System

Abstract

An object of a system according to an embodiment is to provide an environment in which an employee can anonymously consult about psychological distress or stress.SOLUTION: A system according to an embodiment includes an anonymous consultation platform, a generation AI, and a feedback unit. The anonymous consultation platform allows employees to anonymously consult about psychological distress or stress. The generation AI analyzes the consultation content received by the anonymous consultation platform. The feedback unit anonymizes the consultation contents analyzed by the generated AI, and feeds back the anonymized consultation contents to the personnel department as statistic data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately provide employees with a means to anonymously seek advice about psychological concerns and stress, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an environment in which employees can anonymously seek advice about psychological worries and stress. [Means for solving the problem]

[0006] The system according to the embodiment includes an anonymous consultation platform, a generation AI, and a feedback unit. The anonymous consultation platform allows employees to anonymously seek advice about psychological worries and stress. The generation AI analyzes the consultation content received by the anonymous consultation platform. The feedback unit anonymizes the consultation content analyzed by the generation AI and provides feedback to the human resources department as statistical data. [Effects of the Invention]

[0007] The system according to the embodiment can provide an environment in which employees can anonymously seek advice about psychological worries and stress. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The psychological consultation system according to an embodiment of the present invention provides a platform on which employees can anonymously seek advice about psychological worries and stress, and the information can be appropriately utilized by the human resources department. As a result, the psychological consultation system allows employees to consult about psychological worries and stress in a safe manner, and the human resources department can grasp the psychological health status of the entire workplace and take appropriate measures.

[0029] A psychological consultation system according to an embodiment includes an anonymous consultation platform, a generation AI, and a feedback unit. The anonymous consultation platform allows employees to anonymously seek advice about psychological concerns and stress. For example, employees can access the anonymous consultation platform via a web browser or a mobile app and enter their consultation details. The anonymous consultation platform also encrypts and stores the consultation details to protect employees' privacy. The generation AI analyzes the consultation details received by the anonymous consultation platform. For example, the generation AI may analyze the consultation details using natural language processing technology and provide appropriate advice and support. The generation AI may also provide more personalized advice by taking into account employees' past consultation history and behavioral patterns. The feedback unit anonymizes the consultation details analyzed by the generation AI and provides feedback to the human resources department as statistical data. For example, if stress levels in a specific department are high, the feedback unit may propose measures to reduce stress for that department. The feedback unit may also use statistical data to understand the psychological health status of the entire workplace and take necessary measures. As a result, the psychological consultation system of the embodiment allows employees to anonymously consult about psychological worries and stress, and enables the human resources department to grasp the psychological health state of the entire workplace and take appropriate measures.

[0030] Generative AI can provide more personalized advice by taking into account the client's past consultation history and behavioral patterns. For example, generative AI can retrieve an employee's past consultation history from a database and analyze it in comparison with the current consultation content. For example, if an employee who previously consulted about stress management comes to the same consultation again, new advice can be provided taking into account the effectiveness of the previous advice. Generative AI can also analyze an employee's behavioral patterns to provide personalized advice. For example, if an employee is prone to feeling stressed at certain times of the day, it can suggest relaxation techniques for those times. This allows for more personalized advice to be provided to employees.

[0031] The generation AI can propose specific work improvement measures by taking into account the job content and work environment of the person seeking advice. For example, the generation AI retrieves an employee's job content from a database and analyzes it in comparison with the content of the consultation. For example, if a sales employee is feeling stressed, the generation AI can propose measures to improve the efficiency of sales activities and customer service. The generation AI can also analyze an employee's work environment and propose specific work improvement measures. For example, it can make a proposal to improve work efficiency by changing the office layout. This makes it possible to propose specific work improvement measures tailored to the employee's job content and work environment.

[0032] Generative AI can be linked to employee schedules and task management tools to propose specific action plans. For example, generative AI can analyze employee schedules and propose specific action plans based on advice. For example, it can incorporate relaxation time into daily schedules to manage stress. Generative AI can also be linked to task management tools to make suggestions for optimizing employee tasks. For example, it can review task priorities and provide advice for working more efficiently. Generative AI can also be linked to employee schedules and task management tools to propose specific action plans. For example, it can conduct weekly task reviews and adjust action plans based on progress. This allows generative AI to propose specific action plans by linking with employee schedules and task management tools.

[0033] Generative AI can develop algorithms that completely remove personally identifiable information while preserving the context of the consultation content. Generative AI can, for example, develop algorithms that completely remove personally identifiable information while preserving the context of the consultation content. For example, it can automatically mask personal information such as names and addresses. Generative AI can also develop algorithms that completely remove personally identifiable information while preserving the context of the consultation content. For example, it can detect information that indicates a specific individual within the consultation content and anonymize it. Generative AI can also develop algorithms that completely remove personally identifiable information while preserving the context of the consultation content. For example, it can detect information that indicates a specific place or organization within the consultation content and anonymize it. This makes it possible to completely remove personally identifiable information while preserving the context of the consultation content.

[0034] The generation AI can analyze trends and patterns of consultation content based on statistical data and build a predictive model. The generation AI, for example, analyzes trends and patterns of consultation content based on statistical data and builds a predictive model. For example, it predicts a tendency for stress to increase at specific times. The generation AI can also analyze trends and patterns of consultation content based on statistical data and build a predictive model. For example, it can predict periods when stress will increase in a specific department and take measures to address the situation. The generation AI can also analyze trends and patterns of consultation content based on statistical data and build a predictive model. For example, it can predict increases in stress for specific work content and propose work improvement measures. This makes it possible to analyze trends and patterns of consultation content and build a predictive model.

