System

Abstract

An object of a system according to an embodiment is to efficiently perform mental health care of employees.SOLUTION: A system according to an embodiment includes a counseling reception unit, a response generation unit, an emotion estimation unit, and a cognitive behavioral therapy unit. The counseling reception unit receives counseling. The response generation unit generates a response based on the counseling content received by the counseling reception unit. The emotion estimation unit estimates the employee's emotion based on the response generated by the response generation unit. The cognitive behavioral therapy unit provides cognitive behavioral therapy based on the emotion estimated by the emotion estimation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately provide employees with mental health care, which can worsen mental health problems.

[0005] The system according to the embodiment aims to efficiently provide mental health care for employees. [Means for solving the problem]

[0006] A system according to an embodiment includes a counseling reception unit, a response generation unit, an emotion estimation unit, and a cognitive behavioral therapy unit. The counseling reception unit receives counseling. The response generation unit generates a response based on the consultation content received by the counseling reception unit. The emotion estimation unit estimates the emotion of the employee based on the response generated by the response generation unit. The cognitive behavioral therapy unit provides cognitive behavioral therapy based on the emotion estimated by the emotion estimation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide mental health care for employees. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​counseling app according to an embodiment of the present invention is a system that automatically reads consultation content written by employees, generates responses using a generative AI, estimates emotions, and provides cognitive behavioral therapy. This enables the AI ​​counseling app to support the mental health of employees and reduce corporate losses.

[0029] An AI counseling app according to an embodiment includes a counseling reception unit, a response generation unit, an emotion estimation unit, and a cognitive behavioral therapy unit. The counseling reception unit receives written consultation information from employees. For example, the consultation information can be entered through an online form. The counseling reception unit can also receive consultation information over the phone or in person. For example, when an employee transmits the consultation information over the phone, the counseling reception unit stores the information as digital data. The response generation unit generates a response based on the consultation information received by the counseling reception unit. For example, the response generation unit analyzes the consultation information using natural language processing technology and generates an appropriate response. The response generation unit can also generate a response by referring to expert advice. For example, the response generation unit generates a response based on the advice of a psychologist. The emotion estimation unit estimates the employee's emotion based on the response generated by the response generation unit. For example, the emotion estimation unit estimates the employee's emotion using facial expression recognition technology. The emotion estimation unit can also estimate the emotion using voice analysis technology. For example, the emotion estimation unit estimates the employee's emotion by analyzing the employee's tone of voice. The cognitive behavioral therapy unit provides cognitive behavioral therapy based on the emotions estimated by the emotion estimation unit. For example, the cognitive behavioral therapy unit suggests an approach to correct cognitive distortions in employees. The cognitive behavioral therapy unit can also provide specific methods for improving employee behavior. For example, the cognitive behavioral therapy unit suggests exercises for employees to develop positive thinking patterns. This enables the AI ​​counseling app according to the embodiment to support employees' mental health and reduce corporate losses. For example, maintaining employees' mental health reduces the risk of employees taking leave or quitting, leading to cost savings for the company. Improving employees' mental health improves organizational vitality and productivity and reduces employee turnover.

[0030] The counseling reception unit can generate individually optimized counseling plans based on the employee's consultation history. For example, the counseling reception unit analyzes each employee's past consultation history and generates individually optimized counseling plans. For example, it provides continuous support for specific issues based on the content and frequency of past consultations. The counseling reception unit also automatically adjusts counseling plans using AI based on the employee's consultation history. For example, it suggests relaxation techniques during periods of high stress and provides specific advice to resolve the problem. The counseling reception unit also generates counseling plans tailored to the employee's needs based on the consultation history and regularly evaluates progress. For example, it monitors goal setting and achievement levels and adjusts the plan as necessary. This enables more effective support by providing individually optimized counseling plans based on the employee's past consultation history.

[0031] The response generation unit can refer to expert advice in real time depending on the content of the consultation and generate a more accurate response. For example, the response generation unit analyzes the content of the employee's consultation and builds a system in which an AI counselor refers to expert advice in real time. For example, it generates a response based on the knowledge of psychologists and counselors. The response generation unit also refers to expert advice in real time and proposes specific solutions to the employee. For example, it provides advice on stress management and communication techniques. The response generation unit also generates a more accurate response to the employee's consultation by referring to expert advice in real time. For example, it utilizes specialized knowledge of a specific problem. In this way, by referring to expert advice in real time, a more accurate response can be generated and appropriate support can be provided to the employee's consultation.

[0032] The system can link with employee health data to provide comprehensive health management. For example, the system can link with fitness trackers and sleep data to build a system that comprehensively manages employees' health status. For example, the content of counseling can be adjusted based on the amount of exercise and quality of sleep. The system can also evaluate employees' stress levels and health status based on health data and provide appropriate counseling. For example, it can provide advice on lack of exercise and sleep. The system can also link with fitness trackers and sleep data to develop a system that monitors employees' health status in real time. For example, it can adjust the frequency and content of counseling based on health data. In this way, by linking with employees' health data, comprehensive health management can be provided and employees' physical and mental health can be supported.

[0033] The system can introduce multilingual AI counselors that support different languages ​​or cultures, making it possible to support global companies. For example, the system can develop multilingual AI counselors that support different languages ​​and cultures, making it possible to support global companies. For example, the system can support multiple languages ​​such as English, Spanish, and Chinese. The system can also introduce multilingual AI counselors to provide appropriate counseling to employees with different cultural backgrounds. For example, the system can provide advice on culture-specific stressors. The system can also introduce AI counselors that support different languages ​​and cultures to provide consistent counseling services to employees of global companies. For example, the system can generate customized responses according to language and culture. This allows the system to provide appropriate counseling to employees of global companies by supporting different languages ​​and cultures.

