system

Abstract

The system according to the embodiment aims to provide prompt and appropriate advice to employees in response to their concerns. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives consultations about worries from employees. The analysis unit analyzes the consultation content received by the reception unit. The generation unit generates advice based on the content analyzed by the analysis unit. The provision unit provides the advice generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to provide prompt and appropriate advice to employees seeking advice.

[0005] The system according to the embodiment aims to provide prompt and appropriate advice to employees in response to their concerns. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives consultations about worries from employees. The analysis unit analyzes the consultation content received by the reception unit. The generation unit generates advice based on the content analyzed by the analysis unit. The provision unit provides the advice generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide prompt and appropriate advice to employees in response to their concerns. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses AI to provide advice to employees on a 24-hour basis in response to their concerns. In this system, employees input their concerns, and AI analyzes the content of the consultation, generates appropriate advice, and provides it to the employee in real time. For example, if an employee inputs a message such as "I'm feeling stressed from work," the AI ​​analyzes the content and provides advice on stress management methods and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve employees' mental health and work performance. This allows the system to provide advice to employees on their concerns 24 hours a day.

[0029] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives consultations from employees about their concerns. The employee consultations may include, but are not limited to, interpersonal relationships in the workplace, work procedures, and career paths. The reception unit may receive consultations input in text format, for example. The analysis unit analyzes the consultation content received by the reception unit. The analysis unit may understand the consultation content and generate appropriate advice using, for example, natural language processing technology. The analysis unit may use methods such as text analysis, sentiment analysis, and keyword extraction. The generation unit generates advice based on the content analyzed by the analysis unit. The generation unit may generate specific guidelines for action, such as stress management methods and relaxation advice. The generation unit may generate the advice in text format or audio format, for example. The provision unit provides the advice generated by the generation unit. The provision unit may display the advice to employees in real time, for example. The provision unit may provide the advice in text format or audio format, for example. As a result, the system according to the embodiment can provide advice to employees on their concerns 24 hours a day.

[0030] The system includes a voice input unit that accepts voice input. The voice input unit allows employees to input their concerns by voice. For example, when an employee uses a microphone to input their concerns by voice, the voice input unit accepts the voice. The voice input unit can convert the voice data into text data using voice recognition technology. For example, the voice input unit converts the voice into text in real time using voice recognition software. The voice input unit also has a function to learn the voice characteristics of specific speakers to improve the accuracy of the voice input. For example, the voice input unit accumulates employee voice data and learns the voice characteristics of each employee, thereby improving the accuracy of the voice recognition. This allows the voice input unit to input employees' concerns by voice, allowing the content of the consultation to be accepted more quickly and accurately.

[0031] The system includes an image input unit that accepts image input. The image input unit allows employees to input their concerns using images. For example, an employee takes an image using a smartphone camera and uploads it to the system, where the image input unit accepts the image. The image input unit can analyze the image data using image recognition technology. For example, the image input unit uses image recognition software to analyze the content of the image and convert it into text data. The image input unit also has a function for learning specific image characteristics to improve the accuracy of image input. For example, the image input unit learns image patterns frequently used by employees to improve the accuracy of image recognition. This allows the image input unit to allow employees to input their concerns using images, allowing the system to accept consultation content more quickly and accurately.

[0032] The system includes a history analysis unit that performs analysis based on past consultation history. The history analysis unit makes it possible to analyze the content of a current consultation by taking into account the employee's past consultation history. For example, the history analysis unit can store past consultation contents made by employees in a database and compare them with the current consultation content. The history analysis unit can store, for example, text data or audio data and refer to the past consultation contents. The history analysis unit can also analyze trends in past consultation contents and provide appropriate advice for the current consultation content. For example, if a similar consultation content has occurred in the past, the history analysis unit can generate advice for the current consultation content by referring to the advice given at that time. In this way, the history analysis unit can provide more appropriate advice by taking into account the past consultation history.

[0033] The system includes an expert knowledge learning unit that learns expert knowledge. The expert knowledge learning unit learns expert knowledge and enables the quality of advice to be improved. For example, the expert knowledge learning unit can learn from specialized books, papers, past consultation cases, etc., and accumulate specialized knowledge. The expert knowledge learning unit automatically learns expert knowledge using, for example, a machine learning algorithm. The expert knowledge learning unit can also improve the quality of advice by incorporating the opinions of experts. For example, the expert knowledge learning unit learns advice provided by experts and generates advice based on that advice. In this way, the expert knowledge learning unit can provide more specialized advice by learning expert knowledge.

[0034] The system includes a feedback collection unit that collects feedback after providing advice. The feedback collection unit makes it possible to collect feedback on the advice received by employees. For example, the feedback collection unit can collect employee feedback through questionnaires or interviews. The feedback collection unit, for example, conducts an online questionnaire to collect employees' evaluations of the advice they received. The feedback collection unit can also collect employees' opinions and impressions through interviews. For example, the feedback collection unit directly interacts with employees to collect information on the effectiveness of the advice and areas for improvement. In this way, the feedback collection unit can collect feedback after providing advice and use it to improve the system.

[0035] The reception unit can select the most appropriate reception method by referring to the user's past consultation history when receiving a call. For example, the reception unit preferentially suggests reception methods that the user has frequently used in the past. The reception unit can also automatically select the most appropriate reception method from the user's past consultation history. Furthermore, the reception unit can also suggest an appropriate reception method based on the content of the user's past consultation. In this way, the reception unit can select the most appropriate reception method by referring to the user's past consultation history.

[0036] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving consultations. For example, the reception unit preferentially receives consultation contents related to the user's current situation. The reception unit can also filter appropriate consultation contents based on the user's areas of interest. Furthermore, the reception unit can combine the user's current situation and areas of interest to propose optimal consultation contents. In this way, the reception unit can receive more appropriate consultation contents by filtering based on the user's current situation and areas of interest.

[0037] The reception unit can prioritize receiving highly relevant consultations by taking into account the user's geographical location information when receiving consultations. For example, if the user is in a specific area, the reception unit prioritizes receiving consultations related to that area. The reception unit can also suggest optimal consultation content based on the user's geographical location information. Furthermore, the reception unit can filter appropriate consultation content based on information related to the user's current location. In this way, the reception unit can prioritize receiving highly relevant consultations by taking into account the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity at the time of reception and receive related consultations. For example, the reception unit analyzes the user's current interests from the user's social media activity and receives related consultations on a priority basis. The reception unit can also suggest appropriate consultation content based on the content posted by the user on social media. Furthermore, the reception unit can analyze the user's social media activity history and filter out the most appropriate consultation content. In this way, the reception unit can analyze the user's social media activity and receive related consultations on a priority basis.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content. For example, the analysis unit performs a detailed analysis for consultation content with a high level of importance. The analysis unit can also perform a concise analysis for consultation content with a low level of importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the consultation content. In this way, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the consultation content.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the consultation content. For example, the analysis unit applies a stress analysis algorithm to a consultation about stress management. The analysis unit can also apply a career analysis algorithm to a career consultation. Furthermore, the analysis unit can also apply a human relationship analysis algorithm to a consultation about human relationships. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of the consultation content.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the consultation content. For example, the analysis unit prioritizes the analysis of the most recently submitted consultation content. The analysis unit can also postpone the analysis of consultation content that was submitted earlier. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the time of submission. In this way, the analysis unit can provide analysis results more quickly by determining the priority of analysis based on the time of submission of the consultation content.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the consultation contents. For example, the analysis unit prioritizes the analysis of consultation contents with high relevance. The analysis unit can also postpone consultation contents with low relevance. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the consultation contents. In this way, the analysis unit can prioritize the analysis of consultation contents with high relevance by adjusting the order of analysis based on the relevance of the consultation contents.

