System

Abstract

An object of the system according to the embodiment is to efficiently match the skills of employees with the needs of departments and to continuously manage the conditions of employees.SOLUTION: A system includes a skill matching unit, a AI interview unit, a condition check unit, and a smart watch cooperation unit. The skill matching unit associates the in-house database with the generated AI, and performs matching between the skill required by the department and the work desired by the employee. The AI interview unit conducts a AI interview with an employee. The condition check part periodically checks the condition of the employee. The smart watch cooperation unit analyzes the data of the smart watch.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 faced challenges in efficiently matching employee skills with departmental needs and continuously managing employee conditions.

[0005] The system according to the embodiment aims to efficiently match employee skills with departmental needs and continuously manage employee conditions. [Means for solving the problem]

[0006] The system according to the embodiment comprises a skill matching unit, an AI interview unit, a condition check unit, and a smartwatch linkage unit. The skill matching unit links the internal database with the generation AI to match the skills required by the department with the work desired by the employee. The AI ​​interview unit conducts AI interviews with employees. The condition check unit periodically checks the condition of employees. The smartwatch linkage unit analyzes data from the smartwatch. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently match employee skills with departmental needs and continuously manage employee conditions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The information provision system according to the embodiment of the present invention is a system that supports the optimization of "human resources" by linking an internal database with a generation AI and matching the skills required by a department with the jobs desired by employees. As a result, the information provision system can optimize the matching of employees' skills with jobs and support the creation of a resilient organization.

[0029] The information provision system according to the embodiment includes a skill matching unit, an AI interview unit, a condition check unit, and a smartwatch linkage unit. The skill matching unit matches employee skills with department requirements. For example, the skill matching unit obtains employee skill information from an internal database, and the generation AI analyzes the information to recommend the most suitable department. The skill matching unit can also match employees based on their skill information and the department's skill requirements. The skill matching unit can also periodically update employee skill information and perform matching based on the latest skill information. The AI ​​interview unit conducts AI interviews with employees. For example, the AI ​​interview unit uses the generation AI to analyze the content of employee interviews and propose appropriate career paths. The AI ​​interview unit can also analyze past interview data and make more effective proposals. The AI ​​interview unit can also share the content of the interviews with other employees and provide mutual feedback. The condition check unit regularly checks the condition of employees. For example, the condition check unit uses the generation AI to analyze the employee's health condition and propose appropriate measures. The condition check unit can also propose a personalized health management plan based on the employee's condition data. The condition check unit can also use an emotion estimation function to analyze the employee's emotional state, detect emotional problems early, and propose countermeasures. The smartwatch linkage unit analyzes smartwatch data. For example, the smartwatch linkage unit uses a generation AI to analyze the smartwatch data and make suggestions to encourage appropriate rest and exercise. The smartwatch linkage unit can also monitor the employee's health status in real time based on the smartwatch data and issue an alert if an abnormality is detected. The smartwatch linkage unit can also use the emotion estimation function to analyze the employee's emotional state from the smartwatch data and manage stress. As a result, the information provision system according to the embodiment can optimize the matching of employee skills and jobs, supporting the creation of a resilient organization.For example, information provision systems can improve work efficiency and employee satisfaction by optimizing the matching of employee skills with jobs. Furthermore, regular health checks and the use of smartwatches can help maintain employee health and improve the work environment. This is expected to improve overall work efficiency and enhance the performance of the entire organization.

[0030] The skill matching unit inputs employee skill information, project history, or evaluation data into the generation AI, enabling more accurate matching. For example, the skill matching unit inputs employee skill information and past project history into the generation AI, and recommends the most suitable department based on the project success rate and evaluation data. For example, the unit analyzes data on employee A's past successful projects and recommends him / her to department B, which is working on a similar project. The skill matching unit also inputs employee skill information and evaluation data into the generation AI, and prioritizes matching with employees with highly rated skills. For example, if employee B has highly rated skills, he / she is recommended to department C, which needs those skills. The skill matching unit also integrates employee skill information, project history, and evaluation data, and the generation AI performs a comprehensive analysis to perform optimal matching. For example, based on data on projects where employee C received high evaluations in the past, he / she is recommended to department D, which is working on a similar project. This enables more accurate matching based on employee skills, project history, and evaluation data.

[0031] The skill matching department periodically updates employee skill sets, allowing the generation AI to perform matching based on the latest skill information. For example, the skill matching department will build a system in which employee skill sets are periodically updated, and the generation AI performs matching based on the latest skill information. For example, employee A adds newly acquired skills to the database and recommends them to department B, which requires those skills. The skill matching department will also periodically update employee skill information, and the generation AI will perform optimal matching based on the latest skill data. For example, employee B registers newly acquired qualifications in the database and recommends them to department C, which requires those qualifications. The skill matching department will also develop a system in which employee skill sets are automatically updated, and the generation AI performs matching based on the latest skill information. For example, information about the training that employee C participated in will be added to the database, and the training content will be recommended to department D, which requires it. This keeps employee skill sets up to date, enabling more appropriate matching.

[0032] The skill matching department can link the skill information registered in the internal database with external job information to support employee job changes. For example, the skill matching department builds a system that links the skill information registered in the internal database with external job information to support employee job changes. For example, it searches external job information based on employee A's skill information and recommends appropriate job changes. The skill matching department also links the skill information in the internal database with external job information to support employee career advancement. For example, it analyzes external job information based on employee B's skill set and recommends job changes suitable for career advancement. The skill matching department also integrates the skill information in the internal database with external job information to develop a system that supports employee job changes. For example, it analyzes external job information based on employee C's skill information and recommends the most suitable job change. This makes it possible to support job changes by linking external job information based on employee skill information.

[0033] The skill matching department can automatically generate in-house training programs based on employee skill information and promote skill development. For example, the skill matching department builds a system that automatically generates in-house training programs based on employee skill information. For example, it analyzes employee A's skill set and proposes a training program that complements the necessary skills. The skill matching department also automatically generates individualized training programs based on employee skill information and promotes skill development. For example, it analyzes employee B's skill gap and proposes a training program to fill that gap. The skill matching department also develops a system in which a generation AI automatically generates optimal training programs based on employee skill information and supports skill development. For example, it analyzes employee C's skill data and proposes the training program necessary to improve his skills. This makes it possible to automatically generate training programs based on employee skill information and promote skill development.