[0034] The system can analyze the frequency and patterns of sticker and text usage to assess employee stress levels. For example, the system analyzes the frequency and patterns of sticker and text usage to build a system that assesses employee stress levels. For example, if negative stickers are used frequently, the stress level is assessed as high. The system also performs text analysis of chat content to assess employee stress levels. For example, it detects signs of stress based on frequently used keywords and phrases. The system also analyzes sticker and text usage patterns to develop a system that assesses employee stress levels in real time. For example, if there are a lot of negative stickers during a specific time period, the stress level is assessed as high. In this way, by analyzing the frequency and patterns of sticker and text usage, the system can assess employee stress levels and provide appropriate support.

[0035] The system can automatically summarize chat content, making it easy for employees to review it later. For example, the system builds a system that automatically summarizes chat content, making it easy for employees to review it later. For example, it summarizes and displays important points and advice. The system also develops a system that concisely summarizes chat content using an automatic summarization function. For example, it converts long chat content into short summary sentences. The system also builds a system that automatically summarizes chat content, making it easy for employees to review it later. For example, it displays the summarized content in a timeline format. In this way, automatically summarizing chat content makes it easy for employees to review it later and easily confirm important points.

[0036] The system can add voice input and video call functions in addition to the chat format, providing more diverse means of communication. For example, a system is developed that adds voice input and video call functions in addition to the chat format. For example, the system allows employees to input their consultation details by voice. The system also adds voice input and video call functions, allowing employees to use more diverse means of communication. For example, the system allows employees to interact with an AI counselor via video call. The system is also developed that provides voice input and video call functions in addition to the chat format. For example, the system uses voice recognition technology to convert voice input into text. This allows employees to use more diverse means of communication by adding voice input and video call functions.

[0037] The system can analyze chat content, identify common worries and problems among employees, and provide opportunities for group counseling. For example, the system analyzes chat content and builds a system that identifies common worries and problems among employees. For example, it groups employees who have the same problem. The system also provides group counseling opportunities for employees who have common worries and problems. For example, it holds group sessions on the same topic. The system also analyzes chat content and develops a system that identifies common worries and problems among employees. For example, it extracts common problems based on specific keywords or phrases. In this way, common worries and problems among employees can be identified and opportunities for group counseling can be provided, thereby promoting support between employees.

[0038] The system can analyze the content of employees' complaints and whining, identify common problems, and propose improvement measures for the entire company. For example, the system analyzes the content of employees' complaints and whining and builds a system that identifies common problems. For example, it extracts problems based on frequently appearing keywords and phrases. The system also analyzes the content of complaints and whining and proposes improvement measures for the entire company. For example, it identifies problems related to specific departments or tasks and proposes improvement measures. The system also develops a system that analyzes the content of employees' complaints and whining and identifies common problems. For example, it extracts problems using text mining technology. In this way, the system analyzes the content of employees' complaints and whining, identifies common problems, and proposes improvement measures for the entire company.

[0039] The system is capable of having an AI counselor provide appropriate resources depending on the content of the complaints or whining. For example, the system will build a system in which an AI counselor provides appropriate resources depending on the content of the complaints or whining. For example, it will suggest stress management techniques or relaxation methods. The system will also analyze the content of employees' complaints or whining and provide appropriate resources. For example, it will suggest relaxation music or meditation guides. The system will also develop a system in which an AI counselor provides appropriate resources depending on the content of the complaints or whining. For example, it will suggest exercises or breathing techniques for stress relief. In this way, by providing appropriate resources depending on the content of the complaints or whining, it will support employees' stress management and relaxation.

[0040] The system provides a platform where employees can anonymously share their complaints and weaknesses, and can receive sympathy and advice from other employees. For example, the system builds a platform where employees can anonymously share their complaints and weaknesses. For example, it provides an anonymous message board or chat room. The system also develops a system where employees can anonymously share their complaints and weaknesses and receive sympathy and advice from other employees. For example, it adds a sympathy button or comment function. The system also provides a platform where employees can anonymously share their complaints and weaknesses, and collects feedback from other employees. For example, it adds an anonymous voting function or advice posting function. This allows employees to anonymously share their complaints and weaknesses and receive sympathy and advice from other employees.

[0041] The system analyzes the content of complaints and whining and can use the information to improve a company's mental health program. For example, the system analyzes the content of complaints and whining and builds a system that helps improve a company's mental health program. For example, the system adjusts the program based on frequently occurring problems. The system also analyzes the content of employees' complaints and whining and suggests improvements to the mental health program. For example, the system strengthens measures against specific stress factors. The system also analyzes the content of complaints and whining and develops a system that helps improve a company's mental health program. For example, text mining technology is used to extract problems and reflect them in the program. In this way, analyzing the content of complaints and whining can be used to help improve a company's mental health program.

[0042] The system can identify an employee's cognitive distortions and provide an individualized cognitive behavioral therapy program based on the identified distortions. For example, the system builds a system that identifies an employee's cognitive distortions and provides an individualized cognitive behavioral therapy program based on the identified distortions. For example, it suggests a specific approach for correcting the cognitive distortions. The system also identifies an employee's cognitive distortions based on the theory of cognitive behavioral therapy and provides an individualized program. For example, it provides exercises to change negative thought patterns into positive ones. The system also develops a system that identifies an employee's cognitive distortions and provides an individualized cognitive behavioral therapy program based on the identified distortions. For example, it sets specific tasks to correct the cognitive distortions. In this way, the system identifies an employee's cognitive distortions and provides an individualized cognitive behavioral therapy program based on the identified distortions, thereby supporting the employee's mental health.

