System

Abstract

An object of the system according to the embodiment is to efficiently match a desire of an employee with a request of a department, and to grasp and improve stress and problems of the employee at an early stage.SOLUTION: A system includes a database cooperation part, a matching part, an interview execution part, a stress check part, and an improvement proposal part. The database cooperation unit is configured to 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] Previous technology had the problem of not being able to efficiently match employee wishes with departmental demands, and not being able to identify and improve employee stress and problems early on.

[0005] The system according to the embodiment aims to efficiently match employee wishes with departmental demands, and to identify and improve employee stress and problems at an early stage. [Means for solving the problem]

[0006] The system according to the embodiment includes a database linking unit, a matching unit, an interview implementation unit, a stress check unit, and an improvement proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment efficiently matches employee wishes with departmental demands, enabling early detection and improvement of employee stress and problems. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The human resource optimization system according to an embodiment of the present invention is a system that links an internal "human resource" database with a generation AI, matches the skills required by a department with the work desired by employees, and optimizes "human resources." As a result, the human resource optimization system can support the optimization of internal "human resources" and the strengthening of an organization.

[0029] A human resource optimization system according to an embodiment includes a database linking unit, a matching unit, an interview implementation unit, a stress check unit, and an improvement proposal unit. The database linking unit links an internal "human resource" database with a generation AI. For example, the database linking unit provides information such as employee skills, experience, and desired work to the generation AI. The matching unit matches departmental requirements with employee preferences based on the data linked by the database linking unit. For example, the matching unit uses the generation AI to search for employees with project management skills and propose optimal candidates. The interview implementation unit periodically conducts AI interviews based on the matching results obtained by the matching unit. For example, the interview implementation unit uses the generation AI to hear about employees' current situations and preferences and make appropriate proposals. The stress check unit performs stress checks during AI interviews conducted by the interview implementation unit. For example, the stress check unit uses the generation AI to evaluate employees' stress levels. The improvement proposal unit identifies problems early based on the results obtained by the stress check unit and the interview content and proposes improvement proposals. For example, the improvement proposal unit uses the generation AI to suggest team-building workshops when there are problems with interpersonal relationships in the workplace. This allows the human resource optimization system according to the embodiment to support the optimization of internal human resources and strengthening of the organization. For example, by allowing employees to take on jobs that match their skills and aspirations, motivation improves and work efficiency increases. Furthermore, stress checks and early detection of problems can improve employee health and the work environment.

[0030] The matching department analyzes not only employees' skills and experience, but also their performance data in projects, enabling more accurate matching. For example, the matching department collects performance data on employees in past projects, and the generation AI analyzes that data. For example, it evaluates the success rate of projects and leadership skills, and matches employees to the most suitable departments and projects. This enables more accurate matching.

[0031] The matching unit also adds employees' hobbies and personality traits to the database, allowing the generation AI to perform more personalized matching. For example, the matching unit registers employees' hobbies and personality traits in the database, and the generation AI performs matching based on that information. For example, employees who are good at teamwork can be placed on project teams. This allows for more personalized matching.

[0032] The database linking unit links the company's internal "human resources" database with an external talent pool, enabling optimal matching, including with external talent. For example, the database linking unit links the company's internal "human resources" database with an external talent pool, and the generation AI performs optimal matching based on that data. For example, external experts can be invited to a project. This makes it possible to optimally match, including with external talent.

[0033] The database linking unit adds employee health data to the database and can perform matching that takes health status into consideration. For example, the database linking unit registers employee health data in the database, and the generation AI performs matching based on that information. For example, employees in good health can be assigned to important projects. This makes it possible to perform matching that takes health status into consideration.

[0034] During interviews, the interview implementation unit allows the generation AI to refer to the employee's past work history and evaluations in real time, and provide more specific advice.For example, the interview implementation unit allows the generation AI to refer to the employee's past work history and evaluations in real time, and provide specific advice during interviews.For example, advice can be given based on past success stories.This makes it possible to provide more specific advice.

[0035] The interview implementation unit automatically records the contents of the interview, and can provide continuous support by referring to the previous content at the next interview. For example, the interview implementation unit will build a system in which the generation AI automatically records the contents of the interview, and can provide continuous support by referring to the previous content at the next interview. For example, it will follow up based on the proposals made in the previous interview. This makes continuous support possible.