[0034] The AI ​​Interview Department uses a generation AI to analyze the content of interviews and propose specific action plans based not only on the employee's career path but also on their growth goals. For example, the AI ​​Interview Department uses a generation AI to analyze the content of interviews and propose specific action plans based not only on the employee's career path but also on their growth goals. For example, if employee A wishes to advance his or her career, specific training and projects will be proposed based on that wish. The AI ​​Interview Department also builds a system in which a generation AI analyzes the content of interviews and proposes action plans based on employees' growth goals. For example, if employee B aims to improve his or her skills, a specific learning plan will be proposed based on that goal. The AI ​​Interview Department also uses a generation AI to analyze the content of interviews and propose action plans based on the employee's career path and growth goals. For example, if employee C wants to demonstrate leadership, specific training and projects will be proposed based on that goal. This makes it possible to propose specific action plans based on employees' career paths and growth goals.

[0035] The AI ​​interview department can refer to an employee's past performance data or feedback during an interview and make more personalized suggestions. For example, the AI ​​interview department refers to an employee's past performance data and feedback during an interview, and the generation AI makes more personalized suggestions. For example, it proposes a specific career path based on employee A's past evaluation data. The AI ​​interview department also builds a system in which the generation AI analyzes an employee's past performance data during an interview and makes individualized suggestions. For example, it proposes an appropriate growth plan based on employee B's past project data. The AI ​​interview department also refers to an employee's feedback data during an interview, and the generation AI makes personalized suggestions. For example, it proposes specific areas for improvement and a growth plan based on employee C's past feedback. This makes it possible to make more personalized suggestions based on employees' past performance data and feedback.

[0036] The AI ​​Interview Department can promote the growth of the entire organization by sharing the content of interviews with other employees and providing mutual feedback. For example, the AI ​​Interview Department could build a system where interview content is shared with other employees and they can provide mutual feedback. For example, the interview content of employee A is shared and areas for improvement are identified based on feedback from other employees. The AI ​​Interview Department could also develop a system where a generative AI analyzes the interview content and shares it with other employees to provide mutual feedback. For example, the interview content of employee B is shared and opinions from other employees are incorporated. The AI ​​Interview Department could also build a system where interview content is shared with other employees and they can provide mutual feedback to promote the growth of the entire organization. For example, the interview content of employee C is shared and a growth plan is created based on feedback from other employees. In this way, the sharing of interview content and mutual feedback can promote the growth of the entire organization.

[0037] The AI ​​Interview Department can optimize internal personnel allocation and improve team performance based on the results of interviews. For example, the AI ​​Interview Department builds a system that optimizes internal personnel allocation based on the results of interviews. For example, employee A is assigned to the most appropriate department based on his or her skills and preferences. The AI ​​Interview Department also develops a system in which the generative AI analyzes interview results and optimizes internal personnel allocation. For example, employee B is assigned to the most appropriate team based on his or her career path. The AI ​​Interview Department also builds a system that optimizes internal personnel allocation and improves team performance based on the results of interviews. For example, employee C is assigned to the most appropriate project based on his or her growth goals. This makes it possible to optimize internal personnel allocation and improve team performance based on the results of interviews.

[0038] The condition check unit can analyze employees' condition data and propose personalized health management plans based on their individual health conditions. For example, the condition check unit uses a generation AI to analyze employees' condition data and propose personalized health management plans based on their individual health conditions. For example, the condition check unit analyzes employee A's stress level and proposes specific measures to reduce stress. The condition check unit also builds a system in which the generation AI analyzes employees' condition data and proposes personalized health management plans. For example, the condition check unit analyzes employee B's sleep data and provides specific advice for improving sleep. The condition check unit also uses a generation AI to propose personalized health management plans based on employees' condition data. For example, the condition check unit analyzes employee C's exercise data and proposes an appropriate exercise plan. This makes it possible to propose personalized health management plans based on employees' condition data.

[0039] The Condition Check Department can make suggestions for improving the workplace environment based on the results of the condition check, thereby reducing employee stress. For example, the Condition Check Department builds a system in which a generation AI analyzes the results of the condition check and makes suggestions for improving the workplace environment. For example, the condition check department analyzes employee A's stress level and suggests changing the workplace layout or improving the break space. The Condition Check Department also develops a system in which a generation AI makes suggestions for improving the workplace environment based on the results of the condition check. For example, the condition check department analyzes employee B's fatigue data and suggests adjusting lighting and air conditioning. The condition check department can also make suggestions for improving the workplace environment based on the results of the condition check, thereby reducing employee stress. For example, the condition check department analyzes employee C's health data and suggests reducing noise levels in the workplace and arranging plants. In this way, suggestions for improving the workplace environment can be made based on the results of the condition check, thereby reducing employee stress.

[0040] The Condition Check Department can automatically generate an in-house health promotion program based on the condition check data, supporting employees in maintaining their health. For example, the Condition Check Department builds a system in which a generation AI automatically generates an in-house health promotion program based on the condition check data. For example, it analyzes employee A's health data and suggests an appropriate exercise program. The Condition Check Department also develops a system in which a generation AI automatically generates an individualized health promotion program based on the condition check data. For example, it analyzes employee B's stress data and suggests a program for stress reduction. The Condition Check Department also uses a generation AI to automatically generate a health promotion program based on the condition check data, supporting employees in maintaining their health. For example, it analyzes employee C's sleep data and suggests a program for improving sleep. In this way, a health promotion program can be automatically generated based on the condition check data, supporting employees in maintaining their health.

[0041] The condition check department can link employees' condition data with external medical institutions to provide specialized health advice. For example, the condition check department builds a system that links employees' condition data with external medical institutions to provide specialized health advice. For example, employee A's health data is sent to a medical institution and the employee receives advice from a specialist. The condition check department also develops a system in which a generative AI links employees' condition data with external medical institutions to provide specialized health advice. For example, employee B's stress data is sent to a medical institution and the employee receives stress management advice from a specialist. The condition check department also links employees' condition data with external medical institutions to provide specialized health advice. For example, employee C's sleep data is sent to a medical institution and the employee receives advice on improving sleep from a specialist. This makes it possible to link employees' condition data with external medical institutions to provide specialized health advice.

