats to evaluate the frequency of negative language. They can also conduct regular surveys to evaluate workplace satisfaction and stress levels. They can also analyze the content of posts on the company's internal social media accounts to evaluate the frequency of positive posts. This allows them to analyze crew internal communication data and evaluate stress levels and the workplace atmosphere, thereby improving the accuracy of predicting absenteeism and resignation.

[0076] The data collection unit can also monitor the emotional state of crew members in real time and collect emotional data using emotion estimation functions. For example, wearable devices can be used to measure heart rate and electrodermal activity. Cameras installed in the office can also be used to analyze facial expressions and estimate emotions. Furthermore, voice analysis technology can be used to analyze the tone and pitch of voices during phone calls to collect emotional data. This allows for real-time monitoring of crew members' emotional states and collection of emotional data, improving the accuracy of predicting absenteeism and resignation.

[0077] The data analysis department can also combine and analyze crew health data and attendance data to predict the impact of health status on the risk of absence. For example, it can compare and analyze past health checkup results with absence data. It can also train a machine learning model on fluctuation patterns in health data. It can also combine and analyze health data with other attribute data. This allows for the combined analysis of crew health data and attendance data to predict the impact of health status on the risk of absence, thereby improving the accuracy of absenteeism predictions.

[0078] The data analysis unit can also use the emotion estimation function to analyze crew emotional data and predict the impact of emotional fluctuations on absenteeism and resignation. For example, it can compare and analyze past emotional data with absenteeism and resignation data. It can also train a machine learning model on patterns of emotional fluctuations. It can also analyze emotion data in combination with other attribute data. This allows the emotion estimation function to analyze crew emotional data and predict the impact of emotional fluctuations on absenteeism and resignation, thereby improving the accuracy of absenteeism and resignation predictions.

[0079] The operation planning department can also use the crew's health data to create plans to deploy backup crew during periods when their health is likely to deteriorate. For example, it can analyze past health checkup results and absenteeism data to identify patterns of fluctuations in health status. It can also combine health data with other attribute data for analysis to evaluate the impact of fluctuations in health status on absenteeism. It can also monitor health data in real time and respond immediately if a change in health status is detected. This makes it possible to reduce the risk of absenteeism by creating plans to deploy backup crew during periods when their health is likely to deteriorate based on the crew's health data.

[0080] The operation planning department can also use the emotion estimation function to create plans to assign backup crew members during periods when emotional fluctuations are predicted based on the crew's emotional data. For example, it can analyze past emotional data and absence data to identify patterns of emotional fluctuations. It can also combine and analyze emotional data with other attribute data to evaluate the impact of emotional fluctuations on absences. It can also monitor emotional data in real time and respond immediately if emotional fluctuations are detected. This makes it possible to reduce the risk of absenteeism and resignation by using the emotion estimation function to create plans to assign backup crew members during periods when emotional fluctuations are predicted based on the crew's emotional data.

[0081] The operation planning department can use the crew's home environment data to create plans to deploy backup crew members during periods when their home environment is likely to change. For example, they can analyze family composition and commuting time to assess the risk of absenteeism and resignation. They can also analyze the usage of the company's employee benefits program to assess the impact of home environment on absenteeism and resignation. Furthermore, they can obtain commuting time from GPS data and analyze the impact of home environment on absenteeism and resignation. This makes it possible to reduce the risk of absenteeism and resignation by creating plans to deploy backup crew members during periods when their home environment is likely to change, based on the crew's home environment data.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The data collection department collects crew attribute data, attendance data, and external information. For example, attribute data such as crew age, gender, and work history, attendance data such as number of days worked, number of days absent, and number of times late, and external information such as job market trends and the hiring situation of competitors are collected. This data is obtained from sources such as human resources systems, attendance management systems, and the Internet. Step 2: The data storage unit stores the data collected by the data collection unit. For example, it stores the data in a database, cleanses the data to maintain its integrity, and encrypts it to ensure its security. Step 3: The data analysis unit analyzes the data accumulated by the data accumulation unit. For example, it uses AI to predict absenteeism and resignation, analyzes past data to learn patterns of absenteeism and resignation, and analyzes crew attribute data and attendance data to identify crew members at high risk of resigning. Step 4: The Operation Planning Department formulates an operation plan based on the results of the analysis by the Data Analysis Department. For example, it may plan to deploy backup crew members during periods when there are predicted to be many absentees, plan to take early action for crew members at high risk of quitting, and propose measures to improve crew motivation and satisfaction.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 data collection department that collects crew attribute data, attendance data, and external information; a data storage unit that stores the data collected by the data collection unit; a data analysis unit that analyzes the data accumulated by the data accumulation unit; an operation plan formulation unit that formulates an operation plan based on the results of the analysis by the data analysis unit. A system characterized by:

2. The data collection unit Monitor crew emotional states in real time and collect emotional data The system of claim 1 .

3. The data collection unit Collect crew health data and analyze the relationship between health status and risk of absenteeism The system of claim 1 .

4. The data collection unit Analyzing crew internal communication data to assess stress levels and workplace atmosphere The system of claim 1 .

5. The data collection unit Collect data on crew members' home environments and analyze the impact of home environments on absenteeism and resignation The system of claim 1 .

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