[0043] The system can regularly evaluate the progress of cognitive behavioral therapy and adjust the program as needed. For example, the system builds a system that regularly evaluates the progress of cognitive behavioral therapy and adjusts the program as needed. For example, it updates the content of the program based on the regular evaluations. The system also monitors the progress of employees' cognitive behavioral therapy and adjusts the program as needed. For example, it provides additional support if progress is lagging behind. The system also develops a system that regularly evaluates the progress of cognitive behavioral therapy and adjusts the program as needed. For example, it customizes the content of the program based on the evaluation results. In this way, the mental health of employees can be supported by regularly evaluating the progress of cognitive behavioral therapy and adjusting the program as needed.

[0044] The system can provide a comprehensive mental health program that incorporates mindfulness and meditation techniques in addition to cognitive behavioral therapy. For example, the system is constructed to provide a comprehensive mental health program that incorporates mindfulness and meditation techniques in addition to cognitive behavioral therapy. For example, the system suggests meditation guides and mindfulness exercises. The system also provides a mental health program that incorporates mindfulness and meditation techniques to support the mental health of employees. For example, the system suggests techniques for stress management and relaxation. The system is also developed to provide a comprehensive mental health program that incorporates mindfulness and meditation techniques in addition to cognitive behavioral therapy. For example, the system adjusts the content of the program according to changes in emotions. In this way, the system can comprehensively support the mental health of employees by incorporating mindfulness and meditation techniques in addition to cognitive behavioral therapy.

[0045] The system provides the content of cognitive behavioral therapy as visual notes and interactive teaching materials, thereby deepening understanding. For example, the system builds a system that provides the content of cognitive behavioral therapy as visual notes, making it easier for employees to understand. For example, the concept of cognitive behavioral therapy is explained using diagrams and illustrations. The system also provides the content of cognitive behavioral therapy using interactive teaching materials, deepening employees' understanding. For example, cognitive behavioral therapy techniques are learned through quizzes and simulations. The system also develops a system that provides the content of cognitive behavioral therapy as visual notes and interactive teaching materials, making it easier for employees to understand. For example, explanations are made using videos and animations. In this way, the content of cognitive behavioral therapy is provided as visual notes and interactive teaching materials, deepening employees' understanding.

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

[0047] The system can link with employee health data to provide comprehensive health management. For example, a system can be built that links with fitness trackers and sleep data to provide comprehensive management of employee health. For example, the content of counseling can be adjusted based on the amount of exercise and quality of sleep. The system can also evaluate employees' stress levels and health status based on health data and provide appropriate counseling. For example, advice can be given on lack of exercise or sleep. The system can also link with fitness trackers and sleep data to develop a system that monitors employees' health in real time. For example, the frequency and content of counseling can be adjusted based on health data. In this way, by linking with employee health data, comprehensive health management can be provided to support the physical and mental health of employees.

[0048] The system can introduce multilingual AI counselors that support different languages ​​or cultures, making it possible to support global companies. For example, a multilingual AI counselor that supports different languages ​​and cultures can be developed to support global companies. For example, it can support multiple languages ​​such as English, Spanish, and Chinese. The system can also introduce multilingual AI counselors to provide appropriate counseling to employees with different cultural backgrounds. For example, it can provide advice on culture-specific stressors. The system can also introduce AI counselors that support different languages ​​and cultures to provide consistent counseling services to employees of global companies. For example, it can generate customized responses according to language and culture. This allows it to support different languages ​​and cultures and provide appropriate counseling to employees of global companies.

[0049] The system can analyze the frequency and patterns of sticker and text usage to assess employee stress levels. For example, a system can be built to analyze the frequency and patterns of sticker and text usage to assess employee stress levels. For example, if negative stickers are used frequently, the stress level can be assessed as high. The system can also perform text analysis of chat content to assess employee stress levels. For example, it can detect signs of stress based on frequently used keywords and phrases. The system can also analyze sticker and text usage patterns to develop a system that assesses employee stress levels in real time. For example, if there are a lot of negative stickers during a particular time period, the stress level can be assessed as high. In this way, by analyzing the frequency and patterns of sticker and text usage, the stress level of employees can be assessed and appropriate support can be provided.

[0050] The system can automatically summarize chat content, making it easier for employees to review later. For example, a system can be built that automatically summarizes chat content, making it easier for employees to review later. For example, important points and advice can be summarized and displayed. The system can also develop a system that uses an automatic summarization function to concisely summarize chat content. For example, long chat content can be converted into short summary sentences. The system can also build a system that automatically summarizes chat content, making it easier for employees to review later. For example, the summarized content can be displayed in a timeline format. In this way, automatically summarizing chat content makes it easier for employees to review later and easily confirm important points.

[0051] The system can add voice input and video calling functions in addition to the chat format, providing more diverse means of communication. For example, a system can be built that adds voice input and video calling functions in addition to the chat format. For example, it can allow employees to input their consultation details by voice. The system can also add voice input and video calling functions, allowing employees to use more diverse means of communication. For example, it can interact with an AI counselor via video calling. The system can also be developed that provides voice input and video calling functions in addition to the chat format. For example, it can convert voice input into text using voice recognition technology. This allows employees to use more diverse means of communication by adding voice input and video calling functions.