[0042] The smartwatch linkage unit can monitor employees' health conditions in real time based on data collected from smartwatches and issue alerts if an abnormality is detected. For example, the smartwatch linkage unit builds a system in which a generation AI monitors employees' health conditions in real time based on data collected from smartwatches and issues alerts if an abnormality is detected. For example, if employee A's heart rate is abnormally high, an alert is issued. The smartwatch linkage unit also develops a system in which a generation AI analyzes smartwatch data and monitors employees' health conditions in real time. For example, if employee B's blood pressure is abnormally high, an alert is issued. The smartwatch linkage unit also monitors employees' health conditions in real time based on data collected from smartwatches and issues alerts if an abnormality is detected. For example, if employee C's body temperature is abnormally high, an alert is issued. This makes it possible to monitor employees' health conditions in real time based on data collected from smartwatches and issue alerts if an abnormality is detected.

[0043] The smartwatch integration department can analyze smartwatch data and propose specific action plans to improve employees' lifestyle habits. For example, the smartwatch integration department builds a system in which a generation AI analyzes smartwatch data and proposes specific action plans to improve employees' lifestyle habits. For example, it analyzes employee A's exercise data and proposes an appropriate exercise plan. The smartwatch integration department also develops a system in which a generation AI analyzes smartwatch data and proposes specific action plans to improve employees' lifestyle habits. For example, it analyzes employee B's sleep data and provides specific advice for improving sleep. The smartwatch integration department also develops a system in which a generation AI analyzes smartwatch data and proposes specific action plans to improve employees' lifestyle habits based on smartwatch data. For example, it analyzes employee C's dietary data and proposes a balanced diet plan. This makes it possible to propose specific action plans to improve employees' lifestyle habits based on smartwatch data.

[0044] The smartwatch integration department can automatically plan internal health events and activities based on smartwatch data, encouraging employee participation. For example, the smartwatch integration department builds a system in which a generation AI automatically plans internal health events and activities based on smartwatch data. For example, it analyzes employee A's exercise data and suggests an appropriate exercise event. The smartwatch integration department also develops a system in which a generation AI analyzes smartwatch data and automatically plans internal health events and activities. For example, it suggests a walking event based on employee B's activity data. The smartwatch integration department also develops a system in which a generation AI automatically plans internal health events and activities based on smartwatch data, encouraging employee participation. For example, it analyzes employee C's heart rate data and suggests a relaxation event. This makes it possible to automatically plan internal health events and activities based on smartwatch data, encouraging employee participation.

[0045] The smartwatch linking unit can link smartwatch data with external fitness apps to provide comprehensive support for employee health management. For example, the smartwatch linking unit can link smartwatch data with external fitness apps to build a system that provides comprehensive support for employee health management. For example, employee A's exercise data can be linked to a fitness app to suggest an individualized exercise plan. The smartwatch linking unit can also develop a system in which a generative AI analyzes smartwatch data and links it to an external fitness app. For example, employee B's heart rate data can be linked to a fitness app to suggest an appropriate exercise intensity. The smartwatch linking unit can also link to external fitness apps based on smartwatch data to provide comprehensive support for employee health management. For example, employee C's activity data can be linked to a fitness app to suggest a balanced exercise plan. This allows smartwatch data to be linked to an external fitness app to provide comprehensive support for employee health management.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] In addition to employee skill information, the Skill Matching Department can input employees' hobbies and special skills into the generation AI, enabling more multifaceted matching. For example, if employee A is into programming as a hobby, they can be recommended to department B where they can utilize that hobby. Also, if employee B has a special skill in design, they can be recommended to project C where they can utilize that skill. Furthermore, if employee C is interested in marketing as a hobby, they can be recommended to department D where they can utilize that interest. This makes it possible to perform multifaceted matching that takes into account employees' hobbies and special skills.

[0048] In addition to employee skill information, the Skill Matching Department can input the employee's lifestyle and work style preferences into the generation AI, allowing for more flexible matching. For example, if employee A wishes to work remotely, they can be recommended to department B, which can accommodate that preference. Also, if employee B wishes to work flextime, they can be recommended to project C, which can accommodate that preference. Furthermore, if employee C wishes to work shorter hours, they can be recommended to department D, which can accommodate that preference. This enables flexible matching that takes into account employees' lifestyles and work style preferences.

[0049] In addition to employee skill information, the Skill Matching Department can input the employee's learning history and self-development activities into the generation AI, enabling more growth-oriented matching. For example, if employee A is taking an online course, it can recommend him to department B, where he can utilize the content of that learning. Similarly, if employee B is attending a self-development seminar, it can recommend him to project C, where he can utilize the content of that seminar. Furthermore, if employee C is aiming to obtain a qualification, it can recommend him to department D, where he can utilize that qualification. This makes it possible to perform growth-oriented matching that takes into account employees' learning history and self-development activities.

[0050] In addition to employee skill information, the Skill Matching Department can input the employee's past experiences of failure and setbacks into the generation AI to perform matching to support recovery. For example, data on employee A's past failed projects can be analyzed and the department can recommend employee A to department B, where that experience can be utilized. Also, if employee B has a history of setbacks, the department can recommend the employee to project C, where that experience can be utilized. Furthermore, if employee C has a skill that they failed at in the past, the department can recommend the employee to department D, where they can try again to use that skill. This makes it possible to match employees for recovery support that takes into account their past experiences of failure and setbacks.

[0051] In addition to employee skill information, the Skill Matching Department can input the employee's family structure and home environment into the generation AI to perform matching that takes into account the balance between work and home. For example, if employee A has small children, they can be recommended to department B, which takes their home environment into consideration. Also, if employee B has a family member who requires care, they can be recommended to project C, which also takes their home environment into consideration. Furthermore, if employee C is working away from home, they can be recommended to department D, which also takes their home environment into consideration. This makes it possible to match employees with a balance between work and home that takes into account their family structure and home environment.

