System

Abstract

An object of a system according to an embodiment is to appropriately distribute inquiries in a company to improve business efficiency.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a distribution unit, and a monitoring unit. The reception unit receives an inquiry content. The analysis unit analyzes the content of the inquiry received by the reception unit. The distribution unit distributes the document to a department or a person in charge based on the content analyzed by the analysis unit. The monitoring part monitors the number of inquiries of the whole company.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to assign internal inquiries to the appropriate department or person in charge, which can lead to a decrease in work efficiency.

[0005] The system according to the embodiment aims to appropriately distribute inquiries within a company and improve business efficiency. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a distribution unit, and a monitoring unit. The reception unit receives inquiries. The analysis unit analyzes the inquiries received by the reception unit. The distribution unit distributes inquiries to departments or personnel based on the analysis results. The monitoring unit monitors the number of inquiries across the company. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately distribute inquiries within a company and improve business efficiency. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The inquiry efficiency improvement system according to an embodiment of the present invention allows employees to make inquiries in one go when they are unsure which department or person to contact. In this system, employees input their inquiries, and a generation AI analyzes the inquiries and automatically determines the appropriate contact point or department. For example, technical issues are routed to the technical department, and accounting issues are routed to the accounting department. In this way, inquiries are automatically directed to the most appropriate contact point depending on the inquiry. Furthermore, the system can track the total number of inquiries across the entire company. This allows for real-time monitoring of which departments are receiving the most inquiries. For example, if inquiries are concentrated in a particular department, measures can be taken to reduce the department's workload. This relieves employee stress and significantly improves work efficiency. Furthermore, the company can identify problems early, enabling a faster response. For example, adding resources to departments receiving a large number of inquiries can improve overall work efficiency. This allows the inquiry efficiency improvement system to efficiently process employee inquiries and route them to the appropriate department. For example, even if an employee is unsure which department to contact, they can still receive a prompt and accurate response. In addition, by understanding the status of inquiries across the company in real time, problems can be detected early and appropriate measures can be taken, improving work efficiency and reducing employee stress.

[0029] An inquiry efficiency improvement system according to an embodiment includes a reception unit, an analysis unit, a distribution unit, and a monitoring unit. The reception unit receives inquiries entered by employees. The inquiries include, but are not limited to, technical questions, product inquiries, and complaints. The reception unit can receive inquiries via, for example, a web form, email, or a chatbot. The analysis unit uses a generation AI to analyze the inquiries received by the reception unit. The analysis can be performed using, for example, text analysis, sentiment analysis, keyword extraction, or other methods, but is not limited to these. For example, the generation AI analyzes the inquiries using a text generation AI (e.g., LLM) to extract important keywords. The analysis unit can also grasp the emotional nuances of the inquiries using sentiment analysis. The distribution unit distributes the inquiries to appropriate departments or staff members based on the analysis results of the analysis unit. The distribution can be performed based on, for example, the staff member's field of expertise or the department's work, but is not limited to these examples. For example, the distribution unit distributes technical issues to the engineering department and accounting issues to the accounting department. The allocation unit can also allocate inquiries to personnel who are knowledgeable about a particular technology. The monitoring unit monitors the number of inquiries across the entire company. Monitoring can be performed, for example, based on the tallying of the number of inquiries and anomaly detection criteria, but is not limited to these examples. For example, the monitoring unit can issue an alert if a particular department is overloaded. This allows the inquiry efficiency system according to the embodiment to efficiently process employee inquiries and allocate them to appropriate departments. This reduces employee stress and improves work efficiency. Furthermore, by understanding the status of inquiries across the entire company in real time, problems can be discovered early and appropriate measures can be taken. This improves work efficiency across the entire company.

[0030] The inquiry efficiency improvement system includes a history storage unit that stores inquiry history. The history storage unit stores the inquiry history. The stored history includes, for example, inquiry content, inquiry date and time, and response status, but is not limited to these examples. The history storage unit stores the inquiry history using, for example, a database. The history storage unit can also store the inquiry history using cloud storage. For example, the history storage unit stores the inquiry content as text data so that it can be referenced later. The history storage unit can also update the inquiry response status in real time and retain the latest information. In this way, by storing the inquiry history, past inquiry content can be referenced. For example, if an employee has made a similar inquiry in the past, the history can be referenced to quickly respond. The history storage unit can also analyze the inquiry history and understand inquiry trends. This enables efficient responses based on the inquiry history.

[0031] The inquiry efficiency improvement system includes a reference unit that can reference past inquiry content. The reference unit can reference past inquiry content. The referenced content includes, for example, inquiry content, response status, and response results, but is not limited to these examples. The reference unit, for example, searches past inquiry content using a search algorithm. The reference unit can also narrow down inquiry content based on specific conditions using a filtering function. For example, the reference unit searches for and displays inquiry content containing specific keywords. The reference unit can also prioritize displaying unresolved inquiries based on the response status of the inquiry. This allows for efficient response by referring to past inquiry content. For example, if an employee has made a similar inquiry in the past, the history can be referenced to quickly respond. The reference unit can also analyze past inquiry content and understand inquiry trends. This allows the reference unit to support efficient response.

[0032] The inquiry efficiency improvement system includes an FAQ generation unit that automatically generates an FAQ based on the inquiry content. The FAQ generation unit automatically generates the FAQ based on the inquiry content. The generated FAQs include, for example, frequently asked questions and their answers, troubleshooting procedures, etc., but are not limited to these examples. The FAQ generation unit, for example, uses a generation AI to analyze the inquiry content and generate the FAQ. The generation AI generates the FAQ using, for example, a text generation AI (e.g., LLM). The FAQ generation unit can also generate the FAQ using a template. For example, the FAQ generation unit analyzes the inquiry content and selects an appropriate template to generate the FAQ. The FAQ generation unit can also store the generated FAQ in a database for later reference. In this way, automatically generating FAQs based on the inquiry content enables efficient information provision. For example, employees can quickly obtain answers to frequently asked questions. The FAQ generation unit can also analyze the generated FAQs and improve the quality of the FAQs. In this way, the FAQ generation unit can support efficient information provision.

[0033] The allocation unit can allocate to a person who is knowledgeable about a technology. For example, the allocation unit allocates to a person who is knowledgeable about a specific technology. A person who is knowledgeable about a technology includes, but is not limited to, a person with a specific qualification or a person with a specific number of years of experience. For example, the allocation unit allocates technical issues to experts in the technical department. The allocation unit can also allocate inquiries about a specific technology to experts preferentially. For example, the allocation unit allocates to experts in order to quickly resolve technical issues. By allocating to an expert who is knowledgeable about a specific technology, a quick and appropriate response is possible. For example, by having a person with specialized knowledge respond to a technical issue, the issue is quickly resolved. The allocation unit can also make optimal allocations based on the person's area of ​​expertise. This allows the allocation unit to support efficient responses.

[0034] The monitoring unit can issue an alert to respond when a load is concentrated in a specific department. The monitoring unit issues an alert to respond when a load is concentrated in a specific department. Examples of a state in which a load is concentrated include, but are not limited to, a state in which a large number of inquiries are received within a certain period of time or a state in which the response capacity of a person in charge is exceeded. The monitoring unit, for example, monitors the number of inquiries in real time and detects an abnormality. Furthermore, the monitoring unit can issue an alert to prompt a prompt response when a load is concentrated in a specific department. For example, the monitoring unit instructs the allocation of additional resources when inquiries are concentrated in a specific department. Furthermore, the monitoring unit can monitor the status of the department where the load is concentrated in real time and take appropriate measures. As a result, by issuing an alert to promptly respond when a load is concentrated in a specific department, business efficiency can be improved. For example, by promptly responding to the department where the load is concentrated, business delays can be prevented. Furthermore, the monitoring unit can take measures to distribute the load. As a result, the monitoring unit can support efficient business operations.

[0035] The reception unit can select a reception method by referring to the user's past inquiry history when receiving an inquiry. For example, the reception unit can prioritize and suggest inquiry methods (email, chat, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest the method to be used during a specific time period based on the user's past inquiry history. Furthermore, the reception unit can analyze the success rate of inquiry methods used by the user in the past and suggest the optimal method. This allows the optimal reception method to be selected by referring to the user's past inquiry history, enabling efficient inquiry reception. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past inquiry history data into a generation AI and have the generation AI select the optimal reception method.

[0036] The reception unit can perform filtering based on the user's current work situation and areas of interest when receiving inquiries. For example, the reception unit prioritizes receiving inquiries related to a project the user is currently working on. The reception unit can also filter related inquiries based on the user's areas of interest. Furthermore, the reception unit can analyze the user's work situation in real time and suggest the optimal means of inquiry. This enables efficient inquiry reception by filtering based on the user's current work situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's work situation data into a generation AI and have the generation AI perform filtering.

[0037] The reception unit can select a reception means according to the user's input method when receiving an inquiry. For example, when the user makes an inquiry by voice, the reception unit receives the inquiry using voice recognition technology. Furthermore, when the user makes an inquiry by text, the reception unit can also receive the inquiry using text analysis technology. Furthermore, when the user makes an inquiry by attaching an image, the reception unit can also receive the inquiry using image analysis technology. This enables efficient inquiry reception by selecting the optimal reception means according to the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data into a generation AI and have the generation AI select the optimal reception means.