[0043] The generation unit can adjust the level of detail of the advice based on the importance of the consultation content when generating the advice. For example, the generation unit generates detailed advice for consultation content with a high level of importance. The generation unit can also generate concise advice for consultation content with a low level of importance. Furthermore, the generation unit can dynamically adjust the level of detail of the advice according to the importance of the consultation content. In this way, the generation unit can provide more appropriate advice by adjusting the level of detail of the advice based on the importance of the consultation content.

[0044] The generation unit can apply different generation algorithms depending on the category of the consultation content during generation. For example, the generation unit applies a stress management advice generation algorithm to a consultation about stress management. The generation unit can also apply a career advice generation algorithm to a career consultation. Furthermore, the generation unit can also apply a human relationship advice generation algorithm to a consultation about human relationships. In this way, the generation unit can provide more appropriate advice by applying different generation algorithms depending on the category of the consultation content.

[0045] The generation unit can determine the priority of advice based on the time of submission of the consultation content when generating the advice. For example, the generation unit generates advice with priority for the consultation content that was submitted most recently. The generation unit can also postpone advice for consultation content that was submitted earlier. Furthermore, the generation unit can dynamically adjust the priority of advice based on the time of submission. In this way, the generation unit can provide advice more quickly by determining the priority of advice based on the time of submission of the consultation content.

[0046] The generation unit can adjust the order of advice based on the relevance of the consultation content when generating the advice. For example, the generation unit generates advice preferentially for consultation content with high relevance. The generation unit can also postpone consultation content with low relevance. Furthermore, the generation unit can dynamically adjust the order of advice based on the relevance of the consultation content. In this way, the generation unit can adjust the order of advice based on the relevance of the consultation content, thereby providing advice preferentially for consultation content with high relevance.

[0047] When providing the service, the providing unit can select the optimal providing method by referring to the user's past consultation history. For example, the providing unit preferentially suggests a providing method that the user has used in the past. The providing unit can also automatically select the optimal providing method from the user's past consultation history. Furthermore, the providing unit can also suggest an appropriate providing method based on the content of the user's past consultation. In this way, the providing unit can select the optimal providing method by referring to the user's past consultation history.

[0048] The providing unit can customize the means for providing advice based on the user's current situation when providing the advice. For example, when the user is in the office, the providing unit provides the advice in text format. Furthermore, when the user is on the move, the providing unit can also provide the advice in audio format. Furthermore, when the user is at home, the providing unit can also provide the advice in video format. In this way, the providing unit can provide advice in a more appropriate manner by customizing the means for providing advice based on the user's current situation.

[0049] The providing unit can provide optimal advice by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit provides advice related to that area. The providing unit can also suggest optimal advice based on the user's geographical location information. Furthermore, the providing unit can also provide appropriate advice based on information related to the user's current location. In this way, the providing unit can provide optimal advice by taking into account the user's geographical location information.

[0050] At the time of providing advice, the providing unit can analyze the user's social media activity and suggest a means of providing the advice. For example, the providing unit can analyze the user's current interests from the user's social media activity and provide related advice. The providing unit can also suggest appropriate advice based on the content of the user's social media posts. Furthermore, the providing unit can analyze the user's social media activity history and provide optimal advice. In this way, the providing unit can suggest a more appropriate means of providing advice by analyzing the user's social media activity.

[0051] The voice input unit can select the optimal analysis method by referring to the user's past voice input history when inputting voice. For example, the voice input unit preferentially suggests voice input methods that the user has used in the past. The voice input unit can also automatically select the optimal analysis method from the user's past voice input history. Furthermore, the voice input unit can also suggest an appropriate analysis method based on the content of the user's past voice input. In this way, the voice input unit can select the optimal analysis method by referring to the user's past voice input history.

[0052] The voice input unit can select the optimal voice input method by taking into account the user's geographical location information when inputting voice. For example, if the user is in a specific area, the voice input unit suggests a voice input method related to that area. The voice input unit can also select the optimal voice input method based on the user's geographical location information. Furthermore, the voice input unit can also suggest an appropriate voice input method based on information related to the user's current location. In this way, the voice input unit can select the optimal voice input method by taking into account the user's geographical location information.

[0053] When inputting an image, the image input unit can select the optimal analysis method by referring to the user's past image input history. For example, the image input unit preferentially suggests image input methods that the user has used in the past. The image input unit can also automatically select the optimal analysis method from the user's past image input history. Furthermore, the image input unit can also suggest an appropriate analysis method based on the content of the user's past image input. In this way, the image input unit can select the optimal analysis method by referring to the user's past image input history.

[0054] The image input unit can select the optimal image input method by taking into account the user's geographical location information when inputting an image. For example, if the user is in a specific area, the image input unit suggests an image input method related to that area. The image input unit can also select the optimal image input method based on the user's geographical location information. Furthermore, the image input unit can also suggest an appropriate image input method based on information related to the user's current location. This allows the image input unit to select the optimal image input method by taking into account the user's geographical location information.

[0055] During emotion analysis, the emotion analysis unit can select the optimal analysis method by referring to the user's past emotion history. For example, the emotion analysis unit preferentially suggests emotion analysis methods that the user has used in the past. The emotion analysis unit can also automatically select the optimal analysis method from the user's past emotion history. Furthermore, the emotion analysis unit can also suggest an appropriate analysis method based on the content of the user's past emotions. In this way, the emotion analysis unit can select the optimal analysis method by referring to the user's past emotion history.

[0056] The sentiment analysis unit can select the optimal analysis method by taking into account the user's geographical location information when analyzing sentiment. For example, if the user is in a specific area, the sentiment analysis unit suggests a sentiment analysis method related to that area. The sentiment analysis unit can also select the optimal sentiment analysis method based on the user's geographical location information. Furthermore, the sentiment analysis unit can also suggest an appropriate sentiment analysis method based on information related to the user's current location. In this way, the sentiment analysis unit can select the optimal sentiment analysis method by taking into account the user's geographical location information.

[0057] During history analysis, the history analysis unit can select the optimal analysis method by referring to the user's past consultation history. For example, the history analysis unit preferentially suggests a history analysis method that the user has used in the past. The history analysis unit can also automatically select the optimal analysis method from the user's past consultation history. Furthermore, the history analysis unit can also suggest an appropriate analysis method based on the content of the user's past consultation. In this way, the history analysis unit can select the optimal analysis method by referring to the user's past consultation history.

[0058] When analyzing history, the history analysis unit can select the optimal analysis method by taking into account the user's geographical location information. For example, if the user is in a specific area, the history analysis unit can suggest a history analysis method related to that area. The history analysis unit can also select the optimal history analysis method based on the user's geographical location information. Furthermore, the history analysis unit can also suggest an appropriate history analysis method based on information related to the user's current location. In this way, the history analysis unit can select the optimal history analysis method by taking into account the user's geographical location information.

[0059] The expert knowledge learning unit can select the optimal learning algorithm by referring to past learning data when learning expert knowledge. The expert knowledge learning unit selects the most effective learning algorithm based on, for example, past learning data. The expert knowledge learning unit can also select a learning algorithm specialized for a specific field from past learning data. Furthermore, the expert knowledge learning unit can analyze past learning data and dynamically select the optimal learning algorithm. In this way, the expert knowledge learning unit can select the optimal learning algorithm by referring to past learning data.