[0035] Generative AI can compare statistical data with data from other companies or industries to perform benchmark analysis. Generative AI can, for example, compare statistical data with data from other companies or industries to perform benchmark analysis. For example, comparing with the stress levels of companies in the same industry. Generative AI can also compare statistical data with data from other companies or industries to perform benchmark analysis. For example, incorporating best practices for stress management from different industries. Generative AI can also compare statistical data with data from other companies or industries to perform benchmark analysis. For example, comparing with the stress levels of the entire industry to evaluate the effectiveness of your company's stress management. This allows you to compare with data from other companies or industries to perform benchmark analysis.

[0036] Generative AI can display statistical data in a visual dashboard to enable the HR department to intuitively understand it. For example, generative AI can display statistical data in a visual dashboard to enable the HR department to intuitively understand it. For example, it can visualize the data using graphs and charts. Generative AI can also display statistical data in a visual dashboard to enable the HR department to intuitively understand it. For example, it can provide an interactive dashboard to allow the HR department to check the details of the data. Generative AI can also display statistical data in a visual dashboard to enable the HR department to intuitively understand it. For example, it can visually display trends and patterns in the data to support quick decision-making. This makes it possible to display statistical data in a visual dashboard to enable the HR department to intuitively understand it.

[0037] Generative AI can identify an employee's individual stressors and suggest counseling and resources that correspond to them. For example, generative AI can identify an employee's individual stressors and suggest counseling that corresponds to them. For example, it can suggest stress management counseling for an employee who is feeling stressed due to work pressure. Generative AI can also identify an employee's individual stressors and suggest resources that correspond to them. For example, it can introduce relaxation apps or online courses for stress management. Generative AI can also identify an employee's individual stressors and suggest counseling and resources that correspond to them. For example, it can suggest collaboration with a mental health professional. This makes it possible to suggest counseling and resources that correspond to an employee's individual stressors.

[0038] The generative AI can reflect employee feedback in real time and continuously improve the support content. For example, the generative AI collects employee feedback in real time and continuously improves the support content. For example, it adjusts the next piece of advice based on feedback on the advice provided. The generative AI also reflects employee feedback in real time and continuously improves the support content. For example, it analyzes the content of the feedback and improves the quality of the advice. The generative AI also reflects employee feedback in real time and continuously improves the support content. For example, it can update the generative AI's algorithm based on the feedback and provide more appropriate advice. This allows the generative AI to reflect employee feedback in real time and continuously improve the support content.

[0039] Generative AI can extend psychological support to employees' families and friends, building support networks outside of the workplace. For example, generative AI can extend psychological support to employees' families, building support networks outside of the workplace. For example, it can make stress management resources available to families. Generative AI can also extend psychological support to employees' friends, building support networks outside of the workplace. For example, it can make relaxation apps available to friends. Generative AI can also extend psychological support to employees' family and friends, building support networks outside of the workplace. For example, it can make family and friends connect with mental health professionals. This allows psychological support to be extended to employees' family and friends, building support networks outside of the workplace.

[0040] Generative AI can link psychological support with employees' health and fitness data to provide comprehensive health management. Generative AI can, for example, link with employees' health data to provide comprehensive health management. For example, it can provide health management advice based on the results of health checkups. Generative AI can also link with employees' fitness data to provide comprehensive health management. For example, it can manage exercise volume and calorie consumption based on fitness tracker data. Generative AI can also link with employees' health and fitness data to provide comprehensive health management. For example, it can provide health management advice based on the results of health checkups and fitness tracker data. This allows for comprehensive health management by linking with employees' health and fitness data.

[0041] When anonymizing the consultation content, the generation AI can protect the data using the latest encryption technology. For example, when anonymizing the consultation content, the generation AI can protect the data using the latest encryption technology. For example, it can encrypt the data using AES-256 encryption. Furthermore, when anonymizing the consultation content, the generation AI can protect the data using the latest encryption technology. For example, it can encrypt the data using RSA encryption. Furthermore, when anonymizing the consultation content, the generation AI can protect the data using the latest encryption technology. For example, it can encrypt the data using quantum cryptography. This allows the data to be protected using the latest encryption technology.

[0042] Generative AI can incorporate privacy protection guidelines into advice and support, allowing employees to use the service with peace of mind. Generative AI can, for example, incorporate privacy protection guidelines into advice and support, allowing employees to use the service with peace of mind. For example, it can clearly state guidelines regarding the handling of personal information. Generative AI can also incorporate privacy protection guidelines into advice and support, allowing employees to use the service with peace of mind. For example, it can clearly state data handling policies and access controls. Generative AI can also incorporate privacy protection guidelines into advice and support, allowing employees to use the service with peace of mind. For example, it can clearly state data deletion policies, allowing employees to use the service with peace of mind. This makes it possible to incorporate privacy protection guidelines so that employees can use the service with peace of mind.

[0043] The emotion estimation function can add a filtering function to protect privacy when analyzing an employee's emotional state. For example, the emotion estimation function adds a filtering function to protect privacy when analyzing an employee's emotional state. For example, it automatically masks information that can identify individuals. The emotion estimation function also adds a filtering function to protect privacy when analyzing an employee's emotional state. For example, it develops an algorithm to anonymize emotional data. The emotion estimation function also adds a filtering function to protect privacy when analyzing an employee's emotional state. For example, it can filter emotional data and remove information that can identify individuals. This makes it possible to add a filtering function to protect privacy when analyzing an employee's emotional state.