[0036] The interview department can share the contents of the interview with other employees and departments, promoting information sharing throughout the organization. For example, the interview department can build a system in which a generation AI automatically records the contents of the interview and shares them with other employees and departments. For example, by sharing the contents of the interview, information sharing throughout the organization can be promoted. This promotes information sharing throughout the organization.

[0037] The interview implementation department will use the generation AI to propose career paths for employees during interviews, and support their long-term career development. The interview implementation department will build a system in which the generation AI proposes career paths for employees during interviews and supports their long-term career development. For example, it will propose specific training plans for improving skills. This will support long-term career development.

[0038] The improvement suggestion unit can analyze past interview data, identify the most effective proposal pattern, and reflect it in the next interview. The improvement suggestion unit, for example, builds a system that analyzes past interview data and identifies the most effective proposal pattern. For example, it reflects the proposal pattern with a high success rate in the next interview. This allows the most effective proposal pattern to be reflected in the next interview.

[0039] The Improvement Suggestion Department can compare the interview content with other success stories and introduce best practices. For example, the Improvement Suggestion Department will build a system where a generative AI compares the interview content with other success stories and introduces best practices. For example, it will make suggestions based on the success stories of other employees. This will allow best practices to be introduced.

[0040] The improvement proposal section can link the interview content with other AI systems to make more multifaceted proposals. For example, the generation AI can link the interview content with other AI systems to build a system that makes more multifaceted proposals. For example, it can make proposals based on data from other AI systems. This allows for more multifaceted proposals.

[0041] The Improvement Suggestion Department can customize the content of interview suggestions by taking into account the employee's family and private circumstances. For example, the Improvement Suggestion Department will build a system in which the generative AI customizes the content of interview suggestions by taking into account the employee's family and private circumstances. For example, suggestions will be made that take into account the health status of the family. This will result in suggestions that take into account the family and private circumstances.

[0042] The stress check department can customize the content of the stress check questions based on the employee's work content and past stress levels. For example, the stress check department will build a system in which a generation AI customizes the content of the stress check questions based on the employee's work content and past stress levels. For example, it will ask questions that identify stress factors related to specific work. This will customize the content of the questions.

[0043] The stress check unit can analyze the results of stress checks in real time and provide instant feedback. For example, the stress check unit will build a system in which a generation AI analyzes the results of stress checks in real time and provides instant feedback based on that data. For example, if the stress level is high, it will suggest ways to relax. This will provide instant feedback.

[0044] The stress check unit can integrate the results of the stress check with other health data to perform a comprehensive health assessment. For example, the stress check unit constructs a system in which a generative AI integrates the results of the stress check with other health data to perform a comprehensive health assessment. For example, the results of the health check and the results of the stress check are integrated and evaluated. This allows for a comprehensive health assessment.

[0045] The Stress Check Department can suggest relaxation and mental health resources to employees based on the results of the stress check. For example, the Stress Check Department will build a system in which a generative AI will suggest relaxation and mental health resources to employees based on the results of the stress check. For example, it will suggest yoga classes or counseling services. This will suggest relaxation and mental health resources.

[0046] The Improvement Proposal Department can analyze stress check and interview data and develop algorithms that detect signs of problems early. For example, the Improvement Proposal Department develops algorithms that use generative AI to analyze stress check and interview data and detect signs of problems early. For example, it can detect sudden changes in stress levels. This allows signs of problems to be detected early.

[0047] The improvement proposal unit can automatically generate customized improvement proposals according to the type of problem. For example, the improvement proposal unit constructs a system in which a generation AI automatically generates customized improvement proposals according to the type of problem. For example, it proposes a specific action plan for stress management. This automatically generates customized improvement proposals.

[0048] The Improvement Proposal Department can share improvement proposals for problems with other departments and external experts, and seek more multifaceted solutions. For example, the Improvement Proposal Department will build a system in which the generative AI shares improvement proposals for problems with other departments and external experts, and seek more multifaceted solutions. For example, it will propose improvement proposals that incorporate the opinions of other departments. This will allow multifaceted solutions to be sought.

[0049] The Improvement Proposal Department can track the effects of improvement proposals after they are implemented and build a feedback loop that ensures continuous improvement. For example, the Improvement Proposal Department will develop a system in which the generative AI tracks the effects of improvement proposals after they are implemented and builds a feedback loop that ensures continuous improvement. For example, the effects of improvement proposals will be regularly evaluated and revised as necessary. This will ensure continuous improvement.

