system

Abstract

The system according to this embodiment aims to streamline responses to residents' inquiries and reduce the burden on staff. [Solution] The system according to this embodiment comprises a reception unit, a search unit, a response unit, a proposal unit, a coordination unit, and an improvement unit. The reception unit receives inquiries from residents. The search unit searches for relevant laws and regulations based on the inquiries received by the reception unit. The response unit provides immediate answers based on the laws and regulations found by the search unit. The proposal unit searches for relevant past consultation cases and FAQs in accordance with the inquiries received by the reception unit and makes suggestions. The coordination unit manages the schedules of each department in the city hall based on the inquiries received by the reception unit and adjusts and reserves the optimal consultation time. The improvement unit collects feedback from the consultants based on the inquiries received by the reception unit and proposes improvements to the service.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the response to the consultation content of residents is not efficient and the burden on staff is large.

[0005] The system according to the embodiment aims to improve the efficiency of responding to the consultation content of residents and reduce the burden on staff.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a search unit, a response unit, a proposal unit, a coordination unit, and an improvement unit. The reception unit receives inquiries from residents. The search unit searches for relevant laws and regulations based on the inquiries received by the reception unit. The response unit provides immediate responses based on the laws and regulations found by the search unit. The proposal unit searches for relevant past consultation cases and FAQs in accordance with the inquiries received by the reception unit and makes suggestions. The coordination unit manages the schedules of each department in the city hall based on the inquiries received by the reception unit and adjusts and reserves the optimal consultation time. The improvement unit collects feedback from the consultants based on the inquiries received by the reception unit and proposes improvements to the service. [Effects of the Invention]

[0007] The system according to this embodiment can streamline responses to residents' inquiries and reduce the burden on staff. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. 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 data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The system utilizing the autonomous AI agent according to an embodiment of the present invention is a system for streamlining administrative consultation services. This system begins with residents providing their consultation details via voice input, image, or document upload. The autonomous AI agent searches for relevant laws and regulations in real time based on the consultation details and provides an immediate response. Furthermore, it has a function to automatically search for and suggest relevant past consultation cases and FAQs according to the consultation details. This eliminates the need for staff to refer to past cases, enabling more efficient responses. The AI ​​agent also has a function to manage the schedules of each department in the city hall in real time and automatically adjust and reserve the optimal consultation time. This minimizes the waiting time for those seeking consultation. Furthermore, it has a function to automatically collect feedback from those seeking consultation, and the AI ​​analyzes the data to suggest areas for service improvement. This allows for continuous improvement of service quality. For example, a resident might input details such as "I would like to consult about changing my resident registration due to moving" via voice input, or upload relevant documents. This information is input into the autonomous AI agent. The autonomous AI agent analyzes the input consultation details and searches for relevant laws and regulations in real time. For example, it can search for information on laws and procedures related to changing residency registration and provide immediate answers. This allows residents to quickly obtain the information they need. Furthermore, it has a function that automatically searches for and suggests relevant past consultation cases and FAQs based on the content of the consultation. For example, if a similar consultation has occurred in the past, it will refer to that case and suggest it to the resident. This saves staff the trouble of referring to past cases, enabling more efficient responses. The AI ​​agent also has a function that manages the schedules of each department in the city hall in real time and automatically adjusts and reserves the optimal consultation time. For example, it checks the availability of the appropriate department based on the content of the consultation resident and makes a reservation. This minimizes the waiting time for residents. Furthermore, it has a function that automatically collects feedback from consultants and the AI ​​analyzes that data to suggest ways to improve the service. For example, a resident provides feedback after a consultation, and the AI ​​analyzes that data to suggest ways to improve the service.This will enable continuous improvement in service quality. This system utilizes multimodal autonomous AI agents for inquiry and consultation tasks, freeing staff from consultation and response duties and allowing them to focus on proactive administrative services. This optimizes administration in the AI ​​era, benefiting both residents and city hall staff. The system, utilizing autonomous AI agents, will efficiently respond by searching for relevant laws and regulations based on residents' inquiries and providing immediate answers.

[0029] The system utilizing the autonomous AI agent according to this embodiment comprises a reception unit, a search unit, a response unit, a proposal unit, a coordination unit, and an improvement unit. The reception unit receives inquiries from residents. These inquiries include, but are not limited to, legal consultations, consultations regarding administrative procedures, and consultations regarding daily life. The reception unit accepts, for example, voice input and uploads of images and documents. The search unit searches for relevant laws and regulations based on the inquiries received by the reception unit. The search unit searches for relevant laws and regulations in real time based on the inquiries. The search unit searches for relevant laws and regulations using, for example, a search engine. The search unit searches for relevant laws and regulations using, for example, a real-time database update function. The response unit provides immediate answers based on the laws and regulations found by the search unit. The response unit provides answers within, for example, a few seconds. The response unit provides answers in, for example, real time. The proposal unit searches for relevant past consultation cases and FAQs according to the inquiries received by the reception unit. The proposal unit searches for consultation cases from the past year, for example. The Proposal Department searches for FAQs related to a specific category, for example. The Proposal Department makes proposals based on the searched past consultation cases and FAQs. The Proposal Department makes proposals by referring to past consultation cases, for example. The Proposal Department makes proposals by referring to FAQs, for example. The Coordination Department manages the schedules of each department in the city hall based on the consultation content received by the Reception Department and adjusts and reserves the optimal consultation time. The Coordination Department adjusts the optimal consultation time based on the time requested by the consultant, for example. The Coordination Department adjusts the optimal consultation time based on the availability of each department, for example. The Coordination Department manages the schedules of each department in real time using a schedule management system, for example. The Coordination Department manages the schedules of each department using a real-time update function, for example. The Improvement Department collects feedback from consultants based on the consultation content received by the Reception Department and proposes improvements to the service. The Improvement Department collects feedback using surveys, for example. The Improvement Department collects feedback using direct opinion gathering, for example. The Improvement Department automatically collects feedback using a feedback collection tool, for example.The improvement unit automatically collects feedback using, for example, periodic data collection. This enables the system utilizing the autonomous AI agent according to the embodiment to efficiently respond by searching for relevant laws and regulations based on the content of residents' inquiries and providing immediate answers.

