system

Abstract

The system according to this embodiment aims to efficiently receive inquiries regarding the workplace environment and provide appropriate solutions. [Solution] The system according to the embodiment comprises a reception unit, a questionnaire unit, and a presentation unit. The reception unit receives the content of the consultation. The questionnaire unit conducts a questionnaire based on the content of the consultation received by the reception unit. The presentation unit learns the trends of each respondent obtained by the questionnaire unit and presents a solution suitable for each individual.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the consultation response regarding the workplace environment is inefficient, and there is room for improving the workplace environment of both the consultation side and the consulted side.

[0005] The system according to the embodiment aims to efficiently receive consultations regarding the workplace environment and present appropriate solutions.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a questionnaire unit, and a presentation unit. The reception unit receives the consultation content. The questionnaire unit conducts a questionnaire based on the consultation content received by the reception unit. The presentation unit learns the tendency of each respondent obtained by the questionnaire unit and presents a solution suitable for each individual. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently receive inquiries regarding the workplace environment and provide appropriate solutions. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The workplace environment improvement AI agent "Nandemo" according to an embodiment of the present invention is a system that receives and responds quickly to inquiries from employees. This system aims to improve the workplace environment by responding to various inquiries received by the human resources department, general affairs department, and information systems department. For example, it receives inquiries about the workplace environment and responds quickly based on past inquiry history, existing FAQs, and the position of the person making the inquiry. For example, in response to inquiries such as "The sales department's phone calls are too loud," "The setting sun is too bright and makes it difficult to see the PC screen," "The office is too cold," or "I'm feeling mentally distressed," the AI ​​will suggest appropriate solutions. Furthermore, the AI ​​conducts surveys on "room temperature," "noise," "network quality," and "equipment status," and learns the tendencies of each respondent to suggest solutions tailored to each individual. For example, based on the results of a survey on room temperature, it is possible to adjust the air conditioning setting. In addition, by referring to data that has learned individual needs and tendencies, it is possible to optimize the office layout in a way that is easy for everyone in the workplace to work in. For example, if many employees are bothered by noise, it is possible to create a quiet area. Furthermore, when receiving inquiries about the system, it is possible to allocate different resources to each person making the inquiry. For example, it can prioritize allocating resources to employees who feel the network is slow. The AI ​​also selects response methods appropriate to each employee's IT literacy, and when necessary, such as system upgrades, overall resource optimization, or troubleshooting, it presents solutions to the IT department that take into account the individual and departmental "IT resource needs." In this way, the workplace environment-improving AI agent "Nandemo" (Anything) can improve productivity by addressing the needs of each employee and improving the workplace environment. For instance, when addressing the concerns of new employees or mid-career hires, the AI, recognizing them as "new to the company," provides a more reassuring response. It can also explain terminology that employees might hesitate to ask about due to concerns about interrupting a meeting, including how it's used within the company, based on accumulated internal documents. Even when you don't know who to ask, it connects you to the most suitable employee based on HR data.This allows the "Anything" AI agent for improving the workplace environment to respond quickly and appropriately to employee inquiries and improve the work environment.

[0029] The workplace environment improvement AI agent "Nandemo" according to this embodiment comprises a reception unit, a survey unit, and a presentation unit. The reception unit receives inquiries from employees. Inquiries include, but are not limited to, technical inquiries, business inquiries, and personal inquiries. The reception unit can, for example, input inquiries in text format. The reception unit can also receive inquiries using voice input. For example, if an employee speaks about their inquiry, the reception unit converts the voice into text and records it. Furthermore, the reception unit can respond quickly by referring to past inquiry history. For example, if a similar inquiry has been made in the past, it can quickly present a solution based on the response history. The survey unit conducts a survey based on the inquiries received by the reception unit. The content of the survey includes, but is not limited to, room temperature, noise, network quality, and equipment status. The survey unit conducts a survey of employees and collects the results. The survey can be conducted using, for example, an online form. The survey unit also analyzes the survey results and learns the trends of each respondent. For example, the AI ​​agent "Nandemo" (Anything) statistically analyzes survey results to extract specific trends. The presentation unit learns the trends for each respondent obtained by the survey unit and presents solutions tailored to each individual. For example, the presentation unit adjusts the air conditioning temperature based on survey results regarding indoor temperature. For example, if the survey results indicate that many employees feel cold, the air conditioning temperature is raised. The presentation unit can also create quiet areas if many employees are bothered by noise. For example, it identifies areas where noise is a concern and creates a quiet space in those areas. Furthermore, the presentation unit can prioritize resource allocation to employees who feel the network is slow. For example, it monitors network usage and prioritizes bandwidth allocation to employees who feel the network is slow. In this way, the workplace environment improvement AI agent "Nandemo" according to this embodiment can conduct surveys based on the content of employee consultations, learn the trends for each respondent, and present solutions tailored to each individual.

[0030] The reception desk receives inquiries from employees. These inquiries include, but are not limited to, technical, business, and personal matters. The reception desk can, for example, accept inquiries in text format. It can also accept inquiries using voice input. For example, if an employee speaks about their inquiry, the reception desk converts the voice into text and records it. Furthermore, the reception desk can respond quickly by referring to past inquiry history. For example, if a similar inquiry has been made in the past, it can quickly provide a solution based on the response history. The reception desk can also use AI to automatically categorize inquiries and route them to the appropriate person. For example, technical inquiries are routed to the technical department, business inquiries to the sales department, and personal inquiries to the human resources department. This enables quick and appropriate responses according to the content of the inquiry. In addition, the reception desk can evaluate the importance and urgency of inquiries and prioritize them. For example, high-urgency inquiries are addressed immediately, and high-importance inquiries are processed with priority. This allows for a quick response to employee needs. Furthermore, the reception department can save a history of consultations in a database, which can then be used for future analysis and improvement. For example, by analyzing past consultations, common problems and areas for improvement can be identified, which can then be used to improve the workplace environment. This allows the reception department to efficiently receive employee consultations and provide prompt and appropriate responses.

[0031] The Survey Department conducts surveys based on inquiries received by the Reception Department. Survey content may include, but is not limited to, questions about room temperature, noise levels, network quality, and equipment status. The Survey Department can, for example, conduct surveys with employees and collect the results. Surveys can be conducted using online forms. The Survey Department also analyzes survey results and learns trends from individual respondents. For example, it can statistically analyze survey results to extract specific trends. The Survey Department can use AI to automatically analyze survey results and identify patterns and trends. For example, by analyzing survey results regarding room temperature and finding that many employees feel cold, it can identify the cause and implement appropriate measures. Similarly, by analyzing survey results regarding noise and finding that noise is a problem in a specific area, it can identify that area and create a quiet space. Furthermore, by analyzing survey results regarding network quality and finding that many employees feel the network is slow during certain times or in specific areas, it can identify the cause and prioritize resource allocation. Based on the survey results, the Survey Department can understand employee needs and problems and use this information to improve the workplace environment. For example, based on the survey results, specific measures can be taken, such as adjusting indoor temperature, reducing noise, and improving network quality. This allows the survey department to understand employees' needs and problems and use that information to improve the workplace environment.

[0032] The suggestion unit learns the trends of each respondent obtained from the survey unit and presents solutions tailored to each individual. For example, the suggestion unit adjusts the air conditioning temperature based on the survey results regarding indoor temperature. For instance, if the survey results indicate that many employees feel cold, it raises the air conditioning temperature. The suggestion unit can also create quiet areas if many employees are bothered by noise. For example, it identifies areas where noise is a concern and creates a quiet space in those areas. Furthermore, the suggestion unit can prioritize resource allocation to employees who feel the network is slow. For example, it monitors network usage and prioritizes bandwidth allocation to employees who feel the network is slow. The suggestion unit can use AI to analyze survey results and automatically propose optimal solutions. For example, it can analyze survey results regarding indoor temperature and propose the optimal air conditioning temperature. It can also analyze survey results regarding noise and propose specific measures to create quiet areas. Furthermore, it can analyze survey results regarding network quality and propose specific measures to prioritize resource allocation. The suggestion unit can also provide specific procedures and methods for implementing the proposed solutions. For example, it can provide specific procedures for adjusting the air conditioning temperature and specific methods for creating quiet areas. This allows the presentation unit to propose solutions tailored to each employee, contributing to improvements in the work environment.

[0033] The display unit can adjust the air conditioning temperature setting based on the results of a survey regarding indoor temperature. For example, the display unit can conduct a survey on indoor temperature and collect the results. For example, if the survey results indicate that many employees feel cold, the display unit can raise the air conditioning temperature setting. The display unit can also use AI to calculate the optimal temperature when adjusting the air conditioning temperature setting based on the survey results. For example, the AI ​​can analyze the survey results and suggest the optimal air conditioning temperature setting. Furthermore, the display unit can also consider the individual needs of employees when adjusting the air conditioning temperature setting. For example, if a particular employee is sensitive to the cold, the temperature around that employee's desk can be adjusted. In this way, a comfortable work environment can be provided by adjusting the air conditioning temperature setting based on the survey results regarding indoor temperature. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the survey results into a generating AI and have the generating AI perform the adjustment of the air conditioning temperature setting.