[0038] The reception unit can prioritize receiving inquiries by taking into account the user's geographical location information. For example, if the user is in a specific office, the reception unit can prioritize receiving inquiries related to that office. Furthermore, if the user is on a business trip, the reception unit can prioritize receiving inquiries related to the business trip destination. Furthermore, if the user is working remotely, the reception unit can prioritize receiving inquiries related to the user's home. In this way, by taking the user's geographical location information into consideration, it is possible to prioritize receiving highly relevant inquiries. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data into the generation AI and have the generation AI select the optimal reception method.

[0039] The reception unit can analyze the user's social media activity and receive related inquiries at the time of reception. For example, the reception unit prioritizes reception of inquiries related to issues the user mentioned on social media. The reception unit can also analyze the content of the user's social media posts and receive related inquiries. Furthermore, the reception unit can also receive related inquiries by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related inquiries can be efficiently received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to receive related inquiries.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a call. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also analyze the success rate of a specific reception method from the user's past feedback and suggest a method. Furthermore, the reception unit can provide a customized reception method by referring to the user's past feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI select the optimal reception method.

[0041] During analysis, the analysis unit can adjust the level of analysis based on the importance of the query. For example, the analysis unit performs a detailed analysis for queries with high importance. The analysis unit can also perform a simplified analysis for queries with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail for queries with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the query. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input query importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the inquiry. For example, the analysis unit can apply a technical analysis algorithm to a technical inquiry. The analysis unit can also apply an accounting analysis algorithm to an accounting inquiry. The analysis unit can also apply a human resources analysis algorithm to an inquiry regarding human resources. This enables efficient analysis by applying different analysis algorithms depending on the inquiry category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input inquiry category data into the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the current analysis based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can analyze the user's past analysis results and provide a highly accurate analysis. This can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI optimize the analysis.

[0044] During analysis, the analysis unit can determine the order of analysis based on the time of submission of the inquiry. For example, the analysis unit may give top priority to analysis of urgent inquiries. The analysis unit may also perform analysis of normal inquiries with normal priority. Furthermore, the analysis unit may postpone analysis of past inquiries. This enables efficient analysis by determining the analysis priority based on the time of submission of the inquiry. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input inquiry submission time data into the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the queries. For example, the analysis unit prioritizes analysis of queries with high relevance. The analysis unit can also postpone analysis of queries with low relevance. Furthermore, the analysis unit can also analyze queries with moderate relevance with an appropriate priority. This enables efficient analysis by adjusting the order of analysis based on the relevance of the queries. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input query relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the selection of technical terms according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide an analysis that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide an analysis in simple language. Furthermore, the analysis unit can provide an analysis that uses appropriate technical terms according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI select the technical terms.

[0047] The distribution unit can improve the accuracy of distribution by taking into account the interrelationships between inquiries during distribution. For example, the distribution unit distributes related inquiries to the same department. The distribution unit can also group interrelated inquiries and distribute them to appropriate departments. Furthermore, the distribution unit can analyze the interrelationships between inquiries and perform optimal distribution. In this way, by taking into account the interrelationships between inquiries, the accuracy of distribution can be improved. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input interrelationship data between inquiries to a generation AI and have the generation AI perform optimization of distribution.

[0048] The allocation unit can allocate inquiries taking into consideration attribute information of the person who submitted the inquiry. For example, the allocation unit allocates inquiries to an appropriate department based on the person's job title. The allocation unit can also allocate inquiries to related departments based on the person's field of expertise. Furthermore, the allocation unit can allocate inquiries to the most appropriate department based on the person's past inquiry history. In this way, inquiries can be allocated to the most appropriate department by taking into consideration the attribute information of the person who submitted the inquiry. Some or all of the above-described processing in the allocation unit may be performed using AI, for example, or may be performed without using AI. For example, the allocation unit can input attribute information data of the person who submitted the inquiry into a generation AI and have the generation AI perform the most appropriate allocation.

[0049] The allocation unit can prioritize allocation based on the frequency of inquiries submitted during allocation. For example, the allocation unit allocates inquiries submitted frequently to a department that can respond quickly. The allocation unit can also handle inquiries submitted infrequently using normal allocation criteria. Furthermore, the allocation unit can allocate inquiries submitted at a medium frequency with appropriate weighting. This allows for efficient allocation by weighting the allocation based on the frequency of inquiries submitted. Some or all of the above-mentioned processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit can input submission frequency data to a generation AI and have the generation AI perform the allocation weighting.

[0050] The allocation unit can allocate inquiries to departments taking into account the geographical distribution of the inquiries. For example, if a user is in a specific area, the allocation unit can allocate the inquiries to a department related to that area. Furthermore, if a user is on a business trip, the allocation unit can also allocate the inquiries to a department related to the user's business trip destination. Furthermore, if a user is working remotely, the allocation unit can also allocate the inquiries to a department related to the user's home. In this way, by taking the geographical distribution of inquiries into consideration, the inquiries can be allocated to the optimal department. Some or all of the above-described processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit can input user geographical distribution data into a generation AI and cause the generation AI to perform optimal allocation.

[0051] The allocation unit can improve the accuracy of allocation by referring to literature related to the inquiry during allocation. The allocation unit, for example, allocates to the most appropriate department based on related literature. The allocation unit can also refer to literature related to the inquiry content and allocate to the appropriate department. Furthermore, the allocation unit can analyze related literature and improve the accuracy of allocation. In this way, by referring to literature related to the inquiry, the accuracy of allocation can be improved. Some or all of the above-mentioned processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit can input related literature data to a generation AI and have the generation AI perform allocation optimization.

[0052] The allocation unit can allocate inquiries to departments taking into account the market value of the inquiries when allocating them. For example, the allocation unit allocates inquiries with high market value to a department that can respond quickly. The allocation unit can also handle inquiries with low market value using normal allocation standards. Furthermore, the allocation unit can allocate inquiries with medium market value with appropriate weighting. In this way, inquiries can be allocated to the most appropriate department by taking into account the market value of the inquiry. Some or all of the above-mentioned processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit can input market value data into a generation AI and have the generation AI perform allocation optimization.

[0053] During monitoring, the monitoring unit can improve current monitoring by referring to past monitoring data. The monitoring unit, for example, optimizes current monitoring based on past monitoring data. The monitoring unit can also extract specific patterns from past monitoring data and reflect them in current monitoring. Furthermore, the monitoring unit can analyze past monitoring data to provide highly accurate monitoring. This makes it possible to optimize current monitoring by referring to past monitoring data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past monitoring data into a generation AI and have the generation AI execute monitoring optimization.

[0054] During monitoring, the monitoring unit can apply different monitoring methods to different inquiry categories. For example, the monitoring unit can apply a technical-specific monitoring method to technical inquiries. The monitoring unit can also apply an accounting-specific monitoring method to accounting-related inquiries. The monitoring unit can also apply a human resources-specific monitoring method to human resources-related inquiries. This enables efficient monitoring by applying different monitoring methods to different inquiry categories. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input inquiry category data into a generation AI and have the generation AI apply the optimal monitoring method.

[0055] During monitoring, the monitoring unit can perform monitoring while taking into account attribute information of the person submitting the inquiry. The monitoring unit can apply an appropriate monitoring method, for example, depending on the position of the person submitting the inquiry. The monitoring unit can also apply a relevant monitoring method depending on the person's field of expertise. Furthermore, the monitoring unit can apply the optimal monitoring method by referring to the submitter's past inquiry history. This enables optimal monitoring by taking into account the attribute information of the person submitting the inquiry. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input attribute information data of the submitter into the generation AI and cause the generation AI to apply the optimal monitoring method.

[0056] During monitoring, the monitoring unit can analyze monitoring fluctuations based on the time of inquiry submission. For example, for urgent inquiries, the monitoring unit analyzes monitoring changes based on the time of submission. The monitoring unit can also analyze monitoring changes for normal inquiries based on the time of submission. Furthermore, the monitoring unit can analyze monitoring changes for past inquiries based on the time of submission. This enables efficient monitoring by analyzing monitoring changes based on the time of inquiry submission. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input submission time data into a generation AI and have the generation AI analyze monitoring changes.

[0057] During monitoring, the monitoring unit can perform monitoring by referring to market data related to the inquiry. The monitoring unit, for example, performs optimal monitoring based on the related market data. The monitoring unit can also perform appropriate monitoring by referring to market data related to the inquiry content. Furthermore, the monitoring unit can analyze the related market data and improve the accuracy of monitoring. This enables efficient monitoring by referring to the market data related to the inquiry. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the related market data into a generation AI and cause the generation AI to perform optimal monitoring.

[0058] The monitoring unit can perform monitoring while taking into account the technical maturity of the inquiry. For example, the monitoring unit performs normal monitoring for technically mature inquiries. The monitoring unit can also perform detailed monitoring for technically immature inquiries. Furthermore, the monitoring unit can apply an appropriate monitoring method depending on the technical maturity. This enables efficient monitoring by taking into account the technical maturity of the inquiry. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input technical maturity data into the generation AI and cause the generation AI to apply the optimal monitoring method.