[0030] The reception desk receives inquiries from residents. These inquiries include, but are not limited to, legal consultations, consultations regarding administrative procedures, and consultations regarding daily life. The reception desk accepts inquiries via voice input, image and document uploads, and other methods. Specifically, residents can use their smartphones or computers to input their inquiries by voice or upload related documents and images. In the case of voice input, the system uses speech recognition technology to convert the voice data into text data. This allows residents to easily communicate their inquiries. Furthermore, by using the image and document upload function, residents can provide concrete evidence and materials, enabling an accurate understanding of their inquiries. The reception desk centrally manages this input data and can quickly pass it on to the next processing step. In addition, the reception desk has implemented security measures to appropriately protect residents' personal information, including data encryption and access control. This allows residents to provide their inquiries with peace of mind. To enhance convenience for residents, the reception desk has built a system that is available 24 hours a day, 365 days a year, allowing inquiries to be received anytime, anywhere. This allows residents to seek advice whenever needed, without being restricted by time or location.

[0031] The search unit searches for relevant laws and regulations based on the consultation content received by the reception unit. For example, the search unit searches for relevant laws and regulations in real time based on the consultation content. Specifically, the search unit uses natural language processing technology to analyze the content of the consultation and extract appropriate keywords. This makes it possible to identify the laws and regulations that are most relevant to the consultation content. For example, the search unit uses a search engine to search for relevant laws and regulations. The search engine searches publicly available databases on the internet and legal compilations to obtain the latest information. For example, the search unit uses the real-time update function of the database to search for relevant laws and regulations. This makes it possible to always provide the latest legal information. The search unit organizes the search results and provides them in an easy-to-understand format for residents. For example, it creates a summary that summarizes the key points of the relevant laws and regulations and presents it to residents. This makes it possible for residents to quickly grasp the necessary information. Furthermore, the search unit learns from past search history and consultation content and has built a feedback loop to improve search accuracy. As a result, the search unit is continuously improved and can provide more accurate and faster search results.

[0032] The response unit provides immediate answers based on the laws and regulations searched by the search unit. For example, it can respond within seconds. Specifically, the response unit uses AI to analyze search results and generate appropriate answers to residents' inquiries. The AI ​​uses natural language generation technology to create easy-to-understand explanations of the laws and regulations. The response unit provides answers in real-time, for example. This allows residents to receive answers quickly without waiting. When presenting the generated answers to residents, the response unit emphasizes key points and provides relevant additional information, presenting the information in a format that is easy for residents to understand. For example, it explains not only the legal text but also its background and application examples, allowing residents to deepen their understanding in a way that is relevant to their specific situation. Furthermore, the response unit can quickly respond to additional questions and ambiguities from residents. For example, if a resident requests more detailed information about the answer, the response unit performs additional searches and provides supplementary information. This allows residents to continue their consultation until they are satisfied. The response unit has a mechanism to continuously evaluate and improve the quality of its answers to enhance resident satisfaction. For example, by collecting feedback from residents and evaluating the accuracy and clarity of the responses, the AI's learning data is updated, improving the quality of the responses.

[0033] The proposal department searches for relevant past consultation cases and FAQs based on the content of the consultation received by the reception department. For example, the proposal department searches for consultation cases from the past year. Specifically, the proposal department analyzes past consultation cases stored in the database and identifies the case that is most similar to the resident's consultation content. For example, the proposal department searches for FAQs on a specific category. This allows them to provide general solutions and advice to the problems the resident is facing. The proposal department makes proposals based on the searched past consultation cases and FAQs. For example, the proposal department makes proposals by referring to past consultation cases. Specifically, they provide useful information to residents by presenting how they handled similar consultations in the past and the solutions they found. For example, the proposal department makes proposals by referring to FAQs. This allows residents to quickly obtain answers to common questions. The proposal department can also incorporate visual elements to present the proposal content to residents in an easy-to-understand manner. For example, they can deepen residents' understanding by using charts and illustrations to visually explain the proposal content. Furthermore, the proposal department has a system in place to collect feedback from residents and evaluate the accuracy and usefulness of the proposal content. This allows the proposal department to continuously improve and provide more appropriate proposals.

[0034] The Coordination Department manages the schedules of each department within the city hall based on the consultation content received by the Reception Department, and adjusts and reserves the optimal consultation time. For example, the Coordination Department adjusts the optimal consultation time based on the consultationer's preferred time. Specifically, the Coordination Department receives the consultationer's preferred date and time, compares it with the schedules of each department, and proposes the optimal consultation time. For example, the Coordination Department adjusts the optimal consultation time based on the availability of each department. This allows the consultationer to reserve a consultation at their convenience. The Coordination Department manages the schedules of each department in real time, for example, using a schedule management system. The schedule management system has the function of centrally managing the schedules of each department and updating availability and reservation status in real time. For example, the Coordination Department manages the schedules of each department using the real-time update function. This allows adjustments to always be made based on the latest schedule information. The Coordination Department sends a reservation confirmation notice to the consultationer and carries out the procedure to confirm the reservation details. For example, a reservation confirmation notice is sent using email or SMS so that the consultationer can confirm the reservation details. Furthermore, the Coordination Department can flexibly handle changes and cancellations of reservations. For example, if a client wants to change their appointment, the scheduling department will readjust the schedule and propose a new appointment time. This allows the scheduling department to improve client convenience and ensure smooth appointment scheduling.

[0035] The Improvement Department collects feedback from clients based on the consultation content received by the Reception Department and proposes improvements to the service. The Improvement Department collects feedback using, for example, surveys. Specifically, after the consultation ends, they send questionnaires to clients to collect opinions on service satisfaction and areas for improvement. The Improvement Department collects feedback using, for example, direct interviews. This allows them to directly hear specific opinions and requests from clients. The Improvement Department automatically collects feedback using, for example, a feedback collection tool. A feedback collection tool has the function of automatically collecting clients' opinions and storing them in a database. The Improvement Department automatically collects feedback using, for example, periodic data collection. This allows them to continuously collect feedback and use it to improve the service. The Improvement Department analyzes the collected feedback and identifies areas for service improvement. For example, they categorize the content of the feedback and extract common problems and requests. Based on the identified areas for improvement, the Improvement Department proposes and implements specific improvement measures. For example, they propose and implement specific improvement measures such as reviewing the consultation process or adding system functions. This allows the Improvement Department to improve the quality of its services and increase client satisfaction. Furthermore, the Improvement Department has a system in place to evaluate the effectiveness of improvement measures and implement continuous improvements. For example, it collects feedback again after implementing improvement measures and evaluates the effectiveness of the improvements. This allows the Improvement Department to constantly improve the quality of its services and provide better services to clients.