[0034] The system can create quiet areas if many employees are bothered by noise. For example, the system can conduct a survey on noise and collect the results. If the survey results indicate that many employees are bothered by noise, it can create quiet areas. The system can also use AI to suggest the optimal placement of quiet areas based on the survey results. For example, the AI ​​can analyze the survey results and suggest the optimal placement of quiet areas. Furthermore, the system can consider the individual needs of employees when creating quiet areas. For example, if a specific employee needs a quiet environment, a quiet area can be created around that employee's desk. This allows for a more comfortable work environment by providing quiet areas when many employees are bothered by noise. Some or all of the above processes in the system may be performed using AI or not. For example, the system can input the survey results into a generating AI and have the generating AI determine the placement of quiet areas.

[0035] The allocation unit can prioritize resource allocation to employees who feel the network is slow. For example, the allocation unit can monitor network usage and prioritize bandwidth allocation to employees who feel the network is slow. For example, it can monitor network usage in real time and prioritize resource allocation to employees who feel the network is slow. The allocation unit can also use AI to suggest the optimal allocation when allocating network resources. For example, the AI ​​can analyze network usage and suggest the optimal resource allocation. Furthermore, the allocation unit can consider the individual needs of employees when allocating resources. For example, if a particular employee is working on an important project, resources can be prioritized for that employee. This provides a comfortable work environment by prioritizing resource allocation to employees who feel the network is slow. Some or all of the above processes in the allocation unit may be performed using AI or not. For example, the allocation unit can input network usage data into a generating AI and have the generating AI perform resource allocation optimization.

[0036] The presentation unit can select a response method that matches the employee's IT literacy. For example, the presentation unit can evaluate an employee's IT literacy and select a response method appropriate to that level. For instance, it might provide answers using technical terms to employees with high IT literacy and use simple language to employees with low IT literacy. The presentation unit can also use AI to suggest the optimal evaluation method when assessing IT literacy. For example, the AI ​​could analyze an employee's IT literacy and suggest the optimal evaluation method. Furthermore, the presentation unit can consider the individual needs of employees when selecting a response method. For example, if a particular employee asks a question about a specific system, it might request an employee familiar with that system to provide the answer. This allows the presentation unit to provide a solution that is appropriate for the user by selecting a response method that matches their IT literacy. Some or all of the above processes in the presentation unit may be performed using AI or not. For example, the presentation unit can input employee IT literacy data into a generating AI and have the generating AI select the optimal response method.

[0037] The proposal unit can present solutions to the IT department that take into account the "IT resource needs" of individuals and departments at times such as system upgrades, overall resource optimization, and failure response. For example, when system upgrades are needed, the proposal unit analyzes the IT resource usage of each department and proposes an optimal upgrade plan. The proposal unit can also use AI to propose the optimal resource allocation when performing overall resource optimization. For example, the AI ​​analyzes the resource usage of each department and proposes the optimal resource allocation. Furthermore, when responding to failures, the proposal unit can present solutions that take into account the needs of each department. For example, if a particular department is working on an important project, resources will be allocated to that department preferentially. This enables efficient resource management by presenting solutions to the IT department that take into account the "IT resource needs" of individuals and departments at times such as system upgrades, overall resource optimization, and failure response. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input resource usage data from each department into a generating AI and have the generating AI execute a proposal for the optimal resource allocation.

[0038] The information dissemination unit can provide a greater sense of security to new employees and mid-career hires who have concerns. For example, the information dissemination unit can receive concerns from new employees and mid-career hires and respond accordingly. For example, if a new employee is feeling anxious about workplace rules and culture, the information dissemination unit can provide information to reassure them. The information dissemination unit can also use AI to analyze the concerns of new employees and mid-career hires and propose the most suitable response. For example, the AI ​​can analyze a new employee's concerns and provide specific advice to reassure them. Furthermore, the information dissemination unit can also provide support that takes into account the individual needs of new employees and mid-career hires. For example, if a particular new employee is feeling anxious about a particular skill, training related to that skill can be provided. In this way, by providing a greater sense of security to new employees and mid-career hires who have concerns, the workplace environment can be improved. Some or all of the above processes in the information dissemination unit may be performed using AI or not. For example, the presentation unit can input data on the concerns of new employees into a generating AI, and have the AI ​​generate suggestions for the most suitable solutions.

[0039] The presentation unit can explain terms that employees might hesitate to ask about in order to avoid interrupting the flow of a meeting, including how they are used within the company, based on accumulated internal documents. For example, the presentation unit can quickly explain technical terms and abbreviations that come up during a meeting. For instance, if an employee wants to know the meaning of a term that came up during a meeting, the presentation unit will search for the meaning and usage of that term in internal documents and provide it. The presentation unit can also use AI to analyze the meaning of terms and provide the most appropriate explanation. For example, the AI ​​will analyze the meaning of a term and provide an explanation that includes how it is used within the company. Furthermore, when explaining terms, the presentation unit can also consider the individual needs of employees. For example, if a particular employee wants to know about terms related to a specific project, it will provide information related to that project. In this way, by explaining terms that employees might hesitate to ask about in order to avoid interrupting the flow of a meeting, including how they are used within the company, based on accumulated internal documents, the efficiency of meetings is improved. Some or all of the above processing in the presentation unit may be performed using AI, or not. For example, the presentation unit can input terms that came up during a meeting into a generating AI and have the AI ​​generate the most appropriate explanation.

[0040] The referral unit can connect an employee to the most suitable person based on HR data when the appropriate person to ask is unknown. For example, if an employee wants to discuss a specific problem but doesn't know who to ask, the referral unit will identify the most suitable person based on HR data. For example, if the question concerns a specific technology, it will identify and connect the employee with expertise in that technology. The referral unit can also use AI to identify the most suitable person. For example, the AI ​​will analyze HR data and suggest the most suitable person. Furthermore, the referral unit can identify the most suitable person by considering the individual needs of the employee. For example, if the question concerns a specific project, it will identify and connect the employee with an employee involved in that project. This enables rapid problem solving by connecting the employee to the most suitable person based on HR data when the appropriate person to ask is unknown. Some or all of the above processes in the referral unit may be performed using AI or not. For example, the referral unit can input the consultation content into a generating AI and have the generating AI identify the most suitable person.

[0041] The reception department can analyze the user's past consultation history when receiving a consultation request and select the most appropriate reception method. For example, the reception department can suggest the most appropriate reception method based on the types of consultations the user has frequently had in the past. For example, if the user has had many technical consultations in the past, it will select a reception method appropriate to those types of consultations. The reception department can also select the most effective reception method from the user's past consultation history. For example, it can analyze past consultation history and automatically select the most appropriate reception method. Furthermore, the reception department can select the most appropriate reception method considering the user's individual needs. For example, if a particular user prefers a specific method of consultation, that method will be prioritized. In this way, the reception department can select the most appropriate reception method by analyzing the user's past consultation history. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input the user's past consultation history data into a generating AI and have the generating AI select the most appropriate reception method.

[0042] The reception desk can filter inquiries based on the user's current work situation and areas of interest when receiving them. For example, the reception desk can prioritize receiving inquiries that are relevant to the user's current work situation. For instance, if the user is currently working on a specific project, it will prioritize receiving inquiries related to that project. The reception desk can also filter appropriate inquiries based on the user's areas of interest. For example, if the user is interested in a specific technology, it will prioritize receiving inquiries related to that technology. Furthermore, the reception desk can select the most suitable inquiries based on the user's work situation and areas of interest. For example, if the user is interested in a specific task, it will prioritize receiving inquiries related to that task. In this way, relevant inquiries can be prioritized by filtering based on the user's current work situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's work situation and areas of interest data into a generating AI and have the generating AI perform the filtering of the most suitable inquiries.

[0043] The reception desk can prioritize receiving inquiries that are highly relevant, taking into account the user's geographical location. For example, the reception desk can prioritize receiving inquiries that are highly relevant based on the user's geographical location. For instance, if the user is in a specific region, it will prioritize receiving inquiries related to that region. The reception desk can also select the most appropriate inquiries by considering the user's current location. For example, if the user is in a specific location, it will prioritize receiving inquiries related to that location. Furthermore, the reception desk can filter appropriate inquiries based on the user's geographical location. For example, if the user is in a specific area, it will prioritize receiving inquiries related to that area. In this way, by considering the user's geographical location, highly relevant inquiries can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI perform the filtering of the most appropriate inquiries.

[0044] The reception department can analyze the user's social media activity when receiving a consultation request and accept relevant consultation requests. For example, the reception department can prioritize accepting relevant consultation requests based on the user's social media activity. For instance, if a user frequently posts about a particular topic on social media, it will prioritize accepting consultation requests related to that topic. The reception department can also analyze the user's social media activity and select the most appropriate consultation request. For example, if a user makes many posts about a particular theme, it will prioritize accepting consultation requests related to that theme. Furthermore, the reception department can filter appropriate consultation requests based on the user's social media activity. For example, if a user shows a lot of interest in a particular topic, it will prioritize accepting consultation requests related to that topic. In this way, by analyzing the user's social media activity, it is possible to prioritize accepting relevant consultation requests. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the user's social media activity data into a generating AI and have the generating AI perform the filtering of the most appropriate consultation requests.