[0059] When saving history, the history storage unit can select a storage method by referring to past inquiry history. The history storage unit, for example, selects the optimal storage method based on the past inquiry history. The history storage unit can also extract specific patterns from the past inquiry history and select the optimal storage method. Furthermore, the history storage unit can analyze the past inquiry history and provide a highly accurate storage method. This makes it possible to select the optimal storage method by referring to the past inquiry history and efficiently store the history. Some or all of the above-mentioned processing in the history storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the history storage unit can input past inquiry history data into a generation AI and have the generation AI select the optimal storage method.

[0060] The history storage unit can apply different storage algorithms depending on the category of the inquiry when storing the history. For example, the history storage unit can apply a specialized storage algorithm for technical inquiries. The history storage unit can also apply a specialized storage algorithm for accounting inquiries. The history storage unit can also apply a specialized storage algorithm for human resources inquiries. This enables efficient history storage by applying different storage algorithms depending on the category of the inquiry. Some or all of the above-mentioned processing in the history storage unit can be performed using, for example, AI, or without AI. For example, the history storage unit can input inquiry category data into a generation AI and cause the generation AI to apply the optimal storage algorithm.

[0061] The history storage unit can weight the history to be saved based on the time of submission of the inquiry when saving the history. For example, the history storage unit weights the history to be saved based on the time of submission for urgent inquiries. The history storage unit can also weight the history to be saved based on the time of submission for normal inquiries. Furthermore, the history storage unit can weight the history to be saved based on the time of submission for past inquiries. This enables efficient history saving by weighting the history to be saved based on the time of submission of the inquiry. Some or all of the above-mentioned processing in the history storage unit may be performed using AI, for example, or may be performed without using AI. For example, the history storage unit can input submission time data to a generation AI and have the generation AI perform the weighting of the history to be saved.

[0062] The history storage unit can improve the accuracy of storage by referring to literature related to the query when storing the history. The history storage unit, for example, selects an optimal storage method based on related literature. The history storage unit can also select an appropriate storage method by referring to literature related to the query content. Furthermore, the history storage unit can analyze related literature and improve the accuracy of storage. In this way, by referring to literature related to the query, the accuracy of storage can be improved. Some or all of the above-mentioned processing in the history storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the history storage unit can input related literature data to a generation AI and have the generation AI perform storage optimization.

[0063] The reference unit can select the optimal reference method by referring to past inquiry history during reference. The reference unit, for example, selects the optimal reference method based on the past inquiry history. The reference unit can also extract specific patterns from the past inquiry history and select the optimal reference method. Furthermore, the reference unit can analyze the past inquiry history and provide a highly accurate reference method. This allows the optimal reference method to be selected by referring to the past inquiry history, enabling efficient reference. Some or all of the above-described processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input past inquiry history data into a generation AI and have the generation AI select the optimal reference method.

[0064] The reference unit can apply different reference algorithms depending on the category of the inquiry during reference. For example, the reference unit can apply a technical-specialized reference algorithm to a technical inquiry. The reference unit can also apply an accounting-specialized reference algorithm to an accounting inquiry. The reference unit can also apply a human resources-specialized reference algorithm to an inquiry regarding human resources. This enables efficient reference by applying different reference algorithms depending on the inquiry category. Some or all of the above-mentioned processing in the reference unit can be performed using AI, for example, or without AI. For example, the reference unit can input inquiry category data into the generation AI and have the generation AI apply the optimal reference algorithm.

[0065] The reference unit can weight the reference based on the time of submission of the inquiry at the time of reference. For example, for urgent inquiries, the reference unit weights the reference based on the time of submission. The reference unit can also weight the reference based on the time of submission for normal inquiries. Furthermore, the reference unit can weight the reference based on the time of submission for past inquiries. In this way, weighting the reference based on the time of submission of the inquiry enables efficient reference. Some or all of the above-mentioned processing in the reference unit may be performed using AI, for example, or may be performed without using AI. For example, the reference unit can input submission time data into a generation AI and have the generation AI perform reference weighting.

[0066] The reference unit can improve the accuracy of the reference by referring to literature related to the query during the reference. The reference unit, for example, selects an optimal reference method based on related literature. The reference unit can also refer to literature related to the query content and select an appropriate reference method. Furthermore, the reference unit can analyze related literature and improve the accuracy of the reference. In this way, the accuracy of the reference can be improved by referring to literature related to the query. Some or all of the above-mentioned processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input related literature data into a generation AI and have the generation AI perform reference optimization.

[0067] When generating an FAQ, the FAQ generation unit can generate the FAQ by referencing past inquiry history. The FAQ generation unit generates an optimal FAQ based on, for example, the past inquiry history. The FAQ generation unit can also extract specific patterns from the past inquiry history and generate an optimal FAQ. Furthermore, the FAQ generation unit can analyze the past inquiry history and generate a highly accurate FAQ. This makes it possible to generate an optimal FAQ by referencing the past inquiry history and provide information efficiently. Some or all of the above-mentioned processing in the FAQ generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the FAQ generation unit can input past inquiry history data into a generation AI and have the generation AI generate an optimal FAQ.

[0068] When generating an FAQ, the FAQ generation unit can apply different generation algorithms depending on the category of the inquiry. For example, the FAQ generation unit applies a generation algorithm specialized in technology to a technical inquiry. The FAQ generation unit can also apply a generation algorithm specialized in accounting to an inquiry about accounting. Furthermore, the FAQ generation unit can also apply a generation algorithm specialized in human resources to an inquiry about human resources. In this way, by applying different generation algorithms depending on the inquiry category, efficient FAQ generation becomes possible. Some or all of the above-mentioned processing in the FAQ generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the FAQ generation unit can input inquiry category data into the generation AI and cause the generation AI to apply the optimal generation algorithm.

[0069] When generating FAQs, the FAQ generation unit can weight the FAQs based on the time of submission of the inquiry. For example, for urgent inquiries, the FAQ generation unit weights the FAQs based on the time of submission. For normal inquiries, the FAQ generation unit can also weight the FAQs based on the time of submission. Furthermore, for past inquiries, the FAQ generation unit can weight the FAQs based on the time of submission. In this way, weighting the FAQs based on the time of submission of the inquiry enables efficient information provision. Some or all of the above-mentioned processing in the FAQ generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the FAQ generation unit can input submission time data into the generation AI and have the generation AI perform FAQ weighting.

[0070] When generating an FAQ, the FAQ generation unit can improve the accuracy of the FAQ by referring to literature related to the inquiry. The FAQ generation unit, for example, generates an optimal FAQ based on related literature. The FAQ generation unit can also generate an appropriate FAQ by referring to literature related to the content of the inquiry. Furthermore, the FAQ generation unit can analyze related literature and improve the accuracy of the FAQ. In this way, the accuracy of the FAQ can be improved by referring to literature related to the inquiry. Some or all of the above-mentioned processing in the FAQ generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the FAQ generation unit can input related literature data into the generation AI and cause the generation AI to optimize the FAQ.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The inquiry efficiency improvement system can refer to a user's past inquiry history and automatically provide an answer when a similar inquiry is made. For example, if a similar technical problem has occurred in the past, it can automatically present a solution. It can also extract specific patterns from the past inquiry history and suggest proactive measures for predicted problems. It can also analyze the past inquiry history and automatically generate FAQs for frequently occurring problems. This enables quick and efficient responses by utilizing the user's past inquiry history. Some or all of the above-mentioned processing in the inquiry efficiency improvement system may be performed using, for example, AI, or may be performed without using AI. For example, the inquiry efficiency improvement system inputs past inquiry history data into a generation AI and has the generation AI provide the optimal answer.

[0073] The inquiry efficiency improvement system can automatically provide relevant information based on the user's current work situation and areas of interest. For example, it can prioritize technical information related to the project the user is currently working on. It can also present related topics and the latest research results based on the user's areas of interest. It can also analyze the user's work situation in real time and provide optimal information. This enables efficient information provision based on the user's current work situation and areas of interest. Some or all of the above-mentioned processing in the inquiry efficiency improvement system may be performed using, for example, AI, or may be performed without using AI. For example, the inquiry efficiency improvement system inputs the user's work situation data into a generation AI and has the generation AI provide optimal information.

[0074] The inquiry efficiency improvement system can prioritize inquiries by taking into account the user's geographical location information. For example, if the user is in a specific office, inquiries related to that office can be prioritized. Also, if the user is on a business trip, inquiries related to the business trip destination can be prioritized. Furthermore, if the user is working remotely, inquiries related to the user's home can be prioritized. In this way, by taking the user's geographical location information into consideration, it is possible to prioritize inquiries that are highly relevant. Some or all of the above-described processing in the inquiry efficiency improvement system may be performed using AI, for example, or may be performed without using AI. For example, the inquiry efficiency improvement system inputs the user's geographical location information data into a generation AI and causes the generation AI to select the optimal means of reception.