[0044] When anonymizing the consultation content, the generation AI can add customization functions to comply with different privacy protection regulations. For example, when anonymizing the consultation content, the generation AI can add customization functions to comply with different privacy protection regulations. For example, it can process data in compliance with GDPR or CCPA. Furthermore, when anonymizing the consultation content, the generation AI can add customization functions to comply with different privacy protection regulations. For example, it can process data in compliance with HIPAA. Furthermore, when anonymizing the consultation content, the generation AI can add customization functions to comply with different privacy protection regulations. For example, it can process data in compliance with the privacy protection regulations of each country. This makes it possible to add customization functions to comply with different privacy protection regulations.

[0045] The generative AI can add educational content for privacy protection to advice and support to improve employees' privacy awareness. For example, the generative AI can add educational content for privacy protection to advice and support to improve employees' privacy awareness. For example, the generative AI can provide online courses on privacy protection. The generative AI can also add educational content for privacy protection to advice and support to improve employees' privacy awareness. For example, the generative AI can provide video tutorials to educate employees on the importance of privacy protection. The generative AI can also add educational content for privacy protection to advice and support to improve employees' privacy awareness. For example, the generative AI can provide a guidebook to educate employees on specific methods for privacy protection. This makes it possible to add educational content to improve employees' privacy awareness.

[0046] The emotion estimation function can enhance anonymization technology for privacy protection when analyzing the emotional state of an employee. For example, the emotion estimation function can enhance anonymization technology for privacy protection when analyzing the emotional state of an employee. For example, an algorithm for anonymizing emotional data can be developed. Furthermore, the emotion estimation function can enhance anonymization technology for privacy protection when analyzing the emotional state of an employee. For example, a data masking technique can be used to anonymize the emotional data. Furthermore, the emotion estimation function can enhance anonymization technology for privacy protection when analyzing the emotional state of an employee. For example, a pseudo-anonymization technique can be used to anonymize the emotional data. This can enhance anonymization technology for privacy protection when analyzing the emotional state of an employee.

[0047] When learning feedback from employees, the generation AI can evaluate the quality of the feedback and prioritize highly reliable feedback. For example, when learning feedback from employees, the generation AI can evaluate the quality of the feedback and prioritize highly reliable feedback. For example, it can analyze the content of the feedback and prioritize feedback that includes specific and useful information. Furthermore, when learning feedback from employees, the generation AI can evaluate the quality of the feedback and prioritize highly reliable feedback. For example, it can evaluate the consistency of the feedback and the reliability of the person who provided it. Furthermore, when learning feedback from employees, the generation AI can evaluate the quality of the feedback and prioritize highly reliable feedback. For example, it can evaluate the accuracy and relevance of the feedback. This allows it to prioritize highly reliable feedback.

[0048] The generation AI can quantitatively evaluate the effectiveness of advice and support, and prioritize providing those that are highly effective. The generation AI can, for example, quantitatively evaluate the effectiveness of advice and support, and prioritize providing those that are highly effective. For example, it can measure changes in stress levels after the advice is implemented. The generation AI can also quantitatively evaluate the effectiveness of advice and support, and prioritize providing those that are highly effective. For example, it can measure improvements in performance after the advice is implemented. The generation AI can also quantitatively evaluate the effectiveness of advice and support, and prioritize providing those that are highly effective. For example, it can measure employee satisfaction after the advice is implemented. This makes it possible to prioritize providing advice and support that are highly effective.

[0049] When learning from employee feedback, generative AI can integrate data from different industries and job types to make improvements from a more diverse perspective. For example, when learning from employee feedback, generative AI can integrate data from different industries and job types to make improvements from a more diverse perspective. For example, it can incorporate best practices from different industries. When learning from employee feedback, generative AI can integrate data from different industries and job types to make improvements from a more diverse perspective. For example, it can analyze data from different industries and identify common issues. When learning from employee feedback, generative AI can integrate data from different industries and job types to make improvements from a more diverse perspective. For example, it can analyze data from different job types to identify issues specific to each job type. This allows generative AI to integrate data from different industries and job types to make improvements from a more diverse perspective.

[0050] The generative AI can link the effects of advice and support with employee performance data to make a comprehensive evaluation. The generative AI, for example, links the effects of advice and support with employee performance data to make a comprehensive evaluation. For example, it can measure the improvement in performance after the advice is implemented. The generative AI can also link the effects of advice and support with employee performance data to make a comprehensive evaluation. For example, it can measure the improvement in productivity after the advice is implemented. The generative AI can also link the effects of advice and support with employee performance data to make a comprehensive evaluation. For example, it can measure the employee's satisfaction after the advice is implemented. This makes it possible to link the effects of advice and support with employee performance data to make a comprehensive evaluation.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The psychological consultation system can be linked to employees' health and fitness data to provide comprehensive health management. For example, it can provide health management advice based on the results of employees' health checkups. It can also manage exercise volume and calorie consumption based on fitness tracker data. It can also provide health management advice based on the results of health checkups and fitness tracker data. This allows for comprehensive health management by linking with employees' health and fitness data.

[0053] The psychological consultation system can extend psychological support to employees' family and friends, building a support network outside the workplace. For example, it can provide family members with access to stress management resources, friends with access to relaxation apps, and even connect family and friends with mental health professionals. This allows employees to extend psychological support to their family and friends, building a support network outside the workplace.

[0054] The psychological consultation system can collect employee feedback in real time and continuously improve the support content. For example, the next advice can be adjusted based on feedback on the advice provided. The content of the feedback can also be analyzed to improve the quality of the advice. Furthermore, the generative AI algorithm can be updated based on the feedback to provide more appropriate advice. This allows employee feedback to be reflected in real time and the support content to be continuously improved.