[0036] The reception desk can accept voice input and uploads of images and documents. The reception desk can accept voice input, for example. The reception desk can accept voice input using, for example, speech recognition technology. The reception desk can accept voice input using, for example, a microphone. The reception desk can accept uploads of images and documents, for example. The reception desk can accept images in JPEG format, for example. The reception desk can accept documents in PDF format, for example. This allows the reception desk to handle various formats of inquiries by accepting voice input and uploads of images and documents. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input voice input into a generating AI and have the generating AI perform the conversion from voice data to text data.

[0037] The search unit can search for relevant laws and regulations in real time based on the content of the consultation. The search unit can search for relevant laws and regulations using, for example, a search engine. The search unit can search for relevant laws and regulations using, for example, a real-time database update function. The search unit can search for relevant laws and regulations using, for example, natural language processing technology. This enables quick responses by searching for relevant laws and regulations in real time based on the content of the consultation. Some or all of the above processing in the search unit may be performed using, for example, AI, or not using AI. For example, the search unit can input the content of the consultation into a generating AI and have the generating AI perform a search for relevant laws and regulations.

[0038] The proposal department can automatically search for relevant past consultation cases and FAQs based on the content of the consultation. For example, the proposal department can search for consultation cases from the past year. For example, the proposal department can search for FAQs related to a specific category. For example, the proposal department can use a search algorithm to automatically search for past consultation cases and FAQs. This enables efficient proposals by automatically searching for relevant past consultation cases and FAQs based on the content of the consultation. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the consultation content into a generating AI and have the generating AI perform a search for relevant past consultation cases and FAQs.

[0039] The coordination unit can manage the schedules of each department in the city hall in real time and automatically adjust and reserve the optimal consultation time. For example, the coordination unit can adjust the optimal consultation time based on the time requested by the person seeking consultation. For example, the coordination unit can adjust the optimal consultation time based on the availability of each department. For example, the coordination unit can manage the schedules of each department in real time using a schedule management system. For example, the coordination unit can manage the schedules of each department using a real-time update function. This minimizes the waiting time for people seeking consultation by managing the schedules of each department in the city hall in real time and automatically adjusting and reserving the optimal consultation time. Some or all of the above processes in the coordination unit may be performed using AI, for example, or not using AI. For example, the coordination unit can input the consultation content into a generating AI and have the generating AI perform the adjustment of the optimal consultation time.

[0040] The improvement department can automatically collect feedback from clients and propose areas for service improvement. The improvement department can collect feedback using, for example, surveys. The improvement department can collect feedback using, for example, direct opinion gathering. The improvement department can automatically collect feedback using, for example, a feedback collection tool. The improvement department can automatically collect feedback using, for example, periodic data collection. This allows for continuous improvement of service quality by automatically collecting feedback from clients and proposing areas for service improvement. Some or all of the above processes in the improvement department may be performed using, for example, AI, or not using AI. For example, the improvement department can input feedback data into a generating AI and have the generating AI generate suggestions for service improvements.

[0041] The reception department can select the most suitable reception method by referring to the caller's past consultation history at the time of reception. For example, the reception department may prioritize suggesting consultation methods that the caller has frequently used in the past. For example, the reception department may automatically display relevant reception options based on the content of the caller's past consultations. For example, the reception department may predict and suggest a reception method to be used during a specific time period based on the caller's past consultation history. This enables efficient reception by selecting the most suitable reception method by referring to the caller's past consultation history. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department may input the caller's past consultation history data into a generating AI and have the generating AI select the most suitable reception method.

[0042] The reception unit can filter the caller's current situation and areas of interest at the time of reception. For example, when a caller enters their current situation, the reception unit can prioritize displaying relevant consultation content. For example, the reception unit can automatically suggest relevant consultation options based on the caller's areas of interest. For example, the reception unit can analyze the caller's current situation and areas of interest and provide the optimal reception method. This allows for highly relevant reception by filtering based on the caller's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the caller's current situation and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0043] The reception desk can prioritize receiving inquiries that are highly relevant to the caller, taking into account the caller's geographical location. For example, when a caller enters their current location, the reception desk will prioritize displaying relevant inquiries. For example, the reception desk will provide the optimal reception method based on the caller's geographical location. For example, the reception desk will prioritize suggesting the nearest service counter to the caller's current location. This enables efficient handling by prioritizing the reception of inquiries that are highly relevant, taking into account the caller's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the caller's geographical location data into a generating AI and have the generating AI prioritize the reception of inquiries that are highly relevant.

[0044] The reception department can analyze the caller's social media activity and accept relevant consultation content upon receiving the call. For example, the reception department can prioritize suggesting consultation topics of interest based on the caller's social media activity. For example, the reception department can analyze the content of the caller's social media posts and provide relevant consultation options. For example, the reception department can suggest the most appropriate reception method by referring to the activities of the caller's social media followers and friends. This allows for more appropriate responses by analyzing the caller's social media activity and accepting relevant consultation content. Some or all of the above processes in the reception department may be performed using AI, for example, or not. For example, the reception department can input the caller's social media activity data into a generating AI and have the generating AI perform the reception of relevant consultation content.

[0045] The search unit can adjust the level of detail in search results based on the importance of relevant laws and regulations during a search. For example, the search unit may prioritize displaying information about important laws and regulations. For example, the search unit may provide detailed information about relevant laws and regulations. For example, the search unit may display information about less important laws and regulations concisely. This allows for more appropriate search results by adjusting the level of detail in search results based on the importance of relevant laws and regulations. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit may input importance data of relevant laws and regulations into a generating AI and have the generating AI perform the adjustment of the level of detail in search results.

[0046] The search unit can apply different search algorithms depending on the category of the consultation content during a search. For example, the search unit applies a legal-specific search algorithm to consultations concerning legal matters. For example, the search unit applies a regulatory-specific search algorithm to consultations concerning regulations. For example, the search unit applies a general-purpose search algorithm to general consultations. By applying different search algorithms depending on the category of the consultation content, more appropriate search results can be provided. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input consultation content category data into a generating AI and have the generating AI execute the application of different search algorithms.

