System

Abstract

An object of a system according to an embodiment is to propose an optimal placement destination and an optimal transfer destination in consideration of characteristics and desires of employees.SOLUTION: A system includes a hometown and family information analysis part, a career plan analysis part, a personality diagnosis analysis part, and an integration optimizing part. A hometown and family information analysis part analyzes hometown and family information of the employee. The career plan analysis part analyzes the career plan of the employee. The personality diagnosis analyzing unit analyzes the personality diagnosis data of the employee. The integrating and optimizing unit integrates the information analyzed by the hometown and family information analyzing unit, the career plan analyzing unit, and the personality diagnosing and analyzing unit, and proposes an optimal place of assignment or place of transfer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to fully consider the characteristics and wishes of individual employees when assigning or transferring them, which led to issues such as the risk of employees turning over and a decline in motivation.

[0005] The system according to the embodiment aims to propose the most suitable assignment or transfer destination in consideration of the characteristics and wishes of employees. [Means for solving the problem]

[0006] The system according to the embodiment includes a hometown and family information analysis unit, a career plan analysis unit, a personality diagnosis analysis unit, and an integrated optimization unit. The hometown and family information analysis unit analyzes the employee's hometown and family information. The career plan analysis unit analyzes the employee's career plan. The personality diagnosis analysis unit analyzes the employee's personality diagnosis data. The integrated optimization unit integrates the data analyzed by the hometown and family information analysis unit, career plan analysis unit, and personality diagnosis analysis unit to propose optimal assignments or transfer destinations. [Effects of the Invention]

[0007] The system according to the embodiment can propose the most suitable assignment or transfer destination in consideration of the characteristics and wishes of employees. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The assignment suggestion system according to an embodiment of the present invention is a system that analyzes data such as an employee's hometown, family information, career plan, and personality assessment, and proposes the most suitable assignment or transfer destination. As a result, the assignment suggestion system can maintain employee motivation, promote employee growth, and reduce the risk of employee turnover.

[0029] The assignment suggestion system according to the embodiment includes a hometown and family information analysis unit, a career plan analysis unit, a personality analysis unit, and an integrated optimization unit. The hometown and family information analysis unit analyzes an employee's hometown and family information. For example, it analyzes details of the hometown (at the city / ward / town / village level), family structure, and family occupations. Furthermore, if an employee lives with their family, the hometown and family information analysis unit can reduce the risk of employee turnover by assigning them to a position near their family. The career plan analysis unit analyzes an employee's career plan. For example, it analyzes the employee's short-term and long-term goals and desired job type and position. Furthermore, if an employee wants to acquire specific skills, the career plan analysis unit can maintain motivation and promote growth by transferring them to a department where they can utilize those skills. The personality analysis unit analyzes the employee's personality analysis data. For example, it analyzes data such as the MBTI and Big Five personality traits. Furthermore, if an employee is highly cooperative, the personality analysis unit can reduce friction by assigning them to a department where teamwork is emphasized. The integrated optimization unit integrates the data analyzed by the hometown and family information analysis unit, career plan analysis unit, and personality diagnosis analysis unit to propose optimal assignments or transfers. For example, if an employee's hometown is far away but there is a department that matches their career plan, assigning them to that department can maintain motivation and reduce the risk of employee turnover. As a result, the assignment proposal system according to the embodiment can improve the productivity of each department and prevent early employee turnover. For example, assigning employees to a department close to their family makes it easier for them to balance work and family life, improving motivation. Furthermore, transferring employees to a department that matches their career plan promotes their growth. Furthermore, utilizing personality diagnosis data can reduce friction within teams and enable smooth business operations.

[0030] The hometown and family information analysis unit can suggest an assignment that minimizes an employee's commute time and living costs. For example, the hometown and family information analysis unit analyzes an employee's hometown and family information to suggest an assignment that will shorten commute time. For example, it selects the office or branch closest to the employee's home. The hometown and family information analysis unit also assigns an employee to an area with low rent and living costs to minimize the employee's living costs. For example, it selects an office in the suburbs rather than in an urban area. The hometown and family information analysis unit also takes into account the employee's family structure and suggests an assignment that is close to public transportation and facilities that are easy for the family to use. For example, it selects an area close to a nursery school or school. This makes it possible to minimize an employee's commute time and living costs.