[0075] The inquiry efficiency improvement system can analyze a user's social media activity and accept related inquiries. For example, it can prioritize inquiries related to issues the user mentioned on social media. It can also analyze the content of a user's social media posts and accept related inquiries. It can also accept related inquiries by referring to the activities of the user's friends on social media. In this way, by analyzing a user's social media activity, it is possible to efficiently accept related inquiries. Some or all of the above-mentioned processes in the inquiry efficiency improvement system may be performed using, for example, AI, or may be performed without using AI. For example, the inquiry efficiency improvement system inputs a user's social media data into a generation AI and causes the generation AI to accept related inquiries.

[0076] The inquiry efficiency improvement system can customize the reception method by reflecting the user's past feedback. For example, it can suggest the optimal reception method based on feedback provided by the user in the past. It can also analyze the success rate of a specific reception method from the user's past feedback and suggest a method. It can also provide a customized reception method by referring to the user's past feedback. In this way, it is possible to provide the optimal reception method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the inquiry efficiency improvement system may be performed using AI, for example, or may be performed without using AI. For example, the inquiry efficiency improvement system can input the user's past feedback data into a generation AI and have the generation AI select the optimal reception method.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The reception department accepts inquiries entered by employees. Inquiries include technical questions, product inquiries, and complaints. The reception department can accept inquiries via web forms, email, chatbots, etc. Step 2: The analysis unit uses the generation AI to analyze the inquiry content received by the reception unit. The analysis is performed using methods such as text analysis, sentiment analysis, and keyword extraction. For example, the generation AI analyzes the inquiry content using a text generation AI (e.g., LLM) and extracts important keywords. The analysis unit can also use sentiment analysis to grasp the emotional nuances of the inquiry content. Step 3: The allocation department allocates the problem to the appropriate department or person based on the content analyzed by the analysis department. Allocation is based on the person's area of ​​expertise and the work content of the department. For example, technical issues are allocated to the technical department, and accounting issues are allocated to the accounting department. It is also possible to allocate the problem to a person who is knowledgeable about a specific technology. Step 4: The monitoring department monitors the number of inquiries from across the company. Monitoring is based on the tally of inquiries and anomaly detection criteria. For example, an alert can be issued if a particular department is experiencing a high load.

[0079] (Example 2) The inquiry efficiency improvement system according to an embodiment of the present invention allows employees to make inquiries in one go when they are unsure which department or person to contact. In this system, employees input their inquiries, and a generation AI analyzes the inquiries and automatically determines the appropriate contact point or department. For example, technical issues are routed to the technical department, and accounting issues are routed to the accounting department. In this way, inquiries are automatically directed to the most appropriate contact point depending on the inquiry. Furthermore, the system can track the total number of inquiries across the entire company. This allows for real-time monitoring of which departments are receiving the most inquiries. For example, if inquiries are concentrated in a particular department, measures can be taken to reduce the department's workload. This relieves employee stress and significantly improves work efficiency. Furthermore, the company can identify problems early, enabling a faster response. For example, adding resources to departments receiving a large number of inquiries can improve overall work efficiency. This allows the inquiry efficiency improvement system to efficiently process employee inquiries and route them to the appropriate department. For example, even if an employee is unsure which department to contact, they can still receive a prompt and accurate response. In addition, by understanding the status of inquiries across the company in real time, problems can be detected early and appropriate measures can be taken, improving work efficiency and reducing employee stress.

[0080] An inquiry efficiency improvement system according to an embodiment includes a reception unit, an analysis unit, a distribution unit, and a monitoring unit. The reception unit receives inquiries entered by employees. The inquiries include, but are not limited to, technical questions, product inquiries, and complaints. The reception unit can receive inquiries via, for example, a web form, email, or a chatbot. The analysis unit uses a generation AI to analyze the inquiries received by the reception unit. The analysis can be performed using, for example, text analysis, sentiment analysis, keyword extraction, or other methods, but is not limited to these. For example, the generation AI analyzes the inquiries using a text generation AI (e.g., LLM) to extract important keywords. The analysis unit can also grasp the emotional nuances of the inquiries using sentiment analysis. The distribution unit distributes the inquiries to appropriate departments or staff members based on the analysis results of the analysis unit. The distribution can be performed based on, for example, the staff member's field of expertise or the department's work, but is not limited to these examples. For example, the distribution unit distributes technical issues to the engineering department and accounting issues to the accounting department. The allocation unit can also allocate inquiries to personnel who are knowledgeable about a particular technology. The monitoring unit monitors the number of inquiries across the entire company. Monitoring can be performed, for example, based on the tallying of the number of inquiries and anomaly detection criteria, but is not limited to these examples. For example, the monitoring unit can issue an alert if a particular department is overloaded. This allows the inquiry efficiency system according to the embodiment to efficiently process employee inquiries and allocate them to appropriate departments. This reduces employee stress and improves work efficiency. Furthermore, by understanding the status of inquiries across the entire company in real time, problems can be discovered early and appropriate measures can be taken. This improves work efficiency across the entire company.

[0081] The inquiry efficiency improvement system includes a history storage unit that stores inquiry history. The history storage unit stores the inquiry history. The stored history includes, for example, inquiry content, inquiry date and time, and response status, but is not limited to these examples. The history storage unit stores the inquiry history using, for example, a database. The history storage unit can also store the inquiry history using cloud storage. For example, the history storage unit stores the inquiry content as text data so that it can be referenced later. The history storage unit can also update the inquiry response status in real time and retain the latest information. In this way, by storing the inquiry history, past inquiry content can be referenced. For example, if an employee has made a similar inquiry in the past, the history can be referenced to quickly respond. The history storage unit can also analyze the inquiry history and understand inquiry trends. This enables efficient responses based on the inquiry history.

[0082] The inquiry efficiency improvement system includes a reference unit that can reference past inquiry content. The reference unit can reference past inquiry content. The referenced content includes, for example, inquiry content, response status, and response results, but is not limited to these examples. The reference unit, for example, searches past inquiry content using a search algorithm. The reference unit can also narrow down inquiry content based on specific conditions using a filtering function. For example, the reference unit searches for and displays inquiry content containing specific keywords. The reference unit can also prioritize displaying unresolved inquiries based on the response status of the inquiry. This allows for efficient response by referring to past inquiry content. For example, if an employee has made a similar inquiry in the past, the history can be referenced to quickly respond. The reference unit can also analyze past inquiry content and understand inquiry trends. This allows the reference unit to support efficient response.

[0083] The inquiry efficiency improvement system includes an FAQ generation unit that automatically generates an FAQ based on the inquiry content. The FAQ generation unit automatically generates the FAQ based on the inquiry content. The generated FAQs include, for example, frequently asked questions and their answers, troubleshooting procedures, etc., but are not limited to these examples. The FAQ generation unit, for example, uses a generation AI to analyze the inquiry content and generate the FAQ. The generation AI generates the FAQ using, for example, a text generation AI (e.g., LLM). The FAQ generation unit can also generate the FAQ using a template. For example, the FAQ generation unit analyzes the inquiry content and selects an appropriate template to generate the FAQ. The FAQ generation unit can also store the generated FAQ in a database for later reference. In this way, automatically generating FAQs based on the inquiry content enables efficient information provision. For example, employees can quickly obtain answers to frequently asked questions. The FAQ generation unit can also analyze the generated FAQs and improve the quality of the FAQs. In this way, the FAQ generation unit can support efficient information provision.

[0084] The allocation unit can allocate to a person who is knowledgeable about a technology. For example, the allocation unit allocates to a person who is knowledgeable about a specific technology. A person who is knowledgeable about a technology includes, but is not limited to, a person with a specific qualification or a person with a specific number of years of experience. For example, the allocation unit allocates technical issues to experts in the technical department. The allocation unit can also allocate inquiries about a specific technology to experts preferentially. For example, the allocation unit allocates to experts in order to quickly resolve technical issues. By allocating to an expert who is knowledgeable about a specific technology, a quick and appropriate response is possible. For example, by having a person with specialized knowledge respond to a technical issue, the issue is quickly resolved. The allocation unit can also make optimal allocations based on the person's area of ​​expertise. This allows the allocation unit to support efficient responses.

[0085] The monitoring unit can issue an alert to respond when a load is concentrated in a specific department. The monitoring unit issues an alert to respond when a load is concentrated in a specific department. Examples of a state in which a load is concentrated include, but are not limited to, a state in which a large number of inquiries are received within a certain period of time or a state in which the response capacity of a person in charge is exceeded. The monitoring unit, for example, monitors the number of inquiries in real time and detects an abnormality. Furthermore, the monitoring unit can issue an alert to prompt a prompt response when a load is concentrated in a specific department. For example, the monitoring unit instructs the allocation of additional resources when inquiries are concentrated in a specific department. Furthermore, the monitoring unit can monitor the status of the department where the load is concentrated in real time and take appropriate measures. As a result, by issuing an alert to promptly respond when a load is concentrated in a specific department, business efficiency can be improved. For example, by promptly responding to the department where the load is concentrated, business delays can be prevented. Furthermore, the monitoring unit can take measures to distribute the load. As a result, the monitoring unit can support efficient business operations.