[0047] The search unit can prioritize search results based on the effective dates of relevant laws and regulations during a search. For example, the search unit may prioritize displaying information about recently enacted laws and regulations. For example, the search unit may briefly display information about older laws and regulations. For example, the search unit may adjust the priority of relevant laws and regulations based on their effective dates. This allows for more appropriate search results to be provided by prioritizing search results based on the effective dates of relevant laws and regulations. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit may input effective date data for relevant laws and regulations into a generating AI and have the generating AI perform the determination of search result priorities.

[0048] The search unit can adjust the order of search results based on the relevance of relevant laws and regulations during a search. For example, the search unit may prioritize displaying the laws and regulations most relevant to the inquiry. For example, the search unit may briefly display information on less relevant laws and regulations. For example, the search unit adjusts the order of search results based on relevance. This allows for the provision of more appropriate search results by adjusting the order of search results based on the relevance of relevant laws and regulations. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input relevance data of relevant laws and regulations into a generating AI and have the generating AI perform the adjustment of the order of search results.

[0049] The response unit can adjust the level of detail in its response based on the importance of the consultation. For example, the response unit provides a detailed response to important consultations. For example, it provides a concise response to general consultations. The response unit adjusts the level of detail in its response based on importance. This allows for the provision of more appropriate responses by adjusting the level of detail in the response based on the importance of the consultation. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input importance data of the consultation into a generating AI and have the generating AI perform the adjustment of the level of detail in the response.

[0050] The response unit can apply different response algorithms depending on the category of the consultation content when providing a response. For example, the response unit can apply a legal-specific response algorithm to consultations concerning legal matters. For example, the response unit can apply a regulatory-specific response algorithm to consultations concerning regulations. For example, the response unit can apply a general-purpose response algorithm to general consultations. By applying different response algorithms depending on the category of the consultation content, it is possible to provide more appropriate answers. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input consultation content category data into a generating AI and have the generating AI execute the application of different response algorithms.

[0051] The response unit can determine the priority of responses based on when the consultation content was submitted. For example, the response unit will prioritize responses to recently submitted consultation content. For example, the response unit will also adjust the priority of older consultation content according to its importance. For example, the response unit will determine the priority of responses based on the submission date. This allows for the provision of more appropriate responses by determining the priority of responses based on the submission date of the consultation content. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input consultation content submission date data into a generating AI and have the generating AI perform the determination of response priority.

[0052] The response unit can adjust the order of responses based on the relevance of the consultation content when providing answers. For example, the response unit may prioritize providing the most relevant answer to the consultation content. For example, the response unit may also provide a concise answer to less relevant consultation content. The response unit adjusts the order of responses based on relevance. By adjusting the order of responses based on the relevance of the consultation content, more appropriate answers can be provided. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit may input relevance data of the consultation content into a generating AI and have the generating AI perform the adjustment of the order of responses.

[0053] The proposal department can adjust the level of detail in a proposal based on the importance of past consultation cases and FAQs. For example, the proposal department can provide a detailed proposal based on important consultation cases and FAQs. For example, the proposal department can provide a concise proposal based on general consultation cases and FAQs. For example, the proposal department can adjust the level of detail in a proposal based on importance. This allows for the provision of more appropriate proposals by adjusting the level of detail in proposals based on the importance of past consultation cases and FAQs. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input importance data of past consultation cases and FAQs into a generating AI and have the generating AI perform the adjustment of the level of detail in proposals.

[0054] The proposal unit can apply different proposal algorithms depending on the category of the consultation content when making a proposal. For example, the proposal unit can apply a legal-specific proposal algorithm to consultations concerning legal matters. For example, the proposal unit can apply a regulatory-specific proposal algorithm to consultations concerning regulations. For example, the proposal unit can apply a general-purpose proposal algorithm to general consultations. By applying different proposal algorithms depending on the category of the consultation content, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input consultation content category data into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0055] The proposal department can determine the priority of proposals based on the submission dates of past consultation cases and FAQs. For example, the proposal department will prioritize recently submitted consultation cases and FAQs. The proposal department will also adjust the priority of older consultation cases and FAQs according to their importance. The proposal department will determine the priority of proposals based on the submission date. This allows for the provision of more appropriate proposals by determining the priority of proposals based on the submission dates of past consultation cases and FAQs. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on the submission dates of past consultation cases and FAQs into a generating AI and have the generating AI perform the determination of proposal priorities.

[0056] The proposal unit can adjust the order of proposals based on the relevance of past consultation cases and FAQs. For example, the proposal unit will prioritize proposing consultation cases and FAQs that are most relevant to the consultation content. For example, the proposal unit will also provide concise proposals for consultation cases and FAQs that are less relevant. The proposal unit will adjust the order of proposals based on relevance. This allows for the provision of more appropriate proposals by adjusting the order of proposals based on the relevance of past consultation cases and FAQs. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input relevance data of past consultation cases and FAQs into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0057] The adjustment unit can adjust the level of detail of the adjustments based on the importance of each department's schedule during the adjustment process. For example, the adjustment unit prioritizes adjusting the schedules of important departments. For example, the adjustment unit also adjusts the level of detail of the adjustments for general departments according to their importance. For example, the adjustment unit adjusts the level of detail of the adjustments based on their importance. This allows for more appropriate adjustments by adjusting the level of detail of the adjustments based on the importance of each department's schedule. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input importance data for each department's schedule into a generating AI and have the generating AI perform the adjustment of the level of detail of the adjustments.

[0058] The adjustment unit can apply different adjustment algorithms depending on the category of the consultation content during the adjustment process. For example, the adjustment unit applies a legal adjustment algorithm to consultations concerning legal matters. For example, the adjustment unit applies a regulatory adjustment algorithm to consultations concerning regulations. For example, the adjustment unit applies a general adjustment algorithm to general consultations. By applying different adjustment algorithms depending on the category of the consultation content, more appropriate adjustments become possible. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input consultation content category data into a generating AI and have the generating AI execute the application of different adjustment algorithms.

[0059] The coordination unit can determine the priority of adjustments based on the submission timing of each department's schedules during the adjustment process. For example, the coordination unit may prioritize recently submitted schedules. For example, the coordination unit may also adjust the priority of older schedules according to their importance. For example, the coordination unit may determine the priority of adjustments based on the submission timing. This allows for more appropriate adjustments by determining the priority of adjustments based on the submission timing of each department's schedules. Some or all of the above processes in the coordination unit may be performed using AI, for example, or not using AI. For example, the coordination unit may input the submission timing data of each department's schedules into a generating AI and have the generating AI perform the determination of the adjustment priority.

