System

Abstract

An object of the system according to the embodiment is to accurately grasp the abilities and desires of employees and propose an optimal deployment.SOLUTION: A system according to an embodiment includes a capability grasping unit, a desire grasping unit, and an arrangement proposal unit. The ability grasping unit grasps the ability of the employee through a conversation with the employee. The desire identification unit identifies a desire of an employee. The arrangement proposal unit proposes an optimum arrangement based on the information grasped by the capability grasping unit and the desire grasping unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to accurately grasp employees' abilities and desires and to allocate them optimally.

[0005] The system according to the embodiment aims to accurately grasp the capabilities and desires of employees and propose optimal placement. [Means for solving the problem]

[0006] The system according to the embodiment includes a capability grasping unit, a desire grasping unit, and a placement proposal unit. The capability grasping unit grasps the capabilities of employees through dialogue with the employees. The desire grasping unit grasps the desires of the employees. The placement proposal unit proposes an optimal placement based on the information grasped by the capability grasping unit and the desire grasping unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately grasp the abilities and desires of employees and propose optimal placement. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 AI ​​HR assistant according to the embodiment of the present invention is a system that deeply understands the abilities and desires of employees and proposes optimal personnel placement. As a result, the AI ​​HR assistant can grasp the abilities and desires of employees and carry out optimal personnel placement.

[0029] An AI human resources assistant according to an embodiment includes a conversational AI, a capability assessment unit, a preference assessment unit, and a placement proposal unit. The conversational AI includes a capability assessment unit that assesses an employee's capabilities through dialogue with the employee, a preference assessment unit that assesses the employee's preferences, and a placement proposal unit that proposes optimal placements based on the information assessed by the capability assessment unit and the preference assessment unit. The capability assessment unit, for example, asks an employee, "What are your strengths?" and analyzes the employee's response. The capability assessment unit can also analyze an employee's past work history and evaluation data and reflect the results in the dialogue. The capability assessment unit can also evaluate an employee's capabilities using, for example, a skill matrix. The preference assessment unit, for example, asks an employee, "What kind of career do you want in the future?" and analyzes the response. The preference assessment unit can also analyze an employee's non-verbal communication (facial expressions and gestures) and reflect the results in the dialogue. The preference assessment unit can also assess an employee's preferences using, for example, a questionnaire survey. For example, if an employee responds, "I'm good at project management and would like to demonstrate leadership skills in the future," the placement suggestion department will suggest placing the employee as a project manager. The placement suggestion department can also suggest placements that take into account team dynamics and compatibility based on the employee's abilities and aspirations. The placement suggestion department can also consider the employee's long-term career path and suggest placements that anticipate future growth. This allows the AI ​​HR assistant to deeply understand employees' abilities and aspirations and suggest optimal placements, thereby improving the organization's competitiveness.

[0030] The ability assessment unit can analyze an employee's past work history and evaluation data and reflect the results in the dialogue. For example, the ability assessment unit analyzes an employee's past work history, and the conversational AI asks questions based on that information. For example, the ability assessment unit identifies an employee's areas of expertise based on past successful projects and highly evaluated work content. The ability assessment unit can also analyze an employee's evaluation data, and the conversational AI can understand the employee's abilities based on that information. For example, the ability assessment unit identifies an employee's strengths and weaknesses based on the results of performance reviews and 360-degree evaluations. The ability assessment unit can also evaluate an employee's abilities using, for example, an employee's skill matrix and reflect the results in the dialogue. In this way, by analyzing an employee's past work history and evaluation data, it is possible to more accurately understand their abilities and aspirations.

[0031] The desire grasping unit grasps an employee's hobbies and interests and can provide information useful for activities outside of work. For example, the desire grasping unit uses a conversational AI to grasp an employee's hobbies and interests and, based on that information, provides information useful for activities outside of work. For example, if an employee is interested in sports, it introduces related events and club activities. The desire grasping unit also grasps an employee's hobbies and interests and, based on that information, provides information useful for activities outside of work. For example, if an employee is interested in music, it introduces related concerts and music classes. The desire grasping unit also grasps an employee's hobbies and interests and, based on that information, provides information useful for activities outside of work. For example, if an employee is interested in cooking, it introduces related cooking classes and recipes. In this way, by grasping an employee's hobbies and interests and providing information useful for activities outside of work, employee satisfaction is improved.

[0032] The preference understanding unit can grasp the stress level of an employee and propose appropriate stress management measures. For example, an interactive AI may grasp the stress level of an employee and propose appropriate stress management measures based on that information. For example, if an employee is in a high stress state, the preference understanding unit may propose relaxation methods. Furthermore, the preference understanding unit may grasp the stress level of an employee and propose appropriate stress management measures based on that information. For example, if an employee is in a moderate stress state, the preference understanding unit may propose exercise or hobbies to reduce stress. Furthermore, the preference understanding unit may grasp the stress level of an employee and propose appropriate stress management measures based on that information. For example, if an employee is in a low stress state, the preference understanding unit may propose relaxation methods to prevent stress. In this way, by grasping the stress level of an employee and proposing appropriate stress management measures, the health and performance of the employee are improved.

[0033] The placement proposal unit can propose placements that take into consideration team dynamics and compatibility in addition to employee abilities and desires. The placement proposal unit, for example, analyzes team dynamics in addition to employee abilities and desires to propose optimal placements. For example, the placement proposal unit forms optimal teams taking into consideration the skill sets and personalities of team members. The placement proposal unit also analyzes team compatibility in addition to employee abilities and desires to propose optimal placements. For example, it selects team members with good compatibility based on past collaboration experiences and personality diagnostic tests. The placement proposal unit also proposes placements that take into consideration team dynamics and compatibility in addition to employee abilities and desires. For example, it makes optimal placements taking into consideration the division of team roles and communication patterns. This enables more effective personnel placements by taking team dynamics and compatibility into consideration.