[0086] The reception unit can estimate the user's emotions and adjust the inquiry reception method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly enter the inquiry content. This reduces the user's stress and enables efficient inquiry reception by adjusting the inquiry reception method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's input data into the generation AI and have the generation AI perform emotion estimation.

[0087] The reception unit can select a reception method by referring to the user's past inquiry history when receiving an inquiry. For example, the reception unit can prioritize and suggest inquiry methods (email, chat, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest the method to be used during a specific time period based on the user's past inquiry history. Furthermore, the reception unit can analyze the success rate of inquiry methods used by the user in the past and suggest the optimal method. This allows the optimal reception method to be selected by referring to the user's past inquiry history, enabling efficient inquiry reception. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past inquiry history data into a generation AI and have the generation AI select the optimal reception method.

[0088] The reception unit can perform filtering based on the user's current work situation and areas of interest when receiving inquiries. For example, the reception unit prioritizes receiving inquiries related to a project the user is currently working on. The reception unit can also filter related inquiries based on the user's areas of interest. Furthermore, the reception unit can analyze the user's work situation in real time and suggest the optimal means of inquiry. This enables efficient inquiry reception by filtering based on the user's current work situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's work situation data into a generation AI and have the generation AI perform filtering.

[0089] The reception unit can select a reception means according to the user's input method when receiving an inquiry. For example, when the user makes an inquiry by voice, the reception unit receives the inquiry using voice recognition technology. Furthermore, when the user makes an inquiry by text, the reception unit can also receive the inquiry using text analysis technology. Furthermore, when the user makes an inquiry by attaching an image, the reception unit can also receive the inquiry using image analysis technology. This enables efficient inquiry reception by selecting the optimal reception means according to the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data into a generation AI and have the generation AI select the optimal reception means.

[0090] The reception unit can estimate the user's emotions and determine the priority of inquiries to be received based on the estimated user emotions. For example, if the user feels urgent, the reception unit processes the inquiry with the highest priority. Furthermore, if the user feels relaxed, the reception unit can also process the inquiry with normal priority. Furthermore, if the user feels anxious, the reception unit can set a priority for a quick response. This enables a quick and appropriate response by determining the priority of inquiries according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input user input data into the generation AI and have the generation AI perform emotion estimation.

[0091] The reception unit can prioritize receiving inquiries by taking into account the user's geographical location information. For example, if the user is in a specific office, the reception unit can prioritize receiving inquiries related to that office. Furthermore, if the user is on a business trip, the reception unit can prioritize receiving inquiries related to the business trip destination. Furthermore, if the user is working remotely, the reception unit can prioritize receiving inquiries related to the user's home. In this way, by taking the user's geographical location information into consideration, it is possible to prioritize receiving highly relevant inquiries. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data into the generation AI and have the generation AI select the optimal reception method.

[0092] The reception unit can analyze the user's social media activity and receive related inquiries at the time of reception. For example, the reception unit prioritizes reception of inquiries related to issues the user mentioned on social media. The reception unit can also analyze the content of the user's social media posts and receive related inquiries. Furthermore, the reception unit can also receive related inquiries by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related inquiries can be efficiently received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to receive related inquiries.

[0093] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a call. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also analyze the success rate of a specific reception method from the user's past feedback and suggest a method. Furthermore, the reception unit can provide a customized reception method by referring to the user's past feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI select the optimal reception method.

[0094] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that are easy to understand for the user by adjusting the way the analysis is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input user input data into the generation AI and have the generation AI perform emotion estimation.

[0095] During analysis, the analysis unit can adjust the level of analysis based on the importance of the query. For example, the analysis unit performs a detailed analysis for queries with high importance. The analysis unit can also perform a simplified analysis for queries with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail for queries with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the query. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input query importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0096] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the inquiry. For example, the analysis unit can apply a technical analysis algorithm to a technical inquiry. The analysis unit can also apply an accounting analysis algorithm to an accounting inquiry. The analysis unit can also apply a human resources analysis algorithm to an inquiry regarding human resources. This enables efficient analysis by applying different analysis algorithms depending on the inquiry category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input inquiry category data into the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0097] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the current analysis based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can analyze the user's past analysis results and provide a highly accurate analysis. This can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI optimize the analysis.

[0098] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. If the user is excited, the analysis unit can also provide a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, optimal analysis results can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input user input data into the generation AI and have the generation AI perform emotion estimation.

[0099] During analysis, the analysis unit can determine the order of analysis based on the time of submission of the inquiry. For example, the analysis unit may give top priority to analysis of urgent inquiries. The analysis unit may also perform analysis of normal inquiries with normal priority. Furthermore, the analysis unit may postpone analysis of past inquiries. This enables efficient analysis by determining the analysis priority based on the time of submission of the inquiry. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input inquiry submission time data into the generation AI and have the generation AI determine the analysis priority.

[0100] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the queries. For example, the analysis unit prioritizes analysis of queries with high relevance. The analysis unit can also postpone analysis of queries with low relevance. Furthermore, the analysis unit can also analyze queries with moderate relevance with an appropriate priority. This enables efficient analysis by adjusting the order of analysis based on the relevance of the queries. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input query relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0101] During analysis, the analysis unit can adjust the selection of technical terms according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide an analysis that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide an analysis in simple language. Furthermore, the analysis unit can provide an analysis that uses appropriate technical terms according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI select the technical terms.

[0102] The sorting unit can estimate the user's emotions and adjust sorting criteria based on the estimated user emotions. For example, if the user is nervous, the sorting unit can assign the user to a department that can respond quickly. Furthermore, if the user is relaxed, the sorting unit can also respond using normal sorting criteria. Furthermore, if the user is feeling anxious, the sorting unit can assign the user to a department that can receive professional support. This allows for a quick and appropriate response by adjusting the sorting criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the sorting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sorting unit can input user input data to the generation AI and have the generation AI perform emotion estimation.

[0103] The distribution unit can improve the accuracy of distribution by taking into account the interrelationships between inquiries during distribution. For example, the distribution unit distributes related inquiries to the same department. The distribution unit can also group interrelated inquiries and distribute them to appropriate departments. Furthermore, the distribution unit can analyze the interrelationships between inquiries and perform optimal distribution. In this way, by taking into account the interrelationships between inquiries, the accuracy of distribution can be improved. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input interrelationship data between inquiries to a generation AI and have the generation AI perform optimization of distribution.

[0104] The allocation unit can allocate inquiries taking into consideration attribute information of the person who submitted the inquiry. For example, the allocation unit allocates inquiries to an appropriate department based on the person's job title. The allocation unit can also allocate inquiries to related departments based on the person's field of expertise. Furthermore, the allocation unit can allocate inquiries to the most appropriate department based on the person's past inquiry history. In this way, inquiries can be allocated to the most appropriate department by taking into consideration the attribute information of the person who submitted the inquiry. Some or all of the above-described processing in the allocation unit may be performed using AI, for example, or may be performed without using AI. For example, the allocation unit can input attribute information data of the person who submitted the inquiry into a generation AI and have the generation AI perform the most appropriate allocation.

[0105] The allocation unit can prioritize allocation based on the frequency of inquiries submitted during allocation. For example, the allocation unit allocates inquiries submitted frequently to a department that can respond quickly. The allocation unit can also handle inquiries submitted infrequently using normal allocation criteria. Furthermore, the allocation unit can allocate inquiries submitted at a medium frequency with appropriate weighting. This allows for efficient allocation by weighting the allocation based on the frequency of inquiries submitted. Some or all of the above-mentioned processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit can input submission frequency data to a generation AI and have the generation AI perform the allocation weighting.

[0106] The sorting unit can estimate the user's emotions and adjust the order in which sorting results are displayed based on the estimated user emotions. For example, if the user is nervous, the sorting unit can display the most relevant results first. Furthermore, if the user is relaxed, the sorting unit can also display results in a normal order. Furthermore, if the user is in a hurry, the sorting unit can also display results that can be quickly addressed first. By adjusting the order in which sorting results are displayed based on the user's emotions, optimal results can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the sorting unit can be performed using, for example, an AI, or without an AI. For example, the sorting unit can input user input data to the generation AI and have the generation AI perform emotion estimation.

[0107] The allocation unit can allocate inquiries to departments taking into account the geographical distribution of the inquiries. For example, if a user is in a specific area, the allocation unit can allocate the inquiries to a department related to that area. Furthermore, if a user is on a business trip, the allocation unit can also allocate the inquiries to a department related to the user's business trip destination. Furthermore, if a user is working remotely, the allocation unit can also allocate the inquiries to a department related to the user's home. In this way, by taking the geographical distribution of inquiries into consideration, the inquiries can be allocated to the optimal department. Some or all of the above-described processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit can input user geographical distribution data into a generation AI and cause the generation AI to perform optimal allocation.

[0108] The allocation unit can improve the accuracy of allocation by referring to literature related to the inquiry during allocation. The allocation unit, for example, allocates to the most appropriate department based on related literature. The allocation unit can also refer to literature related to the inquiry content and allocate to the appropriate department. Furthermore, the allocation unit can analyze related literature and improve the accuracy of allocation. In this way, by referring to literature related to the inquiry, the accuracy of allocation can be improved. Some or all of the above-mentioned processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit can input related literature data to a generation AI and have the generation AI perform allocation optimization.