[0060] When learning expert knowledge, the expert knowledge learning unit can select the optimal study method by taking into account the user's geographical location information. For example, if the user is in a specific area, the expert knowledge learning unit suggests a study method related to that area. The expert knowledge learning unit can also select the optimal study method based on the user's geographical location information. Furthermore, the expert knowledge learning unit can also suggest an appropriate study method based on information related to the user's current location. In this way, the expert knowledge learning unit can select the optimal study method by taking into account the user's geographical location information.

[0061] When collecting feedback, the feedback collection unit can select the optimal collection method by referring to the user's past feedback history. For example, the feedback collection unit preferentially suggests feedback collection methods that the user has used in the past. The feedback collection unit can also automatically select the optimal collection method from the user's past feedback history. Furthermore, the feedback collection unit can also suggest an appropriate collection method based on the content of the user's past feedback. In this way, the feedback collection unit can select the optimal collection method by referring to the user's past feedback history.

[0062] The feedback collection unit can select the optimal collection method when collecting feedback by taking into account the user's geographical location information. For example, if the user is in a specific area, the feedback collection unit suggests a feedback collection method related to that area. The feedback collection unit can also select the optimal feedback collection method based on the user's geographical location information. Furthermore, the feedback collection unit can also suggest an appropriate feedback collection method based on information related to the user's current location. In this way, the feedback collection unit can select the optimal feedback collection method by taking into account the user's geographical location information.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The system uses AI to provide 24-hour advice to employees regarding their concerns. Employees input their concerns, and the AI ​​analyzes the content of the input, generates appropriate advice, and delivers it to the employee in real time. For example, if an employee inputs a comment such as "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve employees' mental health and work performance. The system can also include an expert recommendation module that recommends appropriate experts based on the content of the consultation. For example, if an employee consults about a concern related to a specific field, the system will recommend an expert in that field. The expert recommendation module can also recommend the most appropriate expert based on the employee's past consultation history. Furthermore, the expert recommendation module can recommend appropriate experts based on the employee's current situation and areas of interest. This allows the expert recommendation module to help employees receive more specialized advice.

[0065] The system uses AI to provide advice 24 hours a day to employees who have concerns. Employees input their concerns, and the AI ​​analyzes the content of the input, generates appropriate advice, and provides it to the employee in real time. For example, if an employee inputs a comment like "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve employees' mental health and work performance. The system can also include a resource provider that provides appropriate resources based on the content of the consultation. For example, if an employee requests a consultation about stress management, the resource provider recommends books or online courses on stress management. The resource provider can also provide optimal resources by taking into account the employee's past consultation history. Furthermore, the resource provider can provide appropriate resources based on the employee's current situation and areas of interest. This allows the resource provider to help employees access more specific resources.

[0066] The system uses AI to provide advice 24 hours a day to employees who have concerns. Employees input their concerns, and the AI ​​analyzes the content of the consultation, generates appropriate advice, and provides it to the employee in real time. For example, if an employee inputs something like "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve employees' mental health and work performance. The system can also include a training provider that provides appropriate training programs based on the content of the consultation. For example, if an employee consults about stress management, the training provider recommends a stress management training program. The training provider can also provide the most appropriate training program by taking into account the employee's past consultation history. Furthermore, the training provider can provide appropriate training programs based on the employee's current situation and areas of interest. This allows the training provider to support employees in receiving more specific training.

[0067] The system uses AI to provide 24-hour advice to employees regarding their concerns. Employees input their concerns, and the AI ​​analyzes the content of the input, generates appropriate advice, and provides it to the employee in real time. For example, if an employee inputs a comment such as "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve their mental health and work performance. The system can also include a community recommendation module that recommends appropriate communities based on the content of the consultation. For example, if an employee consults about a concern related to a specific field, the system recommends online communities related to that field. The community recommendation module can also recommend the most appropriate community based on the employee's past consultation history. Furthermore, the community recommendation module can recommend appropriate communities based on the employee's current situation and areas of interest. This allows the community recommendation module to help employees interact and share information with others who share the same concerns.

[0068] The system uses AI to provide 24-hour advice to employees regarding their concerns. Employees input their concerns, and the AI ​​analyzes the content of the input, generates appropriate advice, and provides it to the employee in real time. For example, if an employee inputs a message such as "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve employees' mental health and work performance. The system can also include an event recommendation unit that recommends appropriate events based on the content of the consultation. For example, if an employee requests a consultation about stress management, the event recommendation unit will recommend a workshop or seminar on stress management. The event recommendation unit can also recommend appropriate events based on the employee's past consultation history. Furthermore, the event recommendation unit can recommend appropriate events based on the employee's current situation and areas of interest. This allows the event recommendation unit to help employees participate in more specific events.

[0069] The system uses AI to provide advice to employees 24 hours a day in response to their concerns. Employees input their concerns, and the AI ​​analyzes the content of the input, generates appropriate advice, and provides it to the employee in real time. For example, if an employee inputs something like "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve employees' mental health and work performance. The system can also include a relaxation provider that provides appropriate relaxation methods based on the content of the input. For example, if an employee requests advice on stress management, the relaxation provider recommends meditation or yoga. The relaxation provider can also provide optimal relaxation methods by taking into account the employee's past consultation history. Furthermore, the relaxation provider can provide appropriate relaxation methods based on the employee's current situation and areas of interest. This allows the relaxation provider to help employees practice more specific relaxation methods.

[0070] The system uses AI to provide advice to employees 24 hours a day in response to their concerns. In this system, employees input their concerns, and the AI ​​analyzes the content of the consultation, generates appropriate advice, and provides it to the employee in real time. For example, if an employee inputs content such as "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management methods and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive advice quickly. This is expected to improve employees' mental health and work performance. The system can also include a feedback providing unit that provides appropriate feedback based on the content of the consultation. For example, if an employee consults about stress management, the feedback providing unit provides feedback on the progress of stress management. The feedback providing unit can also provide optimal feedback by taking into account the employee's past consultation history. Furthermore, the feedback providing unit can provide appropriate feedback based on the employee's current situation and areas of interest. This allows the feedback providing unit to help employees receive more specific feedback.

[0071] The system uses AI to provide 24-hour advice to employees regarding their concerns. Employees input their concerns, and the AI ​​analyzes the content of the input, generates appropriate advice, and delivers it to the employee in real time. For example, if an employee inputs a comment like "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve employees' mental health and work performance. The system can also include a mental health check module that provides appropriate mental health checks based on the content of the consultation. For example, if an employee consults about stress management, the mental health check module provides a checklist for measuring stress levels. The mental health check module can also provide the most appropriate mental health check by taking into account the employee's past consultation history. Furthermore, the mental health check module can provide appropriate mental health checks based on the employee's current situation and areas of interest. This allows the mental health check module to help employees understand their own mental health status and take appropriate measures.

[0072] The system uses AI to provide 24-hour advice to employees regarding their concerns. Employees input their concerns, and the AI ​​analyzes the content of the input, generates appropriate advice, and delivers it to the employee in real time. For example, if an employee inputs a comment like "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve employees' mental health and work performance. The system can also include a mental health check module that provides appropriate mental health checks based on the content of the consultation. For example, if an employee consults about stress management, the mental health check module provides a checklist for measuring stress levels. The mental health check module can also provide the most appropriate mental health check by taking into account the employee's past consultation history. Furthermore, the mental health check module can provide appropriate mental health checks based on the employee's current situation and areas of interest. This allows the mental health check module to help employees understand their own mental health status and take appropriate measures.

