System

Abstract

An object of a system according to an embodiment is to achieve both mental health care of employees and corporate governance.SOLUTION: A system includes a reception part, an analysis part, a provision part, and a grasping part. The reception part receives worries of employees. The analysis unit analyzes the consultation contents received by the reception unit. The providing unit provides advice on the basis of a result analyzed by the analysis unit. The grasping part grasps the stress situation of the whole organization on the basis of the data analyzed by the analysis part. The providing unit provides the information grasped by the grasping unit to the manager or the executive layer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately balance employee mental health care with corporate governance, and there is room for improvement.

[0005] The system according to the embodiment aims to achieve both employee mental health care and corporate governance. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and an understanding unit. The reception unit receives employee concerns. The analysis unit analyzes the consultation content received by the reception unit. The provision unit provides advice based on the results of the analysis by the analysis unit. The understanding unit understands the stress situation of the entire organization based on the data analyzed by the analysis unit. The provision unit provides the information understood by the understanding unit to managers and executives. [Effects of the Invention]

[0007] The system according to the embodiment can achieve both employee mental health care and corporate governance. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A mental healthcare system according to an embodiment of the present invention efficiently accepts and analyzes employee concerns, provides appropriate advice, and grasps the stress level of the entire organization. The mental healthcare system provides a mechanism for employees to anonymously consult with AI about their concerns and stress. For example, employees can input their concerns into the AI ​​via chat, which then analyzes the content and provides appropriate advice and resources. The AI ​​then analyzes the employee concerns and grasps the stress level of the entire organization. For example, if stress levels are rising in a specific department, the system notifies management so that early action can be taken. Furthermore, the mental healthcare system uses AI to collect and analyze data related to employee mental healthcare, contributing to improved corporate governance. For example, by creating regular reports and providing them to management, the current situation of the organization can be grasped and appropriate measures can be taken. This allows the mental healthcare system to improve employee well-being and the performance of the entire organization. This allows the mental healthcare system to efficiently accept and analyze employee concerns, provide appropriate advice, and grasp the stress level of the entire organization. For example, providing a system that allows employees to anonymously consult AI about their worries and stress lowers the psychological barrier for employees and creates an environment where they can consult more easily. Furthermore, by having AI analyze the content of employees' consultations and grasp the stress level of the entire organization, it reduces the burden on managers and supports the healthy operation of the entire organization. Furthermore, by having AI collect and analyze data on employees' mental health care and contribute to improving corporate governance, it is possible to improve the performance of the entire organization and achieve sustainable growth.

[0029] A mental health care system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and an understanding unit. The reception unit receives employee concerns. Examples of employee concerns include, but are not limited to, work-related concerns, personal concerns, and health-related concerns. The reception unit receives employee concerns, for example, in a chat format. The reception unit can also receive concerns using multiple input methods, such as voice input and text input. For example, when an employee inputs their consultation content by voice, the reception unit uses voice recognition technology to receive the consultation. The analysis unit analyzes the consultation content received by the reception unit. The analysis can be performed using, for example, text analysis, emotion analysis, statistical analysis, or the like, but is not limited to these examples. For example, the analysis unit can extract keywords from the consultation content using text analysis and provide appropriate advice. The analysis unit can also estimate the employee's emotions using emotion analysis and provide advice based on the results. The provision unit provides advice based on the results of the analysis by the analysis unit. The advice can be provided in the form of, for example, written advice, audio advice, video advice, or the like, but is not limited to these examples. For example, the providing unit provides advice in text. The providing unit can also provide advice via audio. The providing unit can also provide advice via video. The understanding unit understands the stress state of the entire organization based on the data analyzed by the analysis unit. The stress state is evaluated by methods such as, but not limited to, stress level scoring, questionnaire results, and biometric data. For example, the understanding unit understands the stress state of the entire organization using stress level scoring. The understanding unit can also understand the stress state using questionnaire results. The understanding unit can also understand the stress state using biometric data. As a result, the mental health care system according to the embodiment can efficiently accept and analyze the concerns of employees, provide appropriate advice, and understand the stress state of the entire organization. For example, the reception unit accepts the concerns of employees. The analysis unit analyzes the consultation content accepted by the reception unit.The provision unit provides advice based on the results of the analysis by the analysis unit. The understanding unit understands the stress situation of the entire organization based on the data analyzed by the analysis unit. This makes it possible to efficiently accept and analyze employees' concerns, provide appropriate advice, and understand the stress situation of the entire organization.

[0030] The reception unit can accept employee concerns in chat format. Examples of chat formats include, but are not limited to, text chat, voice chat, and video chat. The reception unit can accept employee concerns using text chat, for example. The reception unit can also accept concerns using voice chat. For example, voice chat can be performed using voice recognition technology. The reception unit can also accept concerns using video chat. For example, video chat can be performed using video call technology. This can provide an environment where employees can easily seek advice. Some or all of the above-mentioned processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the content of the employee's text chat into a generation AI, which can analyze the text data.

[0031] The analysis unit can analyze the content of the employee's consultation and provide appropriate advice. Examples of appropriate advice include, but are not limited to, expert opinions and advice based on past data. For example, the analysis unit can extract keywords from the content of the consultation using text analysis and provide appropriate advice. The analysis unit can also estimate the employee's emotions using sentiment analysis and provide advice based on the results. For example, the analysis unit can extract keywords from the content of the consultation using text analysis and provide advice based on past data. The analysis unit can also estimate the employee's emotions using sentiment analysis and provide expert opinions based on the results. The analysis unit can also analyze the content of the consultation using statistical analysis and provide appropriate advice. For example, the analysis unit can analyze patterns in the content of the consultation using statistical analysis and provide appropriate advice. This allows appropriate advice to be provided based on the content of the employee's consultation. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the content of the employee's consultation into a generation AI and cause the generation AI to generate appropriate advice.

[0032] If stress is increasing in a specific department, the assessment department can notify management of the information. Specific departments include, but are not limited to, the sales department, development department, and human resources department. For example, if stress is increasing in the sales department, the assessment department can notify management of the information. Furthermore, if stress is increasing in the development department, the assessment department can also notify management of the information. For example, the assessment department can use stress level scoring to assess the stress level of a specific department and notify management. Furthermore, the assessment department can use questionnaire results to assess the stress level of a specific department and notify management. Furthermore, the assessment department can use biometric data to assess the stress level of a specific department and notify management. For example, the assessment department can use stress level scoring to assess the stress level of the sales department and notify management. Furthermore, the assessment department can use questionnaire results to assess the stress level of the development department and notify management. Furthermore, the assessment department can use biometric data to assess the stress level of the human resources department and notify management. This allows measures to be taken early if stress levels are rising in a specific department. Some or all of the above-mentioned processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit may input the stress levels of a specific department into the generating AI, and have the generating AI analyze the stress levels.

[0033] The provision unit can create periodic reports and provide them to management. Periodic reports include, but are not limited to, monthly reports, quarterly reports, and annual reports. For example, the provision unit can create monthly reports and provide them to management. The provision unit can also create quarterly reports and provide them to management. For example, the provision unit can create monthly reports and provide them to management. The provision unit can also create quarterly reports and provide them to management. The provision unit can also create annual reports and provide them to management. For example, the provision unit can create annual reports and provide them to management. This allows management to understand the current state of the organization and take appropriate measures. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the creation of periodic reports into a generation AI, causing the generation AI to generate the reports.