[0031] The career plan analysis department can analyze an employee's past performance data and suggest an assignment where the employee can gain the most successful experience. The career plan analysis department, for example, analyzes an employee's past performance data and suggests an assignment where the employee can gain the most successful experience. For example, it selects a project similar to a project that was successful in the past. The career plan analysis department also suggests an assignment where the employee can gain the most successful experience based on the employee's performance data. For example, it refers to past success cases and selects a department where similar success is expected. The career plan analysis department also analyzes past performance data and suggests an assignment where the employee can gain the most successful experience. For example, it analyzes the factors behind past successes and selects an assignment that matches those. This allows the employee to gain the most successful experience.

[0032] The personality diagnosis analysis unit can suggest an assignment with a boss or coworkers who have an optimal communication style based on the employee's personality traits. The personality diagnosis analysis unit, for example, analyzes personality diagnosis data and suggests an assignment with a boss or coworkers who have an optimal communication style based on the employee's personality traits. For example, for an introverted employee, it selects a boss who is calm and good at supporting. The personality diagnosis analysis unit also suggests an assignment with a colleague who has an optimal communication style based on the employee's personality traits. For example, for an extroverted employee, it selects a colleague who is willing to exchange opinions. The personality diagnosis analysis unit also suggests an assignment with a boss or coworkers who have a communication style that the employee finds most comfortable based on the personality diagnosis data. For example, it selects members who have a communication style that matches the employee's personality traits. This allows the employee to work with a boss or coworkers who have an optimal communication style.

[0033] The hometown and family information analysis department can propose an assignment that optimizes work-life balance by taking into account the employee's family structure and life stage. For example, the hometown and family information analysis department analyzes the employee's family structure and proposes an assignment that takes into account the age of the employee's children and the need for nursing care. For example, if the employee has young children, it selects an area with a good number of childcare facilities. The hometown and family information analysis department also proposes an assignment that optimizes work-life balance according to the employee's life stage. For example, if the employee has family members who need nursing care, it selects an area with a nearby nursing facility. The hometown and family information analysis department also comprehensively analyzes the employee's family structure and life stage and proposes an assignment that makes it easy to balance work and family life. For example, it selects an area where the entire family can live comfortably. This optimizes the employee's work-life balance.

[0034] The career plan analysis department can suggest a post linked to a training program that allows an employee to acquire new skills based on the employee's career plan. The career plan analysis department, for example, analyzes the career plan and suggests a post linked to a training program that allows an employee to acquire new skills. For example, it selects a department that offers extensive training for specific skills. The career plan analysis department also suggests a post linked to a training program that allows an employee to acquire new skills based on the employee's career plan. For example, it selects a department that regularly conducts skill-up training. The career plan analysis department also suggests a post linked to a training program that allows an employee to acquire new skills based on the career plan. For example, it selects a department that connects skill training with practical work. This allows the employee to acquire new skills.

[0035] The personality diagnosis analysis unit can suggest assignments that utilize remote work or flextime systems in accordance with the employee's personality traits. The personality diagnosis analysis unit, for example, analyzes personality diagnosis data and suggests assignments that utilize remote work or flextime systems in accordance with the employee's personality traits. For example, remote work is recommended for employees with high self-management skills. The personality diagnosis analysis unit also suggests assignments that utilize remote work or flextime systems based on the employee's personality traits. For example, a flextime system is offered to employees who prefer flexible working styles. The personality diagnosis analysis unit also suggests assignments that utilize remote work or flextime systems in accordance with the employee's personality traits based on the personality diagnosis data. For example, a flexible working style that suits family circumstances is offered. This allows employees to take advantage of remote work or flextime systems.

[0036] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0037] The assignment suggestion system can further include a health condition analysis unit. The health condition analysis unit analyzes the employee's health data and suggests the most suitable assignment. For example, if an employee has a chronic illness, it selects an assignment that is close to a medical facility that can treat that illness. The health condition analysis unit also analyzes the employee's stress level and suggests an assignment that provides a low-stress environment. For example, it can select an area with a rich natural environment or an area with plenty of relaxation facilities. Furthermore, the health condition analysis unit suggests an assignment that will help maintain health based on the employee's health data. For example, it can select an area with plenty of fitness facilities or an office with a cafeteria that serves healthy meals. This helps maintain employee health and improves work efficiency.