[0073] The system uses AI to provide 24-hour advice to employees regarding their concerns. Employees input their concerns, and the AI ​​analyzes the content of the input, generates appropriate advice, and provides it to the employee in real time. For example, if an employee inputs a comment such as "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management and relaxation. This system allows employees to seek advice anytime, anywhere and receive prompt advice. This is expected to improve employees' mental health and work performance. The system can also include a support group recommendation module that recommends appropriate mental health support groups based on the content of the input. For example, if an employee consults about a specific topic, the system recommends support groups related to that topic. The support group recommendation module can also recommend the most appropriate support group based on the employee's past consultation history. Furthermore, the support group recommendation module can recommend appropriate support groups based on the employee's current situation and areas of interest. This allows employees to interact and share information with others who share the same concerns.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The reception unit accepts employee consultations. Employee consultations may include, but are not limited to, interpersonal relationships in the workplace, work procedures, and career paths. The reception unit may accept consultations entered in text format, for example. Step 2: The analysis unit analyzes the consultation content received by the reception unit. The analysis unit uses, for example, natural language processing technology to understand the consultation content and generate appropriate advice. The analysis unit can use methods such as text analysis, sentiment analysis, and keyword extraction. Step 3: The generator generates advice based on the content analyzed by the analyzer. The generator can generate specific guidelines for action, such as stress management methods or relaxation advice. The generator can generate the advice in text or audio format. Step 4: The providing unit provides the advice generated by the generating unit. The providing unit can, for example, display the advice to the employee in real time. The providing unit can provide the advice in text format or audio format.

[0076] (Example 2) A system according to an embodiment of the present invention uses AI to provide advice to employees on a 24-hour basis in response to their concerns. In this system, employees input their concerns, and AI analyzes the content of the consultation, generates appropriate advice, and provides it to the employee in real time. For example, if an employee inputs a message such as "I'm feeling stressed from work," the AI ​​analyzes the content and provides advice on stress management methods and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve employees' mental health and work performance. This allows the system to provide advice to employees on their concerns 24 hours a day.

[0077] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives consultations from employees about their concerns. The employee consultations may include, but are not limited to, interpersonal relationships in the workplace, work procedures, and career paths. The reception unit may receive consultations input in text format, for example. The analysis unit analyzes the consultation content received by the reception unit. The analysis unit may understand the consultation content and generate appropriate advice using, for example, natural language processing technology. The analysis unit may use methods such as text analysis, sentiment analysis, and keyword extraction. The generation unit generates advice based on the content analyzed by the analysis unit. The generation unit may generate specific guidelines for action, such as stress management methods and relaxation advice. The generation unit may generate the advice in text format or audio format, for example. The provision unit provides the advice generated by the generation unit. The provision unit may display the advice to employees in real time, for example. The provision unit may provide the advice in text format or audio format, for example. As a result, the system according to the embodiment can provide advice to employees on their concerns 24 hours a day.

[0078] The system includes a voice input unit that accepts voice input. The voice input unit allows employees to input their concerns by voice. For example, when an employee uses a microphone to input their concerns by voice, the voice input unit accepts the voice. The voice input unit can convert the voice data into text data using voice recognition technology. For example, the voice input unit converts the voice into text in real time using voice recognition software. The voice input unit also has a function to learn the voice characteristics of specific speakers to improve the accuracy of the voice input. For example, the voice input unit accumulates employee voice data and learns the voice characteristics of each employee, thereby improving the accuracy of the voice recognition. This allows the voice input unit to input employees' concerns by voice, allowing the content of the consultation to be accepted more quickly and accurately.

[0079] The system includes an image input unit that accepts image input. The image input unit allows employees to input their concerns using images. For example, an employee takes an image using a smartphone camera and uploads it to the system, where the image input unit accepts the image. The image input unit can analyze the image data using image recognition technology. For example, the image input unit uses image recognition software to analyze the content of the image and convert it into text data. The image input unit also has a function for learning specific image characteristics to improve the accuracy of image input. For example, the image input unit learns image patterns frequently used by employees to improve the accuracy of image recognition. This allows the image input unit to allow employees to input their concerns using images, allowing the system to accept consultation content more quickly and accurately.

[0080] The system includes an emotion analysis unit that performs emotion analysis. The emotion analysis unit enables analysis of emotions contained in employee consultations. For example, the emotion analysis unit can extract emotions contained in the consultation content using text analysis technology. The emotion analysis unit can analyze emotional nuances in the consultation content using natural language processing technology. The emotion analysis unit can also analyze emotions in consultation content that has been input via voice using speech analysis technology. For example, the emotion analysis unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the emotion analysis unit can analyze emotions in consultation content that has been input via image analysis using facial expression analysis technology. For example, the emotion analysis unit can analyze changes in facial expressions and calculate an emotion score. This allows the emotion analysis unit to perform a detailed analysis of emotions contained in employee consultations and provide more appropriate advice.

[0081] The system includes a history analysis unit that performs analysis based on past consultation history. The history analysis unit makes it possible to analyze the content of a current consultation by taking into account the employee's past consultation history. For example, the history analysis unit can store the content of past consultations made by employees in a database and compare it with the content of the current consultation. The history analysis unit can store, for example, text data or audio data and refer to the content of past consultations. The history analysis unit can also analyze trends in the content of past consultations and provide appropriate advice for the content of the current consultation. For example, if a similar consultation has occurred in the past, the history analysis unit can generate advice for the current consultation by referring to the advice given at that time. This allows the history analysis unit to provide more appropriate advice by taking into account the past consultation history.

[0082] The system includes an expert knowledge learning unit that learns expert knowledge. The expert knowledge learning unit learns expert knowledge and enables the quality of advice to be improved. For example, the expert knowledge learning unit can learn from specialized books, papers, past consultation cases, etc., and accumulate specialized knowledge. The expert knowledge learning unit automatically learns expert knowledge using, for example, a machine learning algorithm. The expert knowledge learning unit can also improve the quality of advice by incorporating the opinions of experts. For example, the expert knowledge learning unit learns advice provided by experts and generates advice based on that advice. In this way, the expert knowledge learning unit can provide more specialized advice by learning expert knowledge.

[0083] The system includes a feedback collection unit that collects feedback after providing advice. The feedback collection unit makes it possible to collect feedback on the advice received by employees. For example, the feedback collection unit can collect employee feedback through questionnaires or interviews. The feedback collection unit, for example, conducts an online questionnaire to collect employees' evaluations of the advice they received. The feedback collection unit can also collect employees' opinions and impressions through interviews. For example, the feedback collection unit directly interacts with employees to collect information on the effectiveness of the advice and areas for improvement. In this way, the feedback collection unit can collect feedback after providing advice and use it to improve the system.

[0084] The reception unit can estimate the user's emotions and adjust the timing of accepting consultations based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can immediately accept consultations. Furthermore, if the user is relaxed, the reception unit can also accept consultations at an appropriate timing. Furthermore, if the user is in a hurry, the reception unit can quickly accept consultations. In this way, the reception unit can adjust the timing of accepting consultations according to the user's emotions, thereby accepting consultations at more appropriate timing.

[0085] The reception unit can select the most appropriate reception method by referring to the user's past consultation history when receiving a call. For example, the reception unit preferentially suggests reception methods that the user has frequently used in the past. The reception unit can also automatically select the most appropriate reception method from the user's past consultation history. Furthermore, the reception unit can also suggest an appropriate reception method based on the content of the user's past consultation. In this way, the reception unit can select the most appropriate reception method by referring to the user's past consultation history.

[0086] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving consultations. For example, the reception unit preferentially receives consultation contents related to the user's current situation. The reception unit can also filter appropriate consultation contents based on the user's areas of interest. Furthermore, the reception unit can combine the user's current situation and areas of interest to propose optimal consultation contents. In this way, the reception unit can receive more appropriate consultation contents by filtering based on the user's current situation and areas of interest.