[0045] The survey department can improve the accuracy of questions by referring to past survey results when conducting a survey. For example, the survey department can optimize the content of questions based on past survey results. For example, it can analyze past survey results and propose the most suitable questions. The survey department can also improve the accuracy of questions by referring to past survey results. For example, it can improve the accuracy of questions based on past survey results. Furthermore, the survey department can analyze past survey results and propose the most suitable questions. For example, it can propose the most suitable questions based on past survey results. In this way, the accuracy of questions can be improved by referring to past survey results. Some or all of the above processing in the survey department may be performed using AI or not. For example, the survey department can input past survey result data into a generating AI and have the generating AI perform the optimization of the question content.

[0046] The survey unit can apply different question algorithms depending on the user's job content when conducting a survey. For example, the survey unit can apply the most suitable question algorithm based on the user's job content. For example, if the user is engaged in a specific job, it can apply a question algorithm related to that job. The survey unit can also select an appropriate question algorithm considering the user's job content. For example, if the user is engaged in a specific task, it can apply a question algorithm related to that task. Furthermore, the survey unit can apply different question algorithms depending on the user's job content. For example, if the user is engaged in a specific project, it can apply a question algorithm related to that project. By applying different question algorithms according to the user's job content, more appropriate questions can be provided. Some or all of the above processing in the survey unit may be performed using AI or not. For example, the survey unit can input user job content data into a generating AI and have the generating AI execute the application of the most suitable question algorithm.

[0047] The survey unit can determine the timing of a survey based on the user's working hours. For example, the survey unit can determine the optimal timing for a survey by considering the user's working hours. For instance, if it is difficult for a user to answer a survey during working hours, the survey can be conducted outside of working hours. The survey unit can also select an appropriate timing for a survey based on the user's working hours. For example, if a user is busy during a particular time, the survey can be conducted at a time that avoids that period. Furthermore, the survey unit can adjust the timing of the survey based on the user's working hours. For example, if a user is concentrating their work during a particular time, the survey can be conducted at a time that avoids that period. By determining the timing of the survey based on the user's working hours, the survey can be conducted at a more appropriate time. Some or all of the above processes in the survey unit may be performed using AI or not. For example, the survey unit can input user working hours data into a generating AI and have the generating AI determine the timing of the survey.

[0048] The survey unit can adjust the order of questions based on user relevance when conducting a survey. For example, the survey unit optimizes the order of questions by considering user relevance. For instance, if a user is interested in a particular topic, questions related to that topic will be prioritized. The survey unit can also adjust the order of questions based on user relevance. For example, if a user shows interest in a particular theme, questions related to that theme will be prioritized. Furthermore, the survey unit can select an appropriate order of questions based on user relevance. For example, if a user is interested in a particular field, questions related to that field will be prioritized. By adjusting the order of questions based on user relevance, a more appropriate order of questions can be provided. Some or all of the above processing in the survey unit may be performed using AI or not. For example, the survey unit can input user relevance data into a generating AI and have the generating AI perform the adjustment of the question order.

[0049] The presentation unit can adjust the level of detail in its solutions based on the importance of the consultation. For example, it will present detailed solutions for highly important consultations. For instance, if the consultation concerns an important project, it will provide a detailed solution. The presentation unit can also present concise solutions for less important consultations. For example, if the consultation concerns an everyday problem, it will provide a concise solution. Furthermore, the presentation unit can adjust the level of detail in its solutions based on the importance of the consultation. For example, if a particular problem is urgent, it will provide a detailed solution for that problem. By adjusting the level of detail in the solutions based on the importance of the consultation, it can provide more appropriate solutions. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the importance data of the consultation into a generating AI and have the generating AI adjust the level of detail in the solutions.

[0050] The presentation unit can apply different presentation algorithms depending on the category of the consultation when presenting solutions. For example, the presentation unit can apply the optimal presentation algorithm based on the category of the consultation. For example, if the consultation is technical, it will apply an algorithm that provides technical solutions. The presentation unit can also select an appropriate presentation algorithm considering the category of the consultation. For example, if the consultation is business-related, it will apply an algorithm that provides business-related solutions. Furthermore, the presentation unit can apply different presentation algorithms depending on the category of the consultation. For example, if the consultation is personal, it will apply an algorithm that provides personal solutions. In this way, by applying different presentation algorithms depending on the category of the consultation, more appropriate solutions can be provided. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input consultation category data into a generating AI and have the generating AI execute the application of the optimal presentation algorithm.

[0051] The presentation unit can determine the priority of solutions based on when the consultation content was submitted. For example, the presentation unit can determine the priority by considering when the consultation content was submitted. For example, it may prioritize consultations that were submitted earlier. The presentation unit can also select an appropriate priority based on when the consultation content was submitted. For example, it may postpone consultations that were submitted later. Furthermore, the presentation unit can adjust the priority of solutions based on when the consultation content was submitted. For example, if the submission date is urgent, it may prioritize that consultation. By determining the priority of solutions based on when the consultation content was submitted, solutions can be provided in a more appropriate order. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input consultation content submission date data into a generating AI and have the generating AI perform the determination of the priority of solutions.

[0052] The presentation unit can adjust the order of presentations based on the relevance of the consultation content when presenting solutions. For example, the presentation unit optimizes the presentation order by considering the relevance of the consultation content. For example, it prioritizes addressing highly relevant consultation content. The presentation unit can also adjust the presentation order based on the relevance of the consultation content. For example, it postpones addressing less relevant consultation content. Furthermore, the presentation unit can select an appropriate presentation order based on the relevance of the consultation content. For example, it prioritizes addressing highly relevant consultation content. By adjusting the presentation order based on the relevance of the consultation content, solutions can be provided in a more appropriate order. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input consultation content relevance data into a generating AI and have the generating AI perform the adjustment of the presentation order.

[0053] The display unit can adjust the air conditioning temperature based on the results of a survey regarding indoor temperature, and can determine the timing of temperature adjustment based on the user's working hours. For example, the display unit can determine the optimal timing for temperature adjustment by considering the user's working hours. For example, if the user wishes to adjust the temperature during their working hours, the unit will adjust the temperature at that time. The display unit can also select an appropriate timing for temperature adjustment based on the user's working hours. For example, if the user wishes to adjust the temperature during a specific time period, the unit will adjust the temperature during that time period. Furthermore, the display unit can adjust the timing of temperature adjustment based on the user's working hours. For example, if the user concentrates their work during a specific time period, the unit will adjust the temperature to avoid that time period. By determining the timing of temperature adjustment based on the user's working hours, a more comfortable indoor environment can be provided. Some or all of the above processing in the display unit may be performed using AI, or it may be performed without using AI. For example, the display unit can input the user's working hours data into a generating AI and have the generating AI determine the timing of temperature adjustment.

[0054] The display unit can adjust the layout of areas based on the user's job duties when creating quiet areas for employees who are sensitive to noise. For example, the display unit can determine the optimal area layout by considering the user's job duties. For instance, if a user is engaged in a specific job, it will arrange the area in a way that suits that job. The display unit can also select an appropriate area layout based on the user's job duties. For example, if a user is engaged in a specific task, it will arrange the area in a way that suits that task. Furthermore, the display unit can adjust the area layout based on the user's job duties. For example, if a user is engaged in a specific project, it will arrange the area in a way that suits that project. By adjusting the area layout based on the user's job duties, a more comfortable work environment can be provided. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input user job content data into a generating AI and have the generating AI perform the area layout adjustment.

[0055] The presentation unit can prioritize resource allocation to employees who feel the network is overloaded, determining resource allocation priorities based on the user's job responsibilities. For example, the presentation unit can determine the optimal resource allocation priority by considering the user's job responsibilities. For instance, if a user is engaged in a specific job, it will allocate resources appropriate to that job. The presentation unit can also select appropriate resource allocation priorities based on the user's job responsibilities. For example, if a user is engaged in a specific task, it will allocate resources appropriate to that task. Furthermore, the presentation unit can adjust resource allocation priorities based on the user's job responsibilities. For example, if a user is engaged in a specific project, it will allocate resources appropriate to that project. This allows for more appropriate resource management by determining resource allocation priorities based on the user's job responsibilities. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input user job responsibilities data into a generating AI and have the generating AI determine resource allocation priorities.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The reception department can analyze a user's past consultation history and select the most suitable reception method. For example, it can suggest the most suitable reception method based on the topics the user has frequently consulted about in the past. For example, if a user has frequently consulted about technical matters in the past, it can select a reception method appropriate to those topics. The reception department can also select the most effective reception method from the user's past consultation history. For example, it can analyze past consultation history and automatically select the most suitable reception method. Furthermore, the reception department can select the most suitable reception method considering the user's individual needs. For example, if a particular user prefers a specific method of consultation, that method will be prioritized. In this way, the reception department can select the most suitable reception method by analyzing the user's past consultation history. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input the user's past consultation history data into a generating AI and have the generating AI select the most suitable reception method.

[0058] The survey department can improve the accuracy of questions by referring to past survey results when conducting a survey. For example, the survey department can optimize the content of questions based on past survey results. For example, it can analyze past survey results and propose the most suitable questions. The survey department can also improve the accuracy of questions by referring to past survey results. For example, it can improve the accuracy of questions based on past survey results. Furthermore, the survey department can analyze past survey results and propose the most suitable questions. For example, it can propose the most suitable questions based on past survey results. In this way, the accuracy of questions can be improved by referring to past survey results. Some or all of the above processing in the survey department may be performed using AI or not. For example, the survey department can input past survey result data into a generating AI and have the generating AI perform the optimization of the question content.