[0038] The assignment suggestion system can further include a hobby and preference analysis unit. The hobby and preference analysis unit analyzes data related to employees' hobbies and preferences and suggests the most suitable assignment. For example, if an employee enjoys outdoor activities, it can select an area with a rich natural environment. The hobby and preference analysis unit can also suggest an assignment that offers a refreshing environment based on the employee's hobbies and preferences. For example, it can select an area with a good selection of cultural and sports facilities. Furthermore, the hobby and preference analysis unit can suggest departments with many colleagues who share the same hobbies based on the employee's hobbies and preferences. This is expected to facilitate smooth communication between employees and improve teamwork.

[0039] The assignment suggestion system can further include an educational background and qualifications analysis unit. The educational background and qualifications analysis unit analyzes data related to employees' educational background and acquired qualifications to suggest the most suitable assignment. For example, for an employee with a specific qualification, it selects a department where those qualifications can be utilized. The educational background and qualifications analysis unit also suggests an assignment where specialized knowledge can be utilized based on the employee's educational background and qualifications. For example, it can select a project team specializing in a specific field. Furthermore, the educational background and qualifications analysis unit suggests an assignment with many opportunities for career advancement based on the employee's educational background and qualifications. This makes it possible to make the most of employees' specialized knowledge and promote their career growth.

[0040] The assignment suggestion system can further include a transportation access analysis unit. The transportation access analysis unit analyzes data related to employees' commuting means and transportation access, and suggests the optimal assignment location. For example, for employees who use public transportation, it selects an office close to the nearest station. The transportation access analysis unit also suggests an assignment location that will shorten commuting time based on the employee's commuting means. For example, it can select an area where a direct bus runs from the employee's home. Furthermore, the transportation access analysis unit suggests areas with less traffic congestion based on the employee's commuting means. This reduces commuting stress for employees and improves work efficiency.

[0041] The assignment suggestion system can further include an environmental adaptability analysis unit. The environmental adaptability analysis unit analyzes data related to an employee's environmental adaptability and suggests the most suitable assignment. For example, it evaluates whether an employee can easily adapt to a new environment and selects an assignment that provides an environment that makes it easy to adapt. The environmental adaptability analysis unit also suggests an assignment with a solid support system based on the employee's environmental adaptability. For example, it can select a department that has an orientation and mentoring system in place to help the employee get used to the new environment. Furthermore, the environmental adaptability analysis unit suggests a team that is easy to adapt to based on the employee's environmental adaptability. This allows the employee to smoothly adapt to the new environment and concentrate on their work.

[0042] The processing flow of the first embodiment will be briefly explained below.

[0043] Step 1: The Hometown and Family Information Analysis Department analyzes the employee's hometown and family information. For example, it analyzes details of the hometown (at the city / ward / town / village level), family structure, and family occupations. In addition, if an employee lives with their family, assigning them to a position close to their family can reduce the risk of them leaving the company. Step 2: The Career Plan Analysis Department analyzes the employee's career plan. For example, it analyzes the employee's short-term and long-term goals, as well as the type of job and position they want. If an employee wants to acquire specific skills, transferring them to a department where they can utilize those skills can help maintain motivation and promote growth. Step 3: The personality analysis department analyzes the employee's personality data. For example, they analyze data such as MBTI and Big Five personality traits. If an employee is highly cooperative, they can be assigned to a department where teamwork is emphasized, which can reduce friction. Step 4: The Integrated Optimization Unit integrates the data analyzed by the Hometown and Family Information Analysis Unit, Career Plan Analysis Unit, and Personality Diagnostic Analysis Unit to propose optimal assignments or transfers. For example, if an employee's hometown is far away but there is a department that matches their career plan, assigning them to that department can maintain motivation and reduce the risk of them leaving the company.

[0044] (Example 2) The assignment suggestion system according to an embodiment of the present invention is a system that analyzes data such as an employee's hometown, family information, career plan, and personality assessment, and proposes the most suitable assignment or transfer destination. As a result, the assignment suggestion system can maintain employee motivation, promote employee growth, and reduce the risk of employee turnover.