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

[0051] The Database Linkage Department can link the company's internal "human resources" database with external educational institutions and training programs to help employees improve their skills. For example, the Database Linkage Department can identify employee skill gaps and propose appropriate training programs, which promotes employee skill improvement. In addition, by linking with external educational institutions, it is possible to incorporate the latest technology and knowledge. Furthermore, the results of training that employees have taken can be reflected in the database and used to determine their future career paths.

[0052] The Matching Department can match employees with positions that take into consideration their lifestyles and work-life balance. For example, it can suggest departments that offer flexible working hours to employees who are raising children. This allows employees to work in a way that suits their lifestyle. It can also suggest projects that allow remote work for employees who wish to work remotely. It can also suggest departments that are close to the employee's workplace in order to reduce their commute time.

[0053] During interviews, the interview department can use the generation AI to propose long-term career plans based on the employee's career goals. For example, the generation AI analyzes the employee's career goals and suggests the necessary skills and experience. This provides a specific plan for the employee's desired career. It can also suggest training and education programs based on the career plan. Furthermore, it is possible to regularly evaluate the progress of the career plan and revise the plan as necessary.

[0054] Based on the results of the stress checks, the Stress Check Department can propose personalized stress management plans to employees. For example, generative AI can analyze the results of the stress checks and identify the employee's stress factors. This allows the department to propose the optimal stress management plan for each employee. The stress management plan may also include relaxation techniques and mental health resources. Furthermore, the effectiveness of the stress management plan can be regularly evaluated and revised as necessary.

[0055] The Improvement Suggestion Department can propose improvements to the work environment based on employee feedback. For example, generative AI can analyze employee feedback and identify common issues. This allows for specific proposals to be made to improve the work environment. Improvement proposals may also include changes to the office layout or the introduction of communication tools. Furthermore, it is possible to track the effectiveness of the proposed improvements after they are implemented, creating a feedback loop for continuous improvement.

[0056] The Improvement Proposal Department can propose improvements to business processes based on employee performance data. For example, the generative AI analyzes employee performance data and identifies bottlenecks in business processes. This allows specific proposals to be made to improve the efficiency of business processes. Improvement proposals may also include reviewing business flows or introducing automation tools. Furthermore, it is possible to track performance data after the implementation of improvement proposals and evaluate their effectiveness.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The database linking unit links the company's internal "human resources" database with the generation AI. For example, the database linking unit provides information such as employee skills, experience, and desired work to the generation AI. Step 2: The Matching Department matches departmental requirements with employee preferences based on the data linked by the Database Linkage Department. For example, the Matching Department uses generative AI to search for employees with project management skills and propose the most suitable candidates. Step 3: The Interviewing Department will conduct regular AI interviews based on the matching results from the Matching Department. For example, the Interviewing Department will use the Generative AI to listen to employees' current situations and wishes and make appropriate proposals. Step 4: The Stress Check Department conducts a stress check during the AI ​​interview conducted by the Interview Implementation Department. For example, the Stress Check Department uses the generated AI to evaluate the employee's stress level. Step 5: The Improvement Proposal Department identifies problems early based on the results obtained by the Stress Check Department and the contents of interviews, and proposes improvement proposals. For example, the Improvement Proposal Department could use generative AI to suggest a team-building workshop if there are problems with interpersonal relationships in the workplace.

[0059] (Example 2) The human resource optimization system according to an embodiment of the present invention is a system that links an internal "human resource" database with a generation AI, matches the skills required by a department with the work desired by employees, and optimizes "human resources." As a result, the human resource optimization system can support the optimization of internal "human resources" and the strengthening of an organization.