[0055] The psychological consultation system can be linked to employees' schedules and task management tools to propose specific action plans. For example, incorporating relaxation time into daily schedules to manage stress. It can also be linked to task management tools to make suggestions for optimizing employees' tasks. Furthermore, it can conduct weekly task reviews and adjust action plans according to progress. This allows the system to propose specific action plans by linking with employees' schedules and task management tools.

[0056] The psychological consultation system can compare statistical data with data from other companies or industries to conduct benchmark analysis. For example, it can compare stress levels with those of companies in the same industry. It can also incorporate best stress management practices from other industries. Furthermore, it can compare stress levels with the stress level of the entire industry to evaluate the effectiveness of its own stress management. This allows it to compare with data from other companies or industries and conduct benchmark analysis.

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

[0058] Step 1: The anonymous consultation platform is a platform where employees can anonymously seek advice about psychological worries and stress. Employees can access the anonymous consultation platform via a web browser or mobile app and enter their consultation details. Furthermore, the anonymous consultation platform encrypts and stores the consultation details to protect employees' privacy. Step 2: The generation AI analyzes the consultation content received by the anonymous consultation platform. The generation AI uses natural language processing technology to analyze the consultation content and provide appropriate advice and support. The generation AI can also provide more personalized advice by taking into account the employee's past consultation history and behavioral patterns. Step 3: The feedback department anonymizes the consultation content analyzed by the generation AI and provides it as statistical data to the human resources department. If stress is increasing in a specific department, the feedback department will propose measures to reduce stress to that department. The feedback department can also use the statistical data to understand the psychological health of the entire workplace and take necessary measures.

[0059] (Example 2) The psychological consultation system according to an embodiment of the present invention provides a platform on which employees can anonymously seek advice about psychological worries and stress, and the information can be appropriately utilized by the human resources department. As a result, the psychological consultation system allows employees to consult about psychological worries and stress in a safe manner, and the human resources department can grasp the psychological health status of the entire workplace and take appropriate measures.

[0060] A psychological consultation system according to an embodiment includes an anonymous consultation platform, a generation AI, and a feedback unit. The anonymous consultation platform allows employees to anonymously seek advice about psychological concerns and stress. For example, employees can access the anonymous consultation platform via a web browser or a mobile app and enter their consultation details. The anonymous consultation platform also encrypts and stores the consultation details to protect employees' privacy. The generation AI analyzes the consultation details received by the anonymous consultation platform. For example, the generation AI may analyze the consultation details using natural language processing technology and provide appropriate advice and support. The generation AI may also provide more personalized advice by taking into account employees' past consultation history and behavioral patterns. The feedback unit anonymizes the consultation details analyzed by the generation AI and provides feedback to the human resources department as statistical data. For example, if stress levels in a specific department are high, the feedback unit may propose measures to reduce stress for that department. The feedback unit may also use statistical data to understand the psychological health status of the entire workplace and take necessary measures. As a result, the psychological consultation system of the embodiment allows employees to anonymously consult about psychological worries and stress, and enables the human resources department to grasp the psychological health state of the entire workplace and take appropriate measures.

[0061] Generative AI can provide more personalized advice by taking into account the client's past consultation history and behavioral patterns. For example, generative AI can retrieve an employee's past consultation history from a database and analyze it in comparison with the current consultation content. For example, if an employee who previously consulted about stress management comes to the same consultation again, new advice can be provided taking into account the effectiveness of the previous advice. Generative AI can also analyze an employee's behavioral patterns to provide personalized advice. For example, if an employee is prone to feeling stressed at certain times of the day, it can suggest relaxation techniques for those times. This allows for more personalized advice to be provided to employees.

[0062] The generation AI can propose specific work improvement measures by taking into account the job content and work environment of the person seeking advice. For example, the generation AI retrieves an employee's job content from a database and analyzes it in comparison with the content of the consultation. For example, if a sales employee is feeling stressed, the generation AI can propose measures to improve the efficiency of sales activities and customer service. The generation AI can also analyze an employee's work environment and propose specific work improvement measures. For example, it can make a proposal to improve work efficiency by changing the office layout. This makes it possible to propose specific work improvement measures tailored to the employee's job content and work environment.

[0063] The generative AI can use its emotion estimation function to analyze the emotional state of the client in real time and provide advice based on that emotion. For example, if the client is feeling highly stressed, the generative AI can suggest relaxation methods and stress management techniques. The generative AI also uses its emotion estimation function to analyze the client's emotional state. For example, it can use voice analysis technology to estimate the client's emotional state from the tone and rhythm of the client's voice. It can also use facial expression recognition technology to estimate the client's emotional state from their facial expressions. This makes it possible to provide advice based on the client's emotional state in real time.

[0064] Generative AI can also analyze the client's non-verbal information using audio and video inputs, allowing it to provide more comprehensive advice. For example, generative AI can analyze audio input and infer the client's emotional state from the tone and rhythm of their voice. For example, if the client's voice is trembling, it can determine that the client is feeling anxious or tense and suggest relaxation methods. Generative AI can also analyze video input and infer the client's emotional state from their facial expressions and gestures. For example, if the client's facial expression is stiff, it can determine that the client is feeling stressed and suggest stress management techniques. Generative AI can also analyze non-verbal information and provide more comprehensive advice. For example, it can comprehensively analyze the client's tone of voice, facial expressions, and gestures to provide appropriate advice. This allows it to analyze non-verbal information and provide more comprehensive advice.

[0065] Generative AI can be linked to employee schedules and task management tools to propose specific action plans. For example, generative AI can analyze employee schedules and propose specific action plans based on advice. For example, it can incorporate relaxation time into daily schedules to manage stress. Generative AI can also be linked to task management tools to make suggestions for optimizing employee tasks. For example, it can review task priorities and provide advice for working more efficiently. Generative AI can also be linked to employee schedules and task management tools to propose specific action plans. For example, it can conduct weekly task reviews and adjust action plans based on progress. This allows generative AI to propose specific action plans by linking with employee schedules and task management tools.