[0034] The provision unit can collect and analyze data related to employees' mental health care to improve corporate governance. Examples of data related to mental health care include, but are not limited to, survey results, biometric data, and consultation history. For example, the provision unit can collect and analyze survey results to improve corporate governance. The provision unit can also collect and analyze biometric data to improve corporate governance. For example, the provision unit can collect and analyze survey results to improve corporate governance. The provision unit can also collect and analyze biometric data to improve corporate governance. The provision unit can also collect and analyze consultation history to improve corporate governance. For example, the provision unit can collect and analyze consultation history to improve corporate governance. This improves corporate governance and the performance of the entire organization. Some or all of the above-described processing in the provision unit can be performed using, or without, AI. For example, the provision unit can input data related to mental health care into a generation AI and have the generation AI analyze the data.

[0035] The reception unit can analyze the employee's past consultation history and select a reception method. Past consultation history includes, but is not limited to, text data, audio data, and video data. For example, the reception unit preferentially suggests consultation methods (chat, audio, etc.) that the employee has used in the past. The reception unit can also select the optimal reception method based on the employee's past consultation content. For example, the reception unit suggests the most effective reception method based on the employee's past consultation history. This makes it possible to provide the optimal reception method based on the employee's past consultation history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the employee's past consultation history into a generation AI, causing the generation AI to select the optimal reception method.

[0036] When receiving a consultation, the reception unit can filter the consultation based on the employee's current work situation and areas of interest. Examples of work situation include, but are not limited to, work progress, work load, and type of work. Examples of areas of interest include, but are not limited to, survey results, past work history, and self-reporting. The reception unit, for example, prioritizes receiving consultation content related to a project the employee is currently working on. The reception unit can also filter related consultation content based on the employee's areas of interest. For example, the reception unit selects appropriate consultation content taking into account the employee's work situation. This allows priority to be received for consultation content that corresponds to the employee's work situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the employee's work situation and areas of interest into a generation AI and have the generation AI perform filtering.

[0037] When accepting a consultation, the reception unit can select a reception means according to the employee's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the employee inputs the consultation content by voice, the reception unit can use voice recognition to accept the consultation. Furthermore, when the employee inputs the consultation content by text, the reception unit can also use text analysis to accept the consultation. For example, when the employee inputs the consultation content using an image, the reception unit can use image analysis to accept the consultation. This makes it possible to provide the optimal reception means according to the employee's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the employee's input data into a generation AI and have the generation AI select the optimal reception means.

[0038] When accepting consultations, the reception unit can prioritize relevant consultations by taking into account the employee's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, if an employee works in a specific area, the reception unit can prioritize consultations related to that area. Furthermore, if an employee is on a business trip, the reception unit can prioritize consultations related to the business trip destination. For example, if an employee is working remotely, the reception unit can prioritize consultations related to their home. This allows for prioritized acceptance of relevant consultations based on the employee's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the employee's geographical location information into a generation AI, causing the generation AI to filter relevant consultations.

[0039] When receiving a consultation, the reception unit can analyze the employee's social media activity and receive related consultations. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the reception unit can receive related consultations based on concerns expressed by the employee on social media. The reception unit can also analyze the employee's social media activity and suggest related consultations. For example, the reception unit can receive related consultations based on the activity of the employee's friends on social media. This makes it possible to receive related consultations based on the employee's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the employee's social media activity data into a generation AI and have the generation AI analyze the related consultations.

[0040] The reception unit can customize the reception method by reflecting the employee's past feedback when receiving a consultation. Past feedback includes, but is not limited to, survey results, comments, and ratings. For example, the reception unit can propose an optimal reception method based on the employee's past feedback. The reception unit can also improve the reception procedure by reflecting the employee's past feedback. For example, the reception unit provides an individually customized reception method based on the employee's feedback. This makes it possible to provide an optimal reception method based on the employee's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the employee's past feedback data into a generation AI and have the generation AI customize the optimal reception method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content. The importance of the consultation content includes, but is not limited to, urgency, impact, and frequency. For example, the analysis unit performs a detailed analysis for consultation content with a high level of importance. The analysis unit can also perform a concise analysis for consultation content with a low level of importance. For example, the analysis unit provides an analysis with an appropriate level of detail depending on the importance. This makes it possible to provide an analysis result according to the importance of the consultation content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the consultation content to a generation AI, causing the generation AI to adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the consultation content. Examples of categories of consultation content include, but are not limited to, work-related consultations, personal consultations, and health-related consultations. For example, the analysis unit can apply a specialized analysis algorithm to consultation content related to mental health. Furthermore, the analysis unit can also apply an analysis algorithm specialized for improving work efficiency to consultation content related to work. For example, the analysis unit can apply a psychological analysis algorithm to consultation content related to interpersonal relationships. This makes it possible to provide analysis results according to the category of the consultation content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the consultation content into a generation AI and have the generation AI apply different analysis algorithms.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past consultation results. Past consultation results include, but are not limited to, text data, audio data, and video data. For example, the analysis unit adjusts the analysis algorithm based on the employee's past consultation results. The analysis unit can also improve the accuracy of the analysis by referring to the employee's past consultation results. For example, the analysis unit creates a database of the employee's past consultation results and uses it for analysis. This allows the accuracy of the analysis to be improved based on the employee's past consultation results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the employee's past consultation result data into a generation AI, which can improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the consultation content. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, and the submission period. The analysis unit can also determine the priority of analysis based on, for example, the time of submission of the consultation content. The analysis unit can also prioritize the analysis of consultation content with high urgency. For example, the analysis unit performs analysis with appropriate priority depending on the time of submission. This allows analysis to be performed with appropriate priority based on the time of submission of the consultation content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of the consultation content to the generation AI, and have the generation AI determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the consultation contents. Examples of the relevance of the consultation contents include, but are not limited to, similarity of the contents, common keywords, and related topics. The analysis unit can adjust the order of analysis based on, for example, the relevance of the consultation contents. The analysis unit can also prioritize the analysis of highly related consultation contents. For example, the analysis unit performs analysis in an appropriate order depending on the relevance. This allows the analysis to be performed in an appropriate order based on the relevance of the consultation contents. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the consultation contents to a generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis depending on the employee's level of expertise. Examples of technical terminology include, but are not limited to, qualifications, work experience, and educational background. For example, the analysis unit can provide analysis results that use a lot of technical terminology to employees with high levels of technical expertise. The analysis unit can also provide analysis results in easy-to-understand language to employees with low levels of technical expertise. For example, the analysis unit can use appropriate technical terminology depending on the employee's level of technical expertise. This allows the analysis results to be provided according to the employee's level of technical expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the employee's level of technical expertise data into the generation AI, causing the generation AI to adjust the use of technical terminology.

[0047] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the consultation content. The importance of the consultation content includes, for example, urgency, impact, frequency, etc., but is not limited to these examples. For example, the providing unit provides detailed advice for consultation content with high importance. The providing unit can also provide concise advice for consultation content with low importance. For example, the providing unit provides advice with an appropriate level of detail depending on the importance. This makes it possible to provide advice according to the importance of the consultation content. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs importance data of the consultation content to a generating AI, and causes the generating AI to adjust the level of detail of the advice.