[0034] The placement proposal department can consider an employee's career path over the long term and propose placements that take future growth into account. The placement proposal department, for example, considers an employee's career path over the long term and proposes placements that take future growth into account. For example, it proposes a role that will allow the employee to demonstrate leadership in the future. The placement proposal department can also consider an employee's career path over the long term and propose placements that take future growth into account. For example, it can have the employee participate in a project that will allow the employee to acquire new skills. The placement proposal department can also consider an employee's career path over the long term and propose placements that take future growth into account. For example, it can assign the employee to a department that will allow the employee to aim for career advancement. In this way, by considering an employee's career path over the long term, it is possible to make placements that take future growth into account.

[0035] The placement proposal unit can propose temporary placement to different projects or departments based on the employee's abilities and wishes, thereby diversifying their skills. The placement proposal unit, for example, proposes temporary placement to different projects based on the employee's abilities and wishes. For example, by having the employee participate in a project to acquire new skills. The placement proposal unit can also propose temporary placement to different departments based on the employee's abilities and wishes. For example, by having the employee experience different work, diversifying their skills. The placement proposal unit can also propose temporary placement to different projects or departments based on the employee's abilities and wishes. For example, by having the employee participate in a short-term project, they can acquire new skills. This makes it possible to diversify the employees' skills through temporary placement to different projects or departments.

[0036] The placement proposal unit can propose at least one flexible working style, either remote work or flextime, based on the employee's capabilities and preferences. The placement proposal unit, for example, proposes remote work based on the employee's capabilities and preferences. For example, it provides an environment in which the employee can work efficiently from home. The placement proposal unit also proposes flextime based on the employee's capabilities and preferences. For example, it provides flexible working hours that allow the employee to work in accordance with their lifestyle. The placement proposal unit also proposes at least one flexible working style, either remote work or flextime, based on the employee's capabilities and preferences. For example, it provides an environment in which the employee can work in accordance with their family circumstances. In this way, by proposing flexible working styles such as remote work or flextime, employee satisfaction is improved.

[0037] The placement proposal unit can analyze the project progress and resource allocation across the entire organization in real time and make optimal personnel placement. The placement proposal unit, for example, analyzes the project progress and resource allocation across the entire organization in real time and makes optimal personnel placement. For example, employees with the required skills are placed based on the progress of projects. The placement proposal unit can also analyze the resource allocation across the entire organization in real time and make optimal personnel placement. For example, appropriate personnel are placed based on resource surpluses and shortages. The placement proposal unit can also analyze the project progress and resource allocation across the entire organization in real time and make optimal personnel placement. For example, appropriate personnel are placed for important projects based on project priorities. In this way, optimal personnel placement is possible by analyzing the project progress and resource allocation across the entire organization in real time.

[0038] The placement proposal unit can optimize personnel placement based on the strategic goals and vision of the organization. The placement proposal unit optimizes personnel placement based on, for example, the strategic goals and vision of the organization. For example, it places employees who have the skills necessary to achieve the organization's long-term goals. The placement proposal unit also optimizes personnel placement based on, for example, the strategic goals and vision of the organization. For example, it places personnel in line with the organization's growth strategy. The placement proposal unit also optimizes personnel placement based on, for example, the strategic goals and vision of the organization. For example, it places personnel who match the organization's values. In this way, optimizing personnel placement based on the organization's strategic goals and vision supports the achievement of the organization's overall goals.

[0039] The placement proposal department can promote personnel exchanges between different departments in order to optimize personnel allocation throughout the organization. The placement proposal department, for example, promotes personnel exchanges between different departments in order to optimize personnel allocation throughout the organization. For example, it provides opportunities for employees from different departments to work together on projects. The placement proposal department also promotes personnel exchanges between different departments in order to optimize personnel allocation throughout the organization. For example, it implements job rotations to provide opportunities for employees to gain experience in different departments. The placement proposal department also promotes personnel exchanges between different departments in order to optimize personnel allocation throughout the organization. For example, it forms cross-functional teams to provide opportunities for employees from different departments to work together on tasks. In this way, promoting personnel exchanges between different departments optimizes personnel allocation throughout the organization.

[0040] The placement proposal unit can utilize external experts and consultants to optimize personnel placement throughout the organization. The placement proposal unit, for example, utilizes external experts and consultants to optimize personnel placement throughout the organization. For example, it proposes optimal placement based on the opinions of experts. The placement proposal unit also utilizes external experts and consultants to optimize personnel placement throughout the organization. For example, it optimizes placement based on the advice of industry experts. The placement proposal unit also utilizes external experts and consultants to optimize personnel placement throughout the organization. For example, it optimizes placement based on the proposals of consultants. In this way, by utilizing external experts and consultants, personnel placement throughout the organization is optimized.

[0041] The placement proposal unit can analyze the content of the feedback and automatically generate specific improvement measures. The placement proposal unit, for example, analyzes the content of the feedback and automatically generates specific improvement measures based on the information. For example, it proposes improvements to a business process based on employee feedback. The placement proposal unit also analyzes the content of the feedback and automatically generates specific improvement measures based on the information. For example, it proposes improvements to a training program based on employee feedback. The placement proposal unit also analyzes the content of the feedback and automatically generates specific improvement measures based on the information. For example, it proposes improvements to the work environment based on employee feedback. In this way, analyzing the content of feedback and automatically generating specific improvement measures promotes improvement in the organization.

[0042] The placement proposal unit can optimize the frequency and timing of feedback to reduce the burden on employees. The placement proposal unit, for example, optimizes the frequency and timing of feedback to build a system that reduces the burden on employees. For example, feedback is collected at the appropriate time to reduce the burden on employees. The placement proposal unit also optimizes the frequency of feedback to reduce the burden on employees. For example, it adjusts the frequency of monthly reviews and quarterly reviews. The placement proposal unit also optimizes the timing of feedback to reduce the burden on employees. For example, it adjusts the timing of end-of-project and regular meetings. In this way, the burden on employees is reduced by optimizing the frequency and timing of feedback.