[0087] The reception unit can estimate the user's emotions and determine the priority of consultations to be accepted based on the estimated user's emotions. For example, the reception unit can prioritize consultations when the user is feeling stressed. The reception unit can also accept consultations with normal priority when the user is relaxed. Furthermore, the reception unit can quickly accept consultations when the user is in a hurry. In this way, the reception unit can prioritize consultations based on the user's emotions, thereby allowing more important consultations to be accepted with priority.

[0088] The reception unit can prioritize receiving highly relevant consultations by taking into account the user's geographical location information when receiving consultations. For example, if the user is in a specific area, the reception unit prioritizes receiving consultations related to that area. The reception unit can also suggest optimal consultation content based on the user's geographical location information. Furthermore, the reception unit can filter appropriate consultation content based on information related to the user's current location. In this way, the reception unit can prioritize receiving highly relevant consultations by taking into account the user's geographical location information.

[0089] The reception unit can analyze the user's social media activity at the time of reception and receive related consultations. For example, the reception unit analyzes the user's current interests from the user's social media activity and receives related consultations on a priority basis. The reception unit can also suggest appropriate consultation content based on the content posted by the user on social media. Furthermore, the reception unit can analyze the user's social media activity history and filter out the most appropriate consultation content. In this way, the reception unit can analyze the user's social media activity and receive related consultations on a priority basis.

[0090] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit uses a simple and easy-to-understand presentation method. The analysis unit can also provide detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. In this way, the analysis unit can provide more appropriate analysis results by adjusting the way the analysis is presented based on the user's emotions.

[0091] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content. For example, the analysis unit performs a detailed analysis for consultation content with a high level of importance. The analysis unit can also perform a concise analysis for consultation content with a low level of importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the consultation content. In this way, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the consultation content.

[0092] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the consultation content. For example, the analysis unit applies a stress analysis algorithm to a consultation about stress management. The analysis unit can also apply a career analysis algorithm to a career consultation. Furthermore, the analysis unit can also apply a human relationship analysis algorithm to a consultation about human relationships. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of the consultation content.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can perform a short, to-the-point analysis. If the user is relaxed, the analysis unit can also perform a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can also perform a quick analysis. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis according to the user's emotions.

[0094] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the consultation content. For example, the analysis unit prioritizes the analysis of the most recently submitted consultation content. The analysis unit can also postpone the analysis of consultation content that was submitted earlier. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the time of submission. In this way, the analysis unit can provide analysis results more quickly by determining the priority of analysis based on the time of submission of the consultation content.

[0095] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the consultation contents. For example, the analysis unit prioritizes the analysis of consultation contents with high relevance. The analysis unit can also postpone consultation contents with low relevance. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the consultation contents. In this way, the analysis unit can prioritize the analysis of consultation contents with high relevance by adjusting the order of analysis based on the relevance of the consultation contents.

[0096] The generation unit can estimate the user's emotions and adjust the advice generation method based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit generates advice about relaxation methods. Furthermore, if the user is relaxed, the generation unit can also generate detailed advice. Furthermore, if the user is in a hurry, the generation unit can also generate advice that can be implemented quickly. In this way, the generation unit can provide more appropriate advice by adjusting the advice generation method according to the user's emotions.

[0097] The generation unit can adjust the level of detail of the advice based on the importance of the consultation content when generating the advice. For example, the generation unit generates detailed advice for consultation content with a high level of importance. The generation unit can also generate concise advice for consultation content with a low level of importance. Furthermore, the generation unit can dynamically adjust the level of detail of the advice according to the importance of the consultation content. In this way, the generation unit can provide more appropriate advice by adjusting the level of detail of the advice based on the importance of the consultation content.

[0098] The generation unit can apply different generation algorithms depending on the category of the consultation content during generation. For example, the generation unit applies a stress management advice generation algorithm to a consultation about stress management. The generation unit can also apply a career advice generation algorithm to a career consultation. Furthermore, the generation unit can also apply a human relationship advice generation algorithm to a consultation about human relationships. In this way, the generation unit can provide more appropriate advice by applying different generation algorithms depending on the category of the consultation content.

[0099] The generation unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. For example, when the user is feeling stressed, the generation unit generates short and to-the-point advice. When the user is relaxed, the generation unit can also generate detailed advice. Furthermore, when the user is in a hurry, the generation unit can also generate advice that can be implemented quickly. In this way, the generation unit can provide more appropriate advice by adjusting the length of the advice according to the user's emotion.

[0100] The generation unit can determine the priority of advice based on the time of submission of the consultation content when generating the advice. For example, the generation unit generates advice with priority for the consultation content that was submitted most recently. The generation unit can also postpone advice for consultation content that was submitted earlier. Furthermore, the generation unit can dynamically adjust the priority of advice based on the time of submission. In this way, the generation unit can provide advice more quickly by determining the priority of advice based on the time of submission of the consultation content.

[0101] The generation unit can adjust the order of advice based on the relevance of the consultation content when generating the advice. For example, the generation unit generates advice preferentially for consultation content with high relevance. The generation unit can also postpone consultation content with low relevance. Furthermore, the generation unit can dynamically adjust the order of advice based on the relevance of the consultation content. In this way, the generation unit can adjust the order of advice based on the relevance of the consultation content, thereby providing advice preferentially for consultation content with high relevance.

[0102] The providing unit can estimate the user's emotions and adjust the method of providing advice based on the estimated user's emotions. For example, when the user is feeling stressed, the providing unit can provide advice in a simple and easy-to-understand manner. Furthermore, when the user is relaxed, the providing unit can provide detailed advice. Furthermore, when the user is in a hurry, the providing unit can provide advice in a manner that can be quickly implemented. In this way, the providing unit can provide advice in a more appropriate manner by adjusting the method of providing advice according to the user's emotions.

[0103] When providing the service, the providing unit can select the optimal providing method by referring to the user's past consultation history. For example, the providing unit preferentially suggests a providing method that the user has used in the past. The providing unit can also automatically select the optimal providing method from the user's past consultation history. Furthermore, the providing unit can also suggest an appropriate providing method based on the content of the user's past consultation. In this way, the providing unit can select the optimal providing method by referring to the user's past consultation history.

[0104] The providing unit can customize the means for providing advice based on the user's current situation when providing the advice. For example, when the user is in the office, the providing unit provides the advice in text format. Furthermore, when the user is on the move, the providing unit can also provide the advice in audio format. Furthermore, when the user is at home, the providing unit can also provide the advice in video format. In this way, the providing unit can provide advice in a more appropriate manner by customizing the means for providing advice based on the user's current situation.

[0105] The providing unit can estimate the user's emotions and determine the priority of advice provision based on the estimated user's emotions. For example, when the user is feeling stressed, the providing unit can provide advice with priority. Furthermore, when the user is relaxed, the providing unit can also provide advice with normal priority. Furthermore, when the user is in a hurry, the providing unit can quickly provide advice. In this way, the providing unit can provide more important advice with priority by determining the priority of advice provision according to the user's emotions.

[0106] The providing unit can provide optimal advice by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit provides advice related to that area. The providing unit can also suggest optimal advice based on the user's geographical location information. Furthermore, the providing unit can also provide appropriate advice based on information related to the user's current location. In this way, the providing unit can provide optimal advice by taking into account the user's geographical location information.

[0107] At the time of providing advice, the providing unit can analyze the user's social media activity and suggest a means of providing the advice. For example, the providing unit can analyze the user's current interests from the user's social media activity and provide related advice. The providing unit can also suggest appropriate advice based on the content of the user's social media posts. Furthermore, the providing unit can analyze the user's social media activity history and provide optimal advice. In this way, the providing unit can suggest a more appropriate means of providing advice by analyzing the user's social media activity.