[0052] The system can analyze chat content, identify common worries and problems among employees, and provide opportunities for group counseling. For example, a system can be built that analyzes chat content and identifies common worries and problems among employees. For example, employees with the same problems can be grouped together. The system can also provide group counseling opportunities for employees with common worries and problems. For example, group sessions on the same topic can be held. The system can also develop a system that analyzes chat content and identifies common worries and problems among employees. For example, common problems can be extracted based on specific keywords or phrases. In this way, common worries and problems among employees can be identified and opportunities for group counseling can be provided, promoting support between employees.

[0053] The system provides a platform where employees can anonymously share their complaints and weaknesses, and can receive sympathy and advice from other employees. For example, a platform is built where employees can anonymously share their complaints and weaknesses. For example, an anonymous message board or chat room is provided. The system also develops a system where employees can anonymously share their complaints and weaknesses and receive sympathy and advice from other employees. For example, a sympathy button or comment function is added. The system also provides a platform where employees can anonymously share their complaints and weaknesses, and collects feedback from other employees. For example, an anonymous voting function or advice posting function is added. This allows employees to anonymously share their complaints and weaknesses and receive sympathy and advice from other employees.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The counseling reception department accepts written consultation details from employees. For example, the consultation details can be entered through an online form. The counseling reception department can also accept consultation details over the phone or in person. For example, when an employee communicates their consultation details over the phone, the counseling reception department saves the details as digital data. Step 2: The response generation unit generates a response based on the consultation content received by the counseling reception unit. For example, the response generation unit analyzes the consultation content using natural language processing technology and generates an appropriate response. The response generation unit can also generate a response by referring to the advice of an expert. For example, the response generation unit generates a response based on the advice of a psychologist. Step 3: The emotion estimation unit estimates the employee's emotion based on the response generated by the response generation unit. For example, the emotion estimation unit estimates the employee's emotion using facial expression recognition technology. The emotion estimation unit can also estimate the emotion using voice analysis technology. For example, the emotion estimation unit estimates the employee's emotion by analyzing the tone of voice. Step 4: The cognitive behavioral therapy department provides cognitive behavioral therapy based on the emotions estimated by the emotion estimation department. For example, the cognitive behavioral therapy department suggests approaches to correct the employee's cognitive distortions. The cognitive behavioral therapy department can also provide specific methods to improve the employee's behavior. For example, the cognitive behavioral therapy department suggests exercises to cultivate positive thinking patterns for the employee.

[0056] (Example 2) The AI ​​counseling app according to an embodiment of the present invention is a system that automatically reads consultation content written by employees, generates responses using a generative AI, estimates emotions, and provides cognitive behavioral therapy. This enables the AI ​​counseling app to support the mental health of employees and reduce corporate losses.

[0057] An AI counseling app according to an embodiment includes a counseling reception unit, a response generation unit, an emotion estimation unit, and a cognitive behavioral therapy unit. The counseling reception unit receives written consultation information from employees. For example, the consultation information can be entered through an online form. The counseling reception unit can also receive consultation information over the phone or in person. For example, when an employee transmits the consultation information over the phone, the counseling reception unit stores the information as digital data. The response generation unit generates a response based on the consultation information received by the counseling reception unit. For example, the response generation unit analyzes the consultation information using natural language processing technology and generates an appropriate response. The response generation unit can also generate a response by referring to expert advice. For example, the response generation unit generates a response based on the advice of a psychologist. The emotion estimation unit estimates the employee's emotion based on the response generated by the response generation unit. For example, the emotion estimation unit estimates the employee's emotion using facial expression recognition technology. The emotion estimation unit can also estimate the emotion using voice analysis technology. For example, the emotion estimation unit estimates the employee's emotion by analyzing the employee's tone of voice. The cognitive behavioral therapy unit provides cognitive behavioral therapy based on the emotions estimated by the emotion estimation unit. For example, the cognitive behavioral therapy unit suggests an approach to correct cognitive distortions in employees. The cognitive behavioral therapy unit can also provide specific methods for improving employee behavior. For example, the cognitive behavioral therapy unit suggests exercises for employees to develop positive thinking patterns. This enables the AI ​​counseling app according to the embodiment to support employees' mental health and reduce corporate losses. For example, maintaining employees' mental health reduces the risk of employees taking leave or quitting, leading to cost savings for the company. Improving employees' mental health improves organizational vitality and productivity and reduces employee turnover.

[0058] The counseling reception unit can generate individually optimized counseling plans based on the employee's consultation history. For example, the counseling reception unit analyzes each employee's past consultation history and generates individually optimized counseling plans. For example, it provides continuous support for specific issues based on the content and frequency of past consultations. The counseling reception unit also automatically adjusts counseling plans using AI based on the employee's consultation history. For example, it suggests relaxation techniques during periods of high stress and provides specific advice to resolve the problem. The counseling reception unit also generates counseling plans tailored to the employee's needs based on the consultation history and regularly evaluates progress. For example, it monitors goal setting and achievement levels and adjusts the plan as necessary. This enables more effective support by providing individually optimized counseling plans based on the employee's past consultation history.

[0059] The response generation unit can refer to expert advice in real time depending on the content of the consultation and generate a more accurate response. For example, the response generation unit analyzes the content of the employee's consultation and builds a system in which an AI counselor refers to expert advice in real time. For example, it generates a response based on the knowledge of psychologists and counselors. The response generation unit also refers to expert advice in real time and proposes specific solutions to the employee. For example, it provides advice on stress management and communication techniques. The response generation unit also generates a more accurate response to the employee's consultation by referring to expert advice in real time. For example, it utilizes specialized knowledge of a specific problem. In this way, by referring to expert advice in real time, a more accurate response can be generated and appropriate support can be provided to the employee's consultation.