[0045] The assignment suggestion system according to the embodiment includes a hometown and family information analysis unit, a career plan analysis unit, a personality analysis unit, and an integrated optimization unit. The hometown and family information analysis unit analyzes an employee's hometown and family information. For example, it analyzes details of the hometown (at the city / ward / town / village level), family structure, and family occupations. Furthermore, if an employee lives with their family, the hometown and family information analysis unit can reduce the risk of employee turnover by assigning them to a position near their family. The career plan analysis unit analyzes an employee's career plan. For example, it analyzes the employee's short-term and long-term goals and desired job type and position. Furthermore, if an employee wants to acquire specific skills, the career plan analysis unit can maintain motivation and promote growth by transferring them to a department where they can utilize those skills. The personality analysis unit analyzes the employee's personality analysis data. For example, it analyzes data such as the MBTI and Big Five personality traits. Furthermore, if an employee is highly cooperative, the personality analysis unit can reduce friction by assigning them to a department where teamwork is emphasized. The integrated optimization unit integrates the data analyzed by the hometown and family information analysis unit, career plan analysis unit, and personality diagnosis analysis unit to propose optimal assignments or transfers. For example, if an employee's hometown is far away but there is a department that matches their career plan, assigning them to that department can maintain motivation and reduce the risk of employee turnover. As a result, the assignment proposal system according to the embodiment can improve the productivity of each department and prevent early employee turnover. For example, assigning employees to a department close to their family makes it easier for them to balance work and family life, improving motivation. Furthermore, transferring employees to a department that matches their career plan promotes their growth. Furthermore, utilizing personality diagnosis data can reduce friction within teams and enable smooth business operations.

[0046] The hometown and family information analysis unit can suggest an assignment that minimizes an employee's commute time and living costs. For example, the hometown and family information analysis unit analyzes an employee's hometown and family information to suggest an assignment that will shorten commute time. For example, it selects the office or branch closest to the employee's home. The hometown and family information analysis unit also assigns an employee to an area with low rent and living costs to minimize the employee's living costs. For example, it selects an office in the suburbs rather than in an urban area. The hometown and family information analysis unit also takes into account the employee's family structure and suggests an assignment that is close to public transportation and facilities that are easy for the family to use. For example, it selects an area close to a nursery school or school. This makes it possible to minimize an employee's commute time and living costs.

[0047] The career plan analysis department can analyze an employee's past performance data and suggest an assignment where the employee can gain the most successful experience. The career plan analysis department, for example, analyzes an employee's past performance data and suggests an assignment where the employee can gain the most successful experience. For example, it selects a project similar to a project that was successful in the past. The career plan analysis department also suggests an assignment where the employee can gain the most successful experience based on the employee's performance data. For example, it refers to past success cases and selects a department where similar success is expected. The career plan analysis department also analyzes past performance data and suggests an assignment where the employee can gain the most successful experience. For example, it analyzes the factors behind past successes and selects an assignment that matches those. This allows the employee to gain the most successful experience.

[0048] The personality diagnosis analysis unit can suggest an assignment with a boss or coworkers who have an optimal communication style based on the employee's personality traits. The personality diagnosis analysis unit, for example, analyzes personality diagnosis data and suggests an assignment with a boss or coworkers who have an optimal communication style based on the employee's personality traits. For example, for an introverted employee, it selects a boss who is calm and good at supporting. The personality diagnosis analysis unit also suggests an assignment with a colleague who has an optimal communication style based on the employee's personality traits. For example, for an extroverted employee, it selects a colleague who is willing to exchange opinions. The personality diagnosis analysis unit also suggests an assignment with a boss or coworkers who have a communication style that the employee finds most comfortable based on the personality diagnosis data. For example, it selects members who have a communication style that matches the employee's personality traits. This allows the employee to work with a boss or coworkers who have an optimal communication style.

[0049] The integrated optimization unit can use the emotion estimation function to evaluate the employee's emotions and suggest an assignment that will elicit the most positive emotions. The integrated optimization unit, for example, uses the emotion estimation function to evaluate the employee's emotions based on personality diagnosis data and suggest an assignment that will elicit the most positive emotions. For example, it selects a department that suits the personality traits. The integrated optimization unit also evaluates the employee's emotions based on the personality diagnosis data and suggests an assignment that will elicit positive emotions. For example, it selects a department that provides the employee with the most comfortable working environment. The integrated optimization unit also evaluates the employee's emotions based on the personality diagnosis data based on the emotion estimation data and suggests an assignment that will elicit positive emotions. For example, it selects a team that suits the employee's personality traits. In this way, it is possible to suggest an assignment that will elicit positive emotions from the employee.