[0108] The voice input unit can estimate the user's emotions and adjust the voice input analysis method based on the estimated user's emotions. For example, if the user is feeling stressed, the voice input unit uses a simple and easy-to-understand analysis method. Furthermore, if the user is relaxed, the voice input unit can perform a detailed analysis. Furthermore, if the user is in a hurry, the voice input unit can perform a quick analysis. In this way, the voice input unit can provide more appropriate analysis results by adjusting the voice input analysis method according to the user's emotions.

[0109] The voice input unit can select the optimal analysis method by referring to the user's past voice input history when inputting voice. For example, the voice input unit preferentially suggests voice input methods that the user has used in the past. The voice input unit can also automatically select the optimal analysis method from the user's past voice input history. Furthermore, the voice input unit can also suggest an appropriate analysis method based on the content of the user's past voice input. In this way, the voice input unit can select the optimal analysis method by referring to the user's past voice input history.

[0110] The voice input unit can estimate the user's emotion and determine the priority of voice inputs based on the estimated user's emotion. For example, the voice input unit prioritizes analyzing voice inputs when the user is feeling stressed. The voice input unit can also analyze voice inputs with normal priority when the user is relaxed. Furthermore, the voice input unit can quickly analyze voice inputs when the user is in a hurry. In this way, the voice input unit can prioritize analyzing more important voice inputs by determining the priority of voice inputs according to the user's emotion.

[0111] The voice input unit can select the optimal voice input method by taking into account the user's geographical location information when inputting voice. For example, if the user is in a specific area, the voice input unit suggests a voice input method related to that area. The voice input unit can also select the optimal voice input method based on the user's geographical location information. Furthermore, the voice input unit can also suggest an appropriate voice input method based on information related to the user's current location. In this way, the voice input unit can select the optimal voice input method by taking into account the user's geographical location information.

[0112] The image input unit can estimate the user's emotions and adjust the analysis method of the image input based on the estimated user's emotions. For example, if the user is feeling stressed, the image input unit uses a simple and easy-to-understand analysis method. Furthermore, if the user is relaxed, the image input unit can also perform a detailed analysis. Furthermore, if the user is in a hurry, the image input unit can also perform a quick analysis. In this way, the image input unit can provide more appropriate analysis results by adjusting the analysis method of the image input according to the user's emotions.

[0113] When inputting an image, the image input unit can select the optimal analysis method by referring to the user's past image input history. For example, the image input unit preferentially suggests image input methods that the user has used in the past. The image input unit can also automatically select the optimal analysis method from the user's past image input history. Furthermore, the image input unit can also suggest an appropriate analysis method based on the content of the user's past image input. In this way, the image input unit can select the optimal analysis method by referring to the user's past image input history.

[0114] The image input unit can estimate the user's emotions and determine the priority of image inputs based on the estimated user's emotions. For example, when the user is feeling stressed, the image input unit prioritizes analyzing image inputs. Furthermore, when the user is relaxed, the image input unit can also analyze image inputs with normal priority. Furthermore, when the user is in a hurry, the image input unit can quickly analyze image inputs. In this way, the image input unit can prioritize analyzing more important image inputs by determining the priority of image inputs according to the user's emotions.

[0115] The image input unit can select the optimal image input method by taking into account the user's geographical location information when inputting an image. For example, if the user is in a specific area, the image input unit suggests an image input method related to that area. The image input unit can also select the optimal image input method based on the user's geographical location information. Furthermore, the image input unit can also suggest an appropriate image input method based on information related to the user's current location. This allows the image input unit to select the optimal image input method by taking into account the user's geographical location information.

[0116] The emotion analysis unit can estimate the user's emotion and adjust the emotion analysis method based on the estimated user emotion. For example, if the user is feeling stressed, the emotion analysis unit uses a simple and easy-to-understand emotion analysis method. If the user is relaxed, the emotion analysis unit can also perform a detailed emotion analysis. Furthermore, if the user is in a hurry, the emotion analysis unit can also perform a quick emotion analysis. In this way, the emotion analysis unit can provide more appropriate emotion analysis results by adjusting the emotion analysis method according to the user's emotion.

[0117] During emotion analysis, the emotion analysis unit can select the optimal analysis method by referring to the user's past emotion history. For example, the emotion analysis unit preferentially suggests emotion analysis methods that the user has used in the past. The emotion analysis unit can also automatically select the optimal analysis method from the user's past emotion history. Furthermore, the emotion analysis unit can also suggest an appropriate analysis method based on the content of the user's past emotions. In this way, the emotion analysis unit can select the optimal analysis method by referring to the user's past emotion history.

[0118] The emotion analysis unit can estimate the user's emotion and determine the priority of emotion analysis based on the estimated user emotion. For example, the emotion analysis unit can prioritize emotion analysis when the user is feeling stressed. The emotion analysis unit can also perform emotion analysis with normal priority when the user is relaxed. Furthermore, the emotion analysis unit can also perform emotion analysis quickly when the user is in a hurry. In this way, the emotion analysis unit can prioritize emotion analysis according to the user's emotion, thereby allowing more important emotion analysis to be performed with priority.

[0119] The sentiment analysis unit can select the optimal analysis method by taking into account the user's geographical location information when analyzing sentiment. For example, if the user is in a specific area, the sentiment analysis unit suggests a sentiment analysis method related to that area. The sentiment analysis unit can also select the optimal sentiment analysis method based on the user's geographical location information. Furthermore, the sentiment analysis unit can also suggest an appropriate sentiment analysis method based on information related to the user's current location. In this way, the sentiment analysis unit can select the optimal sentiment analysis method by taking into account the user's geographical location information.

[0120] The history analysis unit can estimate the user's emotions and adjust the history analysis method based on the estimated user emotions. For example, if the user is feeling stressed, the history analysis unit uses a simple and easy-to-understand history analysis method. Furthermore, if the user is relaxed, the history analysis unit can perform a detailed history analysis. Furthermore, if the user is in a hurry, the history analysis unit can perform a quick history analysis. In this way, the history analysis unit can provide more appropriate history analysis results by adjusting the history analysis method according to the user's emotions.

[0121] During history analysis, the history analysis unit can select the optimal analysis method by referring to the user's past consultation history. For example, the history analysis unit preferentially suggests a history analysis method that the user has used in the past. The history analysis unit can also automatically select the optimal analysis method from the user's past consultation history. Furthermore, the history analysis unit can also suggest an appropriate analysis method based on the content of the user's past consultation. In this way, the history analysis unit can select the optimal analysis method by referring to the user's past consultation history.

[0122] The history analysis unit can estimate the user's emotions and determine the priority of history analysis based on the estimated user's emotions. For example, the history analysis unit can prioritize history analysis when the user is feeling stressed. The history analysis unit can also perform history analysis with normal priority when the user is relaxed. Furthermore, the history analysis unit can also perform history analysis quickly when the user is in a hurry. In this way, the history analysis unit can prioritize history analysis according to the user's emotions, thereby allowing more important history analysis to be performed with priority.

[0123] When analyzing history, the history analysis unit can select the optimal analysis method by taking into account the user's geographical location information. For example, if the user is in a specific area, the history analysis unit can suggest a history analysis method related to that area. The history analysis unit can also select the optimal history analysis method based on the user's geographical location information. Furthermore, the history analysis unit can also suggest an appropriate history analysis method based on information related to the user's current location. In this way, the history analysis unit can select the optimal history analysis method by taking into account the user's geographical location information.