[0060] A human resource optimization system according to an embodiment includes a database linking unit, a matching unit, an interview implementation unit, a stress check unit, and an improvement proposal unit. The database linking unit links an internal "human resource" database with a generation AI. For example, the database linking unit provides information such as employee skills, experience, and desired work to the generation AI. The matching unit matches departmental requirements with employee preferences based on the data linked by the database linking unit. For example, the matching unit uses the generation AI to search for employees with project management skills and propose optimal candidates. The interview implementation unit periodically conducts AI interviews based on the matching results obtained by the matching unit. For example, the interview implementation unit uses the generation AI to hear about employees' current situations and preferences and make appropriate proposals. The stress check unit performs stress checks during AI interviews conducted by the interview implementation unit. For example, the stress check unit uses the generation AI to evaluate employees' stress levels. The improvement proposal unit identifies problems early based on the results obtained by the stress check unit and the interview content and proposes improvement proposals. For example, the improvement proposal unit uses the generation AI to suggest team-building workshops when there are problems with interpersonal relationships in the workplace. This allows the human resource optimization system according to the embodiment to support the optimization of internal human resources and strengthening of the organization. For example, by allowing employees to take on jobs that match their skills and aspirations, motivation improves and work efficiency increases. Furthermore, stress checks and early detection of problems can improve employee health and the work environment.

[0061] The matching department analyzes not only employees' skills and experience, but also their performance data in projects, enabling more accurate matching. For example, the matching department collects performance data on employees in past projects, and the generation AI analyzes that data. For example, it evaluates the success rate of projects and leadership skills, and matches employees to the most suitable departments and projects. This enables more accurate matching.

[0062] The matching unit also adds employees' hobbies and personality traits to the database, allowing the generation AI to perform more personalized matching. For example, the matching unit registers employees' hobbies and personality traits in the database, and the generation AI performs matching based on that information. For example, employees who are good at teamwork can be placed on project teams. This allows for more personalized matching.

[0063] The matching unit uses the emotion estimation function to perform matching that takes into account the employee's current emotional state, thereby realizing a less stressful placement. For example, the matching unit uses the emotion estimation function to analyze the employee's current emotional state in real time and perform matching based on the results. For example, it suggests departments or projects that are less stressful. This makes it possible to achieve a less stressful placement.

[0064] The database linking unit links the company's internal "human resources" database with an external talent pool, enabling optimal matching, including with external talent. For example, the database linking unit links the company's internal "human resources" database with an external talent pool, and the generation AI performs optimal matching based on that data. For example, external experts can be invited to a project. This makes it possible to optimally match, including with external talent.

[0065] The database linking unit adds employee health data to the database and can perform matching that takes health status into consideration. For example, the database linking unit registers employee health data in the database, and the generation AI performs matching based on that information. For example, employees in good health can be assigned to important projects. This makes it possible to perform matching that takes health status into consideration.

[0066] The matching unit can use the emotion estimation function to predict how an employee will feel about a new position and propose a placement that will elicit positive emotions. For example, the matching unit uses the emotion estimation function to build a system that predicts how an employee will feel about a new position. For example, it proposes a placement that will elicit positive emotions. This makes it possible to create a placement that elicits positive emotions.

[0067] During interviews, the interview implementation unit allows the generation AI to refer to the employee's past work history and evaluations in real time, and provide more specific advice.For example, the interview implementation unit allows the generation AI to refer to the employee's past work history and evaluations in real time, and provide specific advice during interviews.For example, advice can be given based on past success stories.This makes it possible to provide more specific advice.

[0068] The interview implementation unit automatically records the contents of the interview, and can provide continuous support by referring to the previous content at the next interview. For example, the interview implementation unit will build a system in which the generation AI automatically records the contents of the interview, and can provide continuous support by referring to the previous content at the next interview. For example, it will follow up based on the proposals made in the previous interview. This makes continuous support possible.

[0069] The interview implementation unit can use the emotion estimation function to analyze the emotions of employees during interviews in real time and respond accordingly. The interview implementation unit, for example, uses the emotion estimation function to analyze the emotions of employees during interviews in real time and builds a system that responds based on the results. For example, if the emotion is negative, words of encouragement can be offered. This makes it possible to respond according to the emotion.

[0070] The interview department can share the contents of the interview with other employees and departments, promoting information sharing throughout the organization. For example, the interview department can build a system in which a generation AI automatically records the contents of the interview and shares them with other employees and departments. For example, by sharing the contents of the interview, information sharing throughout the organization can be promoted. This promotes information sharing throughout the organization.

[0071] The interview implementation department will use the generation AI to propose career paths for employees during interviews, and support their long-term career development. The interview implementation department will build a system in which the generation AI proposes career paths for employees during interviews and supports their long-term career development. For example, it will propose specific training plans for improving skills. This will support long-term career development.