[0059] The presentation unit can adjust the level of detail in its solutions based on the importance of the consultation. For example, it will present detailed solutions for highly important consultations. For instance, if the consultation concerns an important project, it will provide a detailed solution. The presentation unit can also present concise solutions for less important consultations. For example, if the consultation concerns an everyday problem, it will provide a concise solution. Furthermore, the presentation unit can adjust the level of detail in its solutions based on the importance of the consultation. For example, if a particular problem is urgent, it will provide a detailed solution for that problem. By adjusting the level of detail in the solutions based on the importance of the consultation, it can provide more appropriate solutions. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the importance data of the consultation into a generating AI and have the generating AI adjust the level of detail in the solutions.

[0060] The presentation unit can apply different presentation algorithms depending on the category of the consultation when presenting solutions. For example, the presentation unit can apply the optimal presentation algorithm based on the category of the consultation. For example, if the consultation is technical, it will apply an algorithm that provides technical solutions. The presentation unit can also select an appropriate presentation algorithm considering the category of the consultation. For example, if the consultation is business-related, it will apply an algorithm that provides business-related solutions. Furthermore, the presentation unit can apply different presentation algorithms depending on the category of the consultation. For example, if the consultation is personal, it will apply an algorithm that provides personal solutions. In this way, by applying different presentation algorithms depending on the category of the consultation, more appropriate solutions can be provided. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input consultation category data into a generating AI and have the generating AI execute the application of the optimal presentation algorithm.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives inquiries from employees. These inquiries can include technical questions, business-related questions, and personal questions. The reception desk can receive inquiries via text input or voice input. In the case of voice input, the reception desk converts the voice into text and records it. It can also refer to past inquiry history to respond quickly. Step 2: The Survey Department conducts a survey based on the inquiries received by the Reception Department. The survey includes questions about room temperature, noise levels, network quality, and equipment status. The Survey Department conducts the survey with employees and collects the results. The survey can be conducted using an online form. The Survey Department analyzes the survey results and learns the trends of each respondent. Step 3: The presentation unit learns the trends of each respondent obtained from the survey unit and presents solutions tailored to each individual. For example, it can adjust the air conditioning temperature based on the survey results regarding indoor temperature, create a quiet area if many employees are bothered by noise, or prioritize resource allocation for employees who feel the network is slow.

[0063] (Example of form 2) The workplace environment improvement AI agent "Nandemo" according to an embodiment of the present invention is a system that receives and responds quickly to inquiries from employees. This system aims to improve the workplace environment by responding to various inquiries received by the human resources department, general affairs department, and information systems department. For example, it receives inquiries about the workplace environment and responds quickly based on past inquiry history, existing FAQs, and the position of the person making the inquiry. For example, in response to inquiries such as "The sales department's phone calls are too loud," "The setting sun is too bright and makes it difficult to see the PC screen," "The office is too cold," or "I'm feeling mentally distressed," the AI ​​will suggest appropriate solutions. Furthermore, the AI ​​conducts surveys on "room temperature," "noise," "network quality," and "equipment status," and learns the tendencies of each respondent to suggest solutions tailored to each individual. For example, based on the results of a survey on room temperature, it is possible to adjust the air conditioning setting. In addition, by referring to data that has learned individual needs and tendencies, it is possible to optimize the office layout in a way that is easy for everyone in the workplace to work in. For example, if many employees are bothered by noise, it is possible to create a quiet area. Furthermore, when receiving inquiries about the system, it is possible to allocate different resources to each person making the inquiry. For example, it can prioritize allocating resources to employees who feel the network is slow. The AI ​​also selects response methods appropriate to each employee's IT literacy, and when necessary, such as system upgrades, overall resource optimization, or troubleshooting, it presents solutions to the IT department that take into account the individual and departmental "IT resource needs." In this way, the workplace environment-improving AI agent "Nandemo" (Anything) can improve productivity by addressing the needs of each employee and improving the workplace environment. For instance, when addressing the concerns of new employees or mid-career hires, the AI, recognizing them as "new to the company," provides a more reassuring response. It can also explain terminology that employees might hesitate to ask about due to concerns about interrupting a meeting, including how it's used within the company, based on accumulated internal documents. Even when you don't know who to ask, it connects you to the most suitable employee based on HR data.This allows the "Anything" AI agent for improving the workplace environment to respond quickly and appropriately to employee inquiries and improve the work environment.

[0064] The workplace environment improvement AI agent "Nandemo" according to this embodiment comprises a reception unit, a survey unit, and a presentation unit. The reception unit receives inquiries from employees. Inquiries include, but are not limited to, technical inquiries, business inquiries, and personal inquiries. The reception unit can, for example, input inquiries in text format. The reception unit can also receive inquiries using voice input. For example, if an employee speaks about their inquiry, the reception unit converts the voice into text and records it. Furthermore, the reception unit can respond quickly by referring to past inquiry history. For example, if a similar inquiry has been made in the past, it can quickly present a solution based on the response history. The survey unit conducts a survey based on the inquiries received by the reception unit. The content of the survey includes, but is not limited to, room temperature, noise, network quality, and equipment status. The survey unit conducts a survey of employees and collects the results. The survey can be conducted using, for example, an online form. The survey unit also analyzes the survey results and learns the trends of each respondent. For example, the AI ​​agent "Nandemo" (Anything) statistically analyzes survey results to extract specific trends. The presentation unit learns the trends for each respondent obtained by the survey unit and presents solutions tailored to each individual. For example, the presentation unit adjusts the air conditioning temperature based on survey results regarding indoor temperature. For example, if the survey results indicate that many employees feel cold, the air conditioning temperature is raised. The presentation unit can also create quiet areas if many employees are bothered by noise. For example, it identifies areas where noise is a concern and creates a quiet space in those areas. Furthermore, the presentation unit can prioritize resource allocation to employees who feel the network is slow. For example, it monitors network usage and prioritizes bandwidth allocation to employees who feel the network is slow. In this way, the workplace environment improvement AI agent "Nandemo" according to this embodiment can conduct surveys based on the content of employee consultations, learn the trends for each respondent, and present solutions tailored to each individual.

[0065] The reception desk receives inquiries from employees. These inquiries include, but are not limited to, technical, business, and personal matters. The reception desk can, for example, accept inquiries in text format. It can also accept inquiries using voice input. For example, if an employee speaks about their inquiry, the reception desk converts the voice into text and records it. Furthermore, the reception desk can respond quickly by referring to past inquiry history. For example, if a similar inquiry has been made in the past, it can quickly provide a solution based on the response history. The reception desk can also use AI to automatically categorize inquiries and route them to the appropriate person. For example, technical inquiries are routed to the technical department, business inquiries to the sales department, and personal inquiries to the human resources department. This enables quick and appropriate responses according to the content of the inquiry. In addition, the reception desk can evaluate the importance and urgency of inquiries and prioritize them. For example, high-urgency inquiries are addressed immediately, and high-importance inquiries are processed with priority. This allows for a quick response to employee needs. Furthermore, the reception department can save a history of consultations in a database, which can then be used for future analysis and improvement. For example, by analyzing past consultations, common problems and areas for improvement can be identified, which can then be used to improve the workplace environment. This allows the reception department to efficiently receive employee consultations and provide prompt and appropriate responses.

[0066] The Survey Department conducts surveys based on inquiries received by the Reception Department. Survey content may include, but is not limited to, questions about room temperature, noise levels, network quality, and equipment status. The Survey Department can, for example, conduct surveys with employees and collect the results. Surveys can be conducted using online forms. The Survey Department also analyzes survey results and learns trends from individual respondents. For example, it can statistically analyze survey results to extract specific trends. The Survey Department can use AI to automatically analyze survey results and identify patterns and trends. For example, by analyzing survey results regarding room temperature and finding that many employees feel cold, it can identify the cause and implement appropriate measures. Similarly, by analyzing survey results regarding noise and finding that noise is a problem in a specific area, it can identify that area and create a quiet space. Furthermore, by analyzing survey results regarding network quality and finding that many employees feel the network is slow during certain times or in specific areas, it can identify the cause and prioritize resource allocation. Based on the survey results, the Survey Department can understand employee needs and problems and use this information to improve the workplace environment. For example, based on the survey results, specific measures can be taken, such as adjusting indoor temperature, reducing noise, and improving network quality. This allows the survey department to understand employees' needs and problems and use that information to improve the workplace environment.

[0067] The suggestion unit learns the trends of each respondent obtained from the survey unit and presents solutions tailored to each individual. For example, the suggestion unit adjusts the air conditioning temperature based on the survey results regarding indoor temperature. For instance, if the survey results indicate that many employees feel cold, it raises the air conditioning temperature. The suggestion unit can also create quiet areas if many employees are bothered by noise. For example, it identifies areas where noise is a concern and creates a quiet space in those areas. Furthermore, the suggestion unit can prioritize resource allocation to employees who feel the network is slow. For example, it monitors network usage and prioritizes bandwidth allocation to employees who feel the network is slow. The suggestion unit can use AI to analyze survey results and automatically propose optimal solutions. For example, it can analyze survey results regarding indoor temperature and propose the optimal air conditioning temperature. It can also analyze survey results regarding noise and propose specific measures to create quiet areas. Furthermore, it can analyze survey results regarding network quality and propose specific measures to prioritize resource allocation. The suggestion unit can also provide specific procedures and methods for implementing the proposed solutions. For example, it can provide specific procedures for adjusting the air conditioning temperature and specific methods for creating quiet areas. This allows the presentation unit to propose solutions tailored to each employee, contributing to improvements in the work environment.