[0060] The coordination unit can adjust the order of adjustments based on the relevance of each department's schedule during the adjustment process. For example, the coordination unit prioritizes adjusting the schedule of the department most relevant to the consultation content. The coordination unit also makes concise adjustments to the schedules of less relevant departments. The coordination unit adjusts the order of adjustments based on relevance. This allows for more appropriate adjustments by adjusting the order of adjustments based on the relevance of each department's schedule. Some or all of the above-described processes in the coordination unit may be performed using AI, for example, or without AI. For example, the coordination unit can input the relevance data of each department's schedule into a generating AI and have the generating AI perform the adjustment of the order of adjustments.

[0061] The improvement unit can adjust the level of detail of suggestions based on the importance of past feedback data when proposing improvements. For example, the improvement unit can provide detailed suggestions based on important feedback data. For example, the improvement unit can provide concise suggestions based on general feedback data. For example, the improvement unit can adjust the level of detail of suggestions based on importance. This allows for more appropriate suggestions by adjusting the level of detail of suggestions based on the importance of past feedback data. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input importance data of past feedback data into a generating AI and have the generating AI perform the adjustment of the level of detail of suggestions.

[0062] The improvement unit can apply different suggestion algorithms depending on the category of the feedback data when proposing improvements. For example, the improvement unit can apply a legal-specific suggestion algorithm to legal feedback data. For example, it can apply a regulatory-specific suggestion algorithm to regulatory feedback data. For example, it can apply a general-purpose suggestion algorithm to general feedback data. By applying different suggestion algorithms depending on the category of the feedback data, more appropriate suggestions can be made. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the category data of the feedback data into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0063] The improvement department can prioritize suggestions based on the timing of feedback data submission when proposing improvements. For example, the improvement department may prioritize recently submitted feedback data. The improvement department may also adjust the priority of older feedback data according to its importance. The improvement department may prioritize suggestions based on the submission date. This allows for more appropriate suggestions by prioritizing suggestions based on the timing of feedback data submission. Some or all of the above processes in the improvement department may be performed using AI, for example, or not using AI. For example, the improvement department may input feedback data submission timing data into a generating AI and have the generating AI perform the determination of suggestion priorities.

[0064] The improvement unit can adjust the order of suggestions based on the relevance of the feedback data when proposing improvements. For example, the improvement unit will prioritize suggesting the feedback data most relevant to the consultation. For example, the improvement unit will also provide concise suggestions for less relevant feedback data. The improvement unit will adjust the order of suggestions based on relevance. By adjusting the order of suggestions based on the relevance of the feedback data, more appropriate suggestions can be made. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the relevance data of the feedback data into a generating AI and have the generating AI perform the adjustment of the order of suggestions.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The reception desk can select the most suitable reception method by referring to the caller's past consultation history. For example, it can prioritize suggesting the consultation method the caller has frequently used in the past. It can also automatically display relevant reception options based on the caller's past consultation content. Furthermore, it can predict and suggest the reception method the caller will use at a specific time of day based on their past consultation history. This enables efficient reception by selecting the most suitable reception method based on the caller's past consultation history.

[0067] The search function can retrieve relevant laws and regulations in real time based on the content of the inquiry. For example, it can use a search engine to search for relevant laws and regulations. It is also possible to search for relevant laws and regulations using the real-time update function of the database. Furthermore, it can also use natural language processing technology to search for relevant laws and regulations. As a result, by searching for relevant laws and regulations in real time based on the content of the inquiry, a quick response is possible.

[0068] The proposal department can automatically search for relevant past consultation cases and FAQs based on the content of the consultation. For example, it can search for consultation cases from the past year. It can also search for FAQs related to specific categories. Furthermore, it can use a search algorithm to automatically search for past consultation cases and FAQs. This enables efficient proposals by automatically searching for relevant past consultation cases and FAQs based on the content of the consultation.

[0069] The coordination department can manage the schedules of each department in the city hall in real time and automatically adjust and book the optimal consultation time. For example, it can adjust the optimal consultation time based on the time requested by the person seeking consultation. It can also adjust the optimal consultation time based on the availability of each department. Furthermore, it can manage the schedules of each department in real time using a schedule management system. As a result, by managing the schedules of each department in the city hall in real time and automatically adjusting and booking the optimal consultation time, the waiting time for those seeking consultation can be minimized.

[0070] The improvement department can automatically collect feedback from clients and propose areas for service improvement. For example, feedback can be collected using surveys. It is also possible to collect feedback through direct interviews. Furthermore, feedback can be collected automatically using feedback collection tools. This allows for continuous improvement of service quality by automatically collecting client feedback and proposing areas for service improvement.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The reception desk receives inquiries from residents. These inquiries include legal advice, advice on administrative procedures, and advice on daily life. The reception desk accepts voice input and uploads of images and documents. Step 2: The search unit searches for relevant laws and regulations based on the consultation content received by the reception unit. The search unit uses search engines and real-time database update functions to search for relevant laws and regulations in real time. Step 3: The response unit provides immediate answers based on the laws and regulations searched by the search unit. The response unit provides real-time answers within seconds. Step 4: The proposal department searches for relevant past consultation cases and FAQs based on the consultation content received by the reception department, and makes proposals based on the past consultation cases and FAQs found. The proposal department searches for consultation cases from the past year and FAQs related to specific categories, and makes proposals based on these references. Step 5: The Coordination Department manages the schedules of each department at the city hall based on the consultation content received by the Reception Department, and adjusts and reserves the optimal consultation time. The Coordination Department adjusts the optimal consultation time based on the consultationer's preferred time and the availability of each department, and manages each department's schedule in real time using the schedule management system. Step 6: The Improvement Department collects feedback from callers based on the content of the consultations received by the Reception Department and proposes ways to improve the service. The Improvement Department collects feedback automatically using surveys, direct interviews, and feedback collection tools, and conducts regular data collection.