[0109] The allocation unit can allocate inquiries to departments taking into account the market value of the inquiries when allocating them. For example, the allocation unit allocates inquiries with high market value to a department that can respond quickly. The allocation unit can also handle inquiries with low market value using normal allocation standards. Furthermore, the allocation unit can allocate inquiries with medium market value with appropriate weighting. In this way, inquiries can be allocated to the most appropriate department by taking into account the market value of the inquiry. Some or all of the above-mentioned processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit can input market value data into a generation AI and have the generation AI perform allocation optimization.

[0110] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring based on the estimated user emotions. For example, if the user is nervous, the monitoring unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the monitoring unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the monitoring unit can provide a display method that focuses on the main points. By adjusting the display method of the monitoring according to the user's emotions, a highly visible display can be achieved. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the monitoring unit can be performed using, for example, an AI, or without an AI. For example, the monitoring unit can input user input data into the generation AI and have the generation AI perform emotion estimation.

[0111] During monitoring, the monitoring unit can improve current monitoring by referring to past monitoring data. The monitoring unit, for example, optimizes current monitoring based on past monitoring data. The monitoring unit can also extract specific patterns from past monitoring data and reflect them in current monitoring. Furthermore, the monitoring unit can analyze past monitoring data to provide highly accurate monitoring. This makes it possible to optimize current monitoring by referring to past monitoring data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past monitoring data into a generation AI and have the generation AI execute monitoring optimization.

[0112] During monitoring, the monitoring unit can apply different monitoring methods to different inquiry categories. For example, the monitoring unit can apply a technical-specific monitoring method to technical inquiries. The monitoring unit can also apply an accounting-specific monitoring method to accounting-related inquiries. The monitoring unit can also apply a human resources-specific monitoring method to human resources-related inquiries. This enables efficient monitoring by applying different monitoring methods to different inquiry categories. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input inquiry category data into a generation AI and have the generation AI apply the optimal monitoring method.

[0113] During monitoring, the monitoring unit can perform monitoring while taking into account attribute information of the person submitting the inquiry. The monitoring unit can apply an appropriate monitoring method, for example, depending on the position of the person submitting the inquiry. The monitoring unit can also apply a relevant monitoring method depending on the person's field of expertise. Furthermore, the monitoring unit can apply the optimal monitoring method by referring to the submitter's past inquiry history. This enables optimal monitoring by taking into account the attribute information of the person submitting the inquiry. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input attribute information data of the submitter into the generation AI and cause the generation AI to apply the optimal monitoring method.

[0114] The monitoring unit can estimate the user's emotions and adjust the importance of monitoring based on the estimated user emotions. For example, if the user is nervous, the monitoring unit prioritizes monitoring with a higher importance. Furthermore, if the user is relaxed, the monitoring unit can also perform monitoring with a normal importance. Furthermore, if the user is in a hurry, the monitoring unit can also prioritize monitoring with a higher importance. This enables efficient monitoring by adjusting the importance of monitoring according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input user input data into the generation AI and have the generation AI perform emotion estimation.

[0115] During monitoring, the monitoring unit can analyze monitoring fluctuations based on the time of inquiry submission. For example, for urgent inquiries, the monitoring unit analyzes monitoring changes based on the time of submission. The monitoring unit can also analyze monitoring changes for normal inquiries based on the time of submission. Furthermore, the monitoring unit can analyze monitoring changes for past inquiries based on the time of submission. This enables efficient monitoring by analyzing monitoring changes based on the time of inquiry submission. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input submission time data into a generation AI and have the generation AI analyze monitoring changes.

[0116] During monitoring, the monitoring unit can perform monitoring by referring to market data related to the inquiry. The monitoring unit, for example, performs optimal monitoring based on the related market data. The monitoring unit can also perform appropriate monitoring by referring to market data related to the inquiry content. Furthermore, the monitoring unit can analyze the related market data and improve the accuracy of monitoring. This enables efficient monitoring by referring to the market data related to the inquiry. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the related market data into a generation AI and cause the generation AI to perform optimal monitoring.

[0117] The monitoring unit can perform monitoring while taking into account the technical maturity of the inquiry. For example, the monitoring unit performs normal monitoring for technically mature inquiries. The monitoring unit can also perform detailed monitoring for technically immature inquiries. Furthermore, the monitoring unit can apply an appropriate monitoring method depending on the technical maturity. This enables efficient monitoring by taking into account the technical maturity of the inquiry. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input technical maturity data into the generation AI and cause the generation AI to apply the optimal monitoring method.

[0118] The history storage unit can estimate the user's emotions and adjust the history storage method based on the estimated user emotions. For example, if the user is nervous, the history storage unit can provide a simple, highly visible history storage method. Furthermore, if the user is relaxed, the history storage unit can provide a detailed history storage method. Furthermore, if the user is in a hurry, the history storage unit can provide a history storage method that focuses on the main points. By adjusting the history storage method according to the user's emotions, it is possible to optimally store history for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the history storage unit can be performed using, for example, an AI, or without an AI. For example, the history storage unit can input user input data to the generation AI and have the generation AI perform emotion estimation.

[0119] When saving history, the history storage unit can select a storage method by referring to past inquiry history. The history storage unit, for example, selects the optimal storage method based on the past inquiry history. The history storage unit can also extract specific patterns from the past inquiry history and select the optimal storage method. Furthermore, the history storage unit can analyze the past inquiry history and provide a highly accurate storage method. This makes it possible to select the optimal storage method by referring to the past inquiry history and efficiently store the history. Some or all of the above-mentioned processing in the history storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the history storage unit can input past inquiry history data into a generation AI and have the generation AI select the optimal storage method.

[0120] The history storage unit can apply different storage algorithms depending on the category of the inquiry when storing the history. For example, the history storage unit can apply a specialized storage algorithm for technical inquiries. The history storage unit can also apply a specialized storage algorithm for accounting inquiries. The history storage unit can also apply a specialized storage algorithm for human resources inquiries. This enables efficient history storage by applying different storage algorithms depending on the category of the inquiry. Some or all of the above-mentioned processing in the history storage unit can be performed using, for example, AI, or without AI. For example, the history storage unit can input inquiry category data into a generation AI and cause the generation AI to apply the optimal storage algorithm.

[0121] The history storage unit can estimate the user's emotions and determine the order of history storage based on the estimated user emotions. For example, if the user is nervous, the history storage unit can prioritize saving important history. Furthermore, if the user is relaxed, the history storage unit can also prioritize saving important history. This enables efficient history storage by determining the priority of history storage according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the history storage unit can be performed using, for example, an AI, or without an AI. For example, the history storage unit can input user input data into the generation AI and have the generation AI perform emotion estimation.

[0122] The history storage unit can weight the history to be saved based on the time of submission of the inquiry when saving the history. For example, the history storage unit weights the history to be saved based on the time of submission for urgent inquiries. The history storage unit can also weight the history to be saved based on the time of submission for normal inquiries. Furthermore, the history storage unit can weight the history to be saved based on the time of submission for past inquiries. This enables efficient history saving by weighting the history to be saved based on the time of submission of the inquiry. Some or all of the above-mentioned processing in the history storage unit may be performed using AI, for example, or may be performed without using AI. For example, the history storage unit can input submission time data to a generation AI and have the generation AI perform the weighting of the history to be saved.

[0123] The history storage unit can improve the accuracy of storage by referring to literature related to the query when storing the history. The history storage unit, for example, selects an optimal storage method based on related literature. The history storage unit can also select an appropriate storage method by referring to literature related to the query content. Furthermore, the history storage unit can analyze related literature and improve the accuracy of storage. In this way, by referring to literature related to the query, the accuracy of storage can be improved. Some or all of the above-mentioned processing in the history storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the history storage unit can input related literature data to a generation AI and have the generation AI perform storage optimization.

[0124] The reference unit can estimate the user's emotions and adjust the display method of the reference based on the estimated user's emotions. For example, if the user is nervous, the reference unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the reference unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the reference unit can provide a display method that focuses on the main points. By adjusting the display method of the reference according to the user's emotions, a highly visible display is possible. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reference unit can be performed using, for example, AI, or without AI. For example, the reference unit can input user input data to the generation AI and have the generation AI perform emotion estimation.

[0125] The reference unit can select the optimal reference method by referring to past inquiry history during reference. The reference unit, for example, selects the optimal reference method based on the past inquiry history. The reference unit can also extract specific patterns from the past inquiry history and select the optimal reference method. Furthermore, the reference unit can analyze the past inquiry history and provide a highly accurate reference method. This allows the optimal reference method to be selected by referring to the past inquiry history, enabling efficient reference. Some or all of the above-described processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input past inquiry history data into a generation AI and have the generation AI select the optimal reference method.