[0068] The display unit can adjust the air conditioning temperature setting based on the results of a survey regarding indoor temperature. For example, the display unit can conduct a survey on indoor temperature and collect the results. For example, if the survey results indicate that many employees feel cold, the display unit can raise the air conditioning temperature setting. The display unit can also use AI to calculate the optimal temperature when adjusting the air conditioning temperature setting based on the survey results. For example, the AI ​​can analyze the survey results and suggest the optimal air conditioning temperature setting. Furthermore, the display unit can also consider the individual needs of employees when adjusting the air conditioning temperature setting. For example, if a particular employee is sensitive to the cold, the temperature around that employee's desk can be adjusted. In this way, a comfortable work environment can be provided by adjusting the air conditioning temperature setting based on the survey results regarding indoor temperature. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the survey results into a generating AI and have the generating AI perform the adjustment of the air conditioning temperature setting.

[0069] The system can create quiet areas if many employees are bothered by noise. For example, the system can conduct a survey on noise and collect the results. If the survey results indicate that many employees are bothered by noise, it can create quiet areas. The system can also use AI to suggest the optimal placement of quiet areas based on the survey results. For example, the AI ​​can analyze the survey results and suggest the optimal placement of quiet areas. Furthermore, the system can consider the individual needs of employees when creating quiet areas. For example, if a specific employee needs a quiet environment, a quiet area can be created around that employee's desk. This allows for a more comfortable work environment by providing quiet areas when many employees are bothered by noise. Some or all of the above processes in the system may be performed using AI or not. For example, the system can input the survey results into a generating AI and have the generating AI determine the placement of quiet areas.

[0070] The allocation unit can prioritize resource allocation to employees who feel the network is slow. For example, the allocation unit can monitor network usage and prioritize bandwidth allocation to employees who feel the network is slow. For example, it can monitor network usage in real time and prioritize resource allocation to employees who feel the network is slow. The allocation unit can also use AI to suggest the optimal allocation when allocating network resources. For example, the AI ​​can analyze network usage and suggest the optimal resource allocation. Furthermore, the allocation unit can consider the individual needs of employees when allocating resources. For example, if a particular employee is working on an important project, resources can be prioritized for that employee. This provides a comfortable work environment by prioritizing resource allocation to employees who feel the network is slow. Some or all of the above processes in the allocation unit may be performed using AI or not. For example, the allocation unit can input network usage data into a generating AI and have the generating AI perform resource allocation optimization.

[0071] The presentation unit can select a response method that matches the employee's IT literacy. For example, the presentation unit can evaluate an employee's IT literacy and select a response method appropriate to that level. For instance, it might provide answers using technical terms to employees with high IT literacy and use simple language to employees with low IT literacy. The presentation unit can also use AI to suggest the optimal evaluation method when assessing IT literacy. For example, the AI ​​could analyze an employee's IT literacy and suggest the optimal evaluation method. Furthermore, the presentation unit can consider the individual needs of employees when selecting a response method. For example, if a particular employee asks a question about a specific system, it might request an employee familiar with that system to provide the answer. This allows the presentation unit to provide a solution that is appropriate for the user by selecting a response method that matches their IT literacy. Some or all of the above processes in the presentation unit may be performed using AI or not. For example, the presentation unit can input employee IT literacy data into a generating AI and have the generating AI select the optimal response method.

[0072] The proposal unit can present solutions to the IT department that take into account the "IT resource needs" of individuals and departments at times such as system upgrades, overall resource optimization, and failure response. For example, when system upgrades are needed, the proposal unit analyzes the IT resource usage of each department and proposes an optimal upgrade plan. The proposal unit can also use AI to propose the optimal resource allocation when performing overall resource optimization. For example, the AI ​​analyzes the resource usage of each department and proposes the optimal resource allocation. Furthermore, when responding to failures, the proposal unit can present solutions that take into account the needs of each department. For example, if a particular department is working on an important project, resources will be allocated to that department preferentially. This enables efficient resource management by presenting solutions to the IT department that take into account the "IT resource needs" of individuals and departments at times such as system upgrades, overall resource optimization, and failure response. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input resource usage data from each department into a generating AI and have the generating AI execute a proposal for the optimal resource allocation.

[0073] The information dissemination unit can provide a greater sense of security to new employees and mid-career hires who have concerns. For example, the information dissemination unit can receive concerns from new employees and mid-career hires and respond accordingly. For example, if a new employee is feeling anxious about workplace rules and culture, the information dissemination unit can provide information to reassure them. The information dissemination unit can also use AI to analyze the concerns of new employees and mid-career hires and propose the most suitable response. For example, the AI ​​can analyze a new employee's concerns and provide specific advice to reassure them. Furthermore, the information dissemination unit can also provide support that takes into account the individual needs of new employees and mid-career hires. For example, if a particular new employee is feeling anxious about a particular skill, training related to that skill can be provided. In this way, by providing a greater sense of security to new employees and mid-career hires who have concerns, the workplace environment can be improved. Some or all of the above processes in the information dissemination unit may be performed using AI or not. For example, the presentation unit can input data on the concerns of new employees into a generating AI, and have the AI ​​generate suggestions for the most suitable solutions.

[0074] The presentation unit can explain terms that employees might hesitate to ask about in order to avoid interrupting the flow of a meeting, including how they are used within the company, based on accumulated internal documents. For example, the presentation unit can quickly explain technical terms and abbreviations that come up during a meeting. For instance, if an employee wants to know the meaning of a term that came up during a meeting, the presentation unit will search for the meaning and usage of that term in internal documents and provide it. The presentation unit can also use AI to analyze the meaning of terms and provide the most appropriate explanation. For example, the AI ​​will analyze the meaning of a term and provide an explanation that includes how it is used within the company. Furthermore, when explaining terms, the presentation unit can also consider the individual needs of employees. For example, if a particular employee wants to know about terms related to a specific project, it will provide information related to that project. In this way, by explaining terms that employees might hesitate to ask about in order to avoid interrupting the flow of a meeting, including how they are used within the company, based on accumulated internal documents, the efficiency of meetings is improved. Some or all of the above processing in the presentation unit may be performed using AI, or not. For example, the presentation unit can input terms that came up during a meeting into a generating AI and have the AI ​​generate the most appropriate explanation.

[0075] The referral unit can connect an employee to the most suitable person based on HR data when the appropriate person to ask is unknown. For example, if an employee wants to discuss a specific problem but doesn't know who to ask, the referral unit will identify the most suitable person based on HR data. For example, if the question concerns a specific technology, it will identify and connect the employee with expertise in that technology. The referral unit can also use AI to identify the most suitable person. For example, the AI ​​will analyze HR data and suggest the most suitable person. Furthermore, the referral unit can identify the most suitable person by considering the individual needs of the employee. For example, if the question concerns a specific project, it will identify and connect the employee with an employee involved in that project. This enables rapid problem solving by connecting the employee to the most suitable person based on HR data when the appropriate person to ask is unknown. Some or all of the above processes in the referral unit may be performed using AI or not. For example, the referral unit can input the consultation content into a generating AI and have the generating AI identify the most suitable person.

[0076] The reception desk can estimate the user's emotions and adjust the timing of receiving the consultation based on the estimated emotions. For example, if the user is feeling stressed, the reception desk will adjust to accept the consultation immediately. For example, if the user is emotionally unstable, it will respond quickly. The reception desk can also adjust to accept the consultation at an appropriate time if the user is relaxed. For example, if the user is calm, it will respond at the normal reception time. Furthermore, the reception desk can adjust to accept the consultation quickly if the user is in a hurry. For example, if the user is in a hurry, it will be given priority. In this way, by adjusting the timing of receiving the consultation according to the user's emotions, consultations can be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generating AI and have the AI ​​adjust the timing of receiving inquiries.

[0077] The reception department can analyze the user's past consultation history when receiving a consultation request and select the most appropriate reception method. For example, the reception department can suggest the most appropriate reception method based on the types of consultations the user has frequently had in the past. For example, if the user has had many technical consultations in the past, it will select a reception method appropriate to those types of consultations. The reception department can also select the most effective reception method from the user's past consultation history. For example, it can analyze past consultation history and automatically select the most appropriate reception method. Furthermore, the reception department can select the most appropriate reception method considering the user's individual needs. For example, if a particular user prefers a specific method of consultation, that method will be prioritized. In this way, the reception department can select the most appropriate reception method by analyzing the user's past consultation history. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input the user's past consultation history data into a generating AI and have the generating AI select the most appropriate reception method.

[0078] The reception desk can filter inquiries based on the user's current work situation and areas of interest when receiving them. For example, the reception desk can prioritize receiving inquiries that are relevant to the user's current work situation. For instance, if the user is currently working on a specific project, it will prioritize receiving inquiries related to that project. The reception desk can also filter appropriate inquiries based on the user's areas of interest. For example, if the user is interested in a specific technology, it will prioritize receiving inquiries related to that technology. Furthermore, the reception desk can select the most suitable inquiries based on the user's work situation and areas of interest. For example, if the user is interested in a specific task, it will prioritize receiving inquiries related to that task. In this way, relevant inquiries can be prioritized by filtering based on the user's current work situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's work situation and areas of interest data into a generating AI and have the generating AI perform the filtering of the most suitable inquiries.