[0066] The generative AI can use the emotion estimation function to analyze the emotional state of the client and suggest relaxation methods and exercises to bring out positive emotions. For example, the generative AI can use the emotion estimation function to analyze the emotional state of the client and suggest relaxation methods to bring out positive emotions. For example, it can introduce deep breathing or meditation techniques. The generative AI can also use the emotion estimation function to analyze the emotional state of the client and suggest exercises to bring out positive emotions. For example, it can recommend yoga or light exercise. The generative AI can also use the emotion estimation function to analyze the emotional state of the client and suggest relaxation methods and exercises to bring out positive emotions. For example, it can recommend listening to relaxation music or taking a walk in nature. This makes it possible to suggest relaxation methods and exercises to bring out positive emotions.

[0067] Generative AI can develop algorithms that completely remove personally identifiable information while preserving the context of the consultation content. Generative AI can, for example, develop algorithms that completely remove personally identifiable information while preserving the context of the consultation content. For example, it can automatically mask personal information such as names and addresses. Generative AI can also develop algorithms that completely remove personally identifiable information while preserving the context of the consultation content. For example, it can detect information that indicates a specific individual within the consultation content and anonymize it. Generative AI can also develop algorithms that completely remove personally identifiable information while preserving the context of the consultation content. For example, it can detect information that indicates a specific place or organization within the consultation content and anonymize it. This makes it possible to completely remove personally identifiable information while preserving the context of the consultation content.

[0068] The generation AI can analyze trends and patterns of consultation content based on statistical data and build a predictive model. The generation AI, for example, analyzes trends and patterns of consultation content based on statistical data and builds a predictive model. For example, it predicts a tendency for stress to increase at specific times. The generation AI can also analyze trends and patterns of consultation content based on statistical data and build a predictive model. For example, it can predict periods when stress will increase in a specific department and take measures to address the situation. The generation AI can also analyze trends and patterns of consultation content based on statistical data and build a predictive model. For example, it can predict increases in stress for specific work content and propose work improvement measures. This makes it possible to analyze trends and patterns of consultation content and build a predictive model.

[0069] The emotion estimation function can add emotional elements to statistical data, allowing the human resources department to grasp emotional trends. The emotion estimation function, for example, adds emotional elements to statistical data. For example, the emotion score of the consultation content is included in the statistical data. The emotion estimation function also adds emotional elements to statistical data. For example, the emotional trend of the consultation content is analyzed and reflected in the statistical data. The emotion estimation function also adds emotional elements to statistical data. For example, the emotional changes in the consultation content are analyzed and reflected in the statistical data. This allows the human resources department to grasp emotional trends.

[0070] Generative AI can compare statistical data with data from other companies or industries to perform benchmark analysis. Generative AI can, for example, compare statistical data with data from other companies or industries to perform benchmark analysis. For example, comparing with the stress levels of companies in the same industry. Generative AI can also compare statistical data with data from other companies or industries to perform benchmark analysis. For example, incorporating best practices for stress management from different industries. Generative AI can also compare statistical data with data from other companies or industries to perform benchmark analysis. For example, comparing with the stress levels of the entire industry to evaluate the effectiveness of your company's stress management. This allows you to compare with data from other companies or industries to perform benchmark analysis.

[0071] Generative AI can display statistical data in a visual dashboard to enable the HR department to intuitively understand it. For example, generative AI can display statistical data in a visual dashboard to enable the HR department to intuitively understand it. For example, it can visualize the data using graphs and charts. Generative AI can also display statistical data in a visual dashboard to enable the HR department to intuitively understand it. For example, it can provide an interactive dashboard to allow the HR department to check the details of the data. Generative AI can also display statistical data in a visual dashboard to enable the HR department to intuitively understand it. For example, it can visually display trends and patterns in the data to support quick decision-making. This makes it possible to display statistical data in a visual dashboard to enable the HR department to intuitively understand it.

[0072] The emotion estimation function can build a system that monitors the emotional health of the workplace in real time based on statistical data. The emotion estimation function, for example, builds a system that monitors the emotional health of the workplace in real time based on statistical data. For example, fluctuations in emotion scores can be displayed in real time. The emotion estimation function can also build a system that monitors the emotional health of the workplace in real time based on statistical data. For example, emotional trends can be analyzed and alerts can be issued in real time. The emotion estimation function can also build a system that monitors the emotional health of the workplace in real time based on statistical data. For example, the emotional health of the workplace can be evaluated based on fluctuations in emotion scores and necessary measures can be taken. This makes it possible to build a system that monitors the emotional health of the workplace in real time.

[0073] Generative AI can identify an employee's individual stressors and suggest counseling and resources that correspond to them. For example, generative AI can identify an employee's individual stressors and suggest counseling that corresponds to them. For example, it can suggest stress management counseling for an employee who is feeling stressed due to work pressure. Generative AI can also identify an employee's individual stressors and suggest resources that correspond to them. For example, it can introduce relaxation apps or online courses for stress management. Generative AI can also identify an employee's individual stressors and suggest counseling and resources that correspond to them. For example, it can suggest collaboration with a mental health professional. This makes it possible to suggest counseling and resources that correspond to an employee's individual stressors.

[0074] The generative AI can reflect employee feedback in real time and continuously improve the support content. For example, the generative AI collects employee feedback in real time and continuously improves the support content. For example, it adjusts the next piece of advice based on feedback on the advice provided. The generative AI also reflects employee feedback in real time and continuously improves the support content. For example, it analyzes the content of the feedback and improves the quality of the advice. The generative AI also reflects employee feedback in real time and continuously improves the support content. For example, it can update the generative AI's algorithm based on the feedback and provide more appropriate advice. This allows the generative AI to reflect employee feedback in real time and continuously improve the support content.