[0124] The expert knowledge learning unit can estimate the user's emotions and adjust the expert knowledge learning method based on the estimated user's emotions. For example, if the user is feeling stressed, the expert knowledge learning unit uses a simple and easy-to-understand learning method. Furthermore, if the user is relaxed, the expert knowledge learning unit can perform detailed learning. Furthermore, if the user is in a hurry, the expert knowledge learning unit can perform quick learning. In this way, the expert knowledge learning unit can provide more appropriate learning results by adjusting the expert knowledge learning method according to the user's emotions.

[0125] The expert knowledge learning unit can select the optimal learning algorithm by referring to past learning data when learning expert knowledge. The expert knowledge learning unit selects the most effective learning algorithm based on, for example, past learning data. The expert knowledge learning unit can also select a learning algorithm specialized for a specific field from past learning data. Furthermore, the expert knowledge learning unit can analyze past learning data and dynamically select the optimal learning algorithm. In this way, the expert knowledge learning unit can select the optimal learning algorithm by referring to past learning data.

[0126] The expert knowledge learning unit can estimate the user's emotions and adjust the learning frequency of the expert knowledge based on the estimated user's emotions. For example, the expert knowledge learning unit can set the learning frequency low when the user is feeling stressed. The expert knowledge learning unit can also set the learning frequency high when the user is relaxed. Furthermore, the expert knowledge learning unit can also quickly learn when the user is in a hurry. In this way, the expert knowledge learning unit can provide more appropriate learning results by adjusting the learning frequency of the expert knowledge according to the user's emotions.

[0127] When learning expert knowledge, the expert knowledge learning unit can select the optimal study method by taking into account the user's geographical location information. For example, if the user is in a specific area, the expert knowledge learning unit suggests a study method related to that area. The expert knowledge learning unit can also select the optimal study method based on the user's geographical location information. Furthermore, the expert knowledge learning unit can also suggest an appropriate study method based on information related to the user's current location. In this way, the expert knowledge learning unit can select the optimal study method by taking into account the user's geographical location information.

[0128] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user's emotions. For example, when the user is feeling stressed, the feedback collection unit uses a simple and easy-to-understand feedback collection method. When the user is relaxed, the feedback collection unit can also collect detailed feedback. Furthermore, when the user is in a hurry, the feedback collection unit can also collect feedback quickly. In this way, the feedback collection unit can collect more appropriate feedback by adjusting the feedback collection method according to the user's emotions.

[0129] When collecting feedback, the feedback collection unit can select the optimal collection method by referring to the user's past feedback history. For example, the feedback collection unit preferentially suggests feedback collection methods that the user has used in the past. The feedback collection unit can also automatically select the optimal collection method from the user's past feedback history. Furthermore, the feedback collection unit can also suggest an appropriate collection method based on the content of the user's past feedback. In this way, the feedback collection unit can select the optimal collection method by referring to the user's past feedback history.

[0130] The feedback collection unit can estimate the user's emotions and determine the priority of feedback collection based on the estimated user's emotions. For example, the feedback collection unit prioritizes feedback collection when the user is feeling stressed. The feedback collection unit can also collect feedback with normal priority when the user is relaxed. Furthermore, the feedback collection unit can quickly collect feedback when the user is in a hurry. In this way, the feedback collection unit can prioritize collecting more important feedback by determining the priority of feedback collection according to the user's emotions.

[0131] The feedback collection unit can select the optimal collection method when collecting feedback by taking into account the user's geographical location information. For example, if the user is in a specific area, the feedback collection unit suggests a feedback collection method related to that area. The feedback collection unit can also select the optimal feedback collection method based on the user's geographical location information. Furthermore, the feedback collection unit can also suggest an appropriate feedback collection method based on information related to the user's current location. In this way, the feedback collection unit can select the optimal feedback collection method by taking into account the user's geographical location information. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, provision unit, voice input unit, image input unit, emotion analysis unit, history analysis unit, expert knowledge learning unit, and feedback collection unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives consultations from employees about their concerns. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of the consultation. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates advice. The provision unit is realized by the control unit 46A of the smart device 14 and provides advice. The voice input unit is realized by the microphone 38B of the smart device 14 and receives voice input. The image input unit is realized by the camera 42 of the smart device 14 and receives image input. The emotion analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes emotions. The history analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs analysis based on past consultation history. The expert knowledge learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns expert knowledge. The feedback collection unit is realized by the control unit 46A of the smart device 14 and collects feedback after advice is provided. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, provision unit, voice input unit, image input unit, emotion analysis unit, history analysis unit, expert knowledge learning unit, and feedback collection unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives consultations from employees. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the consultation content. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates advice. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides advice. The voice input unit is realized by the microphone 238 of the smart glasses 214 and receives voice input. The image input unit is realized by the camera 42 of the smart glasses 214 and receives image input. The emotion analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes emotions. The history analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs analysis based on past consultation history. The expert knowledge learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns expert knowledge. The feedback collection unit is realized by the control unit 46A of the smart glasses 214 and collects feedback after providing advice. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, provision unit, voice input unit, image input unit, emotion analysis unit, history analysis unit, expert knowledge learning unit, and feedback collection unit, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives consultations from employees about their concerns. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of the consultation. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates advice. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides advice. The voice input unit is realized by the microphone 238 of the headset type terminal 314 and receives voice input. The image input unit is realized by the camera 42 of the headset type terminal 314 and receives image input. The emotion analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes emotions. The history analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs analysis based on past consultation history. The expert knowledge learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns expert knowledge. The feedback collection unit is realized by the control unit 46A of the headset terminal 314 and collects feedback after advice is given. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, provision unit, voice input unit, image input unit, emotion analysis unit, history analysis unit, expert knowledge learning unit, and feedback collection unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives consultations from employees about their concerns. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of the consultation. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates advice. The provision unit is realized by the control unit 46A of the robot 414 and provides advice. The voice input unit is realized by the microphone 238 of the robot 414 and receives voice input. The image input unit is realized by the camera 42 of the robot 414 and receives image input. The emotion analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes emotions. The history analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs analysis based on past consultation history. The expert knowledge learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns expert knowledge. The feedback collection unit is realized by the control unit 46A of the robot 414 and collects feedback after advice is given.

[0132] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0133] The system uses AI to provide 24-hour advice to employees regarding their concerns. Employees input their concerns, and the AI ​​analyzes the content of the input, generates appropriate advice, and delivers it to the employee in real time. For example, if an employee inputs a comment such as "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve employees' mental health and work performance. The system can also include an expert recommendation module that recommends appropriate experts based on the content of the consultation. For example, if an employee consults about a concern related to a specific field, the system will recommend an expert in that field. The expert recommendation module can also recommend the most appropriate expert based on the employee's past consultation history. Furthermore, the expert recommendation module can recommend appropriate experts based on the employee's current situation and areas of interest. This allows the expert recommendation module to help employees receive more specialized advice.

[0134] The system uses AI to provide advice 24 hours a day to employees who have concerns. Employees input their concerns, and the AI ​​analyzes the content of the input, generates appropriate advice, and provides it to the employee in real time. For example, if an employee inputs a comment like "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve employees' mental health and work performance. The system can also include a resource provider that provides appropriate resources based on the content of the consultation. For example, if an employee requests a consultation about stress management, the resource provider recommends books or online courses on stress management. The resource provider can also provide optimal resources by taking into account the employee's past consultation history. Furthermore, the resource provider can provide appropriate resources based on the employee's current situation and areas of interest. This allows the resource provider to help employees access more specific resources.