[0043] The placement proposal department can share the content of the feedback with employees and work to improve the entire organization. For example, the placement proposal department builds a system to share the content of the feedback with other employees and work to improve the entire organization. For example, it introduces a platform for sharing feedback. The placement proposal department also shares the content of the feedback at regular meetings and work to improve the entire organization. For example, it discusses areas for improvement based on the feedback. The placement proposal department also shares the content of the feedback on an in-house portal and work to improve the entire organization. For example, it proposes improvement measures based on the feedback. In this way, sharing the content of the feedback promotes improvement throughout the organization.

[0044] The placement proposal unit can customize the employee training program based on the content of the feedback. The placement proposal unit, for example, builds a system that customizes the employee training program based on the content of the feedback. For example, it provides training to fill the employee's skill gaps. The placement proposal unit also customizes the employee training program based on the content of the feedback. For example, it provides training based on the employee's career development plan. The placement proposal unit also customizes the employee training program based on the content of the feedback. For example, it provides training based on the employee's performance review. In this way, customizing the training program based on the content of the feedback supports the improvement of employee skills.

[0045] The placement proposal unit can enhance data encryption technology to provide stronger security. The placement proposal unit, for example, enhances data encryption technology to protect employees' personal information. For example, the latest encryption algorithm is introduced to ensure data security. The placement proposal unit also enhances data encryption technology to protect employees' personal information. For example, the data encryption key is updated regularly. The placement proposal unit also enhances data encryption technology to protect employees' personal information. For example, the data encryption protocol is strengthened. In this way, stronger security is provided by enhancing data encryption technology.

[0046] The placement proposal unit can subdivide data access authority management and set the minimum necessary access rights. The placement proposal unit, for example, subdivides data access authority management and restricts access to employees' personal information. For example, access rights are set according to job title or job content. The placement proposal unit can also subdivide data access authority management and restrict access to employees' personal information. For example, it can introduce role-based access control. The placement proposal unit can also subdivide data access authority management and restrict access to employees' personal information. For example, it can introduce attribute-based access control. This subdivides data access authority management and sets the minimum necessary access rights, thereby reducing security risks.

[0047] The placement proposal unit can cooperate with external security experts to strengthen data safety and privacy protection. The placement proposal unit, for example, cooperates with external security experts to strengthen data safety and privacy protection. For example, the placement proposal unit strengthens security measures based on the expert's advice. The placement proposal unit also cooperates with external security experts to strengthen data safety and privacy protection. For example, the placement proposal unit undergoes an audit by a security expert. The placement proposal unit also cooperates with external security experts to strengthen data safety and privacy protection. For example, the placement proposal unit undergoes training by a security expert. In this way, data safety and privacy protection are strengthened by collaborating with external security experts.

[0048] The placement proposal unit can conduct periodic security audits to strengthen data safety and privacy protection. The placement proposal unit, for example, conducts periodic security audits to strengthen data safety and privacy protection. For example, an internal audit team periodically checks security measures. The placement proposal unit also conducts periodic security audits to strengthen data safety and privacy protection. For example, an external audit team periodically checks security measures. The placement proposal unit also conducts periodic security audits to strengthen data safety and privacy protection. For example, improvement measures are proposed based on the results of the security audit. In this way, conducting periodic security audits strengthens data safety and privacy protection.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The AI ​​HR assistant can understand employees' health status and provide advice for health management. For example, it can analyze employees' health data and provide appropriate exercise and dietary advice. It can also suggest stress management and mental health support based on employees' health status. Furthermore, it can monitor employees' health status in real time and take early action if an abnormality is detected. This helps maintain employee health and improve performance.

[0051] The AI ​​HR assistant can analyze employees' learning history and suggest learning plans for improving their skills. For example, it can suggest the next skills and knowledge an employee should learn based on the training and online courses they have taken in the past. It can also customize learning plans to match employees' career goals and provide appropriate learning resources. It can also monitor learning progress and provide support as needed. This helps employees improve their skills and improves the competitiveness of the organization.

[0052] The AI ​​HR assistant can match mentors based on employees' career goals. For example, if an employee wants to acquire a specific skill, it can introduce them to a mentor who is well-versed in that skill. Also, if an employee is looking to advance their career, it can introduce them to a mentor who has walked the same career path. Furthermore, it can take into account the compatibility between the mentor and mentee to make the optimal match. This can support employees' career development and promote growth throughout the organization.

[0053] The AI ​​HR assistant can analyze employees' work patterns and suggest efficient ways of working. For example, it can analyze employees' working hours and break times and suggest optimal work schedules. It can also suggest work style reforms to improve employee productivity. Furthermore, it can suggest flexible work styles such as remote work and flextime based on employees' work patterns. This can optimize employees' work styles and improve productivity across the organization.

[0054] The AI ​​HR assistant can analyze employees' project participation history and form optimal project teams. For example, it can select members with the appropriate skill sets based on employees' past project participation history. It can also form teams that take into account team dynamics and compatibility based on employees' project participation history. Furthermore, it can analyze employees' project participation history and form project teams that take into account their future career paths. This allows for the formation of optimal project teams and improves the success rate of projects.

[0055] The AI ​​HR assistant can analyze employees' work environments and propose optimal office layouts. For example, it can analyze employees' work patterns and communication frequency to propose efficient office layouts. It can also propose office layouts that are tailored to the employees' work duties. It can also propose office layouts that take into account employees' health and comfort. This optimizes employees' work environments and improves work efficiency and satisfaction.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The ability assessment department assesses employee abilities through dialogue with them. For example, it asks employees, "What are your strengths?" and analyzes their responses. It can also analyze employees' past work history and evaluation data and reflect this in the dialogue. It can also assess abilities using an employee skill matrix. Step 2: The preference understanding unit understands employees' preferences through dialogue with them. For example, it asks employees, "What kind of career do you want in the future?" and analyzes their answers. It can also analyze employees' non-verbal communication (facial expressions and gestures) and reflect this in the content of the dialogue. It can also understand preferences through employee questionnaire surveys. Step 3: The Placement Proposal Department proposes optimal placements based on the information gathered by the Ability Assessment Department and the Desire Assessment Department. For example, if an employee responds, "I'm good at project management and would like to demonstrate leadership in the future," the department will propose placing the employee as a project manager. It is also possible to propose placements that take into account team dynamics and compatibility based on the employee's abilities and desires. Furthermore, it is also possible to consider the employee's long-term career path and propose placements that take future growth into account.