[0060] The emotion estimation unit can monitor the emotional state of employees in real time and provide counseling at the appropriate time. For example, the emotion estimation unit uses the emotion estimation function to build a system that monitors the emotional state of employees in real time. For example, it analyzes facial expressions and vocal tone to calculate an emotion score. The emotion estimation unit also monitors the emotional state of employees in real time and provides counseling at the appropriate time. For example, it suggests relaxation techniques when stress increases. The emotion estimation unit also uses the emotion estimation function to monitor the emotional state of employees and adjust the content and frequency of counseling as needed. For example, it customizes responses according to changes in emotions. In this way, the emotional state of employees can be monitored in real time and counseling can be provided at the appropriate time, thereby supporting the mental health of employees.

[0061] The system can link with employee health data to provide comprehensive health management. For example, the system can link with fitness trackers and sleep data to build a system that comprehensively manages employees' health status. For example, the content of counseling can be adjusted based on the amount of exercise and quality of sleep. The system can also evaluate employees' stress levels and health status based on health data and provide appropriate counseling. For example, it can provide advice on lack of exercise and sleep. The system can also link with fitness trackers and sleep data to develop a system that monitors employees' health status in real time. For example, it can adjust the frequency and content of counseling based on health data. In this way, by linking with employees' health data, comprehensive health management can be provided and employees' physical and mental health can be supported.

[0062] The system can introduce multilingual AI counselors that support different languages ​​or cultures, making it possible to support global companies. For example, the system can develop multilingual AI counselors that support different languages ​​and cultures, making it possible to support global companies. For example, the system can support multiple languages ​​such as English, Spanish, and Chinese. The system can also introduce multilingual AI counselors to provide appropriate counseling to employees with different cultural backgrounds. For example, the system can provide advice on culture-specific stressors. The system can also introduce AI counselors that support different languages ​​and cultures to provide consistent counseling services to employees of global companies. For example, the system can generate customized responses according to language and culture. This allows the system to provide appropriate counseling to employees of global companies by supporting different languages ​​and cultures.

[0063] The system can use the emotion estimation function to provide relaxation content according to the emotional state of employees. For example, the system uses the emotion estimation function to build a system that provides relaxation content according to the emotional state of employees. For example, the system suggests relaxing music when stress levels rise. The system also monitors the emotional state of employees in real time and provides appropriate relaxation content. For example, it provides meditation guides and deep breathing instructions. The system also uses the emotion estimation function to develop a system that customizes relaxation content according to the emotional state of employees. For example, it selects content based on an emotion score. This reduces employee stress by providing relaxation content according to the employee's emotional state.

[0064] The system can analyze the frequency and patterns of sticker and text usage to assess employee stress levels. For example, the system analyzes the frequency and patterns of sticker and text usage to build a system that assesses employee stress levels. For example, if negative stickers are used frequently, the stress level is assessed as high. The system also performs text analysis of chat content to assess employee stress levels. For example, it detects signs of stress based on frequently used keywords and phrases. The system also analyzes sticker and text usage patterns to develop a system that assesses employee stress levels in real time. For example, if there are a lot of negative stickers during a specific time period, the stress level is assessed as high. In this way, by analyzing the frequency and patterns of sticker and text usage, the system can assess employee stress levels and provide appropriate support.

[0065] The system can automatically summarize chat content, making it easy for employees to review it later. For example, the system builds a system that automatically summarizes chat content, making it easy for employees to review it later. For example, it summarizes and displays important points and advice. The system also develops a system that concisely summarizes chat content using an automatic summarization function. For example, it converts long chat content into short summary sentences. The system also builds a system that automatically summarizes chat content, making it easy for employees to review it later. For example, it displays the summarized content in a timeline format. In this way, automatically summarizing chat content makes it easy for employees to review it later and easily confirm important points.

[0066] The system can use the emotion estimation function to suggest stickers and text according to the emotions of employees. For example, the system uses the emotion estimation function to build a system that suggests stickers and text according to the emotions of employees. For example, it suggests encouraging stickers when positive emotions are strong. The system also monitors the emotional state of employees in real time and suggests appropriate stickers and text. For example, it suggests messages that will help employees relax when they are feeling stressed. The system also uses the emotion estimation function to develop a system that customizes stickers and text according to the emotions of employees. For example, it selects stickers and text based on an emotion score. This supports more appropriate communication by suggesting stickers and text according to the emotions of employees.

[0067] The system can add voice input and video call functions in addition to the chat format, providing more diverse means of communication. For example, a system is developed that adds voice input and video call functions in addition to the chat format. For example, the system allows employees to input their consultation details by voice. The system also adds voice input and video call functions, allowing employees to use more diverse means of communication. For example, the system allows employees to interact with an AI counselor via video call. The system is also developed that provides voice input and video call functions in addition to the chat format. For example, the system uses voice recognition technology to convert voice input into text. This allows employees to use more diverse means of communication by adding voice input and video call functions.

[0068] The system can analyze chat content, identify common worries and problems among employees, and provide opportunities for group counseling. For example, the system analyzes chat content and builds a system that identifies common worries and problems among employees. For example, it groups employees who have the same problem. The system also provides group counseling opportunities for employees who have common worries and problems. For example, it holds group sessions on the same topic. The system also analyzes chat content and develops a system that identifies common worries and problems among employees. For example, it extracts common problems based on specific keywords or phrases. In this way, common worries and problems among employees can be identified and opportunities for group counseling can be provided, thereby promoting support between employees.