[0075] The emotion estimation function can analyze an employee's emotional state and provide counseling and resources according to their emotions. For example, the emotion estimation function can analyze an employee's emotional state and provide counseling according to their emotions. For example, it can suggest stress management counseling for an employee who is feeling highly stressed. The emotion estimation function can also analyze an employee's emotional state and provide resources according to their emotions. For example, it can introduce a relaxation app or an online course for stress management. The emotion estimation function can also analyze an employee's emotional state and provide counseling and resources according to their emotions. For example, it can suggest collaboration with a mental health professional. This makes it possible to provide counseling and resources according to the employee's emotional state.

[0076] Generative AI can extend psychological support to employees' families and friends, building support networks outside of the workplace. For example, generative AI can extend psychological support to employees' families, building support networks outside of the workplace. For example, it can make stress management resources available to families. Generative AI can also extend psychological support to employees' friends, building support networks outside of the workplace. For example, it can make relaxation apps available to friends. Generative AI can also extend psychological support to employees' family and friends, building support networks outside of the workplace. For example, it can make family and friends connect with mental health professionals. This allows psychological support to be extended to employees' family and friends, building support networks outside of the workplace.

[0077] Generative AI can link psychological support with employees' health and fitness data to provide comprehensive health management. Generative AI can, for example, link with employees' health data to provide comprehensive health management. For example, it can provide health management advice based on the results of health checkups. Generative AI can also link with employees' fitness data to provide comprehensive health management. For example, it can manage exercise volume and calorie consumption based on fitness tracker data. Generative AI can also link with employees' health and fitness data to provide comprehensive health management. For example, it can provide health management advice based on the results of health checkups and fitness tracker data. This allows for comprehensive health management by linking with employees' health and fitness data.

[0078] The emotion estimation function can analyze an employee's emotional state and suggest relaxation methods or exercises that correspond to their emotions. For example, the emotion estimation function can analyze an employee's emotional state and suggest relaxation methods that correspond to their emotions. For example, it can introduce deep breathing or meditation techniques. The emotion estimation function can also analyze an employee's emotional state and suggest exercises that correspond to their emotions. For example, it can recommend yoga or light exercise. The emotion estimation function can also analyze an employee's emotional state and suggest relaxation methods or exercises that correspond to their emotions. For example, it can recommend listening to relaxation music or taking a walk in nature. This makes it possible to suggest relaxation methods or exercises that correspond to an employee's emotional state.

[0079] When anonymizing the consultation content, the generation AI can protect the data using the latest encryption technology. For example, when anonymizing the consultation content, the generation AI can protect the data using the latest encryption technology. For example, it can encrypt the data using AES-256 encryption. Furthermore, when anonymizing the consultation content, the generation AI can protect the data using the latest encryption technology. For example, it can encrypt the data using RSA encryption. Furthermore, when anonymizing the consultation content, the generation AI can protect the data using the latest encryption technology. For example, it can encrypt the data using quantum cryptography. This allows the data to be protected using the latest encryption technology.

[0080] Generative AI can incorporate privacy protection guidelines into advice and support, allowing employees to use the service with peace of mind. Generative AI can, for example, incorporate privacy protection guidelines into advice and support, allowing employees to use the service with peace of mind. For example, it can clearly state guidelines regarding the handling of personal information. Generative AI can also incorporate privacy protection guidelines into advice and support, allowing employees to use the service with peace of mind. For example, it can clearly state data handling policies and access controls. Generative AI can also incorporate privacy protection guidelines into advice and support, allowing employees to use the service with peace of mind. For example, it can clearly state data deletion policies, allowing employees to use the service with peace of mind. This makes it possible to incorporate privacy protection guidelines so that employees can use the service with peace of mind.

[0081] The emotion estimation function can add a filtering function to protect privacy when analyzing an employee's emotional state. For example, the emotion estimation function adds a filtering function to protect privacy when analyzing an employee's emotional state. For example, it automatically masks information that can identify individuals. The emotion estimation function also adds a filtering function to protect privacy when analyzing an employee's emotional state. For example, it develops an algorithm to anonymize emotional data. The emotion estimation function also adds a filtering function to protect privacy when analyzing an employee's emotional state. For example, it can filter emotional data and remove information that can identify individuals. This makes it possible to add a filtering function to protect privacy when analyzing an employee's emotional state.

[0082] When anonymizing the consultation content, the generation AI can add customization functions to comply with different privacy protection regulations. For example, when anonymizing the consultation content, the generation AI can add customization functions to comply with different privacy protection regulations. For example, it can process data in compliance with GDPR or CCPA. Furthermore, when anonymizing the consultation content, the generation AI can add customization functions to comply with different privacy protection regulations. For example, it can process data in compliance with HIPAA. Furthermore, when anonymizing the consultation content, the generation AI can add customization functions to comply with different privacy protection regulations. For example, it can process data in compliance with the privacy protection regulations of each country. This makes it possible to add customization functions to comply with different privacy protection regulations.

[0083] The generative AI can add educational content for privacy protection to advice and support to improve employees' privacy awareness. For example, the generative AI can add educational content for privacy protection to advice and support to improve employees' privacy awareness. For example, the generative AI can provide online courses on privacy protection. The generative AI can also add educational content for privacy protection to advice and support to improve employees' privacy awareness. For example, the generative AI can provide video tutorials to educate employees on the importance of privacy protection. The generative AI can also add educational content for privacy protection to advice and support to improve employees' privacy awareness. For example, the generative AI can provide a guidebook to educate employees on specific methods for privacy protection. This makes it possible to add educational content to improve employees' privacy awareness.