[0126] The reference unit can apply different reference algorithms depending on the category of the inquiry during reference. For example, the reference unit can apply a technical-specialized reference algorithm to a technical inquiry. The reference unit can also apply an accounting-specialized reference algorithm to an accounting inquiry. The reference unit can also apply a human resources-specialized reference algorithm to an inquiry regarding human resources. This enables efficient reference by applying different reference algorithms depending on the inquiry category. Some or all of the above-mentioned processing in the reference unit can be performed using AI, for example, or without AI. For example, the reference unit can input inquiry category data into the generation AI and have the generation AI apply the optimal reference algorithm.

[0127] The reference unit can estimate the user's emotions and determine the order of reference based on the estimated user emotions. For example, if the user is nervous, the reference unit can display the most relevant information first. Furthermore, if the user is relaxed, the reference unit can also display information in a normal order. Furthermore, if the user is in a hurry, the reference unit can also display information that can be quickly responded to first. This enables efficient reference by determining the order of reference according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reference unit can be performed using AI, for example, or without AI. For example, the reference unit can input user input data into the generation AI and have the generation AI perform emotion estimation.

[0128] The reference unit can weight the reference based on the time of submission of the inquiry at the time of reference. For example, for urgent inquiries, the reference unit weights the reference based on the time of submission. The reference unit can also weight the reference based on the time of submission for normal inquiries. Furthermore, the reference unit can weight the reference based on the time of submission for past inquiries. In this way, weighting the reference based on the time of submission of the inquiry enables efficient reference. Some or all of the above-mentioned processing in the reference unit may be performed using AI, for example, or may be performed without using AI. For example, the reference unit can input submission time data into a generation AI and have the generation AI perform reference weighting.

[0129] The reference unit can improve the accuracy of the reference by referring to literature related to the query during the reference. The reference unit, for example, selects an optimal reference method based on related literature. The reference unit can also refer to literature related to the query content and select an appropriate reference method. Furthermore, the reference unit can analyze related literature and improve the accuracy of the reference. In this way, the accuracy of the reference can be improved by referring to literature related to the query. Some or all of the above-mentioned processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input related literature data into a generation AI and have the generation AI perform reference optimization.

[0130] The FAQ generation unit can estimate the user's emotions and adjust the FAQ generation method based on the estimated user emotions. For example, if the user is nervous, the FAQ generation unit generates a simple, highly visible FAQ. The FAQ generation unit can also generate a detailed FAQ if the user is relaxed. Furthermore, if the user is in a hurry, the FAQ generation unit can generate a FAQ that focuses on the main points. By adjusting the FAQ generation method according to the user's emotions, an FAQ with high visibility for the user is generated. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the FAQ generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the FAQ generation unit can input user input data into the generation AI and have the generation AI perform emotion estimation.

[0131] When generating an FAQ, the FAQ generation unit can generate the FAQ by referencing past inquiry history. The FAQ generation unit generates an optimal FAQ based on, for example, the past inquiry history. The FAQ generation unit can also extract specific patterns from the past inquiry history and generate an optimal FAQ. Furthermore, the FAQ generation unit can analyze the past inquiry history and generate a highly accurate FAQ. This makes it possible to generate an optimal FAQ by referencing the past inquiry history and provide information efficiently. Some or all of the above-mentioned processing in the FAQ generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the FAQ generation unit can input past inquiry history data into a generation AI and have the generation AI generate an optimal FAQ.

[0132] When generating an FAQ, the FAQ generation unit can apply different generation algorithms depending on the category of the inquiry. For example, the FAQ generation unit applies a generation algorithm specialized in technology to a technical inquiry. The FAQ generation unit can also apply a generation algorithm specialized in accounting to an inquiry about accounting. Furthermore, the FAQ generation unit can also apply a generation algorithm specialized in human resources to an inquiry about human resources. In this way, by applying different generation algorithms depending on the inquiry category, efficient FAQ generation becomes possible. Some or all of the above-mentioned processing in the FAQ generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the FAQ generation unit can input inquiry category data into the generation AI and cause the generation AI to apply the optimal generation algorithm.

[0133] The FAQ generation unit can estimate the user's emotions and determine the order of FAQs based on the estimated user emotions. For example, if the user is nervous, the FAQ generation unit can display the most relevant FAQ first. Furthermore, if the user is relaxed, the FAQ generation unit can also display FAQs in a normal order. Furthermore, if the user is in a hurry, the FAQ generation unit can also display FAQs that can be quickly addressed first. This enables efficient information provision by determining the order of FAQs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the FAQ generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the FAQ generation unit can input user input data into the generation AI and have the generation AI perform emotion estimation.

[0134] When generating FAQs, the FAQ generation unit can weight the FAQs based on the time of submission of the inquiry. For example, for urgent inquiries, the FAQ generation unit weights the FAQs based on the time of submission. For normal inquiries, the FAQ generation unit can also weight the FAQs based on the time of submission. Furthermore, for past inquiries, the FAQ generation unit can weight the FAQs based on the time of submission. In this way, weighting the FAQs based on the time of submission of the inquiry enables efficient information provision. Some or all of the above-mentioned processing in the FAQ generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the FAQ generation unit can input submission time data into the generation AI and have the generation AI perform FAQ weighting.

[0135] When generating an FAQ, the FAQ generation unit can improve the accuracy of the FAQ by referring to literature related to the inquiry. The FAQ generation unit, for example, generates an optimal FAQ based on related literature. The FAQ generation unit can also generate an appropriate FAQ by referring to literature related to the content of the inquiry. Furthermore, the FAQ generation unit can analyze related literature and improve the accuracy of the FAQ. In this way, the accuracy of the FAQ can be improved by referring to literature related to the inquiry. Some or all of the above-mentioned processing in the FAQ generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the FAQ generation unit can input related literature data into the generation AI and cause the generation AI to optimize the FAQ. === Hard Collateral 1-1 === Each of the multiple elements including the reception unit, analysis unit, distribution unit, monitoring unit, history storage unit, reference unit, and FAQ generation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive inquiry content using the reception device 38 of the smart device 14. The analysis unit analyzes the inquiry content using the specific processing unit 290 of the data processing device 12. The distribution unit realizes processing of distributing the inquiry to an appropriate department based on the analysis result using the specific processing unit 290 of the data processing device 12. The monitoring unit realizes processing of monitoring the number of inquiries across the company using the specific processing unit 290 of the data processing device 12. The history storage unit realizes processing of saving the inquiry history in the database 24 using the specific processing unit 290 of the data processing device 12. The reference unit realizes processing of searching for past inquiry content using the specific processing unit 290 of the data processing device 12. The FAQ generation unit realizes processing of generating FAQs based on the inquiry content using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, distribution unit, monitoring unit, history storage unit, reference unit, and FAQ generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive inquiry content using the microphone 238 of the smart glasses 214. The analysis unit analyzes the inquiry content using the specific processing unit 290 of the data processing device 12. The distribution unit realizes processing of distributing the inquiry to an appropriate department based on the analysis result using the specific processing unit 290 of the data processing device 12. The monitoring unit realizes processing of monitoring the number of inquiries across the company using the specific processing unit 290 of the data processing device 12. The history storage unit realizes processing of saving the inquiry history in the database 24 using the specific processing unit 290 of the data processing device 12. The reference unit realizes processing of searching for past inquiry content using the specific processing unit 290 of the data processing device 12. The FAQ generation unit realizes processing of generating FAQs based on the inquiry content using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, distribution unit, monitoring unit, history storage unit, reference unit, and FAQ generation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive inquiry content using the microphone 238 of the headset-type terminal 314. The analysis unit analyzes the inquiry content using the specific processing unit 290 of the data processing device 12. The distribution unit realizes processing of distributing the inquiry to an appropriate department based on the analysis results using the specific processing unit 290 of the data processing device 12. The monitoring unit realizes processing of monitoring the number of inquiries across the company using the specific processing unit 290 of the data processing device 12. The history storage unit realizes processing of saving the inquiry history in the database 24 using the specific processing unit 290 of the data processing device 12. The reference unit realizes processing of searching for past inquiry content using the specific processing unit 290 of the data processing device 12. The FAQ generation unit realizes processing of generating FAQs based on the inquiry content using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, distribution unit, monitoring unit, history storage unit, reference unit, and FAQ generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive inquiry content using the microphone 238 of the robot 414. The analysis unit analyzes the inquiry content using the specific processing unit 290 of the data processing device 12. The distribution unit realizes processing of distributing the inquiry to an appropriate department based on the analysis results using the specific processing unit 290 of the data processing device 12. The monitoring unit realizes processing of monitoring the number of inquiries across the company using the specific processing unit 290 of the data processing device 12. The history storage unit realizes processing of saving the inquiry history in the database 24 using the specific processing unit 290 of the data processing device 12. The reference unit realizes processing of searching for past inquiry content using the specific processing unit 290 of the data processing device 12. The FAQ generation unit realizes processing of generating FAQs based on the inquiry content using the specific processing unit 290 of the data processing device 12.