[0058] (Example 2) The AI ​​HR assistant according to the embodiment of the present invention is a system that deeply understands the abilities and desires of employees and proposes optimal personnel placement. As a result, the AI ​​HR assistant can grasp the abilities and desires of employees and carry out optimal personnel placement.

[0059] An AI human resources assistant according to an embodiment includes a conversational AI, a capability assessment unit, a preference assessment unit, and a placement proposal unit. The conversational AI includes a capability assessment unit that assesses an employee's capabilities through dialogue with the employee, a preference assessment unit that assesses the employee's preferences, and a placement proposal unit that proposes optimal placements based on the information assessed by the capability assessment unit and the preference assessment unit. The capability assessment unit, for example, asks an employee, "What are your strengths?" and analyzes the employee's response. The capability assessment unit can also analyze an employee's past work history and evaluation data and reflect the results in the dialogue. The capability assessment unit can also evaluate an employee's capabilities using, for example, a skill matrix. The preference assessment unit, for example, asks an employee, "What kind of career do you want in the future?" and analyzes the response. The preference assessment unit can also analyze an employee's non-verbal communication (facial expressions and gestures) and reflect the results in the dialogue. The preference assessment unit can also assess an employee's preferences using, for example, a questionnaire survey. For example, if an employee responds, "I'm good at project management and would like to demonstrate leadership skills in the future," the placement suggestion department will suggest placing the employee as a project manager. The placement suggestion department can also suggest placements that take into account team dynamics and compatibility based on the employee's abilities and aspirations. The placement suggestion department can also consider the employee's long-term career path and suggest placements that anticipate future growth. This allows the AI ​​HR assistant to deeply understand employees' abilities and aspirations and suggest optimal placements, thereby improving the organization's competitiveness.

[0060] The ability assessment unit can analyze an employee's past work history and evaluation data and reflect the results in the dialogue. For example, the ability assessment unit analyzes an employee's past work history, and the conversational AI asks questions based on that information. For example, the ability assessment unit identifies an employee's areas of expertise based on past successful projects and highly evaluated work content. The ability assessment unit can also analyze an employee's evaluation data, and the conversational AI can understand the employee's abilities based on that information. For example, the ability assessment unit identifies an employee's strengths and weaknesses based on the results of performance reviews and 360-degree evaluations. The ability assessment unit can also evaluate an employee's abilities using, for example, an employee's skill matrix and reflect the results in the dialogue. In this way, by analyzing an employee's past work history and evaluation data, it is possible to more accurately understand their abilities and aspirations.

[0061] The desire understanding unit can analyze the employee's non-verbal communication and reflect it in the content of the dialogue. The desire understanding unit, for example, analyzes the employee's facial expressions, and the conversational AI understands the employee's emotional state based on that information. For example, facial expressions such as smiles and frowns are analyzed to understand the employee's emotions. The desire understanding unit can also analyze the employee's gestures, and the conversational AI can understand the employee's desires based on that information. For example, gestures such as hand movements and posture are analyzed to understand the employee's intentions. The desire understanding unit can also analyze the employee's voice, and the conversational AI can understand the employee's desires based on that information. For example, the tone and speed of voice are analyzed to understand the employee's emotions and intentions. In this way, by analyzing the employee's non-verbal communication, it is possible to understand the employee's desires more deeply.

[0062] The desire grasping unit can grasp the emotional state of the employee in real time using the emotion estimation function and ask questions according to the emotion. The desire grasping unit, for example, uses the emotion estimation function to analyze the emotional state of the employee in real time, and the conversational AI adjusts the questions based on that information. For example, if the employee is nervous, it asks questions to relax them. The desire grasping unit can also grasp the emotional state of the employee in real time using the emotion estimation function, and the conversational AI grasps the desires based on that information. For example, if the employee is excited, it asks questions to calm the employee. The desire grasping unit can also analyze the emotional state of the employee in real time using the emotion estimation function, and the conversational AI grasps the desires based on that information. For example, if the employee is anxious, it asks questions to alleviate the anxiety. In this way, the desire grasping unit can grasp the emotional state of the employee in real time and ask appropriate questions, making it possible to grasp the desires more accurately.

[0063] The desire grasping unit grasps an employee's hobbies and interests and can provide information useful for activities outside of work. For example, the desire grasping unit uses a conversational AI to grasp an employee's hobbies and interests and, based on that information, provides information useful for activities outside of work. For example, if an employee is interested in sports, it introduces related events and club activities. The desire grasping unit also grasps an employee's hobbies and interests and, based on that information, provides information useful for activities outside of work. For example, if an employee is interested in music, it introduces related concerts and music classes. The desire grasping unit also grasps an employee's hobbies and interests and, based on that information, provides information useful for activities outside of work. For example, if an employee is interested in cooking, it introduces related cooking classes and recipes. In this way, by grasping an employee's hobbies and interests and providing information useful for activities outside of work, employee satisfaction is improved.

[0064] The preference understanding unit can grasp the stress level of an employee and propose appropriate stress management measures. For example, an interactive AI may grasp the stress level of an employee and propose appropriate stress management measures based on that information. For example, if an employee is in a high stress state, the preference understanding unit may propose relaxation methods. Furthermore, the preference understanding unit may grasp the stress level of an employee and propose appropriate stress management measures based on that information. For example, if an employee is in a moderate stress state, the preference understanding unit may propose exercise or hobbies to reduce stress. Furthermore, the preference understanding unit may grasp the stress level of an employee and propose appropriate stress management measures based on that information. For example, if an employee is in a low stress state, the preference understanding unit may propose relaxation methods to prevent stress. In this way, by grasping the stress level of an employee and proposing appropriate stress management measures, the health and performance of the employee are improved.