[0050] The hometown and family information analysis department can propose an assignment that optimizes work-life balance by taking into account the employee's family structure and life stage. For example, the hometown and family information analysis department analyzes the employee's family structure and proposes an assignment that takes into account the age of the employee's children and the need for nursing care. For example, if the employee has young children, it selects an area with a good number of childcare facilities. The hometown and family information analysis department also proposes an assignment that optimizes work-life balance according to the employee's life stage. For example, if the employee has family members who need nursing care, it selects an area with a nearby nursing facility. The hometown and family information analysis department also comprehensively analyzes the employee's family structure and life stage and proposes an assignment that makes it easy to balance work and family life. For example, it selects an area where the entire family can live comfortably. This optimizes the employee's work-life balance.

[0051] The career plan analysis department can suggest a post linked to a training program that allows an employee to acquire new skills based on the employee's career plan. The career plan analysis department, for example, analyzes the career plan and suggests a post linked to a training program that allows an employee to acquire new skills. For example, it selects a department that offers extensive training for specific skills. The career plan analysis department also suggests a post linked to a training program that allows an employee to acquire new skills based on the employee's career plan. For example, it selects a department that regularly conducts skill-up training. The career plan analysis department also suggests a post linked to a training program that allows an employee to acquire new skills based on the career plan. For example, it selects a department that connects skill training with practical work. This allows the employee to acquire new skills.

[0052] The personality diagnosis analysis unit can suggest assignments that utilize remote work or flextime systems in accordance with the employee's personality traits. The personality diagnosis analysis unit, for example, analyzes personality diagnosis data and suggests assignments that utilize remote work or flextime systems in accordance with the employee's personality traits. For example, remote work is recommended for employees with high self-management skills. The personality diagnosis analysis unit also suggests assignments that utilize remote work or flextime systems based on the employee's personality traits. For example, a flextime system is offered to employees who prefer flexible working styles. The personality diagnosis analysis unit also suggests assignments that utilize remote work or flextime systems in accordance with the employee's personality traits based on the personality diagnosis data. For example, a flexible working style that suits family circumstances is offered. This allows employees to take advantage of remote work or flextime systems.

[0053] The integrated optimization unit can use the emotion estimation function to consider the emotions of the employee's family and suggest an assignment that maximizes the happiness of the entire family. The integrated optimization unit, for example, uses the emotion estimation function to analyze the emotions of the employee's family and suggest an assignment that maximizes the happiness of the entire family. For example, it selects an area where all family members can have positive emotions. The integrated optimization unit also proposes an assignment that maximizes the happiness of the entire family based on the emotion data of the employee's family. For example, it selects an area where all family members can live comfortably. The integrated optimization unit also considers the emotions of the employee's family based on the emotion estimation data and suggests an assignment that maximizes the happiness of the entire family. For example, it selects an area with a wide range of welfare facilities that will satisfy all family members. This makes it possible to maximize the happiness of the entire family of the employee.

[0054] The career plan analysis unit can use the emotion estimation function to suggest a department that will reduce the anxiety and stress that the employee feels about their career plan. The career plan analysis unit, for example, uses the emotion estimation function to analyze the anxiety and stress that the employee feels about their career plan and suggests a department that will reduce that anxiety and stress. For example, it selects a department with less anxiety and stress. The career plan analysis unit also suggests a department that will reduce anxiety and stress based on the emotion data about the employee's career plan. For example, it selects a department with a comprehensive support system. The career plan analysis unit also suggests a department that will reduce the anxiety and stress that the employee feels about their career plan based on the emotion estimation data. For example, it selects a department with comprehensive mental health support. This makes it possible to reduce the employee's anxiety and stress.

[0055] The personality diagnosis analysis unit can monitor emotional fluctuations based on an employee's personality traits and continuously adjust the optimal assignment. The personality diagnosis analysis unit, for example, uses an emotion estimation function to monitor emotional fluctuations based on an employee's personality traits and build a system that continuously adjusts the optimal assignment. For example, if the emotion score drops, the assignment is reevaluated. The personality diagnosis analysis unit also analyzes the employee's emotion data in real time and monitors emotional fluctuations based on the personality traits. For example, if the emotion score drops, appropriate support is provided. The personality diagnosis analysis unit also monitors emotional fluctuations based on the employee's personality traits based on the emotion estimation data and continuously adjusts the optimal assignment. For example, the assignment is changed if the emotion score drops. In this way, it is possible to monitor the emotional fluctuations of an employee and continuously adjust the optimal assignment.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The assignment suggestion system can further include a health condition analysis unit. The health condition analysis unit analyzes the employee's health data and suggests the most suitable assignment. For example, if an employee has a chronic illness, it selects an assignment that is close to a medical facility that can treat that illness. The health condition analysis unit also analyzes the employee's stress level and suggests an assignment that provides a low-stress environment. For example, it can select an area with a rich natural environment or an area with plenty of relaxation facilities. Furthermore, the health condition analysis unit suggests an assignment that will help maintain health based on the employee's health data. For example, it can select an area with plenty of fitness facilities or an office with a cafeteria that serves healthy meals. This helps maintain employee health and improves work efficiency.