[0069] The system can use the emotion estimation function to provide a customized stamp set according to the employee's emotions. For example, the system uses the emotion estimation function to build a system that provides a customized stamp set according to the employee's emotions. For example, it provides encouraging stamps when positive emotions are strong. The system also monitors the employee's emotional state in real time and suggests an appropriate stamp set. For example, it provides relaxing stamps when stress is high. The system also uses the emotion estimation function to develop a system that provides a customized stamp set according to the employee's emotions. For example, it selects a stamp set based on an emotion score. This provides a customized stamp set according to the employee's emotions, supporting more appropriate communication.

[0070] The system can analyze the content of employees' complaints and whining, identify common problems, and propose improvement measures for the entire company. For example, the system analyzes the content of employees' complaints and whining and builds a system that identifies common problems. For example, it extracts problems based on frequently appearing keywords and phrases. The system also analyzes the content of complaints and whining and proposes improvement measures for the entire company. For example, it identifies problems related to specific departments or tasks and proposes improvement measures. The system also develops a system that analyzes the content of employees' complaints and whining and identifies common problems. For example, it extracts problems using text mining technology. In this way, the system analyzes the content of employees' complaints and whining, identifies common problems, and proposes improvement measures for the entire company.

[0071] The system is capable of having an AI counselor provide appropriate resources depending on the content of the complaints or whining. For example, the system will build a system in which an AI counselor provides appropriate resources depending on the content of the complaints or whining. For example, it will suggest stress management techniques or relaxation methods. The system will also analyze the content of employees' complaints or whining and provide appropriate resources. For example, it will suggest relaxation music or meditation guides. The system will also develop a system in which an AI counselor provides appropriate resources depending on the content of the complaints or whining. For example, it will suggest exercises or breathing techniques for stress relief. In this way, by providing appropriate resources depending on the content of the complaints or whining, it will support employees' stress management and relaxation.

[0072] The system uses the emotion estimation function to track changes in employees' emotions in real time and send encouraging messages at the appropriate time. For example, the system uses the emotion estimation function to build a system that tracks changes in employees' emotions in real time. For example, it analyzes facial expressions and voice tone to calculate an emotion score. The system also tracks changes in employees' emotions in real time and sends encouraging messages at the appropriate time. For example, it sends encouraging messages when stress levels rise. The system also uses the emotion estimation function to develop a system that monitors changes in employees' emotions and sends encouraging messages as needed. For example, it customizes messages based on the emotion score. In this way, the system supports employees' mental health by tracking changes in employees' emotions in real time and sending encouraging messages at the appropriate time.

[0073] The system provides a platform where employees can anonymously share their complaints and weaknesses, and can receive sympathy and advice from other employees. For example, the system builds a platform where employees can anonymously share their complaints and weaknesses. For example, it provides an anonymous message board or chat room. The system also develops a system where employees can anonymously share their complaints and weaknesses and receive sympathy and advice from other employees. For example, it adds a sympathy button or comment function. The system also provides a platform where employees can anonymously share their complaints and weaknesses, and collects feedback from other employees. For example, it adds an anonymous voting function or advice posting function. This allows employees to anonymously share their complaints and weaknesses and receive sympathy and advice from other employees.

[0074] The system analyzes the content of complaints and whining and can use the information to improve a company's mental health program. For example, the system analyzes the content of complaints and whining and builds a system that helps improve a company's mental health program. For example, the system adjusts the program based on frequently occurring problems. The system also analyzes the content of employees' complaints and whining and suggests improvements to the mental health program. For example, the system strengthens measures against specific stress factors. The system also analyzes the content of complaints and whining and develops a system that helps improve a company's mental health program. For example, text mining technology is used to extract problems and reflect them in the program. In this way, analyzing the content of complaints and whining can be used to help improve a company's mental health program.

[0075] The system can use the emotion estimation function to suggest relaxation exercises according to the employee's emotions. For example, the system uses the emotion estimation function to build a system that suggests relaxation exercises according to the employee's emotions. For example, it suggests deep breathing or meditation when stress levels rise. The system also monitors the employee's emotional state in real time and suggests appropriate relaxation exercises. For example, it selects exercises based on an emotion score. The system also uses the emotion estimation function to develop a system that customizes relaxation exercises according to the employee's emotions. For example, it adjusts the content of the exercises according to changes in emotions. In this way, the system reduces employee stress by suggesting relaxation exercises according to the employee's emotions.

[0076] The system can identify an employee's cognitive distortions and provide an individualized cognitive behavioral therapy program based on the identified distortions. For example, the system builds a system that identifies an employee's cognitive distortions and provides an individualized cognitive behavioral therapy program based on the identified distortions. For example, it suggests a specific approach for correcting the cognitive distortions. The system also identifies an employee's cognitive distortions based on the theory of cognitive behavioral therapy and provides an individualized program. For example, it provides exercises to change negative thought patterns into positive ones. The system also develops a system that identifies an employee's cognitive distortions and provides an individualized cognitive behavioral therapy program based on the identified distortions. For example, it sets specific tasks to correct the cognitive distortions. In this way, the system identifies an employee's cognitive distortions and provides an individualized cognitive behavioral therapy program based on the identified distortions, thereby supporting the employee's mental health.

[0077] The system can regularly evaluate the progress of cognitive behavioral therapy and adjust the program as needed. For example, the system builds a system that regularly evaluates the progress of cognitive behavioral therapy and adjusts the program as needed. For example, it updates the content of the program based on the regular evaluations. The system also monitors the progress of employees' cognitive behavioral therapy and adjusts the program as needed. For example, it provides additional support if progress is lagging behind. The system also develops a system that regularly evaluates the progress of cognitive behavioral therapy and adjusts the program as needed. For example, it customizes the content of the program based on the evaluation results. In this way, the mental health of employees can be supported by regularly evaluating the progress of cognitive behavioral therapy and adjusting the program as needed.