[0048] When providing advice, the providing unit can apply different advice algorithms depending on the category of the consultation content. Examples of categories of consultation content include, but are not limited to, work-related consultations, personal consultations, and health-related consultations. For example, the providing unit can apply a specialized advice algorithm to consultation content related to mental health. Furthermore, the providing unit can also apply an advice algorithm specialized for improving work efficiency to consultation content related to work. For example, the providing unit can apply a psychological advice algorithm to consultation content related to interpersonal relationships. This makes it possible to provide advice according to the category of the consultation content. Some or all of the above-described processing in the providing unit may be performed using, or without using, AI. For example, the providing unit can input category data of the consultation content into a generation AI and cause the generation AI to apply a different advice algorithm.

[0049] When providing advice, the providing unit can improve the accuracy of the advice by referring to the employee's past advice results. Past advice results include, but are not limited to, for example, text data, audio data, and video data. For example, the providing unit adjusts the advice algorithm based on the employee's past advice results. The providing unit can also improve the accuracy of the advice by referring to the employee's past advice results. For example, the providing unit creates a database of the employee's past advice results and uses them for advice. This allows the accuracy of advice to be improved based on the employee's past advice results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the employee's past advice result data into a generation AI, which can improve the accuracy of advice.

[0050] When providing advice, the providing unit can determine the priority of advice based on the time of submission of the consultation content. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, and the submission period. The providing unit can also determine the priority of advice based on, for example, the time when the consultation content was submitted. The providing unit can also prioritize advice on consultation content with high urgency. For example, the providing unit provides advice with an appropriate priority depending on the time of submission. This makes it possible to provide advice with an appropriate priority based on the time when the consultation content was submitted. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the time when the consultation content was submitted to a generation AI, and have the generation AI determine the priority of advice.

[0051] When providing advice, the providing unit can adjust the order of advice based on the relevance of the consultation content. The relevance of the consultation content includes, but is not limited to, for example, similarity of the content, common keywords, related topics, etc. The providing unit can adjust the order of advice based on, for example, the relevance of the consultation content. The providing unit can also prioritize advice on consultation content with high relevance. For example, the providing unit provides advice in an appropriate order according to the relevance. This makes it possible to provide advice in an appropriate order based on the relevance of the consultation content. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of the consultation content to a generating AI and have the generating AI adjust the order of advice.

[0052] When providing advice, the providing unit can adjust the use of technical terminology in the advice depending on the employee's level of expertise. Examples of expertise levels include, but are not limited to, qualifications, work experience, and educational background. For example, the providing unit can provide advice that uses a lot of technical terminology to employees with high levels of expertise. The providing unit can also provide advice in easy-to-understand language to employees with low levels of expertise. For example, the providing unit uses appropriate technical terminology depending on the employee's level of expertise. This makes it possible to provide advice that is appropriate for the employee's level of expertise. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input employee expertise level data into a generating AI, causing the generating AI to adjust the use of technical terminology.

[0053] When assessing a stress state, the assessment unit can predict a current stress state by referring to past stress data. Past stress data includes, but is not limited to, for example, questionnaire results, biometric data, and consultation history. For example, the assessment unit predicts a current stress state based on the employee's past stress data. The assessment unit can also predict stress fluctuations by referring to the past stress data. For example, the assessment unit analyzes the employee's past stress data to assess the current stress state. This makes it possible to predict a current stress state based on the past stress data. Some or all of the above-described processing in the assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assessment unit can input past stress data into a generation AI and have the generation AI predict a current stress state.

[0054] When grasping the stress state, the grasping unit can apply different stress analysis methods to different categories of consultation content. Examples of categories of consultation content include, but are not limited to, work-related consultations, personal consultations, and health-related consultations. For example, the grasping unit can apply a specialized stress analysis method to consultation content related to mental health. Furthermore, the grasping unit can also apply a stress analysis method specialized for work efficiency to consultation content related to work. For example, the grasping unit can apply a psychological stress analysis method to consultation content related to interpersonal relationships. This allows stress analysis to be performed according to the category of consultation content. Some or all of the above-described processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit can input category data of the consultation content into a generation AI and have the generation AI apply different stress analysis methods.

[0055] When assessing a stress state, the assessment unit can analyze stress by taking into account the employee's attribute information. Attribute information includes, but is not limited to, for example, age, gender, and job position. The assessment unit analyzes stress by taking into account attribute information such as the employee's age and gender. The assessment unit can also analyze stress based on attribute information such as the employee's job type and position. For example, the assessment unit analyzes stress by referring to attribute information such as the employee's working hours and working style. This makes it possible to analyze stress based on the employee's attribute information. Some or all of the above-mentioned processing in the assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assessment unit can input the employee's attribute information data into a generation AI and have the generation AI analyze stress.

[0056] When grasping the stress state, the grasping unit can analyze changes in stress based on the time when the consultation content was submitted. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, and the submission period. The grasping unit can analyze changes in stress based on, for example, the time when the consultation content was submitted. The grasping unit can also prioritize the analysis of consultation content with high urgency. For example, the grasping unit analyzes changes in stress in an appropriate order depending on the time of submission. This makes it possible to analyze changes in stress based on the time when the consultation content was submitted. Some or all of the above-mentioned processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit can input data on the time when the consultation content was submitted to a generation AI and have the generation AI analyze changes in stress.

[0057] When grasping the stress state, the grasping unit can analyze stress by referring to related market data. Examples of related market data include, but are not limited to, market research data, industry reports, and competitive analysis data. For example, the grasping unit analyzes the employee's stress state based on the related market data. The grasping unit can also predict changes in stress by referring to market fluctuations. For example, the grasping unit analyzes market data to grasp the employee's stress state. This makes it possible to analyze stress based on the related market data. Some or all of the above-mentioned processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit can input the related market data to a generation AI and have the generation AI analyze stress.

[0058] When grasping the stress state, the grasping unit can analyze stress taking into account technological maturity. Technological maturity includes, for example, the prevalence of technology, the evolutionary stage of technology, and the adoption rate of technology, but is not limited to these examples. The grasping unit, for example, analyzes the employee's stress state based on technological maturity. The grasping unit can also predict changes in stress by referring to technological advances. For example, the grasping unit grasps the employee's stress state taking into account technological maturity. This makes it possible to analyze stress based on technological maturity. Some or all of the above-mentioned processing in the grasping unit may be performed, for example, using AI, or may be performed without using AI. For example, the grasping unit can input technological maturity data to a generation AI and have the generation AI analyze stress.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When accepting an employee's consultation, the reception unit can refer to the employee's past consultation history and suggest the optimal reception method. For example, if the employee has preferred text chat in the past, the reception unit will preferentially suggest text chat. Also, if the employee has preferred voice chat, the reception unit can suggest voice chat. Furthermore, if the employee has preferred video chat, the reception unit can suggest video chat. This makes it possible to provide the optimal reception method based on the employee's past consultation history.

[0061] When analyzing the content of an employee's consultation, the analysis unit can adjust the level of detail of the analysis according to the employee's level of expertise. For example, it can provide detailed analysis results that use a lot of technical terminology to employees with high levels of expertise. It can also provide concise, easy-to-understand analysis results to employees with low levels of expertise. Furthermore, it can select an appropriate analysis algorithm according to the employee's level of expertise. This makes it possible to provide analysis results that are appropriate for the employee's level of expertise.