[0079] The reception desk can estimate the user's emotions and determine the priority of the consultation content to be received based on the estimated emotions. For example, if the user is feeling stressed, the reception desk will prioritize the consultation content. For example, if the user is emotionally unstable, it will respond quickly. The reception desk can also accept the consultation content with the normal priority if the user is relaxed. For example, if the user is calm, it will respond at the normal reception time. Furthermore, if the reception desk is in a hurry, it will also accept the consultation content quickly. For example, if the user is in a hurry, it will respond quickly. In this way, by determining the priority of consultation content according to the user's emotions, consultations can be received in a more appropriate order. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform the determination of the priority of consultation content.

[0080] The reception desk can prioritize receiving inquiries that are highly relevant, taking into account the user's geographical location. For example, the reception desk can prioritize receiving inquiries that are highly relevant based on the user's geographical location. For instance, if the user is in a specific region, it will prioritize receiving inquiries related to that region. The reception desk can also select the most appropriate inquiries by considering the user's current location. For example, if the user is in a specific location, it will prioritize receiving inquiries related to that location. Furthermore, the reception desk can filter appropriate inquiries based on the user's geographical location. For example, if the user is in a specific area, it will prioritize receiving inquiries related to that area. In this way, by considering the user's geographical location, highly relevant inquiries can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI perform the filtering of the most appropriate inquiries.

[0081] The reception department can analyze the user's social media activity when receiving a consultation request and accept relevant consultation requests. For example, the reception department can prioritize accepting relevant consultation requests based on the user's social media activity. For instance, if a user frequently posts about a particular topic on social media, it will prioritize accepting consultation requests related to that topic. The reception department can also analyze the user's social media activity and select the most appropriate consultation request. For example, if a user makes many posts about a particular theme, it will prioritize accepting consultation requests related to that theme. Furthermore, the reception department can filter appropriate consultation requests based on the user's social media activity. For example, if a user shows a lot of interest in a particular topic, it will prioritize accepting consultation requests related to that topic. In this way, by analyzing the user's social media activity, it is possible to prioritize accepting relevant consultation requests. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the user's social media activity data into a generating AI and have the generating AI perform the filtering of the most appropriate consultation requests.

[0082] The survey unit can estimate the user's emotions and adjust the survey questions based on those emotions. For example, if the user is stressed, the survey unit can adjust the questions to be simpler. For example, if the user is emotionally unstable, it can ask simple questions. The survey unit can also adjust the questions to be more detailed if the user is relaxed. For example, if the user is calm, it can ask detailed questions. Furthermore, if the user is in a hurry, the survey unit can adjust the questions to be quick to answer. For example, if the user is in a hurry, it can ask concise questions. By adjusting the survey questions according to the user's emotions, more appropriate questions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the survey unit may be performed using AI or not. For example, the survey unit can input user emotion data into a generative AI and have the generative AI adjust the survey questions.

[0083] The survey department can improve the accuracy of questions by referring to past survey results when conducting a survey. For example, the survey department can optimize the content of questions based on past survey results. For example, it can analyze past survey results and propose the most suitable questions. The survey department can also improve the accuracy of questions by referring to past survey results. For example, it can improve the accuracy of questions based on past survey results. Furthermore, the survey department can analyze past survey results and propose the most suitable questions. For example, it can propose the most suitable questions based on past survey results. In this way, the accuracy of questions can be improved by referring to past survey results. Some or all of the above processing in the survey department may be performed using AI or not. For example, the survey department can input past survey result data into a generating AI and have the generating AI perform the optimization of the question content.

[0084] The survey unit can apply different question algorithms depending on the user's job content when conducting a survey. For example, the survey unit can apply the most suitable question algorithm based on the user's job content. For example, if the user is engaged in a specific job, it can apply a question algorithm related to that job. The survey unit can also select an appropriate question algorithm considering the user's job content. For example, if the user is engaged in a specific task, it can apply a question algorithm related to that task. Furthermore, the survey unit can apply different question algorithms depending on the user's job content. For example, if the user is engaged in a specific project, it can apply a question algorithm related to that project. By applying different question algorithms according to the user's job content, more appropriate questions can be provided. Some or all of the above processing in the survey unit may be performed using AI or not. For example, the survey unit can input user job content data into a generating AI and have the generating AI execute the application of the most suitable question algorithm.

[0085] The survey unit can estimate the user's emotions and adjust the survey response method based on the estimated emotions. For example, if the user is feeling stressed, the survey unit can provide a simple response method. For example, if the user is emotionally unstable, it can provide a simple response method. The survey unit can also provide a detailed response method if the user is relaxed. For example, if the user is calm, it can provide a detailed response method. Furthermore, if the user is in a hurry, the survey unit can provide a method that allows for quick responses. For example, if the user is in a hurry, it can provide a concise response method. In this way, by adjusting the survey response method according to the user's emotions, a more appropriate response method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the survey unit may be performed using AI or not using AI. For example, the survey unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the survey response method.

[0086] The survey unit can determine the timing of a survey based on the user's working hours. For example, the survey unit can determine the optimal timing for a survey by considering the user's working hours. For instance, if it is difficult for a user to answer a survey during working hours, the survey can be conducted outside of working hours. The survey unit can also select an appropriate timing for a survey based on the user's working hours. For example, if a user is busy during a particular time, the survey can be conducted at a time that avoids that period. Furthermore, the survey unit can adjust the timing of the survey based on the user's working hours. For example, if a user is concentrating their work during a particular time, the survey can be conducted at a time that avoids that period. By determining the timing of the survey based on the user's working hours, the survey can be conducted at a more appropriate time. Some or all of the above processes in the survey unit may be performed using AI or not. For example, the survey unit can input user working hours data into a generating AI and have the generating AI determine the timing of the survey.

[0087] The survey unit can adjust the order of questions based on user relevance when conducting a survey. For example, the survey unit optimizes the order of questions by considering user relevance. For instance, if a user is interested in a particular topic, questions related to that topic will be prioritized. The survey unit can also adjust the order of questions based on user relevance. For example, if a user shows interest in a particular theme, questions related to that theme will be prioritized. Furthermore, the survey unit can select an appropriate order of questions based on user relevance. For example, if a user is interested in a particular field, questions related to that field will be prioritized. By adjusting the order of questions based on user relevance, a more appropriate order of questions can be provided. Some or all of the above processing in the survey unit may be performed using AI or not. For example, the survey unit can input user relevance data into a generating AI and have the generating AI perform the adjustment of the question order.

[0088] The presentation unit can estimate the user's emotions and adjust the method of presenting solutions based on the estimated emotions. For example, if the user is stressed, the presentation unit will present a concise solution. For example, if the user is emotionally unstable, it will provide a concise solution. The presentation unit can also present a detailed solution if the user is relaxed. For example, if the user is calm, it will provide a detailed solution. Furthermore, if the user is in a hurry, the presentation unit can provide a quick solution. For example, if the user is in a hurry, it will respond quickly. In this way, by adjusting the method of presenting solutions according to the user's emotions, more appropriate solutions can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI adjust the method of presenting solutions.

[0089] The presentation unit can adjust the level of detail in its solutions based on the importance of the consultation. For example, it will present detailed solutions for highly important consultations. For instance, if the consultation concerns an important project, it will provide a detailed solution. The presentation unit can also present concise solutions for less important consultations. For example, if the consultation concerns an everyday problem, it will provide a concise solution. Furthermore, the presentation unit can adjust the level of detail in its solutions based on the importance of the consultation. For example, if a particular problem is urgent, it will provide a detailed solution for that problem. By adjusting the level of detail in the solutions based on the importance of the consultation, it can provide more appropriate solutions. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the importance data of the consultation into a generating AI and have the generating AI adjust the level of detail in the solutions.

[0090] The presentation unit can apply different presentation algorithms depending on the category of the consultation when presenting solutions. For example, the presentation unit can apply the optimal presentation algorithm based on the category of the consultation. For example, if the consultation is technical, it will apply an algorithm that provides technical solutions. The presentation unit can also select an appropriate presentation algorithm considering the category of the consultation. For example, if the consultation is business-related, it will apply an algorithm that provides business-related solutions. Furthermore, the presentation unit can apply different presentation algorithms depending on the category of the consultation. For example, if the consultation is personal, it will apply an algorithm that provides personal solutions. In this way, by applying different presentation algorithms depending on the category of the consultation, more appropriate solutions can be provided. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input consultation category data into a generating AI and have the generating AI execute the application of the optimal presentation algorithm.

[0091] The presentation unit can estimate the user's emotions and determine the priority of solutions based on the estimated emotions. For example, if the user is feeling stressed, the presentation unit will prioritize solutions. For example, if the user is emotionally unstable, it will respond quickly. The presentation unit can also present solutions with normal priority if the user is relaxed. For example, if the user is calm, it will respond with normal priority. Furthermore, if the user is in a hurry, the presentation unit can also provide solutions quickly. For example, if the user is in a hurry, it will respond with priority. This allows for the provision of solutions in a more appropriate order by determining the priority of solutions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI determine the priority of solutions.