[0052] In addition to employee skill information, the Skill Matching Department can input the employee's cultural background and values ​​into the generation AI to perform matching that respects diversity. For example, if employee A has a specific cultural background, they can be recommended to department B, where that background can be respected. Similarly, if employee B has specific values, they can be recommended to project C, where those values ​​can be respected. Furthermore, if employee C has a diverse cultural background, they can be recommended to department D, where that diversity can be utilized. This makes it possible to perform matching that respects diversity by taking into account employees' cultural backgrounds and values.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The skill matching unit matches employee skills with department requirements. Specifically, employee skill information is obtained from the company's internal database, and the generation AI analyzes that information to recommend the most suitable department. The generation AI can also perform matching based on employee skill information and department skill requirements. Furthermore, employee skill information can be updated regularly, and matching can be performed based on the latest skill information. Step 2: The AI ​​Interview Department conducts AI interviews with employees. Specifically, the generating AI analyzes the content of the interview and suggests appropriate career paths. It can also analyze past interview data to make more effective suggestions. Furthermore, the content of the interview can be shared with other employees, allowing them to provide mutual feedback. Step 3: The Condition Check Department periodically checks the employee's condition. Specifically, the Generative AI analyzes the employee's health status and proposes appropriate measures. It can also propose personalized health management plans based on the employee's condition data. Furthermore, it can use the emotion estimation function to analyze the employee's emotional state, detect emotional problems early, and propose measures. Step 4: The smartwatch integration unit analyzes the smartwatch data. Specifically, the generative AI analyzes the smartwatch data and makes suggestions to encourage appropriate rest and exercise. It can also monitor employees' health status in real time based on the smartwatch data and issue alerts if an abnormality is detected. Furthermore, the emotion estimation function can be used to analyze employees' emotional state from the smartwatch data and manage stress.

[0055] (Example 2) The information provision system according to the embodiment of the present invention is a system that supports the optimization of "human resources" by linking an internal database with a generation AI and matching the skills required by a department with the jobs desired by employees. As a result, the information provision system can optimize the matching of employees' skills with jobs and support the creation of a resilient organization.

[0056] The information provision system according to the embodiment includes a skill matching unit, an AI interview unit, a condition check unit, and a smartwatch linkage unit. The skill matching unit matches employee skills with department requirements. For example, the skill matching unit obtains employee skill information from an internal database, and the generation AI analyzes the information to recommend the most suitable department. The skill matching unit can also match employees based on their skill information and the department's skill requirements. The skill matching unit can also periodically update employee skill information and perform matching based on the latest skill information. The AI ​​interview unit conducts AI interviews with employees. For example, the AI ​​interview unit uses the generation AI to analyze the content of employee interviews and propose appropriate career paths. The AI ​​interview unit can also analyze past interview data and make more effective proposals. The AI ​​interview unit can also share the content of the interviews with other employees and provide mutual feedback. The condition check unit regularly checks the condition of employees. For example, the condition check unit uses the generation AI to analyze the employee's health condition and propose appropriate measures. The condition check unit can also propose a personalized health management plan based on the employee's condition data. The condition check unit can also use an emotion estimation function to analyze the employee's emotional state, detect emotional problems early, and propose countermeasures. The smartwatch linkage unit analyzes smartwatch data. For example, the smartwatch linkage unit uses a generation AI to analyze the smartwatch data and make suggestions to encourage appropriate rest and exercise. The smartwatch linkage unit can also monitor the employee's health status in real time based on the smartwatch data and issue an alert if an abnormality is detected. The smartwatch linkage unit can also use the emotion estimation function to analyze the employee's emotional state from the smartwatch data and manage stress. As a result, the information provision system according to the embodiment can optimize the matching of employee skills and jobs, supporting the creation of a resilient organization.For example, information provision systems can improve work efficiency and employee satisfaction by optimizing the matching of employee skills with jobs. Furthermore, regular health checks and the use of smartwatches can help maintain employee health and improve the work environment. This is expected to improve overall work efficiency and enhance the performance of the entire organization.

[0057] The skill matching unit inputs employee skill information, project history, or evaluation data into the generation AI, enabling more accurate matching. For example, the skill matching unit inputs employee skill information and past project history into the generation AI, and recommends the most suitable department based on the project success rate and evaluation data. For example, the unit analyzes data on employee A's past successful projects and recommends him / her to department B, which is working on a similar project. The skill matching unit also inputs employee skill information and evaluation data into the generation AI, and prioritizes matching with employees with highly rated skills. For example, if employee B has highly rated skills, he / she is recommended to department C, which needs those skills. The skill matching unit also integrates employee skill information, project history, and evaluation data, and the generation AI performs a comprehensive analysis to perform optimal matching. For example, based on data on projects where employee C received high evaluations in the past, he / she is recommended to department D, which is working on a similar project. This enables more accurate matching based on employee skills, project history, and evaluation data.

[0058] The skill matching department periodically updates employee skill sets, allowing the generation AI to perform matching based on the latest skill information. For example, the skill matching department will build a system in which employee skill sets are periodically updated, and the generation AI performs matching based on the latest skill information. For example, employee A adds newly acquired skills to the database and recommends them to department B, which requires those skills. The skill matching department will also periodically update employee skill information, and the generation AI will perform optimal matching based on the latest skill data. For example, employee B registers newly acquired qualifications in the database and recommends them to department C, which requires those qualifications. The skill matching department will also develop a system in which employee skill sets are automatically updated, and the generation AI performs matching based on the latest skill information. For example, information about the training that employee C participated in will be added to the database, and the training content will be recommended to department D, which requires it. This keeps employee skill sets up to date, enabling more appropriate matching.