[0065] The desire grasping unit can grasp the motivation of employees using the emotion estimation function and propose actions to improve motivation. The desire grasping unit, for example, uses the emotion estimation function to grasp the motivation of employees and proposes actions to improve motivation based on that information. For example, if an employee has lost motivation, it proposes a goal setting method. The desire grasping unit can also grasp the motivation of employees using the emotion estimation function, for example, and propose actions to improve motivation based on that information. For example, if an employee wants to increase their motivation, it proposes an incentive program. The desire grasping unit can grasp the motivation of employees using the emotion estimation function, for example, and propose actions to improve motivation based on that information. For example, if an employee wants to maintain their motivation, it proposes a career development plan. In this way, by grasping the motivation of employees and proposing appropriate actions, employee performance is improved.

[0066] The placement proposal unit can propose placements that take into consideration team dynamics and compatibility in addition to employee abilities and desires. The placement proposal unit, for example, analyzes team dynamics in addition to employee abilities and desires to propose optimal placements. For example, the placement proposal unit forms optimal teams taking into consideration the skill sets and personalities of team members. The placement proposal unit also analyzes team compatibility in addition to employee abilities and desires to propose optimal placements. For example, it selects team members with good compatibility based on past collaboration experiences and personality diagnostic tests. The placement proposal unit also proposes placements that take into consideration team dynamics and compatibility in addition to employee abilities and desires. For example, it makes optimal placements taking into consideration the division of team roles and communication patterns. This enables more effective personnel placements by taking team dynamics and compatibility into consideration.

[0067] The placement proposal department can consider an employee's career path over the long term and propose placements that take future growth into account. The placement proposal department, for example, considers an employee's career path over the long term and proposes placements that take future growth into account. For example, it proposes a role that will allow the employee to demonstrate leadership in the future. The placement proposal department can also consider an employee's career path over the long term and propose placements that take future growth into account. For example, it can have the employee participate in a project that will allow the employee to acquire new skills. The placement proposal department can also consider an employee's career path over the long term and propose placements that take future growth into account. For example, it can assign the employee to a department that will allow the employee to aim for career advancement. In this way, by considering an employee's career path over the long term, it is possible to make placements that take future growth into account.

[0068] The placement proposal unit can use the emotion estimation function to propose placements that take into account the emotional state of employees. The placement proposal unit, for example, uses the emotion estimation function to analyze the emotional state of employees and proposes optimal placements based on that information. For example, if an employee is feeling stressed, it proposes a role that causes less stress. The placement proposal unit can also, for example, use the emotion estimation function to analyze the emotional state of employees and propose optimal placements based on that information. For example, if an employee wants to increase their motivation, it proposes a role that will increase their motivation. The placement proposal unit can also, for example, use the emotion estimation function to analyze the emotional state of employees and propose optimal placements based on that information. For example, if an employee is feeling anxious, it proposes a role that will reduce anxiety. This makes it possible to make more appropriate placements by taking into account the emotional state of employees.

[0069] The placement proposal unit can propose temporary placement to different projects or departments based on the employee's abilities and wishes, thereby diversifying their skills. The placement proposal unit, for example, proposes temporary placement to different projects based on the employee's abilities and wishes. For example, by having the employee participate in a project to acquire new skills. The placement proposal unit can also propose temporary placement to different departments based on the employee's abilities and wishes. For example, by having the employee experience different work, diversifying their skills. The placement proposal unit can also propose temporary placement to different projects or departments based on the employee's abilities and wishes. For example, by having the employee participate in a short-term project, they can acquire new skills. This makes it possible to diversify the employees' skills through temporary placement to different projects or departments.

[0070] The placement proposal unit can propose at least one flexible working style, either remote work or flextime, based on the employee's capabilities and preferences. The placement proposal unit, for example, proposes remote work based on the employee's capabilities and preferences. For example, it provides an environment in which the employee can work efficiently from home. The placement proposal unit also proposes flextime based on the employee's capabilities and preferences. For example, it provides flexible working hours that allow the employee to work in accordance with their lifestyle. The placement proposal unit also proposes at least one flexible working style, either remote work or flextime, based on the employee's capabilities and preferences. For example, it provides an environment in which the employee can work in accordance with their family circumstances. In this way, by proposing flexible working styles such as remote work or flextime, employee satisfaction is improved.

[0071] The placement proposal unit can use the emotion estimation function to propose flexible placements in accordance with the emotional state of employees. The placement proposal unit, for example, uses the emotion estimation function to analyze the emotional state of employees and proposes flexible placements based on the information. For example, if an employee is feeling stressed, it proposes remote work. The placement proposal unit can also, for example, use the emotion estimation function to analyze the emotional state of employees and propose flexible placements based on the information. For example, if an employee wants to increase their motivation, it proposes flextime. The placement proposal unit can also, for example, use the emotion estimation function to analyze the emotional state of employees and propose flexible placements based on the information. For example, if an employee is feeling anxious, it proposes remote work. In this way, by proposing flexible placements in accordance with the emotional state of employees, employee satisfaction is improved.

[0072] The placement proposal unit can analyze the project progress and resource allocation across the entire organization in real time and make optimal personnel placement. The placement proposal unit, for example, analyzes the project progress and resource allocation across the entire organization in real time and makes optimal personnel placement. For example, employees with the required skills are placed based on the progress of projects. The placement proposal unit can also analyze the resource allocation across the entire organization in real time and make optimal personnel placement. For example, appropriate personnel are placed based on resource surpluses and shortages. The placement proposal unit can also analyze the project progress and resource allocation across the entire organization in real time and make optimal personnel placement. For example, appropriate personnel are placed for important projects based on project priorities. In this way, optimal personnel placement is possible by analyzing the project progress and resource allocation across the entire organization in real time.