[0092] The presentation unit can determine the priority of solutions based on when the consultation content was submitted. For example, the presentation unit can determine the priority by considering when the consultation content was submitted. For example, it may prioritize consultations that were submitted earlier. The presentation unit can also select an appropriate priority based on when the consultation content was submitted. For example, it may postpone consultations that were submitted later. Furthermore, the presentation unit can adjust the priority of solutions based on when the consultation content was submitted. For example, if the submission date is urgent, it may prioritize that consultation. By determining the priority of solutions based on when the consultation content was submitted, solutions can be provided in a more appropriate order. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input consultation content submission date data into a generating AI and have the generating AI perform the determination of the priority of solutions.

[0093] The presentation unit can adjust the order of presentations based on the relevance of the consultation content when presenting solutions. For example, the presentation unit optimizes the presentation order by considering the relevance of the consultation content. For example, it prioritizes addressing highly relevant consultation content. The presentation unit can also adjust the presentation order based on the relevance of the consultation content. For example, it postpones addressing less relevant consultation content. Furthermore, the presentation unit can select an appropriate presentation order based on the relevance of the consultation content. For example, it prioritizes addressing highly relevant consultation content. By adjusting the presentation order based on the relevance of the consultation content, solutions can be provided in a more appropriate order. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input consultation content relevance data into a generating AI and have the generating AI perform the adjustment of the presentation order.

[0094] The display unit can estimate the user's emotions when adjusting the air conditioning temperature based on the results of a survey regarding indoor temperature, and can determine the frequency of temperature adjustments based on the estimated emotions of the user. For example, if the user is feeling stressed, the display unit will adjust the temperature frequently. For example, if the user is emotionally unstable, the display unit will adjust the temperature frequently. The display unit can also adjust the temperature at a moderate frequency if the user is relaxed. For example, if the user is calm, the display unit will adjust the temperature at a moderate frequency. Furthermore, if the user is in a hurry, the display unit will adjust the temperature quickly. For example, if the user is in a hurry, the display unit will adjust the temperature quickly. In this way, by determining the frequency of temperature adjustments according to the user's emotions, a more comfortable indoor environment can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the display unit may be performed using AI or not using AI. For example, the display unit can input user emotion data into a generating AI and have the generating AI determine the frequency of temperature adjustments.

[0095] The presentation unit can estimate the user's emotions and adjust the placement of areas based on their emotions when creating quiet areas, for example, when many employees are bothered by noise. For example, if a user is feeling stressed, the presentation unit will prioritize placing quiet areas. For example, if a user is emotionally unstable, a quiet area will be prioritized. The presentation unit can also create quiet areas at an appropriate placement when a user is relaxed. For example, if a user is calm, a quiet area will be created at an appropriate placement. Furthermore, if a user is in a hurry, the presentation unit can quickly create quiet areas. For example, if a user is in a hurry, a quiet area will be quickly created. In this way, a more comfortable work environment can be provided by adjusting the placement of areas according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the display unit can input user emotion data into a generating AI and have the generating AI adjust the layout of the areas.

[0096] The presentation unit can estimate the user's emotions and determine resource allocation priorities based on the estimated emotions when allocating resources preferentially to employees who feel the network is overloaded. For example, if a user is stressed, the presentation unit will prioritize resource allocation. For example, if a user is emotionally unstable, resources will be prioritized. The presentation unit can also allocate resources with normal priority if a user is relaxed. For example, if a user is calm, resources will be allocated with normal priority. Furthermore, if a user is in a hurry, resources will be allocated quickly. For example, if a user is in a hurry, resources will be allocated quickly. This allows for more appropriate resource management by determining resource allocation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input user emotion data into a generating AI and have the generating AI determine the priority of resource allocation.

[0097] The display unit can adjust the air conditioning temperature based on the results of a survey regarding indoor temperature, and can determine the timing of temperature adjustment based on the user's working hours. For example, the display unit can determine the optimal timing for temperature adjustment by considering the user's working hours. For example, if the user wishes to adjust the temperature during their working hours, the unit will adjust the temperature at that time. The display unit can also select an appropriate timing for temperature adjustment based on the user's working hours. For example, if the user wishes to adjust the temperature during a specific time period, the unit will adjust the temperature during that time period. Furthermore, the display unit can adjust the timing of temperature adjustment based on the user's working hours. For example, if the user concentrates their work during a specific time period, the unit will adjust the temperature to avoid that time period. By determining the timing of temperature adjustment based on the user's working hours, a more comfortable indoor environment can be provided. Some or all of the above processing in the display unit may be performed using AI, or it may be performed without using AI. For example, the display unit can input the user's working hours data into a generating AI and have the generating AI determine the timing of temperature adjustment.

[0098] The display unit can adjust the layout of areas based on the user's job duties when creating quiet areas for employees who are sensitive to noise. For example, the display unit can determine the optimal area layout by considering the user's job duties. For instance, if a user is engaged in a specific job, it will arrange the area in a way that suits that job. The display unit can also select an appropriate area layout based on the user's job duties. For example, if a user is engaged in a specific task, it will arrange the area in a way that suits that task. Furthermore, the display unit can adjust the area layout based on the user's job duties. For example, if a user is engaged in a specific project, it will arrange the area in a way that suits that project. By adjusting the area layout based on the user's job duties, a more comfortable work environment can be provided. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input user job content data into a generating AI and have the generating AI perform the area layout adjustment.

[0099] The presentation unit can prioritize resource allocation to employees who feel the network is overloaded, determining resource allocation priorities based on the user's job responsibilities. For example, the presentation unit can determine the optimal resource allocation priority by considering the user's job responsibilities. For instance, if a user is engaged in a specific job, it will allocate resources appropriate to that job. The presentation unit can also select appropriate resource allocation priorities based on the user's job responsibilities. For example, if a user is engaged in a specific task, it will allocate resources appropriate to that task. Furthermore, the presentation unit can adjust resource allocation priorities based on the user's job responsibilities. For example, if a user is engaged in a specific project, it will allocate resources appropriate to that project. This allows for more appropriate resource management by determining resource allocation priorities based on the user's job responsibilities. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input user job responsibilities data into a generating AI and have the generating AI determine resource allocation priorities.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The reception desk can estimate the user's emotions and adjust the way the consultation is handled based on those emotions. For example, if the user is stressed, the reception desk will immediately accept the consultation in order to respond quickly. If the user is relaxed, the reception desk can handle it using the normal reception method. Furthermore, if the user is in a hurry, the consultation can be given priority. In this way, by adjusting the consultation handling method according to the user's emotions, consultations can be handled at a more appropriate time. Emotion estimation is achieved using an emotion engine or generative AI. For example, the reception desk can input the user's emotion data into the generative AI and have the generative AI adjust the consultation handling method.

[0102] The survey unit can estimate the user's emotions and adjust the timing of the survey based on those emotions. For example, if a user is stressed, the survey unit can delay the survey until the user calms down. If the user is relaxed, the survey can be conducted immediately. Furthermore, if the user is in a hurry, the timing of the survey can be adjusted to allow for quick responses. By adjusting the timing of the survey according to the user's emotions, the survey can be conducted at a more appropriate time. Emotion estimation is achieved using an emotion engine or generative AI. For example, the survey unit can input user emotion data into the generative AI and have the generative AI adjust the timing of the survey.

[0103] The presentation unit can estimate the user's emotions and adjust the order in which solutions are presented based on those emotions. For example, if the user is stressed, the presentation unit will prioritize presenting the most important solutions. If the user is relaxed, it can present solutions in the normal order. Furthermore, if the user is in a hurry, it can present solutions quickly. By adjusting the order in which solutions are presented according to the user's emotions, solutions can be provided in a more appropriate order. Emotion estimation is achieved using an emotion engine or generative AI. For example, the presentation unit can input the user's emotion data into the generative AI and have the generative AI adjust the order in which solutions are presented.

[0104] The reception desk can estimate the user's emotions and adjust the way the consultation is handled based on those emotions. For example, if the user is stressed, the reception desk will immediately accept the consultation in order to respond quickly. If the user is relaxed, the reception desk can handle it using the normal reception method. Furthermore, if the user is in a hurry, the consultation can be given priority. In this way, by adjusting the consultation handling method according to the user's emotions, consultations can be handled at a more appropriate time. Emotion estimation is achieved using an emotion engine or generative AI. For example, the reception desk can input the user's emotion data into the generative AI and have the generative AI adjust the consultation handling method.

[0105] The survey unit can estimate the user's emotions and adjust the timing of the survey based on those emotions. For example, if a user is stressed, the survey unit can delay the survey until the user calms down. If the user is relaxed, the survey can be conducted immediately. Furthermore, if the user is in a hurry, the timing of the survey can be adjusted to allow for quick responses. By adjusting the timing of the survey according to the user's emotions, the survey can be conducted at a more appropriate time. Emotion estimation is achieved using an emotion engine or generative AI. For example, the survey unit can input user emotion data into the generative AI and have the generative AI adjust the timing of the survey.

[0106] The reception department can analyze a user's past consultation history and select the most suitable reception method. For example, it can suggest the most suitable reception method based on the topics the user has frequently consulted about in the past. For example, if a user has frequently consulted about technical matters in the past, it can select a reception method appropriate to those topics. The reception department can also select the most effective reception method from the user's past consultation history. For example, it can analyze past consultation history and automatically select the most suitable reception method. Furthermore, the reception department can select the most suitable reception method considering the user's individual needs. For example, if a particular user prefers a specific method of consultation, that method will be prioritized. In this way, the reception department can select the most suitable reception method by analyzing the user's past consultation history. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input the user's past consultation history data into a generating AI and have the generating AI select the most suitable reception method.