[0059] The skill matching unit can use the emotion estimation function to analyze an employee's current motivation or interests and recommend the most suitable department based on that analysis. For example, the skill matching unit uses the emotion estimation function to analyze an employee's current motivation and recommend the most suitable department based on the results. For example, it analyzes the field in which employee A is highly motivated and recommends department B, which is in charge of that field. The skill matching unit also analyzes employees' interests using the emotion estimation function and builds a system that recommends the most suitable department based on the results. For example, it analyzes the project in which employee B is interested and recommends department C, which is working on that project. The skill matching unit also uses the emotion estimation function to analyze employees' motivation and interests in real time and recommends the most suitable department based on the results. For example, it analyzes the field in which employee C is currently interested and recommends department D, which is in charge of that field. This makes it possible to match employees to more suitable departments based on their motivation and interests.

[0060] The skill matching department can link the skill information registered in the internal database with external job information to support employee job changes. For example, the skill matching department builds a system that links the skill information registered in the internal database with external job information to support employee job changes. For example, it searches external job information based on employee A's skill information and recommends appropriate job changes. The skill matching department also links the skill information in the internal database with external job information to support employee career advancement. For example, it analyzes external job information based on employee B's skill set and recommends job changes suitable for career advancement. The skill matching department also integrates the skill information in the internal database with external job information to develop a system that supports employee job changes. For example, it analyzes external job information based on employee C's skill information and recommends the most suitable job change. This makes it possible to support job changes by linking external job information based on employee skill information.

[0061] The skill matching department can automatically generate in-house training programs based on employee skill information and promote skill development. For example, the skill matching department builds a system that automatically generates in-house training programs based on employee skill information. For example, it analyzes employee A's skill set and proposes a training program that complements the necessary skills. The skill matching department also automatically generates individualized training programs based on employee skill information and promotes skill development. For example, it analyzes employee B's skill gap and proposes a training program to fill that gap. The skill matching department also develops a system in which a generation AI automatically generates optimal training programs based on employee skill information and supports skill development. For example, it analyzes employee C's skill data and proposes the training program necessary to improve his skills. This makes it possible to automatically generate training programs based on employee skill information and promote skill development.

[0062] The skill matching unit uses the emotion estimation function to analyze an employee's interest or motivation when learning a new skill and can suggest the optimal learning method. For example, the skill matching unit uses the emotion estimation function to analyze an employee's interest and motivation when learning a new skill and suggests the optimal learning method based on the results. For example, it analyzes the learning method that employee A is interested in and suggests a training program based on that method. The skill matching unit also analyzes an employee's learning motivation using the emotion estimation function and builds a system that suggests the optimal learning method based on the results. For example, it analyzes the learning style that employee B is interested in and suggests a training program based on that style. The skill matching unit also uses the emotion estimation function to analyze an employee's learning motivation and interests in real time and suggests the optimal learning method based on the results. For example, it analyzes the learning method that employee C is currently interested in and suggests a training program based on that method. In this way, it is possible to suggest the optimal learning method based on an employee's interest and motivation.

[0063] The AI ​​Interview Department uses a generation AI to analyze the content of interviews and propose specific action plans based not only on the employee's career path but also on their growth goals. For example, the AI ​​Interview Department uses a generation AI to analyze the content of interviews and propose specific action plans based not only on the employee's career path but also on their growth goals. For example, if employee A wishes to advance his or her career, specific training and projects will be proposed based on that wish. The AI ​​Interview Department also builds a system in which a generation AI analyzes the content of interviews and proposes action plans based on employees' growth goals. For example, if employee B aims to improve his or her skills, a specific learning plan will be proposed based on that goal. The AI ​​Interview Department also uses a generation AI to analyze the content of interviews and propose action plans based on the employee's career path and growth goals. For example, if employee C wants to demonstrate leadership, specific training and projects will be proposed based on that goal. This makes it possible to propose specific action plans based on employees' career paths and growth goals.

[0064] The AI ​​interview department can refer to an employee's past performance data or feedback during an interview and make more personalized suggestions. For example, the AI ​​interview department refers to an employee's past performance data and feedback during an interview, and the generation AI makes more personalized suggestions. For example, it proposes a specific career path based on employee A's past evaluation data. The AI ​​interview department also builds a system in which the generation AI analyzes an employee's past performance data during an interview and makes individualized suggestions. For example, it proposes an appropriate growth plan based on employee B's past project data. The AI ​​interview department also refers to an employee's feedback data during an interview, and the generation AI makes personalized suggestions. For example, it proposes specific areas for improvement and a growth plan based on employee C's past feedback. This makes it possible to make more personalized suggestions based on employees' past performance data and feedback.

[0065] The AI ​​interview unit uses the emotion estimation function to analyze the emotions of employees during interviews in real time and provide appropriate feedback. For example, the AI ​​interview unit uses the emotion estimation function to analyze the emotions of employees during interviews in real time and provide appropriate feedback based on the results. For example, if employee A is feeling stressed, the AI ​​interview unit analyzes their emotions and provides advice on how to relax. The AI ​​interview unit also uses the emotion estimation function to analyze the emotions of employees during interviews and builds a system that provides feedback based on the results. For example, if employee B is feeling anxious, the AI ​​interview unit analyzes their emotions and provides feedback that gives them a sense of security. The AI ​​interview unit also uses the emotion estimation function to analyze the emotions of employees during interviews in real time and provides appropriate feedback based on the results. For example, if employee C is feeling motivated, the AI ​​interview unit analyzes their emotions and provides feedback that further increases their motivation. This makes it possible to analyze the emotions of employees during interviews in real time and provide appropriate feedback.

[0066] The AI ​​Interview Department can promote the growth of the entire organization by sharing the content of interviews with other employees and providing mutual feedback. For example, the AI ​​Interview Department could build a system where interview content is shared with other employees and they can provide mutual feedback. For example, the interview content of employee A is shared and areas for improvement are identified based on feedback from other employees. The AI ​​Interview Department could also develop a system where a generative AI analyzes the interview content and shares it with other employees to provide mutual feedback. For example, the interview content of employee B is shared and opinions from other employees are incorporated. The AI ​​Interview Department could also build a system where interview content is shared with other employees and they can provide mutual feedback to promote the growth of the entire organization. For example, the interview content of employee C is shared and a growth plan is created based on feedback from other employees. In this way, the sharing of interview content and mutual feedback can promote the growth of the entire organization.