[0073] (Example of form 2) The system utilizing the autonomous AI agent according to an embodiment of the present invention is a system for streamlining administrative consultation services. This system begins with residents providing their consultation details via voice input, image, or document upload. The autonomous AI agent searches for relevant laws and regulations in real time based on the consultation details and provides an immediate response. Furthermore, it has a function to automatically search for and suggest relevant past consultation cases and FAQs according to the consultation details. This eliminates the need for staff to refer to past cases, enabling more efficient responses. The AI ​​agent also has a function to manage the schedules of each department in the city hall in real time and automatically adjust and reserve the optimal consultation time. This minimizes the waiting time for those seeking consultation. Furthermore, it has a function to automatically collect feedback from those seeking consultation, and the AI ​​analyzes the data to suggest areas for service improvement. This allows for continuous improvement of service quality. For example, a resident might input details such as "I would like to consult about changing my resident registration due to moving" via voice input, or upload relevant documents. This information is input into the autonomous AI agent. The autonomous AI agent analyzes the input consultation details and searches for relevant laws and regulations in real time. For example, it can search for information on laws and procedures related to changing residency registration and provide immediate answers. This allows residents to quickly obtain the information they need. Furthermore, it has a function that automatically searches for and suggests relevant past consultation cases and FAQs based on the content of the consultation. For example, if a similar consultation has occurred in the past, it will refer to that case and suggest it to the resident. This saves staff the trouble of referring to past cases, enabling more efficient responses. The AI ​​agent also has a function that manages the schedules of each department in the city hall in real time and automatically adjusts and reserves the optimal consultation time. For example, it checks the availability of the appropriate department based on the content of the consultation resident and makes a reservation. This minimizes the waiting time for residents. Furthermore, it has a function that automatically collects feedback from consultants and the AI ​​analyzes that data to suggest ways to improve the service. For example, a resident provides feedback after a consultation, and the AI ​​analyzes that data to suggest ways to improve the service.This will enable continuous improvement in service quality. This system utilizes multimodal autonomous AI agents for inquiry and consultation tasks, freeing staff from consultation and response duties and allowing them to focus on proactive administrative services. This optimizes administration in the AI ​​era, benefiting both residents and city hall staff. The system, utilizing autonomous AI agents, will efficiently respond by searching for relevant laws and regulations based on residents' inquiries and providing immediate answers.

[0074] The system utilizing the autonomous AI agent according to this embodiment comprises a reception unit, a search unit, a response unit, a proposal unit, a coordination unit, and an improvement unit. The reception unit receives inquiries from residents. These inquiries include, but are not limited to, legal consultations, consultations regarding administrative procedures, and consultations regarding daily life. The reception unit accepts, for example, voice input and uploads of images and documents. The search unit searches for relevant laws and regulations based on the inquiries received by the reception unit. The search unit searches for relevant laws and regulations in real time based on the inquiries. The search unit searches for relevant laws and regulations using, for example, a search engine. The search unit searches for relevant laws and regulations using, for example, a real-time database update function. The response unit provides immediate answers based on the laws and regulations found by the search unit. The response unit provides answers within, for example, a few seconds. The response unit provides answers in, for example, real time. The proposal unit searches for relevant past consultation cases and FAQs according to the inquiries received by the reception unit. The proposal unit searches for consultation cases from the past year, for example. The Proposal Department searches for FAQs related to a specific category, for example. The Proposal Department makes proposals based on the searched past consultation cases and FAQs. The Proposal Department makes proposals by referring to past consultation cases, for example. The Proposal Department makes proposals by referring to FAQs, for example. The Coordination Department manages the schedules of each department in the city hall based on the consultation content received by the Reception Department and adjusts and reserves the optimal consultation time. The Coordination Department adjusts the optimal consultation time based on the time requested by the consultant, for example. The Coordination Department adjusts the optimal consultation time based on the availability of each department, for example. The Coordination Department manages the schedules of each department in real time using a schedule management system, for example. The Coordination Department manages the schedules of each department using a real-time update function, for example. The Improvement Department collects feedback from consultants based on the consultation content received by the Reception Department and proposes improvements to the service. The Improvement Department collects feedback using surveys, for example. The Improvement Department collects feedback using direct opinion gathering, for example. The Improvement Department automatically collects feedback using a feedback collection tool, for example.The improvement unit automatically collects feedback using, for example, periodic data collection. This enables the system utilizing the autonomous AI agent according to the embodiment to efficiently respond by searching for relevant laws and regulations based on the content of residents' inquiries and providing immediate answers.

[0075] The reception desk receives inquiries from residents. These inquiries include, but are not limited to, legal consultations, consultations regarding administrative procedures, and consultations regarding daily life. The reception desk accepts inquiries via voice input, image and document uploads, and other methods. Specifically, residents can use their smartphones or computers to input their inquiries by voice or upload related documents and images. In the case of voice input, the system uses speech recognition technology to convert the voice data into text data. This allows residents to easily communicate their inquiries. Furthermore, by using the image and document upload function, residents can provide concrete evidence and materials, enabling an accurate understanding of their inquiries. The reception desk centrally manages this input data and can quickly pass it on to the next processing step. In addition, the reception desk has implemented security measures to appropriately protect residents' personal information, including data encryption and access control. This allows residents to provide their inquiries with peace of mind. To enhance convenience for residents, the reception desk has built a system that is available 24 hours a day, 365 days a year, allowing inquiries to be received anytime, anywhere. This allows residents to seek advice whenever needed, without being restricted by time or location.

[0076] The search unit searches for relevant laws and regulations based on the consultation content received by the reception unit. For example, the search unit searches for relevant laws and regulations in real time based on the consultation content. Specifically, the search unit uses natural language processing technology to analyze the content of the consultation and extract appropriate keywords. This makes it possible to identify the laws and regulations that are most relevant to the consultation content. For example, the search unit uses a search engine to search for relevant laws and regulations. The search engine searches publicly available databases on the internet and legal compilations to obtain the latest information. For example, the search unit uses the real-time update function of the database to search for relevant laws and regulations. This makes it possible to always provide the latest legal information. The search unit organizes the search results and provides them in an easy-to-understand format for residents. For example, it creates a summary that summarizes the key points of the relevant laws and regulations and presents it to residents. This makes it possible for residents to quickly grasp the necessary information. Furthermore, the search unit learns from past search history and consultation content and has built a feedback loop to improve search accuracy. As a result, the search unit is continuously improved and can provide more accurate and faster search results.