[0084] The emotion estimation function can enhance anonymization technology for privacy protection when analyzing the emotional state of an employee. For example, the emotion estimation function can enhance anonymization technology for privacy protection when analyzing the emotional state of an employee. For example, an algorithm for anonymizing emotional data can be developed. Furthermore, the emotion estimation function can enhance anonymization technology for privacy protection when analyzing the emotional state of an employee. For example, a data masking technique can be used to anonymize the emotional data. Furthermore, the emotion estimation function can enhance anonymization technology for privacy protection when analyzing the emotional state of an employee. For example, a pseudo-anonymization technique can be used to anonymize the emotional data. This can enhance anonymization technology for privacy protection when analyzing the emotional state of an employee.

[0085] When learning feedback from employees, the generation AI can evaluate the quality of the feedback and prioritize highly reliable feedback. For example, when learning feedback from employees, the generation AI can evaluate the quality of the feedback and prioritize highly reliable feedback. For example, it can analyze the content of the feedback and prioritize feedback that includes specific and useful information. Furthermore, when learning feedback from employees, the generation AI can evaluate the quality of the feedback and prioritize highly reliable feedback. For example, it can evaluate the consistency of the feedback and the reliability of the person who provided it. Furthermore, when learning feedback from employees, the generation AI can evaluate the quality of the feedback and prioritize highly reliable feedback. For example, it can evaluate the accuracy and relevance of the feedback. This allows it to prioritize highly reliable feedback.

[0086] The generation AI can quantitatively evaluate the effectiveness of advice and support, and prioritize providing those that are highly effective. The generation AI can, for example, quantitatively evaluate the effectiveness of advice and support, and prioritize providing those that are highly effective. For example, it can measure changes in stress levels after the advice is implemented. The generation AI can also quantitatively evaluate the effectiveness of advice and support, and prioritize providing those that are highly effective. For example, it can measure improvements in performance after the advice is implemented. The generation AI can also quantitatively evaluate the effectiveness of advice and support, and prioritize providing those that are highly effective. For example, it can measure employee satisfaction after the advice is implemented. This makes it possible to prioritize providing advice and support that are highly effective.

[0087] The emotion estimation function can analyze the emotional state of employees, collect feedback according to their emotions, and reflect this in the learning of the generative AI. For example, the emotion estimation function can analyze the emotional state of employees, collect feedback according to their emotions, and reflect this in the learning of the generative AI. For example, feedback with positive emotions can be prioritized in learning. The emotion estimation function can also analyze the emotional state of employees, collect feedback according to their emotions, and reflect this in the learning of the generative AI. For example, the emotion estimation function can evaluate the importance of feedback based on an emotion score. The emotion estimation function can also analyze the emotional state of employees, collect feedback according to their emotions, and reflect this in the learning of the generative AI. For example, the content of the feedback can be analyzed based on the emotion score, and the generative AI algorithm can be updated. This makes it possible to collect feedback according to the emotional state of employees and reflect this in the learning of the generative AI.

[0088] When learning from employee feedback, generative AI can integrate data from different industries and job types to make improvements from a more diverse perspective. For example, when learning from employee feedback, generative AI can integrate data from different industries and job types to make improvements from a more diverse perspective. For example, it can incorporate best practices from different industries. When learning from employee feedback, generative AI can integrate data from different industries and job types to make improvements from a more diverse perspective. For example, it can analyze data from different industries and identify common issues. When learning from employee feedback, generative AI can integrate data from different industries and job types to make improvements from a more diverse perspective. For example, it can analyze data from different job types to identify issues specific to each job type. This allows generative AI to integrate data from different industries and job types to make improvements from a more diverse perspective.

[0089] The generative AI can link the effects of advice and support with employee performance data to make a comprehensive evaluation. The generative AI, for example, links the effects of advice and support with employee performance data to make a comprehensive evaluation. For example, it can measure the improvement in performance after the advice is implemented. The generative AI can also link the effects of advice and support with employee performance data to make a comprehensive evaluation. For example, it can measure the improvement in productivity after the advice is implemented. The generative AI can also link the effects of advice and support with employee performance data to make a comprehensive evaluation. For example, it can measure the employee's satisfaction after the advice is implemented. This makes it possible to link the effects of advice and support with employee performance data to make a comprehensive evaluation.

[0090] The emotion estimation function can analyze the emotional state of employees, collect feedback according to their emotions in real time, and reflect this in the learning of the generative AI. For example, the emotion estimation function can analyze the emotional state of employees, collect feedback according to their emotions in real time, and reflect this in the learning of the generative AI. For example, the emotion estimation function can evaluate the importance of the feedback based on an emotion score. The emotion estimation function can also analyze the emotional state of employees, collect feedback according to their emotions in real time, and reflect this in the learning of the generative AI. For example, the emotion estimation function can analyze the content of the feedback based on the emotion score and update the algorithm of the generative AI. The emotion estimation function can also analyze the emotional state of employees, collect feedback according to their emotions in real time, and reflect this in the learning of the generative AI. For example, the content of the feedback can be evaluated based on the emotion score and reflected in the learning of the generative AI. This makes it possible to collect feedback according to the emotional state of employees in real time and reflect this in the learning of the generative AI.

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

[0092] The psychological consultation system can be linked to employees' health and fitness data to provide comprehensive health management. For example, it can provide health management advice based on the results of employees' health checkups. It can also manage exercise volume and calorie consumption based on fitness tracker data. It can also provide health management advice based on the results of health checkups and fitness tracker data. This allows for comprehensive health management by linking with employees' health and fitness data.