[0067] The AI ​​Interview Department can optimize internal personnel allocation and improve team performance based on the results of interviews. For example, the AI ​​Interview Department builds a system that optimizes internal personnel allocation based on the results of interviews. For example, employee A is assigned to the most appropriate department based on his or her skills and preferences. The AI ​​Interview Department also develops a system in which the generative AI analyzes interview results and optimizes internal personnel allocation. For example, employee B is assigned to the most appropriate team based on his or her career path. The AI ​​Interview Department also builds a system that optimizes internal personnel allocation and improves team performance based on the results of interviews. For example, employee C is assigned to the most appropriate project based on his or her growth goals. This makes it possible to optimize internal personnel allocation and improve team performance based on the results of interviews.

[0068] The AI ​​interview department can use the emotion estimation function to track changes in employees' emotions after an interview and evaluate the effectiveness of the interview. For example, the AI ​​interview department uses the emotion estimation function to track changes in employees' emotions after an interview and build a system to evaluate the effectiveness of the interview based on the results. For example, it analyzes employee A's emotion score after an interview and evaluates the effectiveness of the interview. The AI ​​interview department can also use the emotion estimation function to track changes in employees' emotions after an interview and develop a system to evaluate the effectiveness of the interview based on the results. For example, it analyzes employee B's emotion data after an interview and identifies areas for improvement in the interview. The AI ​​interview department can also use the emotion estimation function to track changes in employees' emotions after an interview in real time and evaluate the effectiveness of the interview based on the results. For example, it evaluates the effectiveness of the interview based on employee C's emotion score after the interview and reflects this in the next interview. This makes it possible to track changes in employees' emotions after an interview and evaluate the effectiveness of the interview.

[0069] The condition check unit can analyze employees' condition data and propose personalized health management plans based on their individual health conditions. For example, the condition check unit uses a generation AI to analyze employees' condition data and propose personalized health management plans based on their individual health conditions. For example, the condition check unit analyzes employee A's stress level and proposes specific measures to reduce stress. The condition check unit also builds a system in which the generation AI analyzes employees' condition data and proposes personalized health management plans. For example, the condition check unit analyzes employee B's sleep data and provides specific advice for improving sleep. The condition check unit also uses a generation AI to propose personalized health management plans based on employees' condition data. For example, the condition check unit analyzes employee C's exercise data and proposes an appropriate exercise plan. This makes it possible to propose personalized health management plans based on employees' condition data.

[0070] The Condition Check Department can make suggestions for improving the workplace environment based on the results of the condition check, thereby reducing employee stress. For example, the Condition Check Department builds a system in which a generation AI analyzes the results of the condition check and makes suggestions for improving the workplace environment. For example, the condition check department analyzes employee A's stress level and suggests changing the workplace layout or improving the break space. The Condition Check Department also develops a system in which a generation AI makes suggestions for improving the workplace environment based on the results of the condition check. For example, the condition check department analyzes employee B's fatigue data and suggests adjusting lighting and air conditioning. The condition check department can also make suggestions for improving the workplace environment based on the results of the condition check, thereby reducing employee stress. For example, the condition check department analyzes employee C's health data and suggests reducing noise levels in the workplace and arranging plants. In this way, suggestions for improving the workplace environment can be made based on the results of the condition check, thereby reducing employee stress.

[0071] The condition check unit uses the emotion estimation function to analyze employees' emotional states, detect emotional problems early, and propose countermeasures. For example, the condition check unit uses the emotion estimation function to analyze employees' emotional states and build a system that detects emotional problems early. For example, it analyzes employee A's emotional data to detect signs of stress and anxiety early and proposes countermeasures. The condition check unit also develops a system in which the generative AI uses the emotion estimation function to analyze employees' emotional states in real time and detect emotional problems early. For example, it identifies emotional problems based on employee B's emotional data and proposes appropriate counseling. The condition check unit also uses the emotion estimation function to analyze employees' emotional states, detect emotional problems early, and propose countermeasures. For example, it analyzes employee C's emotional data to detect emotional problems early and propose relaxation methods. This makes it possible to analyze employees' emotional states, detect emotional problems early, and propose countermeasures.

[0072] The Condition Check Department can automatically generate an in-house health promotion program based on the condition check data, supporting employees in maintaining their health. For example, the Condition Check Department builds a system in which a generation AI automatically generates an in-house health promotion program based on the condition check data. For example, it analyzes employee A's health data and suggests an appropriate exercise program. The Condition Check Department also develops a system in which a generation AI automatically generates an individualized health promotion program based on the condition check data. For example, it analyzes employee B's stress data and suggests a program for stress reduction. The Condition Check Department also uses a generation AI to automatically generate a health promotion program based on the condition check data, supporting employees in maintaining their health. For example, it analyzes employee C's sleep data and suggests a program for improving sleep. In this way, a health promotion program can be automatically generated based on the condition check data, supporting employees in maintaining their health.

[0073] The condition check department can link employees' condition data with external medical institutions to provide specialized health advice. For example, the condition check department builds a system that links employees' condition data with external medical institutions to provide specialized health advice. For example, employee A's health data is sent to a medical institution and the employee receives advice from a specialist. The condition check department also develops a system in which a generative AI links employees' condition data with external medical institutions to provide specialized health advice. For example, employee B's stress data is sent to a medical institution and the employee receives stress management advice from a specialist. The condition check department also links employees' condition data with external medical institutions to provide specialized health advice. For example, employee C's sleep data is sent to a medical institution and the employee receives advice on improving sleep from a specialist. This makes it possible to link employees' condition data with external medical institutions to provide specialized health advice.

[0074] The condition check department can use the emotion estimation function to propose improvements to workplace communication based on the emotional state of employees. For example, the condition check department uses the emotion estimation function to analyze the emotional state of employees and build a system that proposes improvements to workplace communication based on the results. For example, it analyzes employee A's emotional data and proposes areas for improvement in communication. The condition check department also develops a system in which the generative AI uses the emotion estimation function to analyze employees' emotional states in real time and proposes improvements to workplace communication. For example, it identifies areas for improvement in communication based on employee B's emotional data and proposes specific measures. The condition check department also uses the emotion estimation function to analyze employees' emotional states and proposes improvements to workplace communication based on the results. For example, it analyzes employee C's emotional data, identifies areas for improvement in communication, and proposes team building activities. In this way, it is possible to propose improvements to workplace communication based on employees' emotional states.