[0062] The reception unit can prioritize receiving highly relevant consultations by taking into account the employee's geographical location information. For example, if an employee works in a specific area, it can prioritize receiving consultations related to that area. Also, if an employee is on a business trip, it can prioritize receiving consultations related to the business trip destination. Furthermore, if an employee is working remotely, it can prioritize receiving consultations related to their home. This makes it possible to prioritize receiving highly relevant consultations based on the employee's geographical location information.

[0063] The analysis unit can improve the accuracy of the analysis by referring to the employee's past consultation results. For example, the analysis algorithm can be adjusted based on the employee's past consultation results. The analysis unit can also improve the accuracy of the analysis by referring to the employee's past consultation results. Furthermore, the employee's past consultation results can be compiled into a database and used for analysis. This makes it possible to improve the accuracy of the analysis based on the employee's past consultation results.

[0064] When providing advice, the providing unit can determine the priority of advice based on the time when the consultation content was submitted. For example, the priority of advice can be determined based on the time when the consultation content was submitted. Also, it is possible to give priority to advice on consultation content with high urgency. Furthermore, it is possible to provide advice with an appropriate priority depending on the time of submission. This makes it possible to provide advice with an appropriate priority based on the time when the consultation content was submitted.

[0065] When grasping the stress state, the grasping unit can predict the current stress state by referring to past stress data. For example, the current stress state is predicted based on the employee's past stress data. The grasping unit can also predict stress fluctuations by referring to the past stress data. Furthermore, the grasping unit can analyze the employee's past stress data to grasp the current stress state. This makes it possible to predict the current stress state based on the past stress data.

[0066] When grasping the stress state, the grasping unit can analyze stress by taking into account the employee's attribute information. For example, stress is analyzed by taking into account attribute information such as the employee's age and gender. Stress can also be analyzed based on attribute information such as the employee's job type and position. Furthermore, stress can also be analyzed by referring to attribute information such as the employee's working hours and working style. In this way, stress can be analyzed based on the employee's attribute information.

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

[0068] Step 1: The reception unit receives the employee's concerns. Employee concerns include, but are not limited to, work-related concerns, personal concerns, and health-related concerns. The reception unit receives the employee's concerns, for example, in chat format. The reception unit can also receive concerns using multiple input methods, such as voice input and text input. For example, when an employee inputs their concerns by voice, the reception unit uses voice recognition technology to receive the concerns. Step 2: The analysis unit analyzes the consultation content received by the reception unit. The analysis is performed by, for example, text analysis, sentiment analysis, statistical analysis, or the like, but is not limited to these examples. For example, the analysis unit extracts keywords from the consultation content using text analysis and provides appropriate advice. The analysis unit can also estimate the employee's emotions using sentiment analysis and provide advice based on the results. Step 3: The providing unit provides advice based on the results of the analysis by the analyzing unit. The advice may be provided in the form of, for example, written advice, audio advice, video advice, or the like, but is not limited to these examples. For example, the providing unit provides written advice. The providing unit may also provide audio advice. The providing unit may also provide video advice. Step 4: The assessment unit assesses the stress state of the entire organization based on the data analyzed by the analysis unit. The stress state is evaluated by, for example, but not limited to, methods such as stress level scoring, questionnaire results, and biometric data. For example, the assessment unit assesses the stress state of the entire organization using stress level scoring. The assessment unit can also assess the stress state using questionnaire results. The assessment unit can also assess the stress state using biometric data. Step 5: The information provided by the assessment department is provided to managers and executives, who can then understand the stress situation across the organization and take appropriate measures.

[0069] (Example 2) A mental healthcare system according to an embodiment of the present invention efficiently accepts and analyzes employee concerns, provides appropriate advice, and grasps the stress level of the entire organization. The mental healthcare system provides a mechanism for employees to anonymously consult with AI about their concerns and stress. For example, employees can input their concerns into the AI ​​via chat, which then analyzes the content and provides appropriate advice and resources. The AI ​​then analyzes the employee concerns and grasps the stress level of the entire organization. For example, if stress levels are rising in a specific department, the system notifies management so that early action can be taken. Furthermore, the mental healthcare system uses AI to collect and analyze data related to employee mental healthcare, contributing to improved corporate governance. For example, by creating regular reports and providing them to management, the current situation of the organization can be grasped and appropriate measures can be taken. This allows the mental healthcare system to improve employee well-being and the performance of the entire organization. This allows the mental healthcare system to efficiently accept and analyze employee concerns, provide appropriate advice, and grasp the stress level of the entire organization. For example, providing a system that allows employees to anonymously consult AI about their worries and stress lowers the psychological barrier for employees and creates an environment where they can consult more easily. Furthermore, by having AI analyze the content of employees' consultations and grasp the stress level of the entire organization, it reduces the burden on managers and supports the healthy operation of the entire organization. Furthermore, by having AI collect and analyze data on employees' mental health care and contribute to improving corporate governance, it is possible to improve the performance of the entire organization and achieve sustainable growth.

[0070] A mental health care system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and an understanding unit. The reception unit receives employee concerns. Examples of employee concerns include, but are not limited to, work-related concerns, personal concerns, and health-related concerns. The reception unit receives employee concerns, for example, in a chat format. The reception unit can also receive concerns using multiple input methods, such as voice input and text input. For example, when an employee inputs their consultation content by voice, the reception unit uses voice recognition technology to receive the consultation. The analysis unit analyzes the consultation content received by the reception unit. The analysis can be performed using, for example, text analysis, emotion analysis, statistical analysis, or the like, but is not limited to these examples. For example, the analysis unit can extract keywords from the consultation content using text analysis and provide appropriate advice. The analysis unit can also estimate the employee's emotions using emotion analysis and provide advice based on the results. The provision unit provides advice based on the results of the analysis by the analysis unit. The advice can be provided in the form of, for example, written advice, audio advice, video advice, or the like, but is not limited to these examples. For example, the providing unit provides advice in text. The providing unit can also provide advice via audio. The providing unit can also provide advice via video. The understanding unit understands the stress state of the entire organization based on the data analyzed by the analysis unit. The stress state is evaluated by methods such as, but not limited to, stress level scoring, questionnaire results, and biometric data. For example, the understanding unit understands the stress state of the entire organization using stress level scoring. The understanding unit can also understand the stress state using questionnaire results. The understanding unit can also understand the stress state using biometric data. As a result, the mental health care system according to the embodiment can efficiently accept and analyze the concerns of employees, provide appropriate advice, and understand the stress state of the entire organization. For example, the reception unit accepts the concerns of employees. The analysis unit analyzes the consultation content accepted by the reception unit.The provision unit provides advice based on the results of the analysis by the analysis unit. The understanding unit understands the stress situation of the entire organization based on the data analyzed by the analysis unit. This makes it possible to efficiently accept and analyze employees' concerns, provide appropriate advice, and understand the stress situation of the entire organization.

[0071] The reception unit can accept employee concerns in chat format. Examples of chat formats include, but are not limited to, text chat, voice chat, and video chat. The reception unit can accept employee concerns using text chat, for example. The reception unit can also accept concerns using voice chat. For example, voice chat can be performed using voice recognition technology. The reception unit can also accept concerns using video chat. For example, video chat can be performed using video call technology. This can provide an environment where employees can easily seek advice. Some or all of the above-mentioned processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the content of the employee's text chat into a generation AI, which can analyze the text data.