[0072] The interview implementation unit can use the emotion estimation function to track changes in the employee's emotions after the interview and evaluate the effectiveness of the interview. For example, the interview implementation unit uses the emotion estimation function to track changes in the employee's emotions after the interview and builds a system to evaluate the effectiveness of the interview based on that data. For example, the effectiveness is evaluated based on the emotion score after the interview. This allows the effectiveness of the interview to be evaluated.

[0073] The improvement suggestion unit can analyze past interview data, identify the most effective proposal pattern, and reflect it in the next interview. The improvement suggestion unit, for example, builds a system that analyzes past interview data and identifies the most effective proposal pattern. For example, it reflects the proposal pattern with a high success rate in the next interview. This allows the most effective proposal pattern to be reflected in the next interview.

[0074] The Improvement Suggestion Department can compare the interview content with other success stories and introduce best practices. For example, the Improvement Suggestion Department will build a system where a generative AI compares the interview content with other success stories and introduces best practices. For example, it will make suggestions based on the success stories of other employees. This will allow best practices to be introduced.

[0075] The improvement suggestion unit can use the emotion estimation function to analyze the emotional reactions of employees during interviews and improve proposals based on their emotions. The improvement suggestion unit, for example, uses the emotion estimation function to analyze the emotional reactions of employees during interviews in real time and builds a system that improves proposals based on that data. For example, if the emotion is positive, a specific proposal is made. This allows for improvement of proposals based on emotions.

[0076] The improvement proposal section can link the interview content with other AI systems to make more multifaceted proposals. For example, the generation AI can link the interview content with other AI systems to build a system that makes more multifaceted proposals. For example, it can make proposals based on data from other AI systems. This allows for more multifaceted proposals.

[0077] The Improvement Suggestion Department can customize the content of interview suggestions by taking into account the employee's family and private circumstances. For example, the Improvement Suggestion Department will build a system in which the generative AI customizes the content of interview suggestions by taking into account the employee's family and private circumstances. For example, suggestions will be made that take into account the health status of the family. This will result in suggestions that take into account the family and private circumstances.

[0078] The improvement proposal unit can use the emotion estimation function to track changes in the employee's emotions after the interview and evaluate the effectiveness of the proposal. The improvement proposal unit, for example, uses the emotion estimation function to track changes in the employee's emotions after the interview and builds a system to evaluate the effectiveness of the proposal based on that data. For example, the effectiveness is evaluated based on the emotion score after the interview. This allows the effectiveness of the proposal to be evaluated.

[0079] The stress check department can customize the content of the stress check questions based on the employee's work content and past stress levels. For example, the stress check department will build a system in which a generation AI customizes the content of the stress check questions based on the employee's work content and past stress levels. For example, it will ask questions that identify stress factors related to specific work. This will customize the content of the questions.

[0080] The stress check unit can analyze the results of stress checks in real time and provide instant feedback. For example, the stress check unit will build a system in which a generation AI analyzes the results of stress checks in real time and provides instant feedback based on that data. For example, if the stress level is high, it will suggest ways to relax. This will provide instant feedback.

[0081] The stress check unit can use the emotion estimation function to analyze the emotions of employees during the stress check and perform stress evaluation based on the emotions. The stress check unit, for example, uses the emotion estimation function to analyze the emotions of employees during the stress check in real time and builds a system that performs stress evaluation based on that data. For example, if the emotions are negative, the stress level is evaluated as high. This allows stress evaluation based on emotions.

[0082] The stress check unit can integrate the results of the stress check with other health data to perform a comprehensive health assessment. For example, the stress check unit constructs a system in which a generative AI integrates the results of the stress check with other health data to perform a comprehensive health assessment. For example, the results of the health check and the results of the stress check are integrated and evaluated. This allows for a comprehensive health assessment.

[0083] The Stress Check Department can suggest relaxation and mental health resources to employees based on the results of the stress check. For example, the Stress Check Department will build a system in which a generative AI will suggest relaxation and mental health resources to employees based on the results of the stress check. For example, it will suggest yoga classes or counseling services. This will suggest relaxation and mental health resources.

[0084] The stress check unit can use the emotion estimation function to track changes in the employee's emotions after the stress check and evaluate the effectiveness of stress management. The stress check unit, for example, uses the emotion estimation function to track changes in the employee's emotions after the stress check and builds a system to evaluate the effectiveness of stress management based on that data. For example, the effectiveness is evaluated based on changes in emotion scores. This allows the effectiveness of stress management to be evaluated.