[0075] The smartwatch linkage unit can monitor employees' health conditions in real time based on data collected from smartwatches and issue alerts if an abnormality is detected. For example, the smartwatch linkage unit builds a system in which a generation AI monitors employees' health conditions in real time based on data collected from smartwatches and issues alerts if an abnormality is detected. For example, if employee A's heart rate is abnormally high, an alert is issued. The smartwatch linkage unit also develops a system in which a generation AI analyzes smartwatch data and monitors employees' health conditions in real time. For example, if employee B's blood pressure is abnormally high, an alert is issued. The smartwatch linkage unit also monitors employees' health conditions in real time based on data collected from smartwatches and issues alerts if an abnormality is detected. For example, if employee C's body temperature is abnormally high, an alert is issued. This makes it possible to monitor employees' health conditions in real time based on data collected from smartwatches and issue alerts if an abnormality is detected.

[0076] The smartwatch integration department can analyze smartwatch data and propose specific action plans to improve employees' lifestyle habits. For example, the smartwatch integration department builds a system in which a generation AI analyzes smartwatch data and proposes specific action plans to improve employees' lifestyle habits. For example, it analyzes employee A's exercise data and proposes an appropriate exercise plan. The smartwatch integration department also develops a system in which a generation AI analyzes smartwatch data and proposes specific action plans to improve employees' lifestyle habits. For example, it analyzes employee B's sleep data and provides specific advice for improving sleep. The smartwatch integration department also develops a system in which a generation AI analyzes smartwatch data and proposes specific action plans to improve employees' lifestyle habits based on smartwatch data. For example, it analyzes employee C's dietary data and proposes a balanced diet plan. This makes it possible to propose specific action plans to improve employees' lifestyle habits based on smartwatch data.

[0077] The smartwatch linkage unit can use the emotion estimation function to analyze employees' emotional states from smartwatch data and perform stress management. For example, the smartwatch linkage unit uses the emotion estimation function to analyze employees' emotional states from smartwatch data and build a system for stress management. For example, it analyzes employee A's heart rate data, identifies their stress level, and proposes appropriate measures. The smartwatch linkage unit also develops a system in which a generative AI analyzes smartwatch data and uses the emotion estimation function to analyze employees' emotional states in real time. For example, it provides specific advice for stress management based on employee B's activity data. The smartwatch linkage unit also uses the emotion estimation function to analyze employees' emotional states based on smartwatch data and perform stress management. For example, it analyzes employee C's sleep data and proposes specific measures to reduce stress. This makes it possible to analyze employees' emotional states and perform stress management based on smartwatch data.

[0078] The smartwatch integration department can automatically plan internal health events and activities based on smartwatch data, encouraging employee participation. For example, the smartwatch integration department builds a system in which a generation AI automatically plans internal health events and activities based on smartwatch data. For example, it analyzes employee A's exercise data and suggests an appropriate exercise event. The smartwatch integration department also develops a system in which a generation AI analyzes smartwatch data and automatically plans internal health events and activities. For example, it suggests a walking event based on employee B's activity data. The smartwatch integration department also develops a system in which a generation AI automatically plans internal health events and activities based on smartwatch data, encouraging employee participation. For example, it analyzes employee C's heart rate data and suggests a relaxation event. This makes it possible to automatically plan internal health events and activities based on smartwatch data, encouraging employee participation.

[0079] The smartwatch linking unit can link smartwatch data with external fitness apps to provide comprehensive support for employee health management. For example, the smartwatch linking unit can link smartwatch data with external fitness apps to build a system that provides comprehensive support for employee health management. For example, employee A's exercise data can be linked to a fitness app to suggest an individualized exercise plan. The smartwatch linking unit can also develop a system in which a generative AI analyzes smartwatch data and links it to an external fitness app. For example, employee B's heart rate data can be linked to a fitness app to suggest an appropriate exercise intensity. The smartwatch linking unit can also link to external fitness apps based on smartwatch data to provide comprehensive support for employee health management. For example, employee C's activity data can be linked to a fitness app to suggest a balanced exercise plan. This allows smartwatch data to be linked to an external fitness app to provide comprehensive support for employee health management.

[0080] The smartwatch linkage unit can use the emotion estimation function to analyze employees' emotional states from smartwatch data and provide emotional support. For example, the smartwatch linkage unit uses the emotion estimation function to analyze employees' emotional states from smartwatch data and build a system to provide emotional support. For example, it analyzes employee A's heart rate data and provides specific advice to reduce stress. The smartwatch linkage unit also develops a system in which a generative AI analyzes smartwatch data and uses the emotion estimation function to analyze employees' emotional states in real time. For example, it provides emotional support based on employee B's activity data. The smartwatch linkage unit also uses the emotion estimation function to analyze employees' emotional states based on smartwatch data and provide emotional support. For example, it analyzes employee C's sleep data and suggests specific measures to achieve emotional stability. This makes it possible to analyze employees' emotional states based on smartwatch data and provide emotional support.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] In addition to employee skill information, the Skill Matching Department can input employees' hobbies and special skills into the generation AI, enabling more multifaceted matching. For example, if employee A is into programming as a hobby, they can be recommended to department B where they can utilize that hobby. Also, if employee B has a special skill in design, they can be recommended to project C where they can utilize that skill. Furthermore, if employee C is interested in marketing as a hobby, they can be recommended to department D where they can utilize that interest. This makes it possible to perform multifaceted matching that takes into account employees' hobbies and special skills.

[0083] In addition to employee skill information, the Skill Matching Department can input the employee's lifestyle and work style preferences into the generation AI, allowing for more flexible matching. For example, if employee A wishes to work remotely, they can be recommended to department B, which can accommodate that preference. Also, if employee B wishes to work flextime, they can be recommended to project C, which can accommodate that preference. Furthermore, if employee C wishes to work shorter hours, they can be recommended to department D, which can accommodate that preference. This enables flexible matching that takes into account employees' lifestyles and work style preferences.