[0072] The analysis unit can analyze the content of the employee's consultation and provide appropriate advice. Examples of appropriate advice include, but are not limited to, expert opinions and advice based on past data. For example, the analysis unit can extract keywords from the content of the consultation using text analysis and provide appropriate advice. The analysis unit can also estimate the employee's emotions using sentiment analysis and provide advice based on the results. For example, the analysis unit can extract keywords from the content of the consultation using text analysis and provide advice based on past data. The analysis unit can also estimate the employee's emotions using sentiment analysis and provide expert opinions based on the results. The analysis unit can also analyze the content of the consultation using statistical analysis and provide appropriate advice. For example, the analysis unit can analyze patterns in the content of the consultation using statistical analysis and provide appropriate advice. This allows appropriate advice to be provided based on the content of the employee's consultation. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the content of the employee's consultation into a generation AI and cause the generation AI to generate appropriate advice.

[0073] If stress is increasing in a specific department, the assessment department can notify management of the information. Specific departments include, but are not limited to, the sales department, development department, and human resources department. For example, if stress is increasing in the sales department, the assessment department can notify management of the information. Furthermore, if stress is increasing in the development department, the assessment department can also notify management of the information. For example, the assessment department can use stress level scoring to assess the stress level of a specific department and notify management. Furthermore, the assessment department can use questionnaire results to assess the stress level of a specific department and notify management. Furthermore, the assessment department can use biometric data to assess the stress level of a specific department and notify management. For example, the assessment department can use stress level scoring to assess the stress level of the sales department and notify management. Furthermore, the assessment department can use questionnaire results to assess the stress level of the development department and notify management. Furthermore, the assessment department can use biometric data to assess the stress level of the human resources department and notify management. This allows measures to be taken early if stress levels are rising in a specific department. Some or all of the above-mentioned processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit may input the stress levels of a specific department into the generating AI, and have the generating AI analyze the stress levels.

[0074] The provision unit can create periodic reports and provide them to management. Periodic reports include, but are not limited to, monthly reports, quarterly reports, and annual reports. For example, the provision unit can create monthly reports and provide them to management. The provision unit can also create quarterly reports and provide them to management. For example, the provision unit can create monthly reports and provide them to management. The provision unit can also create quarterly reports and provide them to management. The provision unit can also create annual reports and provide them to management. For example, the provision unit can create annual reports and provide them to management. This allows management to understand the current state of the organization and take appropriate measures. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the creation of periodic reports into a generation AI, causing the generation AI to generate the reports.

[0075] The provision unit can collect and analyze data related to employees' mental health care to improve corporate governance. Examples of data related to mental health care include, but are not limited to, survey results, biometric data, and consultation history. For example, the provision unit can collect and analyze survey results to improve corporate governance. The provision unit can also collect and analyze biometric data to improve corporate governance. For example, the provision unit can collect and analyze survey results to improve corporate governance. The provision unit can also collect and analyze biometric data to improve corporate governance. The provision unit can also collect and analyze consultation history to improve corporate governance. For example, the provision unit can collect and analyze consultation history to improve corporate governance. This improves corporate governance and the performance of the entire organization. Some or all of the above-described processing in the provision unit can be performed using, or without, AI. For example, the provision unit can input data related to mental health care into a generation AI and have the generation AI analyze the data.

[0076] The reception unit can estimate the employee's emotions and adjust the timing of accepting consultations based on the estimated employee emotions. For example, if the employee is feeling stressed, the reception unit can cause the AI ​​to immediately accept the consultation. Furthermore, if the employee is relaxed, the reception unit can also cause the AI ​​to accept the consultation at an appropriate time. For example, if the employee is busy, the reception unit can cause the AI ​​to accept the consultation at an appropriate time. This allows the consultation to be accepted at an appropriate time according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit can be performed using an AI, for example, or without an AI. For example, the reception unit can input the employee's emotion data into the generation AI and have the generation AI estimate the emotion.

[0077] The reception unit can analyze the employee's past consultation history and select a reception method. Past consultation history includes, but is not limited to, text data, audio data, and video data. For example, the reception unit preferentially suggests consultation methods (chat, audio, etc.) that the employee has used in the past. The reception unit can also select the optimal reception method based on the employee's past consultation content. For example, the reception unit suggests the most effective reception method based on the employee's past consultation history. This makes it possible to provide the optimal reception method based on the employee's past consultation history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the employee's past consultation history into a generation AI, causing the generation AI to select the optimal reception method.

[0078] When receiving a consultation, the reception unit can filter the consultation based on the employee's current work situation and areas of interest. Examples of work situation include, but are not limited to, work progress, work load, and type of work. Examples of areas of interest include, but are not limited to, survey results, past work history, and self-reporting. The reception unit, for example, prioritizes receiving consultation content related to a project the employee is currently working on. The reception unit can also filter related consultation content based on the employee's areas of interest. For example, the reception unit selects appropriate consultation content taking into account the employee's work situation. This allows priority to be received for consultation content that corresponds to the employee's work situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the employee's work situation and areas of interest into a generation AI and have the generation AI perform filtering.

[0079] When accepting a consultation, the reception unit can select a reception means according to the employee's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the employee inputs the consultation content by voice, the reception unit can use voice recognition to accept the consultation. Furthermore, when the employee inputs the consultation content by text, the reception unit can also use text analysis to accept the consultation. For example, when the employee inputs the consultation content using an image, the reception unit can use image analysis to accept the consultation. This makes it possible to provide the optimal reception means according to the employee's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the employee's input data into a generation AI and have the generation AI select the optimal reception means.

[0080] The reception unit can estimate the employee's emotions and determine the priority of the consultation content to be received based on the estimated employee emotions. For example, if the employee is feeling highly stressed, the reception unit can receive that consultation content with the highest priority. Furthermore, if the employee is feeling mildly stressed, the reception unit can also receive the consultation content with a moderate priority. For example, if the employee is relaxed, the reception unit can receive the consultation content with a normal priority. This allows the priority of the consultation content to be determined according to the employee's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the employee's emotion data into the generation AI and have the generation AI estimate the emotion.

[0081] When accepting consultations, the reception unit can prioritize relevant consultations by taking into account the employee's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, if an employee works in a specific area, the reception unit can prioritize consultations related to that area. Furthermore, if an employee is on a business trip, the reception unit can prioritize consultations related to the business trip destination. For example, if an employee is working remotely, the reception unit can prioritize consultations related to their home. This allows for prioritized acceptance of relevant consultations based on the employee's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the employee's geographical location information into a generation AI, causing the generation AI to filter relevant consultations.

[0082] When receiving a consultation, the reception unit can analyze the employee's social media activity and receive related consultations. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the reception unit can receive related consultations based on concerns expressed by the employee on social media. The reception unit can also analyze the employee's social media activity and suggest related consultations. For example, the reception unit can receive related consultations based on the activity of the employee's friends on social media. This makes it possible to receive related consultations based on the employee's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the employee's social media activity data into a generation AI and have the generation AI analyze the related consultations.