[0078] The system can use the emotion estimation function to suggest a cognitive behavioral therapy approach according to the employee's emotional state. For example, the system uses the emotion estimation function to build a system that suggests a cognitive behavioral therapy approach according to the employee's emotional state. For example, it suggests relaxation techniques when stress levels rise. The system also monitors the employee's emotional state in real time and suggests an appropriate cognitive behavioral therapy approach. For example, it selects an approach based on an emotion score. The system also uses the emotion estimation function to develop a system that customizes a cognitive behavioral therapy approach according to the employee's emotional state. For example, it adjusts the content of the approach according to changes in emotions. In this way, the system supports the mental health of employees by suggesting a cognitive behavioral therapy approach according to the employee's emotional state.

[0079] The system can provide a comprehensive mental health program that incorporates mindfulness and meditation techniques in addition to cognitive behavioral therapy. For example, the system is constructed to provide a comprehensive mental health program that incorporates mindfulness and meditation techniques in addition to cognitive behavioral therapy. For example, the system suggests meditation guides and mindfulness exercises. The system also provides a mental health program that incorporates mindfulness and meditation techniques to support the mental health of employees. For example, the system suggests techniques for stress management and relaxation. The system is also developed to provide a comprehensive mental health program that incorporates mindfulness and meditation techniques in addition to cognitive behavioral therapy. For example, the system adjusts the content of the program according to changes in emotions. In this way, the system can comprehensively support the mental health of employees by incorporating mindfulness and meditation techniques in addition to cognitive behavioral therapy.

[0080] The system provides the content of cognitive behavioral therapy as visual notes and interactive teaching materials, thereby deepening understanding. For example, the system builds a system that provides the content of cognitive behavioral therapy as visual notes, making it easier for employees to understand. For example, the concept of cognitive behavioral therapy is explained using diagrams and illustrations. The system also provides the content of cognitive behavioral therapy using interactive teaching materials, deepening employees' understanding. For example, cognitive behavioral therapy techniques are learned through quizzes and simulations. The system also develops a system that provides the content of cognitive behavioral therapy as visual notes and interactive teaching materials, making it easier for employees to understand. For example, explanations are made using videos and animations. In this way, the content of cognitive behavioral therapy is provided as visual notes and interactive teaching materials, deepening employees' understanding.

[0081] The system can use the emotion estimation function to automatically schedule cognitive behavioral therapy sessions according to an employee's emotions. For example, the system uses the emotion estimation function to build a system that automatically schedules cognitive behavioral therapy sessions according to an employee's emotions. For example, the system schedules sessions when stress levels rise. The system also monitors the employee's emotional state in real time and schedules cognitive behavioral therapy sessions at appropriate times. For example, the system adjusts the frequency of sessions based on the emotion score. The system also uses the emotion estimation function to develop a system that automatically schedules cognitive behavioral therapy sessions according to an employee's emotions. For example, the system adjusts the content of sessions according to changes in emotions. In this way, the system supports the mental health of employees by automatically scheduling cognitive behavioral therapy sessions according to the employee's emotions.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The system can link with employee health data to provide comprehensive health management. For example, a system can be built that links with fitness trackers and sleep data to provide comprehensive management of employee health. For example, the content of counseling can be adjusted based on the amount of exercise and quality of sleep. The system can also evaluate employees' stress levels and health status based on health data and provide appropriate counseling. For example, advice can be given on lack of exercise or sleep. The system can also link with fitness trackers and sleep data to develop a system that monitors employees' health in real time. For example, the frequency and content of counseling can be adjusted based on health data. In this way, by linking with employee health data, comprehensive health management can be provided to support the physical and mental health of employees.

[0084] The system can introduce multilingual AI counselors that support different languages ​​or cultures, making it possible to support global companies. For example, a multilingual AI counselor that supports different languages ​​and cultures can be developed to support global companies. For example, it can support multiple languages ​​such as English, Spanish, and Chinese. The system can also introduce multilingual AI counselors to provide appropriate counseling to employees with different cultural backgrounds. For example, it can provide advice on culture-specific stressors. The system can also introduce AI counselors that support different languages ​​and cultures to provide consistent counseling services to employees of global companies. For example, it can generate customized responses according to language and culture. This allows it to support different languages ​​and cultures and provide appropriate counseling to employees of global companies.

[0085] The system can use the emotion estimation function to provide relaxation content according to the emotional state of employees. For example, a system is built that uses the emotion estimation function to provide relaxation content according to the emotional state of employees. For example, the system suggests relaxing music when stress levels rise. The system also monitors the emotional state of employees in real time and provides appropriate relaxation content. For example, it provides meditation guides and deep breathing instructions. The system also uses the emotion estimation function to develop a system that customizes relaxation content according to the emotional state of employees. For example, it selects content based on an emotion score. This reduces employee stress by providing relaxation content according to the employee's emotional state.

[0086] The system can analyze the frequency and patterns of sticker and text usage to assess employee stress levels. For example, a system can be built to analyze the frequency and patterns of sticker and text usage to assess employee stress levels. For example, if negative stickers are used frequently, the stress level can be assessed as high. The system can also perform text analysis of chat content to assess employee stress levels. For example, it can detect signs of stress based on frequently used keywords and phrases. The system can also analyze sticker and text usage patterns to develop a system that assesses employee stress levels in real time. For example, if there are a lot of negative stickers during a particular time period, the stress level can be assessed as high. In this way, by analyzing the frequency and patterns of sticker and text usage, the stress level of employees can be assessed and appropriate support can be provided.