[0084] In addition to employee skill information, the Skill Matching Department can input the employee's learning history and self-development activities into the generation AI, enabling more growth-oriented matching. For example, if employee A is taking an online course, it can recommend him to department B, where he can utilize the content of that learning. Similarly, if employee B is attending a self-development seminar, it can recommend him to project C, where he can utilize the content of that seminar. Furthermore, if employee C is aiming to obtain a qualification, it can recommend him to department D, where he can utilize that qualification. This makes it possible to perform growth-oriented matching that takes into account employees' learning history and self-development activities.

[0085] The skill matching unit uses the emotion estimation function to analyze an employee's stress level and recommend a department with low stress. For example, if employee A is feeling high stress, it can recommend him to department B, which can reduce that stress. If employee B is feeling moderate stress, it can also recommend him to project C, which can reduce that stress. Furthermore, if employee C is feeling low stress, it can also recommend him to department D, which can maintain that stress level. This makes it possible to match employees with their stress levels taken into account.

[0086] The skill matching unit uses the emotion estimation function to analyze the emotional state of employees and recommend departments that are emotionally stable. For example, if employee A is feeling anxious, it can recommend him to department B, which can alleviate that anxiety. If employee B feels secure, it can also recommend him to project C, which can help maintain that sense of security. Furthermore, if employee C is highly motivated, it can also recommend him to department D, where that motivation can be utilized. This makes it possible to match employees with their emotional state taken into consideration.

[0087] In addition to employee skill information, the Skill Matching Department can input the employee's past experiences of failure and setbacks into the generation AI to perform matching to support recovery. For example, data on employee A's past failed projects can be analyzed and the department can recommend employee A to department B, where that experience can be utilized. Also, if employee B has a history of setbacks, the department can recommend the employee to project C, where that experience can be utilized. Furthermore, if employee C has a skill that they failed at in the past, the department can recommend the employee to department D, where they can try again to use that skill. This makes it possible to match employees for recovery support that takes into account their past experiences of failure and setbacks.

[0088] The skill matching unit uses the emotion estimation function to analyze the emotional state of employees and recommend departments that require emotional support. For example, if employee A is emotionally unstable, it can recommend him to department B, which has ample support for that. If employee B is emotionally stable, it can also recommend him to project C, where he can maintain that stability. Furthermore, if employee C is highly emotionally motivated, it can also recommend him to department D, where that motivation can be utilized. This makes it possible to match employees to departments that require emotional support, taking their emotional state into consideration.

[0089] In addition to employee skill information, the Skill Matching Department can input the employee's family structure and home environment into the generation AI to perform matching that takes into account the balance between work and home. For example, if employee A has small children, they can be recommended to department B, which takes their home environment into consideration. Also, if employee B has a family member who requires care, they can be recommended to project C, which also takes their home environment into consideration. Furthermore, if employee C is working away from home, they can be recommended to department D, which also takes their home environment into consideration. This makes it possible to match employees with a balance between work and home that takes into account their family structure and home environment.

[0090] The skill matching unit can use the emotion estimation function to analyze an employee's emotional state and recommend departments that will promote their emotional growth. For example, if employee A wants to grow emotionally, it can recommend him to department B, which can support that growth. If employee B is emotionally stable, it can also recommend him to project C, which can promote his growth while maintaining that stability. Furthermore, if employee C is highly emotionally motivated, it can also recommend him to department D, which can promote his growth while making use of that motivation. This makes it possible to match employees to departments that promote their emotional growth, taking their emotional state into consideration.

[0091] In addition to employee skill information, the Skill Matching Department can input the employee's cultural background and values ​​into the generation AI to perform matching that respects diversity. For example, if employee A has a specific cultural background, they can be recommended to department B, where that background can be respected. Similarly, if employee B has specific values, they can be recommended to project C, where those values ​​can be respected. Furthermore, if employee C has a diverse cultural background, they can be recommended to department D, where that diversity can be utilized. This makes it possible to perform matching that respects diversity by taking into account employees' cultural backgrounds and values.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The skill matching unit matches employee skills with department requirements. Specifically, employee skill information is obtained from the company's internal database, and the generation AI analyzes that information to recommend the most suitable department. The generation AI can also perform matching based on employee skill information and department skill requirements. Furthermore, employee skill information can be updated regularly, and matching can be performed based on the latest skill information. Step 2: The AI ​​Interview Department conducts AI interviews with employees. Specifically, the generating AI analyzes the content of the interview and suggests appropriate career paths. It can also analyze past interview data to make more effective suggestions. Furthermore, the content of the interview can be shared with other employees, allowing them to provide mutual feedback. Step 3: The Condition Check Department periodically checks the employee's condition. Specifically, the Generative AI analyzes the employee's health status and proposes appropriate measures. It can also propose personalized health management plans based on the employee's condition data. Furthermore, it can use the emotion estimation function to analyze the employee's emotional state, detect emotional problems early, and propose measures. Step 4: The smartwatch integration unit analyzes the smartwatch data. Specifically, the generative AI analyzes the smartwatch data and makes suggestions to encourage appropriate rest and exercise. It can also monitor employees' health status in real time based on the smartwatch data and issue alerts if an abnormality is detected. Furthermore, the emotion estimation function can be used to analyze employees' emotional state from the smartwatch data and manage stress.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, a 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The Skills Matching Department connects the company's internal database with the generation AI to match the skills required by each department with the jobs employees want. The AI ​​Interview Department conducts AI interviews with employees, A Condition Check Department that periodically checks the condition of employees, Equipped with a smartwatch linkage unit that analyzes smartwatch data A system characterized by:

2. The skill matching unit In addition to the employee's skill information, project history or evaluation data is input into the generation AI to achieve more accurate matching.

2. The system of claim 1.

3. The skill matching unit The employee's skill set is updated regularly, and the generation AI performs matching based on the latest skill information.

2. The system of claim 1.

4. The skill matching unit Analyze the employee's current motivations or interests and recommend the most suitable department based on that 2. The system of claim 1.

5. The skill matching unit Linking the skill information registered in the internal database with external job information to provide job change support for the employee.

2. The system of claim 1.

Images