[0083] The reception unit can customize the reception method by reflecting the employee's past feedback when receiving a consultation. Past feedback includes, but is not limited to, survey results, comments, and ratings. For example, the reception unit can propose an optimal reception method based on the employee's past feedback. The reception unit can also improve the reception procedure by reflecting the employee's past feedback. For example, the reception unit provides an individually customized reception method based on the employee's feedback. This makes it possible to provide an optimal reception method based on the employee's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the employee's past feedback data into a generation AI and have the generation AI customize the optimal reception method.

[0084] The analysis unit can estimate the employee's emotions and adjust the way the analysis is presented based on the estimated employee emotions. For example, if the employee is feeling stressed, the analysis unit can provide a concise and easy-to-understand analysis result. Furthermore, if the employee is relaxed, the analysis unit can provide a detailed analysis result. For example, if the employee is excited, the analysis unit can provide a visually appealing analysis result. This makes it possible to provide an analysis result that corresponds to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input employee emotion data into a generation AI and have the generation AI estimate the emotion.

[0085] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content. The importance of the consultation content includes, but is not limited to, urgency, impact, and frequency. For example, the analysis unit performs a detailed analysis for consultation content with a high level of importance. The analysis unit can also perform a concise analysis for consultation content with a low level of importance. For example, the analysis unit provides an analysis with an appropriate level of detail depending on the importance. This makes it possible to provide an analysis result according to the importance of the consultation content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the consultation content to a generation AI, causing the generation AI to adjust the level of detail of the analysis.

[0086] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the consultation content. Examples of categories of consultation content include, but are not limited to, work-related consultations, personal consultations, and health-related consultations. For example, the analysis unit can apply a specialized analysis algorithm to consultation content related to mental health. Furthermore, the analysis unit can also apply an analysis algorithm specialized for improving work efficiency to consultation content related to work. For example, the analysis unit can apply a psychological analysis algorithm to consultation content related to interpersonal relationships. This makes it possible to provide analysis results according to the category of the consultation content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the consultation content into a generation AI and have the generation AI apply different analysis algorithms.

[0087] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past consultation results. Past consultation results include, but are not limited to, text data, audio data, and video data. For example, the analysis unit adjusts the analysis algorithm based on the employee's past consultation results. The analysis unit can also improve the accuracy of the analysis by referring to the employee's past consultation results. For example, the analysis unit creates a database of the employee's past consultation results and uses it for analysis. This allows the accuracy of the analysis to be improved based on the employee's past consultation results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the employee's past consultation result data into a generation AI, which can improve the accuracy of the analysis.

[0088] The analysis unit can estimate the employee's emotions and adjust the length of the analysis based on the estimated employee emotions. For example, if the employee is in a hurry, the analysis unit can provide a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the employee is relaxed. For example, if the employee is excited, the analysis unit can provide a visually appealing analysis result. This allows the analysis result to be provided according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, or can be performed without AI. For example, the analysis unit can input employee emotion data into a generation AI and have the generation AI estimate the emotion.

[0089] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the consultation content. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, and the submission period. The analysis unit can also determine the priority of analysis based on, for example, the time of submission of the consultation content. The analysis unit can also prioritize the analysis of consultation content with high urgency. For example, the analysis unit performs analysis with appropriate priority depending on the time of submission. This allows analysis to be performed with appropriate priority based on the time of submission of the consultation content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of the consultation content to the generation AI, and have the generation AI determine the priority of analysis.

[0090] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the consultation contents. Examples of the relevance of the consultation contents include, but are not limited to, similarity of the contents, common keywords, and related topics. The analysis unit can adjust the order of analysis based on, for example, the relevance of the consultation contents. The analysis unit can also prioritize the analysis of highly related consultation contents. For example, the analysis unit performs analysis in an appropriate order depending on the relevance. This allows the analysis to be performed in an appropriate order based on the relevance of the consultation contents. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the consultation contents to a generation AI and have the generation AI adjust the order of analysis.

[0091] During analysis, the analysis unit can adjust the use of technical terminology in the analysis depending on the employee's level of expertise. Examples of technical terminology include, but are not limited to, qualifications, work experience, and educational background. For example, the analysis unit can provide analysis results that use a lot of technical terminology to employees with high levels of technical expertise. The analysis unit can also provide analysis results in easy-to-understand language to employees with low levels of technical expertise. For example, the analysis unit can use appropriate technical terminology depending on the employee's level of technical expertise. This allows the analysis results to be provided according to the employee's level of technical expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the employee's level of technical expertise data into the generation AI, causing the generation AI to adjust the use of technical terminology.

[0092] The providing unit can estimate the employee's emotions and adjust the way the advice is expressed based on the estimated employee's emotions. For example, if the employee is feeling stressed, the providing unit can provide advice in gentle words. Furthermore, if the employee is relaxed, the providing unit can also provide detailed advice. For example, if the employee is excited, the providing unit can provide visually appealing advice. This makes it possible to provide advice that matches the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, or can be performed without using AI. For example, the providing unit can input employee emotion data into the generation AI and have the generation AI estimate the emotion.

[0093] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the consultation content. The importance of the consultation content includes, for example, urgency, impact, frequency, etc., but is not limited to these examples. For example, the providing unit provides detailed advice for consultation content with high importance. The providing unit can also provide concise advice for consultation content with low importance. For example, the providing unit provides advice with an appropriate level of detail depending on the importance. This makes it possible to provide advice according to the importance of the consultation content. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs importance data of the consultation content to a generating AI, and causes the generating AI to adjust the level of detail of the advice.

[0094] When providing advice, the providing unit can apply different advice algorithms depending on the category of the consultation content. Examples of categories of consultation content include, but are not limited to, work-related consultations, personal consultations, and health-related consultations. For example, the providing unit can apply a specialized advice algorithm to consultation content related to mental health. Furthermore, the providing unit can also apply an advice algorithm specialized for improving work efficiency to consultation content related to work. For example, the providing unit can apply a psychological advice algorithm to consultation content related to interpersonal relationships. This makes it possible to provide advice according to the category of the consultation content. Some or all of the above-described processing in the providing unit may be performed using, or without using, AI. For example, the providing unit can input category data of the consultation content into a generation AI and cause the generation AI to apply a different advice algorithm.

[0095] When providing advice, the providing unit can improve the accuracy of the advice by referring to the employee's past advice results. Past advice results include, but are not limited to, for example, text data, audio data, and video data. For example, the providing unit adjusts the advice algorithm based on the employee's past advice results. The providing unit can also improve the accuracy of the advice by referring to the employee's past advice results. For example, the providing unit creates a database of the employee's past advice results and uses them for advice. This allows the accuracy of advice to be improved based on the employee's past advice results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the employee's past advice result data into a generation AI, which can improve the accuracy of advice.

[0096] The providing unit can estimate the employee's emotions and adjust the length of the advice based on the estimated employee emotions. For example, if the employee is in a hurry, the providing unit can provide short, to-the-point advice. The providing unit can also provide detailed advice if the employee is relaxed. For example, if the employee is excited, the providing unit can provide visually appealing advice. This allows the provision of advice tailored to the employee's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the employee's emotion data into the generation AI and have the generation AI estimate the emotion.