[0085] The Improvement Proposal Department can analyze stress check and interview data and develop algorithms that detect signs of problems early. For example, the Improvement Proposal Department develops algorithms that use generative AI to analyze stress check and interview data and detect signs of problems early. For example, it can detect sudden changes in stress levels. This allows signs of problems to be detected early.

[0086] The improvement proposal unit can automatically generate customized improvement proposals according to the type of problem. For example, the improvement proposal unit constructs a system in which a generation AI automatically generates customized improvement proposals according to the type of problem. For example, it proposes a specific action plan for stress management. This automatically generates customized improvement proposals.

[0087] The improvement suggestion unit can use the emotion estimation function to evaluate the seriousness of problems based on emotions and set priorities. The improvement suggestion unit, for example, uses the emotion estimation function to build a system that evaluates the seriousness of problems based on emotions and sets priorities based on that data. For example, problems with low emotion scores are given priority. In this way, the seriousness of problems is evaluated based on emotions and priorities are set.

[0088] The Improvement Proposal Department can share improvement proposals for problems with other departments and external experts, and seek more multifaceted solutions. For example, the Improvement Proposal Department will build a system in which the generative AI shares improvement proposals for problems with other departments and external experts, and seek more multifaceted solutions. For example, it will propose improvement proposals that incorporate the opinions of other departments. This will allow multifaceted solutions to be sought.

[0089] The Improvement Proposal Department can track the effects of improvement proposals after they are implemented and build a feedback loop that ensures continuous improvement. For example, the Improvement Proposal Department will develop a system in which the generative AI tracks the effects of improvement proposals after they are implemented and builds a feedback loop that ensures continuous improvement. For example, the effects of improvement proposals will be regularly evaluated and revised as necessary. This will ensure continuous improvement.

[0090] The improvement proposal unit can use the emotion estimation function to track changes in employees' emotions after the implementation of an improvement proposal and evaluate the effectiveness of the improvement. For example, the improvement proposal unit uses the emotion estimation function to track changes in employees' emotions after the implementation of an improvement proposal, and builds a system to evaluate the effectiveness of the improvement based on that data. For example, the effectiveness is evaluated based on changes in emotion scores. This allows the effectiveness of the improvement to be evaluated.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The Database Linkage Department can link the company's internal "human resources" database with external educational institutions and training programs to help employees improve their skills. For example, the Database Linkage Department can identify employee skill gaps and propose appropriate training programs, which promotes employee skill improvement. In addition, by linking with external educational institutions, it is possible to incorporate the latest technology and knowledge. Furthermore, the results of training that employees have taken can be reflected in the database and used to determine their future career paths.

[0093] The Matching Department can match employees with positions that take into consideration their lifestyles and work-life balance. For example, it can suggest departments that offer flexible working hours to employees who are raising children. This allows employees to work in a way that suits their lifestyle. It can also suggest projects that allow remote work for employees who wish to work remotely. It can also suggest departments that are close to the employee's workplace in order to reduce their commute time.

[0094] The matching unit uses the emotion estimation function to evaluate employees' motivation levels and propose placements that will increase their motivation. For example, the emotion estimation function can be used to analyze what types of work motivate employees. This can then be used to suggest projects or departments that employees will find rewarding. It is also possible to provide support to raise motivation for employees whose motivation is declining. It can also create a feedback loop to maintain employee motivation.

[0095] During interviews, the interview department can use the generation AI to propose long-term career plans based on the employee's career goals. For example, the generation AI analyzes the employee's career goals and suggests the necessary skills and experience. This provides a specific plan for the employee's desired career. It can also suggest training and education programs based on the career plan. Furthermore, it is possible to regularly evaluate the progress of the career plan and revise the plan as necessary.

[0096] The interview implementation department can use the emotion estimation function to analyze the emotions of employees during interviews in real time and provide feedback according to their emotions. For example, the emotion estimation function can be used to identify the stress or anxiety an employee is feeling during an interview. This makes it possible to provide appropriate feedback according to the employee's emotions. It can also suggest approaches to elicit positive emotions. Furthermore, it can track changes in emotions after the interview and evaluate the effectiveness of the feedback.