[0073] The placement proposal unit can optimize personnel placement based on the strategic goals and vision of the organization. The placement proposal unit optimizes personnel placement based on, for example, the strategic goals and vision of the organization. For example, it places employees who have the skills necessary to achieve the organization's long-term goals. The placement proposal unit also optimizes personnel placement based on, for example, the strategic goals and vision of the organization. For example, it places personnel in line with the organization's growth strategy. The placement proposal unit also optimizes personnel placement based on, for example, the strategic goals and vision of the organization. For example, it places personnel who match the organization's values. In this way, optimizing personnel placement based on the organization's strategic goals and vision supports the achievement of the organization's overall goals.

[0074] The placement proposal unit can grasp the emotional state of the entire organization using the emotion estimation function and make personnel placements according to the emotions. The placement proposal unit, for example, uses the emotion estimation function to grasp the emotional state of the entire organization and make optimal personnel placements based on the information. For example, placements are made to reduce the stress level of the entire organization. The placement proposal unit can also grasp the emotional state of the entire organization using the emotion estimation function and make optimal personnel placements based on the information. For example, placements are made to improve the motivation of the entire organization. The placement proposal unit can also grasp the emotional state of the entire organization using the emotion estimation function and make optimal personnel placements based on the information. For example, placements are made to reduce anxiety of the entire organization. In this way, by grasping the emotional state of the entire organization and making personnel placements according to the emotions, organizational performance is improved.

[0075] The placement proposal department can promote personnel exchanges between different departments in order to optimize personnel allocation throughout the organization. The placement proposal department, for example, promotes personnel exchanges between different departments in order to optimize personnel allocation throughout the organization. For example, it provides opportunities for employees from different departments to work together on projects. The placement proposal department also promotes personnel exchanges between different departments in order to optimize personnel allocation throughout the organization. For example, it implements job rotations to provide opportunities for employees to gain experience in different departments. The placement proposal department also promotes personnel exchanges between different departments in order to optimize personnel allocation throughout the organization. For example, it forms cross-functional teams to provide opportunities for employees from different departments to work together on tasks. In this way, promoting personnel exchanges between different departments optimizes personnel allocation throughout the organization.

[0076] The placement proposal unit can utilize external experts and consultants to optimize personnel placement throughout the organization. The placement proposal unit, for example, utilizes external experts and consultants to optimize personnel placement throughout the organization. For example, it proposes optimal placement based on the opinions of experts. The placement proposal unit also utilizes external experts and consultants to optimize personnel placement throughout the organization. For example, it optimizes placement based on the advice of industry experts. The placement proposal unit also utilizes external experts and consultants to optimize personnel placement throughout the organization. For example, it optimizes placement based on the proposals of consultants. In this way, by utilizing external experts and consultants, personnel placement throughout the organization is optimized.

[0077] The placement proposal unit can grasp the emotional state of the entire organization using the emotion estimation function and promote personnel exchange between departments according to emotions. The placement proposal unit, for example, uses the emotion estimation function to grasp the emotional state of the entire organization and promote personnel exchange between departments based on that information. For example, it promotes exchange between departments with a good emotional state and other departments. The placement proposal unit can also grasp the emotional state of the entire organization using the emotion estimation function and promote personnel exchange between departments based on that information. For example, it provides support from other departments to a department with a deteriorating emotional state. The placement proposal unit can also grasp the emotional state of the entire organization using the emotion estimation function and promote personnel exchange between departments based on that information. For example, it promotes exchange between departments with a stable emotional state and other departments. In this way, by grasping the emotional state of the entire organization and promoting personnel exchange between departments according to emotions, organizational performance is improved.

[0078] The placement proposal unit can analyze the content of the feedback and automatically generate specific improvement measures. The placement proposal unit, for example, analyzes the content of the feedback and automatically generates specific improvement measures based on the information. For example, it proposes improvements to a business process based on employee feedback. The placement proposal unit also analyzes the content of the feedback and automatically generates specific improvement measures based on the information. For example, it proposes improvements to a training program based on employee feedback. The placement proposal unit also analyzes the content of the feedback and automatically generates specific improvement measures based on the information. For example, it proposes improvements to the work environment based on employee feedback. In this way, analyzing the content of feedback and automatically generating specific improvement measures promotes improvement in the organization.

[0079] The placement proposal unit can optimize the frequency and timing of feedback to reduce the burden on employees. The placement proposal unit, for example, optimizes the frequency and timing of feedback to build a system that reduces the burden on employees. For example, feedback is collected at the appropriate time to reduce the burden on employees. The placement proposal unit also optimizes the frequency of feedback to reduce the burden on employees. For example, it adjusts the frequency of monthly reviews and quarterly reviews. The placement proposal unit also optimizes the timing of feedback to reduce the burden on employees. For example, it adjusts the timing of end-of-project and regular meetings. In this way, the burden on employees is reduced by optimizing the frequency and timing of feedback.

[0080] The placement proposal unit can analyze the emotional aspects of the feedback using the emotion estimation function and propose improvement measures according to the emotions. The placement proposal unit, for example, uses the emotion estimation function to analyze the emotional aspects of the feedback and proposes improvement measures based on the information. For example, if an employee is feeling dissatisfied, it proposes specific improvement measures. The placement proposal unit can also analyze the emotional aspects of the feedback using the emotion estimation function and propose improvement measures based on the information. For example, if an employee is satisfied, it proposes measures to maintain that satisfaction. The placement proposal unit can also analyze the emotional aspects of the feedback using the emotion estimation function and propose improvement measures based on the information. For example, if an employee is feeling anxious, it proposes measures to alleviate that anxiety. In this way, by analyzing the emotional aspects of the feedback and proposing improvement measures according to the emotions, employee satisfaction is improved.