[0097] When providing advice, the providing unit can determine the priority of advice based on the time of submission of the consultation content. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, and the submission period. The providing unit can also determine the priority of advice based on, for example, the time when the consultation content was submitted. The providing unit can also prioritize advice on consultation content with high urgency. For example, the providing unit provides advice with an appropriate priority depending on the time of submission. This makes it possible to provide advice with an appropriate priority based on the time when the consultation content was submitted. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the time when the consultation content was submitted to a generation AI, and have the generation AI determine the priority of advice.

[0098] When providing advice, the providing unit can adjust the order of advice based on the relevance of the consultation content. The relevance of the consultation content includes, but is not limited to, for example, similarity of the content, common keywords, related topics, etc. The providing unit can adjust the order of advice based on, for example, the relevance of the consultation content. The providing unit can also prioritize advice on consultation content with high relevance. For example, the providing unit provides advice in an appropriate order according to the relevance. This makes it possible to provide advice in an appropriate order based on the relevance of the consultation content. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of the consultation content to a generating AI and have the generating AI adjust the order of advice.

[0099] When providing advice, the providing unit can adjust the use of technical terminology in the advice depending on the employee's level of expertise. Examples of expertise levels include, but are not limited to, qualifications, work experience, and educational background. For example, the providing unit can provide advice that uses a lot of technical terminology to employees with high levels of expertise. The providing unit can also provide advice in easy-to-understand language to employees with low levels of expertise. For example, the providing unit uses appropriate technical terminology depending on the employee's level of expertise. This makes it possible to provide advice that is appropriate for the employee's level of expertise. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input employee expertise level data into a generating AI, causing the generating AI to adjust the use of technical terminology.

[0100] The grasping unit can estimate the employee's emotions and adjust the display method of the stress state based on the estimated employee emotions. For example, if the employee is feeling stressed, the grasping unit provides a visually easy-to-understand display method. Furthermore, if the employee is relaxed, the grasping unit can also display a detailed stress state. For example, if the employee is excited, the grasping unit provides a visually appealing display method. This makes it possible to provide a display method of the stress state according to the employee's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the grasping unit can be performed using AI, for example, or without AI. For example, the grasping unit can input the employee's emotion data into the generation AI and have the generation AI estimate the emotion.

[0101] When assessing a stress state, the assessment unit can predict a current stress state by referring to past stress data. Past stress data includes, but is not limited to, for example, questionnaire results, biometric data, and consultation history. For example, the assessment unit predicts a current stress state based on the employee's past stress data. The assessment unit can also predict stress fluctuations by referring to the past stress data. For example, the assessment unit analyzes the employee's past stress data to assess the current stress state. This makes it possible to predict a current stress state based on the past stress data. Some or all of the above-described processing in the assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assessment unit can input past stress data into a generation AI and have the generation AI predict a current stress state.

[0102] When grasping the stress state, the grasping unit can apply different stress analysis methods to different categories of consultation content. Examples of categories of consultation content include, but are not limited to, work-related consultations, personal consultations, and health-related consultations. For example, the grasping unit can apply a specialized stress analysis method to consultation content related to mental health. Furthermore, the grasping unit can also apply a stress analysis method specialized for work efficiency to consultation content related to work. For example, the grasping unit can apply a psychological stress analysis method to consultation content related to interpersonal relationships. This allows stress analysis to be performed according to the category of consultation content. Some or all of the above-described processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit can input category data of the consultation content into a generation AI and have the generation AI apply different stress analysis methods.

[0103] When assessing a stress state, the assessment unit can analyze stress by taking into account the employee's attribute information. Attribute information includes, but is not limited to, for example, age, gender, and job position. The assessment unit analyzes stress by taking into account attribute information such as the employee's age and gender. The assessment unit can also analyze stress based on attribute information such as the employee's job type and position. For example, the assessment unit analyzes stress by referring to attribute information such as the employee's working hours and working style. This makes it possible to analyze stress based on the employee's attribute information. Some or all of the above-mentioned processing in the assessment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assessment unit can input the employee's attribute information data into a generation AI and have the generation AI analyze stress.

[0104] The assessment unit can estimate the employee's emotions and adjust the importance of the stress situation based on the estimated employee emotions. For example, if the employee is feeling highly stressed, the assessment unit can display the stress situation as the highest priority. Furthermore, if the employee is feeling mildly stressed, the assessment unit can also display the stress situation with a moderate importance. For example, if the employee is relaxed, the assessment unit can display the stress situation with a normal importance. This makes it possible to provide the importance of the stress situation according to the employee's emotions. The estimation of emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the assessment unit can be performed using, for example, AI, or without AI. For example, the assessment unit can input the employee's emotion data into the generation AI and have the generation AI estimate the emotion.

[0105] When grasping the stress state, the grasping unit can analyze changes in stress based on the time when the consultation content was submitted. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, and the submission period. The grasping unit can analyze changes in stress based on, for example, the time when the consultation content was submitted. The grasping unit can also prioritize the analysis of consultation content with high urgency. For example, the grasping unit analyzes changes in stress in an appropriate order depending on the time of submission. This makes it possible to analyze changes in stress based on the time when the consultation content was submitted. Some or all of the above-mentioned processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit can input data on the time when the consultation content was submitted to a generation AI and have the generation AI analyze changes in stress.

[0106] When grasping the stress state, the grasping unit can analyze stress by referring to related market data. Examples of related market data include, but are not limited to, market research data, industry reports, and competitive analysis data. For example, the grasping unit analyzes the employee's stress state based on the related market data. The grasping unit can also predict changes in stress by referring to market fluctuations. For example, the grasping unit analyzes market data to grasp the employee's stress state. This makes it possible to analyze stress based on the related market data. Some or all of the above-mentioned processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit can input the related market data to a generation AI and have the generation AI analyze stress.

[0107] When grasping the stress state, the grasping unit can analyze stress taking into account technological maturity. Technological maturity includes, for example, the prevalence of technology, the evolutionary stage of technology, and the adoption rate of technology, but is not limited to these examples. The grasping unit, for example, analyzes the employee's stress state based on technological maturity. The grasping unit can also predict changes in stress by referring to technological advances. For example, the grasping unit grasps the employee's stress state taking into account technological maturity. This makes it possible to analyze stress based on technological maturity. Some or all of the above-mentioned processing in the grasping unit may be performed, for example, using AI, or may be performed without using AI. For example, the grasping unit can input technological maturity data to a generation AI and have the generation AI analyze stress. === Hard Collateral 1-1 === Each of the multiple elements including the reception unit, analysis unit, provision unit, and assessment unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive employee concerns using the reception device 38 of the smart device 14. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and analyze the consultation content received from the reception unit. For example, the provision unit can provide advice using the output device 40 of the smart device 14. For example, the assessment unit can be realized by the specific processing unit 290 of the data processing device 12 and assess the stress situation of the entire organization based on the data analyzed by the analysis unit. === Hard Collateral 1-2 === Each of the multiple elements including the reception unit, analysis unit, provision unit, and assessment unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive employee concerns using the microphone 238 of the smart glasses 214. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and analyze the consultation content received from the reception unit. For example, the provision unit can provide advice using the speaker 240 of the smart glasses 214. For example, the assessment unit can be realized by the specific processing unit 290 of the data processing device 12 and assess the stress situation of the entire organization based on the data analyzed by the analysis unit. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and understanding unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive employee concerns using the microphone 238 of the headset-type terminal 314. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and analyzes the consultation content received from the reception unit. For example, the provision unit can provide advice using the speaker 240 of the headset-type terminal 314. For example, the understanding unit can be realized by the specific processing unit 290 of the data processing device 12 and understands the stress situation of the entire organization based on the data analyzed by the analysis unit. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, provision unit, and understanding unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive the worries of employees using the microphone 238 of the robot 414. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and analyzes the consultation content received from the reception unit. For example, the provision unit can provide advice using the speaker 240 of the robot 414. For example, the understanding unit can be realized by the specific processing unit 290 of the data processing device 12 and understands the stress situation of the entire organization based on the data analyzed by the analysis unit.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] When accepting an employee's consultation, the reception unit can refer to the employee's past consultation history and suggest the optimal reception method. For example, if the employee has preferred text chat in the past, the reception unit will preferentially suggest text chat. Also, if the employee has preferred voice chat, the reception unit can suggest voice chat. Furthermore, if the employee has preferred video chat, the reception unit can suggest video chat. This makes it possible to provide the optimal reception method based on the employee's past consultation history.