[0087] The system can automatically summarize chat content, making it easier for employees to review later. For example, a system can be built that automatically summarizes chat content, making it easier for employees to review later. For example, important points and advice can be summarized and displayed. The system can also develop a system that uses an automatic summarization function to concisely summarize chat content. For example, long chat content can be converted into short summary sentences. The system can also build a system that automatically summarizes chat content, making it easier for employees to review later. For example, the summarized content can be displayed in a timeline format. In this way, automatically summarizing chat content makes it easier for employees to review later and easily confirm important points.

[0088] The system can use the emotion estimation function to suggest stickers and text according to the employee's emotions. For example, a system can be built that uses the emotion estimation function to suggest stickers and text according to the employee's emotions. For example, encouraging stickers can be suggested when positive emotions are strong. The system can also monitor the employee's emotional state in real time and suggest appropriate stickers and text. For example, it can suggest messages that will help employees relax when they are feeling stressed. The system can also use the emotion estimation function to develop a system that customizes stickers and text according to the employee's emotions. For example, it can select stickers and text based on an emotion score. This supports more appropriate communication by suggesting stickers and text according to the employee's emotions.

[0089] The system can add voice input and video calling functions in addition to the chat format, providing more diverse means of communication. For example, a system can be built that adds voice input and video calling functions in addition to the chat format. For example, it can allow employees to input their consultation details by voice. The system can also add voice input and video calling functions, allowing employees to use more diverse means of communication. For example, it can interact with an AI counselor via video calling. The system can also be developed that provides voice input and video calling functions in addition to the chat format. For example, it can convert voice input into text using voice recognition technology. This allows employees to use more diverse means of communication by adding voice input and video calling functions.

[0090] The system can analyze chat content, identify common worries and problems among employees, and provide opportunities for group counseling. For example, a system can be built that analyzes chat content and identifies common worries and problems among employees. For example, employees with the same problems can be grouped together. The system can also provide group counseling opportunities for employees with common worries and problems. For example, group sessions on the same topic can be held. The system can also develop a system that analyzes chat content and identifies common worries and problems among employees. For example, common problems can be extracted based on specific keywords or phrases. In this way, common worries and problems among employees can be identified and opportunities for group counseling can be provided, promoting support between employees.

[0091] The system can use the emotion estimation function to track changes in employees' emotions in real time and send encouraging messages at the appropriate time. For example, a system is built that uses the emotion estimation function to track changes in employees' emotions in real time. For example, facial expressions and voice tone are analyzed to calculate an emotion score. The system also tracks changes in employees' emotions in real time and sends encouraging messages at the appropriate time. For example, sending encouraging messages when stress levels rise. The system also uses the emotion estimation function to develop a system that monitors changes in employees' emotions and sends encouraging messages as needed. For example, messages are customized based on the emotion score. In this way, changes in employees' emotions can be tracked in real time and encouraging messages sent at the appropriate time, supporting employees' mental health.

[0092] The system provides a platform where employees can anonymously share their complaints and weaknesses, and can receive sympathy and advice from other employees. For example, a platform is built where employees can anonymously share their complaints and weaknesses. For example, an anonymous message board or chat room is provided. The system also develops a system where employees can anonymously share their complaints and weaknesses and receive sympathy and advice from other employees. For example, a sympathy button or comment function is added. The system also provides a platform where employees can anonymously share their complaints and weaknesses, and collects feedback from other employees. For example, an anonymous voting function or advice posting function is added. This allows employees to anonymously share their complaints and weaknesses and receive sympathy and advice from other employees.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The counseling reception department accepts written consultation details from employees. For example, the consultation details can be entered through an online form. The counseling reception department can also accept consultation details over the phone or in person. For example, when an employee communicates their consultation details over the phone, the counseling reception department saves the details as digital data. Step 2: The response generation unit generates a response based on the consultation content received by the counseling reception unit. For example, the response generation unit analyzes the consultation content using natural language processing technology and generates an appropriate response. The response generation unit can also generate a response by referring to the advice of an expert. For example, the response generation unit generates a response based on the advice of a psychologist. Step 3: The emotion estimation unit estimates the employee's emotion based on the response generated by the response generation unit. For example, the emotion estimation unit estimates the employee's emotion using facial expression recognition technology. The emotion estimation unit can also estimate the emotion using voice analysis technology. For example, the emotion estimation unit estimates the employee's emotion by analyzing the tone of voice. Step 4: The cognitive behavioral therapy department provides cognitive behavioral therapy based on the emotions estimated by the emotion estimation department. For example, the cognitive behavioral therapy department suggests approaches to correct the employee's cognitive distortions. The cognitive behavioral therapy department can also provide specific methods to improve the employee's behavior. For example, the cognitive behavioral therapy department suggests exercises to cultivate positive thinking patterns for the employee.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 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. Counseling reception and a response generation unit that generates a response based on the consultation content received by the counseling reception unit; an emotion estimation unit that estimates an emotion of an employee based on the response generated by the response generation unit; a cognitive behavioral therapy unit that provides cognitive behavioral therapy based on the emotion estimated by the emotion estimation unit. A system characterized by:

2. The counseling reception unit Generate an individually optimized counseling plan based on the employee's consultation history 2. The system of claim 1.

3. The response generation unit Based on the content of the consultation, expert advice is referenced in real time to generate a more accurate response.

2. The system of claim 1.

4. The emotion estimation unit Monitor the employee's emotional state in real time and provide counseling at the appropriate time.

2. The system of claim 1.

5. The system comprises: Linking with employee health data to provide comprehensive health management 2. The system of claim 1.

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