[0081] The placement proposal department can share the content of the feedback with employees and work to improve the entire organization. For example, the placement proposal department builds a system to share the content of the feedback with other employees and work to improve the entire organization. For example, it introduces a platform for sharing feedback. The placement proposal department also shares the content of the feedback at regular meetings and work to improve the entire organization. For example, it discusses areas for improvement based on the feedback. The placement proposal department also shares the content of the feedback on an in-house portal and work to improve the entire organization. For example, it proposes improvement measures based on the feedback. In this way, sharing the content of the feedback promotes improvement throughout the organization.

[0082] The placement proposal unit can customize the employee training program based on the content of the feedback. The placement proposal unit, for example, builds a system that customizes the employee training program based on the content of the feedback. For example, it provides training to fill the employee's skill gaps. The placement proposal unit also customizes the employee training program based on the content of the feedback. For example, it provides training based on the employee's career development plan. The placement proposal unit also customizes the employee training program based on the content of the feedback. For example, it provides training based on the employee's performance review. In this way, customizing the training program based on the content of the feedback supports the improvement of employee skills.

[0083] The placement proposal unit can analyze the emotional aspects of the feedback using the emotion estimation function and propose a training program according to the emotion. The placement proposal unit, for example, uses the emotion estimation function to analyze the emotional aspects of the feedback and proposes a training program based on the information. For example, if an employee is feeling anxious, stress management training is provided. The placement proposal unit can also analyze the emotional aspects of the feedback using the emotion estimation function and propose a training program based on the information. For example, if an employee wants to increase their motivation, motivation improvement training is provided. The placement proposal unit can also analyze the emotional aspects of the feedback using the emotion estimation function and propose a training program based on the information. For example, if an employee is satisfied, training to maintain that satisfaction is provided. In this way, by analyzing the emotional aspects of the feedback and proposing a training program according to the emotion, employee skill improvement and satisfaction are improved.

[0084] The placement proposal unit can enhance data encryption technology to provide stronger security. The placement proposal unit, for example, enhances data encryption technology to protect employees' personal information. For example, the latest encryption algorithm is introduced to ensure data security. The placement proposal unit also enhances data encryption technology to protect employees' personal information. For example, the data encryption key is updated regularly. The placement proposal unit also enhances data encryption technology to protect employees' personal information. For example, the data encryption protocol is strengthened. In this way, stronger security is provided by enhancing data encryption technology.

[0085] The placement proposal unit can subdivide data access authority management and set the minimum necessary access rights. The placement proposal unit, for example, subdivides data access authority management and restricts access to employees' personal information. For example, access rights are set according to job title or job content. The placement proposal unit can also subdivide data access authority management and restrict access to employees' personal information. For example, it can introduce role-based access control. The placement proposal unit can also subdivide data access authority management and restrict access to employees' personal information. For example, it can introduce attribute-based access control. This subdivides data access authority management and sets the minimum necessary access rights, thereby reducing security risks.

[0086] The placement proposal unit can use the emotion estimation function to monitor the emotional state of the user when accessing data and detect signs of unauthorized access. The placement proposal unit, for example, uses the emotion estimation function to monitor the emotional state of the user when accessing data and detects signs of unauthorized access based on the information. For example, it issues an alert when it detects an abnormal emotional state. The placement proposal unit can also, for example, use the emotion estimation function to monitor the emotional state of the user when accessing data and detect signs of unauthorized access based on the information. For example, it can restrict access when it detects an abnormal emotional state. The placement proposal unit can also, for example, use the emotion estimation function to monitor the emotional state of the user when accessing data and detect signs of unauthorized access based on the information. For example, it can request additional authentication when it detects an abnormal emotional state. In this way, security is enhanced by monitoring the emotional state of the user when accessing data and detecting signs of unauthorized access.

[0087] The placement proposal unit can cooperate with external security experts to strengthen data safety and privacy protection. The placement proposal unit, for example, cooperates with external security experts to strengthen data safety and privacy protection. For example, the placement proposal unit strengthens security measures based on the expert's advice. The placement proposal unit also cooperates with external security experts to strengthen data safety and privacy protection. For example, the placement proposal unit undergoes an audit by a security expert. The placement proposal unit also cooperates with external security experts to strengthen data safety and privacy protection. For example, the placement proposal unit undergoes training by a security expert. In this way, data safety and privacy protection are strengthened by collaborating with external security experts.

[0088] The placement proposal unit can conduct periodic security audits to strengthen data safety and privacy protection. The placement proposal unit, for example, conducts periodic security audits to strengthen data safety and privacy protection. For example, an internal audit team periodically checks security measures. The placement proposal unit also conducts periodic security audits to strengthen data safety and privacy protection. For example, an external audit team periodically checks security measures. The placement proposal unit also conducts periodic security audits to strengthen data safety and privacy protection. For example, improvement measures are proposed based on the results of the security audit. In this way, conducting periodic security audits strengthens data safety and privacy protection.

[0089] The placement proposal unit can use the emotion estimation function to monitor the emotional state of the user when accessing data and propose security measures according to the emotion. The placement proposal unit, for example, uses the emotion estimation function to monitor the emotional state of the user when accessing data and proposes security measures based on the information. For example, if an abnormal emotional state is detected, additional authentication is requested. The placement proposal unit can also, for example, use the emotion estimation function to monitor the emotional state of the user when accessing data and propose security measures based on the information. For example, if an abnormal emotional state is detected, access is restricted. The placement proposal unit can also, for example, use the emotion estimation function to monitor the emotional state of the user when accessing data and propose security measures based on the information. For example, an alert is issued if an abnormal emotional state is detected. In this way, security is enhanced by monitoring the emotional state of the user when accessing data and proposing security measures according to the emotion.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The AI ​​HR assistant can understand employees' health status and provide advice for health management. For example, it can analyze employees' health data and provide appropriate exercise and dietary advice. It can also suggest stress management and mental health support based on employees' health status. Furthermore, it can monitor employees' health status in real time and take early action if an abnormality is detected. This helps maintain employee health and improve performance.