[0093] The psychological consultation system can extend psychological support to employees' family and friends, building a support network outside the workplace. For example, it can provide family members with access to stress management resources, friends with access to relaxation apps, and even connect family and friends with mental health professionals. This allows employees to extend psychological support to their family and friends, building a support network outside the workplace.

[0094] The psychological consultation system can collect employee feedback in real time and continuously improve the support content. For example, the next advice can be adjusted based on feedback on the advice provided. The content of the feedback can also be analyzed to improve the quality of the advice. Furthermore, the generative AI algorithm can be updated based on the feedback to provide more appropriate advice. This allows employee feedback to be reflected in real time and the support content to be continuously improved.

[0095] The psychological consultation system can be linked to employees' schedules and task management tools to propose specific action plans. For example, incorporating relaxation time into daily schedules to manage stress. It can also be linked to task management tools to make suggestions for optimizing employees' tasks. Furthermore, it can conduct weekly task reviews and adjust action plans according to progress. This allows the system to propose specific action plans by linking with employees' schedules and task management tools.

[0096] The psychological consultation system can compare statistical data with data from other companies or industries to conduct benchmark analysis. For example, it can compare stress levels with those of companies in the same industry. It can also incorporate best stress management practices from other industries. Furthermore, it can compare stress levels with the stress level of the entire industry to evaluate the effectiveness of its own stress management. This allows it to compare with data from other companies or industries and conduct benchmark analysis.

[0097] The psychological counseling system uses emotion estimation to analyze an employee's emotional state and provide counseling and resources appropriate to their emotions. For example, it can suggest stress management counseling to an employee who is feeling highly stressed. It can also recommend relaxation apps and online courses for stress management. It can even suggest collaboration with mental health professionals. This allows it to provide counseling and resources appropriate to the employee's emotional state.

[0098] The psychological consultation system uses its emotion estimation function to analyze the emotional state of employees and suggest relaxation methods and exercises according to their emotions. For example, it can introduce deep breathing and meditation techniques. It can also recommend yoga or light exercise. It can also recommend listening to relaxation music or taking a walk in nature. This allows it to suggest relaxation methods and exercises according to the employee's emotional state.

[0099] The psychological consultation system uses an emotion estimation function to analyze the emotional state of employees in real time and provide advice based on their emotions. For example, if the client is feeling highly stressed, it can suggest relaxation methods and stress management techniques. It can also estimate the client's emotional state from the tone and rhythm of their voice using voice analysis technology. It can also estimate the client's emotional state from their facial expressions using facial expression recognition technology. This makes it possible to provide advice based on the client's emotional state in real time.

[0100] The psychological consultation system can use its emotion estimation function to analyze employees' emotional state and suggest relaxation methods and exercises to bring out positive emotions. For example, it can introduce deep breathing and meditation techniques. It can also recommend yoga or light exercise. It can also recommend listening to relaxation music or taking a walk in nature. This allows it to suggest relaxation methods and exercises to bring out positive emotions.

[0101] The psychological consultation system uses an emotion estimation function to analyze the emotional state of employees, collect feedback according to their emotions, and reflect this in the learning of the generative AI. For example, feedback with positive emotions can be prioritized for learning. The importance of feedback can also be evaluated based on the emotion score. Furthermore, the content of the feedback can be analyzed based on the emotion score and the generative AI algorithm can be updated. This makes it possible to collect feedback according to the emotional state of employees and reflect this in the learning of the generative AI.

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

[0103] Step 1: The anonymous consultation platform is a platform where employees can anonymously seek advice about psychological worries and stress. Employees can access the anonymous consultation platform via a web browser or mobile app and enter their consultation details. Furthermore, the anonymous consultation platform encrypts and stores the consultation details to protect employees' privacy. Step 2: The generation AI analyzes the consultation content received by the anonymous consultation platform. The generation AI uses natural language processing technology to analyze the consultation content and provide appropriate advice and support. The generation AI can also provide more personalized advice by taking into account the employee's past consultation history and behavioral patterns. Step 3: The feedback department anonymizes the consultation content analyzed by the generation AI and provides it as statistical data to the human resources department. If stress is increasing in a specific department, the feedback department will propose measures to reduce stress to that department. The feedback department can also use the statistical data to understand the psychological health of the entire workplace and take necessary measures.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0138] 7, 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An anonymous consultation platform where employees can anonymously seek advice about psychological concerns and stress, A generation AI that analyzes the consultation content accepted by the anonymous consultation platform; a feedback unit that anonymizes the consultation content analyzed by the generation AI and provides the anonymized data as statistical data to the human resources department. A system characterized by:

2. The generated AI is Analyzes the emotional state of the client in real time and provides advice tailored to their emotions 2. The system of claim 1.

3. The generated AI is Using voice and video input, non-verbal information from the client can also be analyzed to provide more comprehensive advice.

2. The system of claim 1.

4. The generated AI is Develop an algorithm that preserves the context of the conversation while completely removing any personally identifiable information.

2. The system of claim 1.

5. The emotion estimation function is Based on the above statistical data, we will build a system to monitor the emotional health of the workplace in real time.

2. The system of claim 1.

6. The emotion estimation function is Analyze the employee's emotional state and provide emotionally appropriate counseling and resources 2. The system of claim 1.

7. The generated AI is When anonymizing the consultation, the data is protected using the latest encryption technology.

2. The system of claim 1.

8. The emotion estimation function is Analyzing the emotional state of the employee, collecting the feedback according to the emotion, and reflecting it in the learning of the generating AI.

2. The system of claim 1.

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