[0077] The response unit provides immediate answers based on the laws and regulations searched by the search unit. For example, it can respond within seconds. Specifically, the response unit uses AI to analyze search results and generate appropriate answers to residents' inquiries. The AI ​​uses natural language generation technology to create easy-to-understand explanations of the laws and regulations. The response unit provides answers in real-time, for example. This allows residents to receive answers quickly without waiting. When presenting the generated answers to residents, the response unit emphasizes key points and provides relevant additional information, presenting the information in a format that is easy for residents to understand. For example, it explains not only the legal text but also its background and application examples, allowing residents to deepen their understanding in a way that is relevant to their specific situation. Furthermore, the response unit can quickly respond to additional questions and ambiguities from residents. For example, if a resident requests more detailed information about the answer, the response unit performs additional searches and provides supplementary information. This allows residents to continue their consultation until they are satisfied. The response unit has a mechanism to continuously evaluate and improve the quality of its answers to enhance resident satisfaction. For example, by collecting feedback from residents and evaluating the accuracy and clarity of the responses, the AI's learning data is updated, improving the quality of the responses.

[0078] The proposal department searches for relevant past consultation cases and FAQs based on the content of the consultation received by the reception department. For example, the proposal department searches for consultation cases from the past year. Specifically, the proposal department analyzes past consultation cases stored in the database and identifies the case that is most similar to the resident's consultation content. For example, the proposal department searches for FAQs on a specific category. This allows them to provide general solutions and advice to the problems the resident is facing. The proposal department makes proposals based on the searched past consultation cases and FAQs. For example, the proposal department makes proposals by referring to past consultation cases. Specifically, they provide useful information to residents by presenting how they handled similar consultations in the past and the solutions they found. For example, the proposal department makes proposals by referring to FAQs. This allows residents to quickly obtain answers to common questions. The proposal department can also incorporate visual elements to present the proposal content to residents in an easy-to-understand manner. For example, they can deepen residents' understanding by using charts and illustrations to visually explain the proposal content. Furthermore, the proposal department has a system in place to collect feedback from residents and evaluate the accuracy and usefulness of the proposal content. This allows the proposal department to continuously improve and provide more appropriate proposals.

[0079] The Coordination Department manages the schedules of each department within the city hall based on the consultation content received by the Reception Department, and adjusts and reserves the optimal consultation time. For example, the Coordination Department adjusts the optimal consultation time based on the consultationer's preferred time. Specifically, the Coordination Department receives the consultationer's preferred date and time, compares it with the schedules of each department, and proposes the optimal consultation time. For example, the Coordination Department adjusts the optimal consultation time based on the availability of each department. This allows the consultationer to reserve a consultation at their convenience. The Coordination Department manages the schedules of each department in real time, for example, using a schedule management system. The schedule management system has the function of centrally managing the schedules of each department and updating availability and reservation status in real time. For example, the Coordination Department manages the schedules of each department using the real-time update function. This allows adjustments to always be made based on the latest schedule information. The Coordination Department sends a reservation confirmation notice to the consultationer and carries out the procedure to confirm the reservation details. For example, a reservation confirmation notice is sent using email or SMS so that the consultationer can confirm the reservation details. Furthermore, the Coordination Department can flexibly handle changes and cancellations of reservations. For example, if a client wants to change their appointment, the scheduling department will readjust the schedule and propose a new appointment time. This allows the scheduling department to improve client convenience and ensure smooth appointment scheduling.

[0080] The Improvement Department collects feedback from clients based on the consultation content received by the Reception Department and proposes improvements to the service. The Improvement Department collects feedback using, for example, surveys. Specifically, after the consultation ends, they send questionnaires to clients to collect opinions on service satisfaction and areas for improvement. The Improvement Department collects feedback using, for example, direct interviews. This allows them to directly hear specific opinions and requests from clients. The Improvement Department automatically collects feedback using, for example, a feedback collection tool. A feedback collection tool has the function of automatically collecting clients' opinions and storing them in a database. The Improvement Department automatically collects feedback using, for example, periodic data collection. This allows them to continuously collect feedback and use it to improve the service. The Improvement Department analyzes the collected feedback and identifies areas for service improvement. For example, they categorize the content of the feedback and extract common problems and requests. Based on the identified areas for improvement, the Improvement Department proposes and implements specific improvement measures. For example, they propose and implement specific improvement measures such as reviewing the consultation process or adding system functions. This allows the Improvement Department to improve the quality of its services and increase client satisfaction. Furthermore, the Improvement Department has a system in place to evaluate the effectiveness of improvement measures and implement continuous improvements. For example, it collects feedback again after implementing improvement measures and evaluates the effectiveness of the improvements. This allows the Improvement Department to constantly improve the quality of its services and provide better services to clients.

[0081] The reception desk can accept voice input and uploads of images and documents. The reception desk can accept voice input, for example. The reception desk can accept voice input using, for example, speech recognition technology. The reception desk can accept voice input using, for example, a microphone. The reception desk can accept uploads of images and documents, for example. The reception desk can accept images in JPEG format, for example. The reception desk can accept documents in PDF format, for example. This allows the reception desk to handle various formats of inquiries by accepting voice input and uploads of images and documents. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input voice input into a generating AI and have the generating AI perform the conversion from voice data to text data.

[0082] The search unit can search for relevant laws and regulations in real time based on the content of the consultation. The search unit can search for relevant laws and regulations using, for example, a search engine. The search unit can search for relevant laws and regulations using, for example, a real-time database update function. The search unit can search for relevant laws and regulations using, for example, natural language processing technology. This enables quick responses by searching for relevant laws and regulations in real time based on the content of the consultation. Some or all of the above processing in the search unit may be performed using, for example, AI, or not using AI. For example, the search unit can input the content of the consultation into a generating AI and have the generating AI perform a search for relevant laws and regulations.

[0083] The proposal department can automatically search for relevant past consultation cases and FAQs based on the content of the consultation. For example, the proposal department can search for consultation cases from the past year. For example, the proposal department can search for FAQs related to a specific category. For example, the proposal department can use a search algorithm to automatically search for past consultation cases and FAQs. This enables efficient proposals by automatically searching for relevant past consultation cases and FAQs based on the content of the consultation. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the consultation content into a generating AI and have the generating AI perform a search for relevant past consultation cases and FAQs.

[0084] The coordination unit can manage the schedules of each department in the city hall in real time and automatically adjust and reserve the optimal consultation time. For example, the coordination unit can adjust the optimal consultation time based on the time requested by the person seeking consultation. For example, the coordination unit can adjust the optimal consultation time based on the availability of each department. For example, the coordination unit can manage the schedules of each department in real time using a schedule management system. For example, the coordination unit can manage the schedules of each department using a real-time update function. This minimizes the waiting time for people seeking consultation by managing the schedules of each department in the city hall in real time and automatically adjusting and reserving the optimal consultation time. Some or all of the above processes in the coordination unit may be performed using AI, for example, or not using AI. For example, the coordination unit can input the consultation content into a generating AI and have the generating AI perform the adjustment of the optimal consultation time.