[0135] The system uses AI to provide advice 24 hours a day to employees who have concerns. Employees input their concerns, and the AI ​​analyzes the content of the consultation, generates appropriate advice, and provides it to the employee in real time. For example, if an employee inputs something like "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve employees' mental health and work performance. The system can also include a training provider that provides appropriate training programs based on the content of the consultation. For example, if an employee consults about stress management, the training provider recommends a stress management training program. The training provider can also provide the most appropriate training program by taking into account the employee's past consultation history. Furthermore, the training provider can provide appropriate training programs based on the employee's current situation and areas of interest. This allows the training provider to support employees in receiving more specific training.

[0136] The system uses AI to provide 24-hour advice to employees regarding their concerns. Employees input their concerns, and the AI ​​analyzes the content of the input, generates appropriate advice, and provides it to the employee in real time. For example, if an employee inputs a comment such as "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve their mental health and work performance. The system can also include a community recommendation module that recommends appropriate communities based on the content of the consultation. For example, if an employee consults about a concern related to a specific field, the system recommends online communities related to that field. The community recommendation module can also recommend the most appropriate community based on the employee's past consultation history. Furthermore, the community recommendation module can recommend appropriate communities based on the employee's current situation and areas of interest. This allows the community recommendation module to help employees interact and share information with others who share the same concerns.

[0137] The system uses AI to provide 24-hour advice to employees regarding their concerns. Employees input their concerns, and the AI ​​analyzes the content of the input, generates appropriate advice, and provides it to the employee in real time. For example, if an employee inputs a message such as "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve employees' mental health and work performance. The system can also include an event recommendation unit that recommends appropriate events based on the content of the consultation. For example, if an employee requests a consultation about stress management, the event recommendation unit will recommend a workshop or seminar on stress management. The event recommendation unit can also recommend appropriate events based on the employee's past consultation history. Furthermore, the event recommendation unit can recommend appropriate events based on the employee's current situation and areas of interest. This allows the event recommendation unit to help employees participate in more specific events.

[0138] The system uses AI to provide advice to employees 24 hours a day in response to their concerns. Employees input their concerns, and the AI ​​analyzes the content of the input, generates appropriate advice, and provides it to the employee in real time. For example, if an employee inputs something like "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve employees' mental health and work performance. The system can also include a relaxation provider that provides appropriate relaxation methods based on the content of the input. For example, if an employee requests advice on stress management, the relaxation provider recommends meditation or yoga. The relaxation provider can also provide optimal relaxation methods by taking into account the employee's past consultation history. Furthermore, the relaxation provider can provide appropriate relaxation methods based on the employee's current situation and areas of interest. This allows the relaxation provider to help employees practice more specific relaxation methods.

[0139] The system uses AI to provide advice to employees 24 hours a day in response to their concerns. In this system, employees input their concerns, and the AI ​​analyzes the content of the consultation, generates appropriate advice, and provides it to the employee in real time. For example, if an employee inputs content such as "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management methods and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive advice quickly. This is expected to improve employees' mental health and work performance. The system can also include a feedback providing unit that provides appropriate feedback based on the content of the consultation. For example, if an employee consults about stress management, the feedback providing unit provides feedback on the progress of stress management. The feedback providing unit can also provide optimal feedback by taking into account the employee's past consultation history. Furthermore, the feedback providing unit can provide appropriate feedback based on the employee's current situation and areas of interest. This allows the feedback providing unit to help employees receive more specific feedback.

[0140] The system uses AI to provide 24-hour advice to employees regarding their concerns. Employees input their concerns, and the AI ​​analyzes the content of the input, generates appropriate advice, and delivers it to the employee in real time. For example, if an employee inputs a comment like "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve employees' mental health and work performance. The system can also include a mental health check module that provides appropriate mental health checks based on the content of the consultation. For example, if an employee consults about stress management, the mental health check module provides a checklist for measuring stress levels. The mental health check module can also provide the most appropriate mental health check by taking into account the employee's past consultation history. Furthermore, the mental health check module can provide appropriate mental health checks based on the employee's current situation and areas of interest. This allows the mental health check module to help employees understand their own mental health status and take appropriate measures.

[0141] The system uses AI to provide 24-hour advice to employees regarding their concerns. Employees input their concerns, and the AI ​​analyzes the content of the input, generates appropriate advice, and delivers it to the employee in real time. For example, if an employee inputs a comment like "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve employees' mental health and work performance. The system can also include a mental health check module that provides appropriate mental health checks based on the content of the consultation. For example, if an employee consults about stress management, the mental health check module provides a checklist for measuring stress levels. The mental health check module can also provide the most appropriate mental health check by taking into account the employee's past consultation history. Furthermore, the mental health check module can provide appropriate mental health checks based on the employee's current situation and areas of interest. This allows the mental health check module to help employees understand their own mental health status and take appropriate measures.

[0142] The system uses AI to provide 24-hour advice to employees regarding their concerns. Employees input their concerns, and the AI ​​analyzes the content of the input, generates appropriate advice, and provides it to the employee in real time. For example, if an employee inputs a comment such as "I'm feeling stressed at work," the AI ​​analyzes the content and provides advice on stress management and relaxation. This system allows employees to consult about their concerns anytime, anywhere and receive prompt advice. This is expected to improve employees' mental health and work performance. The system can also include a support group recommendation module that recommends appropriate mental health support groups based on the content of the consultation. For example, if an employee consults about a specific topic, the system recommends support groups related to that topic. The support group recommendation module can also recommend the most appropriate support group based on the employee's past consultation history. Furthermore, the support group recommendation module can recommend appropriate support groups based on the employee's current situation and areas of interest. This allows employees to interact with and share information with others who share the same concerns.

[0143] The processing flow of the second embodiment will be briefly explained below.

[0144] Step 1: The reception unit accepts employee consultations. Employee consultations may include, but are not limited to, interpersonal relationships in the workplace, work procedures, and career paths. The reception unit may accept consultations entered in text format, for example. Step 2: The analysis unit analyzes the consultation content received by the reception unit. The analysis unit uses, for example, natural language processing technology to understand the consultation content and generate appropriate advice. The analysis unit can use methods such as text analysis, sentiment analysis, and keyword extraction. Step 3: The generator generates advice based on the content analyzed by the analyzer. The generator can generate specific guidelines for action, such as stress management methods or relaxation advice. The generator can generate the advice in text or audio format. Step 4: The providing unit provides the advice generated by the generating unit. The providing unit can, for example, display the advice to the employee in real time. The providing unit can provide the advice in text format or audio format.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may 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 AIs including the generative AI may be replaced with rule-based processes, and rule-based processes may be replaced with processes performed by AIs including the generative AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0150] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0163] 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.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0176] 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.

[0177] 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.

[0178] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0179] 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.

[0180] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0181] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0182] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0193] 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.

[0194] 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.

[0195] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0196] 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.

[0197] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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."

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] [Explanation of symbols]

[0217] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk that handles employee consultations and advice requests. An analysis unit analyzes the content of consultations received by the reception unit, A generation unit that generates advice based on the content analyzed by the analysis unit, A providing unit that provides advice generated by the generation unit, Equipped with A system characterized by:

2. It is equipped with a voice input unit that accepts voice input. The system of claim 1 .

3. It is equipped with an image input section that accepts image input. The system of claim 1 .

4. Equipped with a sentiment analysis unit that performs sentiment analysis The system of claim 1 .

5. We have a history analysis department that analyzes past consultation records. The system of claim 1 .

6. It has a specialist knowledge learning department where students can learn the knowledge of experts. The system of claim 1 .

7. It includes a feedback collection unit that collects feedback after advice has been given. The system of claim 1 .

8. The reception unit The system estimates the user's emotions and adjusts the timing of consultations based on those estimated emotions. The system of claim 1 .

Images