[0110] When analyzing the content of an employee's consultation, the analysis unit can adjust the level of detail of the analysis according to the employee's level of expertise. For example, it can provide detailed analysis results that use a lot of technical terminology to employees with high levels of expertise. It can also provide concise, easy-to-understand analysis results to employees with low levels of expertise. Furthermore, it can select an appropriate analysis algorithm according to the employee's level of expertise. This makes it possible to provide analysis results that are appropriate for the employee's level of expertise.

[0111] The providing unit can estimate the employee's emotions and adjust the way the advice is expressed based on the estimated employee's emotions. For example, if the employee is feeling stressed, the advice can be provided in gentle words. If the employee is relaxed, detailed advice can be provided. Furthermore, if the employee is excited, visually appealing advice can be provided. In this way, advice can be provided according to the employee's emotions.

[0112] The grasping unit can estimate the employee's emotions and adjust the display method of the stress state based on the estimated employee's emotions. For example, if the employee is feeling stressed, a visually easy-to-understand display method can be provided. Also, if the employee is relaxed, a detailed stress state can be displayed. Furthermore, if the employee is excited, a visually attractive display method can be provided. In this way, a display method of the stress state according to the employee's emotions can be provided.

[0113] The reception unit can prioritize receiving highly relevant consultations by taking into account the employee's geographical location information. For example, if an employee works in a specific area, it can prioritize receiving consultations related to that area. Also, if an employee is on a business trip, it can prioritize receiving consultations related to the business trip destination. Furthermore, if an employee is working remotely, it can prioritize receiving consultations related to their home. This makes it possible to prioritize receiving highly relevant consultations based on the employee's geographical location information.

[0114] The analysis unit can improve the accuracy of the analysis by referring to the employee's past consultation results. For example, the analysis algorithm can be adjusted based on the employee's past consultation results. The analysis unit can also improve the accuracy of the analysis by referring to the employee's past consultation results. Furthermore, the employee's past consultation results can be compiled into a database and used for analysis. This makes it possible to improve the accuracy of the analysis based on the employee's past consultation results.

[0115] When providing advice, the providing unit can determine the priority of advice based on the time when the consultation content was submitted. For example, the priority of advice can be determined based on the time when the consultation content was submitted. Also, it is possible to give priority to advice on consultation content with high urgency. Furthermore, it is possible to provide advice with an appropriate priority depending on the time of submission. This makes it possible to provide advice with an appropriate priority based on the time when the consultation content was submitted.

[0116] When grasping the stress state, the grasping unit can predict the current stress state by referring to past stress data. For example, the current stress state is predicted based on the employee's past stress data. The grasping unit can also predict stress fluctuations by referring to the past stress data. Furthermore, the grasping unit can analyze the employee's past stress data to grasp the current stress state. This makes it possible to predict the current stress state based on the past stress data.

[0117] The providing unit can estimate the employee's emotions and adjust the length of the advice based on the estimated employee's emotions. For example, if the employee is in a hurry, short and to the point advice can be provided. If the employee is relaxed, detailed advice can be provided. Furthermore, if the employee is excited, visually appealing advice can be provided. In this way, advice can be provided according to the employee's emotions.

[0118] When grasping the stress state, the grasping unit can analyze stress by taking into account the employee's attribute information. For example, stress is analyzed by taking into account attribute information such as the employee's age and gender. Stress can also be analyzed based on attribute information such as the employee's job type and position. Furthermore, stress can also be analyzed by referring to attribute information such as the employee's working hours and working style. In this way, stress can be analyzed based on the employee's attribute information.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The reception unit receives the employee's concerns. Employee concerns include, but are not limited to, work-related concerns, personal concerns, and health-related concerns. The reception unit receives the employee's concerns, for example, in chat format. The reception unit can also receive concerns using multiple input methods, such as voice input and text input. For example, when an employee inputs their concerns by voice, the reception unit uses voice recognition technology to receive the concerns. Step 2: The analysis unit analyzes the consultation content received by the reception unit. The analysis is performed by, for example, text analysis, sentiment analysis, statistical analysis, or the like, but is not limited to these examples. For example, the analysis unit extracts keywords from the consultation content using text analysis and provides appropriate advice. The analysis unit can also estimate the employee's emotions using sentiment analysis and provide advice based on the results. Step 3: The providing unit provides advice based on the results of the analysis by the analyzing unit. The advice may be provided in the form of, for example, written advice, audio advice, video advice, or the like, but is not limited to these examples. For example, the providing unit provides written advice. The providing unit may also provide audio advice. The providing unit may also provide video advice. Step 4: The assessment unit assesses the stress state of the entire organization based on the data analyzed by the analysis unit. The stress state is evaluated by, for example, but not limited to, methods such as stress level scoring, questionnaire results, and biometric data. For example, the assessment unit assesses the stress state of the entire organization using stress level scoring. The assessment unit can also assess the stress state using questionnaire results. The assessment unit can also assess the stress state using biometric data. Step 5: The information provided by the assessment department is provided to managers and executives, who can then understand the stress situation across the organization and take appropriate measures.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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 AI 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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 AI 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0168] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0169] 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.

[0170] 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.

[0171] 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 AI 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.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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, in order to avoid confusion and to 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk that accepts employee concerns, an analysis unit that analyzes the consultation content received by the reception unit; a providing unit that provides advice based on the results of the analysis by the analyzing unit; a grasping unit that grasps the stress state of the entire organization based on the data analyzed by the analyzing unit; a providing unit that provides the information grasped by the grasping unit to managers and executives. A system characterized by:

2. The reception unit Accepting employee concerns via chat 2. The system of claim 1.

3. The analysis unit Analyze employee consultations and provide appropriate advice 2. The system of claim 1.

4. The grasping unit Communicate to management if stress levels are increasing in a particular department 2. The system of claim 1.

5. The providing unit Prepare regular reports and provide them to management 2. The system of claim 1.

6. The providing unit Collecting and analyzing employee mental health care data to improve corporate governance 2. The system of claim 1.

7. The reception unit Estimate employee emotions and adjust the timing of consultations based on the estimated employee emotions 2. The system of claim 1.

8. The reception unit Analyze employees' past consultation history and select the reception method 2. The system of claim 1.

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