[0097] Based on the results of the stress checks, the Stress Check Department can propose personalized stress management plans to employees. For example, generative AI can analyze the results of the stress checks and identify the employee's stress factors. This allows the department to propose the optimal stress management plan for each employee. The stress management plan may also include relaxation techniques and mental health resources. Furthermore, the effectiveness of the stress management plan can be regularly evaluated and revised as necessary.

[0098] The Stress Check Department uses the emotion estimation function to monitor employees' stress levels in real time and detect signs of stress early. For example, the emotion estimation function is used to continuously analyze employees' emotional states. This makes it possible to identify employees whose stress levels are rising early. If signs of stress are detected, it is also possible to immediately propose countermeasures. Furthermore, it is possible to track changes in stress levels and evaluate the effectiveness of stress management.

[0099] The Improvement Suggestion Department can propose improvements to the work environment based on employee feedback. For example, generative AI can analyze employee feedback and identify common issues. This allows for specific proposals to be made to improve the work environment. Improvement proposals may also include changes to the office layout or the introduction of communication tools. Furthermore, it is possible to track the effectiveness of the proposed improvements after they are implemented, creating a feedback loop for continuous improvement.

[0100] The improvement proposal unit can use the emotion estimation function to propose improvements to the work environment based on employees' emotions. For example, the emotion estimation function can be used to analyze how employees feel about the work environment. This makes it possible to propose specific improvement proposals based on employees' emotions. Improvement proposals may also include activities or events designed to make employees' emotions more positive. Furthermore, it is possible to track changes in emotions after the implementation of improvement proposals and evaluate their effectiveness.

[0101] The Improvement Proposal Department can propose improvements to business processes based on employee performance data. For example, the generative AI analyzes employee performance data and identifies bottlenecks in business processes. This allows specific proposals to be made to improve the efficiency of business processes. Improvement proposals may also include reviewing business flows or introducing automation tools. Furthermore, it is possible to track performance data after the implementation of improvement proposals and evaluate their effectiveness.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The database linking unit links the company's internal "human resources" database with the generation AI. For example, the database linking unit provides information such as employee skills, experience, and desired work to the generation AI. Step 2: The Matching Department matches departmental requirements with employee preferences based on the data linked by the Database Linkage Department. For example, the Matching Department uses generative AI to search for employees with project management skills and propose the most suitable candidates. Step 3: The Interviewing Department will conduct regular AI interviews based on the matching results from the Matching Department. For example, the Interviewing Department will use the Generative AI to listen to employees' current situations and wishes and make appropriate proposals. Step 4: The Stress Check Department conducts a stress check during the AI ​​interview conducted by the Interview Implementation Department. For example, the Stress Check Department uses the generated AI to evaluate the employee's stress level. Step 5: The Improvement Proposal Department identifies problems early based on the results obtained by the Stress Check Department and the contents of interviews, and proposes improvement proposals. For example, the Improvement Proposal Department could use generative AI to suggest a team-building workshop if there are problems with interpersonal relationships in the workplace.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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 database linkage department links the company's internal "human resources" database with the generation AI, a matching unit that matches department requests with employee requests based on the data linked by the database linking unit; an interview implementation unit that periodically conducts AI interviews based on the matching results by the matching unit; A stress check unit that performs a stress check during the AI ​​interview conducted by the interview implementation unit; and an improvement proposal section that identifies problems early based on the results obtained by the stress check section and the contents of the interviews and proposes improvement proposals. A system characterized by:

2. The matching unit Analyze not only the skills and experience of the employees but also their performance data in projects to achieve more accurate matching.

2. The system of claim 1.

3. The database linking unit Linking the human resources database with external talent pools to perform optimal matching, including with external talent 2. The system of claim 1.

4. The interview implementation department: During interviews, the AI ​​will refer to the employee's past work history and evaluations in real time to provide more specific advice.

2. The system of claim 1.

5. The improvement suggestion unit Analyze past interview data, identify the most effective proposal patterns, and reflect them in the next interview.

2. The system of claim 1.

6. The stress check section The questions in the stress check are customized based on the employee's work content and past stress level.

2. The system of claim 1.

7. The matching unit Matching is carried out taking into account the employee's current emotional state, resulting in a less stressful placement.

2. The system of claim 1.

8. The stress check section Analyzing the employee's emotions during the stress check and performing a stress assessment based on the emotions 2. The system of claim 1.

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