[0136] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0137] The inquiry efficiency improvement system can estimate a user's emotions and dynamically adjust the priority of inquiries based on the estimated emotions. For example, if a user is feeling very stressed, the inquiry can be processed with the highest priority. Alternatively, if the user is relaxed, the inquiry can be processed with normal priority. Furthermore, if the user is in a hurry, a priority can be set for a quick response. This enables a prompt and appropriate response by dynamically adjusting the priority of inquiries according to the user's emotions. The emotion estimation is realized, for example, using an emotion engine or a generative AI. The generative AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the inquiry efficiency improvement system may be performed using, for example, an AI, or may be performed without using an AI. For example, the inquiry efficiency improvement system inputs user input data into a generative AI and causes the generative AI to estimate emotions.

[0138] The inquiry efficiency improvement system can refer to a user's past inquiry history and automatically provide an answer when a similar inquiry is made. For example, if a similar technical problem has occurred in the past, it can automatically present a solution. It can also extract specific patterns from the past inquiry history and suggest proactive measures for predicted problems. It can also analyze the past inquiry history and automatically generate FAQs for frequently occurring problems. This enables quick and efficient responses by utilizing the user's past inquiry history. Some or all of the above-mentioned processing in the inquiry efficiency improvement system may be performed using, for example, AI, or may be performed without using AI. For example, the inquiry efficiency improvement system inputs past inquiry history data into a generation AI and has the generation AI provide the optimal answer.

[0139] The inquiry efficiency improvement system can automatically provide relevant information based on the user's current work situation and areas of interest. For example, it can prioritize technical information related to the project the user is currently working on. It can also present related topics and the latest research results based on the user's areas of interest. It can also analyze the user's work situation in real time and provide optimal information. This enables efficient information provision based on the user's current work situation and areas of interest. Some or all of the above-mentioned processing in the inquiry efficiency improvement system may be performed using, for example, AI, or may be performed without using AI. For example, the inquiry efficiency improvement system inputs the user's work situation data into a generation AI and has the generation AI provide optimal information.

[0140] The inquiry efficiency system can estimate a user's emotions and adjust the inquiry acceptance method based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided, minimizing input steps. Alternatively, if the user is relaxed, detailed input options can be provided and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized, allowing the user to quickly enter the inquiry content. This reduces user stress and enables efficient inquiry acceptance by adjusting the inquiry acceptance method according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the inquiry efficiency system may be performed using, for example, an AI, or may be performed without using an AI. For example, the inquiry efficiency system inputs user input data into a generative AI and causes the generative AI to estimate emotions.

[0141] The inquiry efficiency improvement system can estimate a user's emotions and prioritize inquiries based on the estimated emotions. For example, if the user feels urgent, the inquiry can be processed with the highest priority. Alternatively, if the user feels relaxed, the inquiry can be processed with normal priority. Furthermore, if the user feels anxious, a priority can be set for a quick response. This enables prompt and appropriate responses by prioritizing inquiries according to the user's emotions. The emotion estimation is realized, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the inquiry efficiency improvement system can be performed using, for example, an AI, or without an AI. For example, the inquiry efficiency improvement system can input user input data into a generative AI and have the generative AI perform emotion estimation.

[0142] The inquiry efficiency improvement system can prioritize inquiries by taking into account the user's geographical location information. For example, if the user is in a specific office, inquiries related to that office can be prioritized. Also, if the user is on a business trip, inquiries related to the business trip destination can be prioritized. Furthermore, if the user is working remotely, inquiries related to the user's home can be prioritized. In this way, by taking the user's geographical location information into consideration, it is possible to prioritize inquiries that are highly relevant. Some or all of the above-described processing in the inquiry efficiency improvement system may be performed using AI, for example, or may be performed without using AI. For example, the inquiry efficiency improvement system inputs the user's geographical location information data into a generation AI and causes the generation AI to select the optimal means of reception.

[0143] The inquiry efficiency improvement system can analyze a user's social media activity and accept related inquiries. For example, it can prioritize inquiries related to issues the user mentioned on social media. It can also analyze the content of a user's social media posts and accept related inquiries. It can also accept related inquiries by referring to the activities of the user's friends on social media. In this way, by analyzing a user's social media activity, it is possible to efficiently accept related inquiries. Some or all of the above-mentioned processes in the inquiry efficiency improvement system may be performed using, for example, AI, or may be performed without using AI. For example, the inquiry efficiency improvement system inputs a user's social media data into a generation AI and causes the generation AI to accept related inquiries.

[0144] The inquiry efficiency improvement system can customize the reception method by reflecting the user's past feedback. For example, it can suggest the optimal reception method based on feedback provided by the user in the past. It can also analyze the success rate of a specific reception method from the user's past feedback and suggest a method. It can also provide a customized reception method by referring to the user's past feedback. In this way, it is possible to provide the optimal reception method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the inquiry efficiency improvement system may be performed using AI, for example, or may be performed without using AI. For example, the inquiry efficiency improvement system can input the user's past feedback data into a generation AI and have the generation AI select the optimal reception method.

[0145] The inquiry efficiency improvement system can estimate a user's emotions and dynamically adjust the priority of inquiries based on the estimated emotions. For example, if a user is feeling very stressed, the inquiry can be processed with the highest priority. Alternatively, if the user is relaxed, the inquiry can be processed with normal priority. Furthermore, if the user is in a hurry, a priority can be set for a quick response. This enables a prompt and appropriate response by dynamically adjusting the priority of inquiries according to the user's emotions. The emotion estimation is realized, for example, using an emotion engine or a generative AI. The generative AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the inquiry efficiency improvement system may be performed using, for example, an AI, or may be performed without using an AI. For example, the inquiry efficiency improvement system inputs user input data into a generative AI and causes the generative AI to estimate emotions.

[0146] The inquiry efficiency improvement system can estimate a user's emotions and dynamically adjust the priority of inquiries based on the estimated emotions. For example, if a user is feeling very stressed, the inquiry can be processed with the highest priority. Alternatively, if the user is relaxed, the inquiry can be processed with normal priority. Furthermore, if the user is in a hurry, a priority can be set for a quick response. This enables a prompt and appropriate response by dynamically adjusting the priority of inquiries according to the user's emotions. The emotion estimation is realized, for example, using an emotion engine or a generative AI. The generative AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the inquiry efficiency improvement system may be performed using, for example, an AI, or may be performed without using an AI. For example, the inquiry efficiency improvement system inputs user input data into a generative AI and causes the generative AI to estimate emotions.

[0147] The processing flow of the second embodiment will be briefly explained below.

[0148] Step 1: The reception department accepts inquiries entered by employees. Inquiries include technical questions, product inquiries, and complaints. The reception department can accept inquiries via web forms, email, chatbots, etc. Step 2: The analysis unit uses the generation AI to analyze the inquiry content received by the reception unit. The analysis is performed using methods such as text analysis, sentiment analysis, and keyword extraction. For example, the generation AI analyzes the inquiry content using a text generation AI (e.g., LLM) and extracts important keywords. The analysis unit can also use sentiment analysis to grasp the emotional nuances of the inquiry content. Step 3: The allocation department allocates the problem to the appropriate department or person based on the content analyzed by the analysis department. Allocation is based on the person's area of ​​expertise and the work content of the department. For example, technical issues are allocated to the technical department, and accounting issues are allocated to the accounting department. It is also possible to allocate the problem to a person who is knowledgeable about a specific technology. Step 4: The monitoring department monitors the number of inquiries from across the company. Monitoring is based on the tally of inquiries and anomaly detection criteria. For example, an alert can be issued if a particular department is experiencing a high load.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0154] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0170] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0177] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0178] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0179] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0180] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0181] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0182] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0186] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0187] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0188] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0189] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0190] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0191] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0192] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0193] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0194] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0195] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0196] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0197] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0198] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0199] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0200] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0201] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0202] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0203] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0204] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0205] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0206] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0207] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0208] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0209] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0210] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0211] 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.

[0212] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0213] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0214] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0215] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0216] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0217] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0218] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0219] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0220] [Explanation of symbols]

[0221] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives inquiries; an analysis unit that analyzes the inquiry content received by the reception unit; a distribution unit that distributes the analyzed results to departments or personnel based on the analyzed results; A monitoring unit that monitors the number of inquiries from the entire company. A system characterized by:

2. Equipped with a history storage unit that stores inquiry history 2. The system of claim 1.

3. Provides a reference section where past inquiries can be referenced 2. The system of claim 1.

4. Equipped with an FAQ generator that automatically generates FAQs based on the content of inquiries 2. The system of claim 1.

5. The sorting unit Assign to technically knowledgeable staff 2. The system of claim 1.

6. The monitoring unit Issue alerts to take action when a specific department is experiencing heavy load 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the way inquiries are received based on the estimated user emotions 2. The system of claim 1.

8. The reception unit When accepting an inquiry, the method of acceptance is selected by referring to the user's past inquiry history.

2. The system of claim 1.

9. The reception unit At the time of check-in, filtering is performed based on the user's current work situation and areas of interest.

2. The system of claim 1.

10. The reception unit At the time of reception, the reception method is selected according to the user's input method.

2. The system of claim 1.

11. The reception unit Estimate the user's emotions and prioritize inquiries based on the estimated user emotions.

2. The system of claim 1.

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