[0058] The assignment suggestion system can further include a hobby and preference analysis unit. The hobby and preference analysis unit analyzes data related to employees' hobbies and preferences and suggests the most suitable assignment. For example, if an employee enjoys outdoor activities, it can select an area with a rich natural environment. The hobby and preference analysis unit can also suggest an assignment that offers a refreshing environment based on the employee's hobbies and preferences. For example, it can select an area with a good selection of cultural and sports facilities. Furthermore, the hobby and preference analysis unit can suggest departments with many colleagues who share the same hobbies based on the employee's hobbies and preferences. This is expected to facilitate smooth communication between employees and improve teamwork.

[0059] The assignment suggestion system can further include an educational background and qualifications analysis unit. The educational background and qualifications analysis unit analyzes data related to employees' educational background and acquired qualifications to suggest the most suitable assignment. For example, for an employee with a specific qualification, it selects a department where those qualifications can be utilized. The educational background and qualifications analysis unit also suggests an assignment where specialized knowledge can be utilized based on the employee's educational background and qualifications. For example, it can select a project team specializing in a specific field. Furthermore, the educational background and qualifications analysis unit suggests an assignment with many opportunities for career advancement based on the employee's educational background and qualifications. This makes it possible to make the most of employees' specialized knowledge and promote their career growth.

[0060] The assignment suggestion system can further include a transportation access analysis unit. The transportation access analysis unit analyzes data related to employees' commuting means and transportation access, and suggests the optimal assignment location. For example, for employees who use public transportation, it selects an office close to the nearest station. The transportation access analysis unit also suggests an assignment location that will shorten commuting time based on the employee's commuting means. For example, it can select an area where a direct bus runs from the employee's home. Furthermore, the transportation access analysis unit suggests areas with less traffic congestion based on the employee's commuting means. This reduces commuting stress for employees and improves work efficiency.

[0061] The assignment suggestion system can further include an environmental adaptability analysis unit. The environmental adaptability analysis unit analyzes data related to an employee's environmental adaptability and suggests the most suitable assignment. For example, it evaluates whether an employee can easily adapt to a new environment and selects an assignment that provides an environment that makes it easy to adapt. The environmental adaptability analysis unit also suggests an assignment with a solid support system based on the employee's environmental adaptability. For example, it can select a department that has an orientation and mentoring system in place to help the employee get used to the new environment. Furthermore, the environmental adaptability analysis unit suggests a team that is easy to adapt to based on the employee's environmental adaptability. This allows the employee to smoothly adapt to the new environment and concentrate on their work.

[0062] The assignment recommendation system also uses an emotion estimation function to monitor employee emotions in real time and adjust assignments according to emotional fluctuations. For example, if an employee's emotions are negative, the system identifies the cause and suggests an assignment that will provide appropriate support. Also, if an employee's emotions are positive, the system uses the emotion estimation function to suggest an assignment that will help maintain that state. For example, it can select a department that provides an environment where the employee can work comfortably. Furthermore, the emotion estimation function is used to continuously adjust assignments according to emotional fluctuations based on employee emotional data. This makes it possible to monitor employee emotions in real time and suggest the most appropriate assignments.

[0063] The assignment suggestion system can further use an emotion estimation function to suggest assignments that optimize the emotional balance of a team based on employee emotions. For example, if the emotional balance within a team is out of balance, it can suggest assignments that will restore that balance. It can also use the emotion estimation function to analyze employee emotional data and suggest assignments that optimize the emotional balance of the entire team. For example, increasing the number of employees with positive emotions can improve team morale. It can also use the emotion estimation function to continuously monitor the emotional balance of a team based on employee emotions and suggest optimal assignments. This can optimize the emotional balance of the entire team and improve work efficiency.