[0092] The AI ​​HR assistant can analyze employees' learning history and suggest learning plans for improving their skills. For example, it can suggest the next skills and knowledge an employee should learn based on the training and online courses they have taken in the past. It can also customize learning plans to match employees' career goals and provide appropriate learning resources. It can also monitor learning progress and provide support as needed. This helps employees improve their skills and improves the competitiveness of the organization.

[0093] An AI HR assistant can estimate an employee's emotional state and provide feedback according to their emotions. For example, if an employee is feeling stressed, it can provide relaxation methods and stress management advice. If an employee wants to increase their motivation, it can also suggest goal setting and incentive programs. Furthermore, if an employee is feeling anxious, it can provide support to reduce anxiety. This allows for appropriate feedback according to the employee's emotional state, improving employee satisfaction and performance.

[0094] The AI ​​HR assistant can match mentors based on employees' career goals. For example, if an employee wants to acquire a specific skill, it can introduce them to a mentor who is well-versed in that skill. Also, if an employee is looking to advance their career, it can introduce them to a mentor who has walked the same career path. Furthermore, it can take into account the compatibility between the mentor and mentee to make the optimal match. This can support employees' career development and promote growth throughout the organization.

[0095] The AI ​​HR assistant can estimate an employee's emotional state and suggest communication methods appropriate to their emotions. For example, if an employee is nervous, it can suggest communication methods to relax them. If an employee is excited, it can suggest communication methods to calm them down. Furthermore, if an employee is feeling anxious, it can suggest communication methods to alleviate their anxiety. This allows for appropriate communication based on the employee's emotional state, improving employee satisfaction and performance.

[0096] The AI ​​HR assistant can analyze employees' work patterns and suggest efficient ways of working. For example, it can analyze employees' working hours and break times and suggest optimal work schedules. It can also suggest work style reforms to improve employee productivity. Furthermore, it can suggest flexible work styles such as remote work and flextime based on employees' work patterns. This can optimize employees' work styles and improve productivity across the organization.

[0097] The AI ​​HR assistant can estimate an employee's emotional state and suggest stress management measures according to their emotions. For example, if an employee is in a high stress state, it can suggest relaxation methods and exercises to reduce stress. If an employee is in a moderate stress state, it can also suggest hobbies and relaxation methods for stress management. Furthermore, if an employee is in a low stress state, it can suggest relaxation methods to prevent stress. This provides appropriate stress management measures according to the employee's emotional state, improving their health and performance.

[0098] The AI ​​HR assistant can analyze employees' project participation history and form optimal project teams. For example, it can select members with the appropriate skill sets based on employees' past project participation history. It can also form teams that take into account team dynamics and compatibility based on employees' project participation history. Furthermore, it can analyze employees' project participation history and form project teams that take into account their future career paths. This allows for the formation of optimal project teams and improves the success rate of projects.

[0099] The AI ​​HR assistant can estimate an employee's emotional state and suggest career development plans based on their emotions. For example, if an employee wants to increase their motivation, it can suggest a training program for career advancement. If an employee feels anxious, it can provide career counseling to alleviate their anxiety. Furthermore, if an employee is satisfied, it can suggest a career development plan to maintain that satisfaction. This allows for the provision of appropriate career development plans based on the employee's emotional state, thereby improving employee satisfaction and performance.

[0100] The AI ​​HR assistant can analyze employees' work environments and propose optimal office layouts. For example, it can analyze employees' work patterns and communication frequency to propose efficient office layouts. It can also propose office layouts that are tailored to the employees' work duties. It can also propose office layouts that take into account employees' health and comfort. This optimizes employees' work environments and improves work efficiency and satisfaction.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The ability assessment department assesses employee abilities through dialogue with them. For example, it asks employees, "What are your strengths?" and analyzes their responses. It can also analyze employees' past work history and evaluation data and reflect this in the dialogue. It can also assess abilities using an employee skill matrix. Step 2: The preference understanding unit understands employees' preferences through dialogue with them. For example, it asks employees, "What kind of career do you want in the future?" and analyzes their answers. It can also analyze employees' non-verbal communication (facial expressions and gestures) and reflect this in the content of the dialogue. It can also understand preferences through employee questionnaire surveys. Step 3: The Placement Proposal Department proposes optimal placements based on the information gathered by the Ability Assessment Department and the Desire Assessment Department. For example, if an employee responds, "I'm good at project management and would like to demonstrate leadership in the future," the department will propose placing the employee as a project manager. It is also possible to propose placements that take into account team dynamics and compatibility based on the employee's abilities and desires. Furthermore, it is also possible to consider the employee's long-term career path and propose placements that take future growth into account.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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. Equipped with conversational AI, The conversational AI is A capability assessment department that assesses employee capabilities through dialogue with employees; A preference understanding department that understands employee preferences; an arrangement proposal unit that proposes an optimal arrangement based on the information grasped by the ability grasping unit and the desire grasping unit; A system characterized by:

2. The desire grasping unit Analyze the employee's non-verbal communication and reflect it in the content of the conversation 2. The system of claim 1.

3. The desire grasping unit Identify the stress levels of said employees and propose appropriate stress management measures 2. The system of claim 1.

4. The placement proposal unit Propose placements that take into account the employee's abilities and aspirations, as well as team dynamics and compatibility.

2. The system of claim 1.

5. The placement proposal unit Analyze the progress of projects and resource allocation across the organization in real time to optimize personnel allocation 2. The system of claim 1.

6. The placement proposal unit Analyzes feedback and automatically generates specific improvement measures 2. The system of claim 1.

7. The placement proposal unit Enhanced data encryption technology provides stronger security 2. The system of claim 1.

8. The desire grasping unit Understand the employee's emotional state in real time and ask questions according to their emotions 2. The system of claim 1.

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