[0107] The survey department can improve the accuracy of questions by referring to past survey results when conducting a survey. For example, the survey department can optimize the content of questions based on past survey results. For example, it can analyze past survey results and propose the most suitable questions. The survey department can also improve the accuracy of questions by referring to past survey results. For example, it can improve the accuracy of questions based on past survey results. Furthermore, the survey department can analyze past survey results and propose the most suitable questions. For example, it can propose the most suitable questions based on past survey results. In this way, the accuracy of questions can be improved by referring to past survey results. Some or all of the above processing in the survey department may be performed using AI or not. For example, the survey department can input past survey result data into a generating AI and have the generating AI perform the optimization of the question content.

[0108] The presentation unit can adjust the level of detail in its solutions based on the importance of the consultation. For example, it will present detailed solutions for highly important consultations. For instance, if the consultation concerns an important project, it will provide a detailed solution. The presentation unit can also present concise solutions for less important consultations. For example, if the consultation concerns an everyday problem, it will provide a concise solution. Furthermore, the presentation unit can adjust the level of detail in its solutions based on the importance of the consultation. For example, if a particular problem is urgent, it will provide a detailed solution for that problem. By adjusting the level of detail in the solutions based on the importance of the consultation, it can provide more appropriate solutions. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the importance data of the consultation into a generating AI and have the generating AI adjust the level of detail in the solutions.

[0109] The presentation unit can apply different presentation algorithms depending on the category of the consultation when presenting solutions. For example, the presentation unit can apply the optimal presentation algorithm based on the category of the consultation. For example, if the consultation is technical, it will apply an algorithm that provides technical solutions. The presentation unit can also select an appropriate presentation algorithm considering the category of the consultation. For example, if the consultation is business-related, it will apply an algorithm that provides business-related solutions. Furthermore, the presentation unit can apply different presentation algorithms depending on the category of the consultation. For example, if the consultation is personal, it will apply an algorithm that provides personal solutions. In this way, by applying different presentation algorithms depending on the category of the consultation, more appropriate solutions can be provided. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input consultation category data into a generating AI and have the generating AI execute the application of the optimal presentation algorithm.

[0110] The presentation unit can estimate the user's emotions and determine the priority of solutions based on the estimated emotions. For example, if the user is stressed, the presentation unit will prioritize solutions. For example, if the user is emotionally unstable, it will respond quickly. The presentation unit can also present solutions with normal priority if the user is relaxed. For example, if the user is calm, it will respond with normal priority. Furthermore, if the user is in a hurry, the presentation unit can also provide solutions quickly. For example, if the user is in a hurry, it will respond with priority. This allows for the provision of solutions in a more appropriate order by determining the priority of solutions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI determine the priority of solutions.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk receives inquiries from employees. These inquiries can include technical questions, business-related questions, and personal questions. The reception desk can receive inquiries via text input or voice input. In the case of voice input, the reception desk converts the voice into text and records it. It can also refer to past inquiry history to respond quickly. Step 2: The Survey Department conducts a survey based on the inquiries received by the Reception Department. The survey includes questions about room temperature, noise levels, network quality, and equipment status. The Survey Department conducts the survey with employees and collects the results. The survey can be conducted using an online form. The Survey Department analyzes the survey results and learns the trends of each respondent. Step 3: The presentation unit learns the trends of each respondent obtained from the survey unit and presents solutions tailored to each individual. For example, it can adjust the air conditioning temperature based on the survey results regarding indoor temperature, create a quiet area if many employees are bothered by noise, or prioritize resource allocation for employees who feel the network is slow.

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0116] Each of the multiple elements described above, including the reception unit, questionnaire unit, and presentation unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives inquiries from employees. The questionnaire unit is implemented by the specific processing unit 290 of the data processing unit 12 and conducts a questionnaire based on the inquiries. The presentation unit is implemented by the specific processing unit 290 of the data processing unit 12 and presents solutions tailored to each individual based on the questionnaire results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 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.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, questionnaire unit, and presentation unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives inquiries from employees. The questionnaire unit is implemented by the specific processing unit 290 of the data processing unit 12 and conducts a questionnaire based on the inquiries. The presentation unit is implemented by the specific processing unit 290 of the data processing unit 12 and presents solutions tailored to each individual based on the questionnaire results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, questionnaire unit, and presentation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives inquiries from employees. The questionnaire unit is implemented by the specific processing unit 290 of the data processing unit 12 and conducts a questionnaire based on the inquiries. The presentation unit is implemented by the specific processing unit 290 of the data processing unit 12 and presents solutions tailored to each individual based on the questionnaire results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, questionnaire unit, and presentation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives inquiries from employees. The questionnaire unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and conducts a questionnaire based on the inquiries. The presentation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and presents solutions tailored to each individual based on the questionnaire results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) The reception desk that receives inquiries, The survey department conducts a questionnaire based on the consultation content received by the aforementioned reception department, The system includes a presentation unit that learns the trends of each respondent obtained from the aforementioned questionnaire unit and presents solutions suitable for each individual. A system characterized by the following features. (Note 2) The aforementioned display unit is, We will adjust the air conditioning temperature setting based on the results of a survey regarding indoor temperature. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is, If many employees are bothered by noise, create a quiet area. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is, Prioritize allocating resources to employees who experience network lag. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is, Select an answer method that matches your IT literacy. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is, At times such as when system upgrades, overall resource optimization, or troubleshooting are needed, we present solutions to the IT department that take into account the "IT resource needs" of individuals and departments. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned display unit is, We will provide support that gives new employees and mid-career hires a greater sense of security regarding their concerns. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned display unit is, This service explains how certain terms are used within the company, based on accumulated internal documentation, addressing the common hesitation to ask about them without interrupting the meeting's flow. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned display unit is, When you don't know who to ask, we connect you with the most suitable employee based on HR data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving inquiries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a consultation request, the system analyzes the user's past consultation history and selects the most suitable method of receiving the request. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving inquiries, the system filters them based on the user's current job situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the types of inquiries it will accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When receiving inquiries, the system prioritizes accepting inquiries that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is When receiving a consultation request, the system analyzes the user's social media activity and accepts consultation requests related to that activity. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned survey department, The system estimates the user's emotions and adjusts the survey questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned survey department, When conducting a survey, we will refer to past survey results to improve the accuracy of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned survey department, When conducting a survey, different question algorithms are applied depending on the user's job responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned survey department, We estimate the user's emotions and adjust the survey response method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned survey department, When conducting a survey, the timing of the survey will be determined based on the user's working hours. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned survey department, When conducting a survey, adjust the order of questions based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, It estimates the user's emotions and adjusts how solutions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is, When presenting solutions, adjust the level of detail based on the importance of the issue being discussed. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is, When providing solutions, different presentation algorithms are applied depending on the category of the consultation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is, It estimates the user's emotions and determines the priority of solutions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is, When presenting solutions, the priority of the suggestions will be determined based on when the consultation details were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is, When presenting solutions, adjust the order of presentation based on the relevance of the issues discussed. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is, When adjusting the air conditioning temperature based on survey results regarding indoor temperature, the system estimates the user's emotions and determines the frequency of temperature adjustments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is, If many employees are bothered by noise, when creating a quiet area, the system will estimate the users' feelings and adjust the area layout based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is, When prioritizing resource allocation for employees who perceive network congestion as slow, the system estimates the user's emotional state and determines resource allocation priorities based on that estimation. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned display unit is, When adjusting the air conditioning temperature based on survey results regarding indoor temperature, the timing of temperature adjustments is determined based on the user's working hours. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned display unit is, If many employees are sensitive to noise, when creating quiet areas, adjust the layout of these areas based on the users' job duties. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned display unit is, When prioritizing resource allocation for employees who experience network congestion, the priority of resource allocation is determined based on the user's job responsibilities. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk that receives inquiries, The survey department conducts a questionnaire based on the consultation content received by the aforementioned reception department, The system includes a presentation unit that learns the trends of each respondent obtained from the aforementioned questionnaire unit and presents solutions suitable for each individual. A system characterized by the following features.

2. The aforementioned display unit is, We will adjust the air conditioning temperature setting based on the results of a survey regarding indoor temperature. The system according to feature 1.

3. The aforementioned display unit is, If many employees are bothered by noise, create a quiet area. The system according to feature 1.

4. The aforementioned display unit is, Prioritize allocating resources to employees who experience network lag. The system according to feature 1.

5. The aforementioned display unit is, Select an answer method that matches your IT literacy. The system according to feature 1.

6. The aforementioned display unit is, At times such as when system upgrades, overall resource optimization, or troubleshooting are needed, we present solutions to the IT department that take into account the IT resource needs of individuals and departments. The system according to feature 1.

7. The aforementioned display unit is, We provide reassuring support to address the concerns of new employees and mid-career hires. The system according to feature 1.

8. The aforementioned display unit is, This service explains how terms you might hesitate to ask about in a meeting, based on accumulated internal documentation, are used within the company, addressing any issues that might otherwise be interrupted. The system according to feature 1.

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