[0064] The assignment suggestion system can further use an emotion estimation function to evaluate stress levels based on an employee's emotions and suggest assignments that will reduce stress. For example, if an employee's stress level is high, the system can identify the cause and suggest assignments that will reduce stress. The emotion estimation function can also be used to analyze an employee's emotional data and evaluate their stress level. For example, it can select a department that offers a low-stress environment. The emotion estimation function can also be used to continuously monitor stress levels based on an employee's emotions and suggest assignments that will reduce stress. This can reduce employee stress and improve work efficiency.

[0065] The assignment suggestion system can further use an emotion estimation function to suggest assignments that will maintain employee motivation based on the employee's emotions. For example, if an employee's motivation is declining, the system can identify the cause and suggest assignments that will improve motivation. The emotion estimation function can also be used to analyze an employee's emotional data and suggest assignments that will maintain motivation. For example, an employee can be assigned to a project that the employee finds rewarding. Furthermore, the emotion estimation function can be used to continuously adjust assignments to maintain motivation based on the employee's emotions. This makes it possible to maintain employee motivation and improve work efficiency.

[0066] The assignment suggestion system can further use an emotion estimation function to evaluate an employee's satisfaction with their career plan based on their emotions and suggest an assignment that will improve satisfaction. For example, if an employee's satisfaction with their career plan is low, the system can identify the cause and suggest an assignment that will improve satisfaction. The emotion estimation function can also be used to analyze an employee's emotional data and evaluate their satisfaction with their career plan. For example, it can select a department that offers many opportunities for career advancement. Furthermore, the emotion estimation function can be used to continuously monitor an employee's satisfaction with their career plan based on their emotions and suggest an assignment that will improve satisfaction. This can improve an employee's satisfaction with their career plan and improve work efficiency.

[0067] The processing flow of the second embodiment will be briefly explained below.

[0068] Step 1: The Hometown and Family Information Analysis Department analyzes the employee's hometown and family information. For example, it analyzes details of the hometown (at the city / ward / town / village level), family structure, and family occupations. In addition, if an employee lives with their family, assigning them to a position close to their family can reduce the risk of them leaving the company. Step 2: The Career Plan Analysis Department analyzes the employee's career plan. For example, it analyzes the employee's short-term and long-term goals, as well as the type of job and position they want. If an employee wants to acquire specific skills, transferring them to a department where they can utilize those skills can help maintain motivation and promote growth. Step 3: The personality analysis department analyzes the employee's personality data. For example, they analyze data such as MBTI and Big Five personality traits. If an employee is highly cooperative, they can be assigned to a department where teamwork is emphasized, which can reduce friction. Step 4: The Integrated Optimization Unit integrates the data analyzed by the Hometown and Family Information Analysis Unit, Career Plan Analysis Unit, and Personality Diagnostic Analysis Unit to propose optimal assignments or transfers. For example, if an employee's hometown is far away but there is a department that matches their career plan, assigning them to that department can maintain motivation and reduce the risk of them leaving the company.

[0069] 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.

[0070] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0071] 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.

[0072] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0073] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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).

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0088] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

[0102] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0103] 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.

[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 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.

[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 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).

[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] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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."

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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]

[0136] 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. The Hometown and Family Information Analysis Department analyzes employees' hometowns and family information; A career plan analysis department that analyzes employees' career plans; A personality diagnosis analysis department that analyzes employee personality diagnosis data; an integrated optimization unit that integrates the data analyzed by the birthplace and family information analysis unit, the career plan analysis unit, and the personality diagnosis analysis unit to propose the most suitable assignment or transfer destination; A system characterized by:

2. The birthplace and family information analysis unit Propose placements that minimize the employee's commute time and living costs 2. The system of claim 1.

3. The career plan analysis unit Analyze the employee's past performance data and suggest the position where they can gain the most success 2. The system of claim 1.

4. The personality diagnosis analysis unit Based on the employee's personality traits, we suggest placements with superiors and colleagues who have the most suitable communication styles.

2. The system of claim 1.

5. The integrated optimization unit Evaluate the employee's emotions and suggest the placement that elicits the most positive emotions 2. The system of claim 1.

6. The birthplace and family information analysis unit Considering the employee's family structure and life stage, we propose an assignment that optimizes work-life balance.

2. The system of claim 1.

7. The career plan analysis unit Based on the employee's career plan, propose the employee's placement in conjunction with a training program that will enable the employee to acquire new skills.

2. The system of claim 1.

8. The personality diagnosis analysis unit Propose the above-mentioned assignments utilizing remote work and flextime systems according to the personality characteristics of the above-mentioned employees.

2. The system of claim 1.

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