[0085] The improvement department can automatically collect feedback from clients and propose areas for service improvement. The improvement department can collect feedback using, for example, surveys. The improvement department can collect feedback using, for example, direct opinion gathering. The improvement department can automatically collect feedback using, for example, a feedback collection tool. The improvement department can automatically collect feedback using, for example, periodic data collection. This allows for continuous improvement of service quality by automatically collecting feedback from clients and proposing areas for service improvement. Some or all of the above processes in the improvement department may be performed using, for example, AI, or not using AI. For example, the improvement department can input feedback data into a generating AI and have the generating AI generate suggestions for service improvements.

[0086] The reception desk can estimate the client's emotions and adjust the reception process based on the estimated emotions. For example, if the client is feeling anxious, the reception desk can provide a gentle voice guidance to reassure them. For example, if the client is in a hurry, the reception desk can provide a simple interface for quick input. For example, if the client is relaxed, the reception desk can provide an interface with detailed explanations and carefully receive the client's inquiry. This allows for a more appropriate response by adjusting the reception process based on the client's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the client's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The reception department can select the most suitable reception method by referring to the caller's past consultation history at the time of reception. For example, the reception department may prioritize suggesting consultation methods that the caller has frequently used in the past. For example, the reception department may automatically display relevant reception options based on the content of the caller's past consultations. For example, the reception department may predict and suggest a reception method to be used during a specific time period based on the caller's past consultation history. This enables efficient reception by selecting the most suitable reception method by referring to the caller's past consultation history. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department may input the caller's past consultation history data into a generating AI and have the generating AI select the most suitable reception method.

[0088] The reception unit can filter the caller's current situation and areas of interest at the time of reception. For example, when a caller enters their current situation, the reception unit can prioritize displaying relevant consultation content. For example, the reception unit can automatically suggest relevant consultation options based on the caller's areas of interest. For example, the reception unit can analyze the caller's current situation and areas of interest and provide the optimal reception method. This allows for highly relevant reception by filtering based on the caller's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the caller's current situation and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0089] The reception desk can estimate the emotions of the caller and determine the priority of the consultation content to be received based on the estimated emotions. For example, if the caller feels urgent, the reception desk provides a method for receiving the call with priority. For example, if the caller is relaxed, the reception desk will receive the call with normal priority. For example, if the caller is feeling anxious, the reception desk will set a priority for a quick response. This allows for more appropriate responses by determining the priority of the consultation content to be received based on the emotions of the caller. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the caller's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The reception desk can prioritize receiving inquiries that are highly relevant to the caller, taking into account the caller's geographical location. For example, when a caller enters their current location, the reception desk will prioritize displaying relevant inquiries. For example, the reception desk will provide the optimal reception method based on the caller's geographical location. For example, the reception desk will prioritize suggesting the nearest service counter to the caller's current location. This enables efficient handling by prioritizing the reception of inquiries that are highly relevant, taking into account the caller's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the caller's geographical location data into a generating AI and have the generating AI prioritize the reception of inquiries that are highly relevant.

[0091] The reception department can analyze the caller's social media activity and accept relevant consultation content upon receiving the call. For example, the reception department can prioritize suggesting consultation topics of interest based on the caller's social media activity. For example, the reception department can analyze the content of the caller's social media posts and provide relevant consultation options. For example, the reception department can suggest the most appropriate reception method by referring to the activities of the caller's social media followers and friends. This allows for more appropriate responses by analyzing the caller's social media activity and accepting relevant consultation content. Some or all of the above processes in the reception department may be performed using AI, for example, or not. For example, the reception department can input the caller's social media activity data into a generating AI and have the generating AI perform the reception of relevant consultation content.

[0092] The search unit can estimate the client's emotions and adjust how search results are displayed based on the estimated emotions. For example, if the client is feeling anxious, the search unit will display simple and highly visible search results. If the client is relaxed, the search unit will display search results containing detailed information. If the client is in a hurry, the search unit will display concise search results. By adjusting how search results are displayed based on the client's emotions, more appropriate search results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI or not. For example, the search unit can input the client's emotion data into the generative AI and have the generative AI adjust how search results are displayed.

[0093] The search unit can adjust the level of detail in search results based on the importance of relevant laws and regulations during a search. For example, the search unit may prioritize displaying information about important laws and regulations. For example, the search unit may provide detailed information about relevant laws and regulations. For example, the search unit may display information about less important laws and regulations concisely. This allows for more appropriate search results by adjusting the level of detail in search results based on the importance of relevant laws and regulations. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit may input importance data of relevant laws and regulations into a generating AI and have the generating AI perform the adjustment of the level of detail in search results.

[0094] The search unit can apply different search algorithms depending on the category of the consultation content during a search. For example, the search unit applies a legal-specific search algorithm to consultations concerning legal matters. For example, the search unit applies a regulatory-specific search algorithm to consultations concerning regulations. For example, the search unit applies a general-purpose search algorithm to general consultations. By applying different search algorithms depending on the category of the consultation content, more appropriate search results can be provided. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input consultation content category data into a generating AI and have the generating AI execute the application of different search algorithms.

[0095] The search unit can estimate the client's emotions and determine the priority of search results based on the estimated emotions. For example, if the client feels urgent, the search unit will prioritize important search results. If the client is relaxed, the search unit will display search results with normal priority. If the client is anxious, the search unit will prioritize search results that provide reassurance. By prioritizing search results based on the client's emotions, more appropriate search results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI or not. For example, the search unit can input the client's emotion data into a generative AI and have the generative AI determine the priority of search results.

[0096] The search unit can prioritize search results based on the effective dates of relevant laws and regulations during a search. For example, the search unit may prioritize displaying information about recently enacted laws and regulations. For example, the search unit may briefly display information about older laws and regulations. For example, the search unit may adjust the priority of relevant laws and regulations based on their effective dates. This allows for more appropriate search results to be provided by prioritizing search results based on the effective dates of relevant laws and regulations. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit may input effective date data for relevant laws and regulations into a generating AI and have the generating AI perform the determination of search result priorities.

[0097] The search unit can adjust the order of search results based on the relevance of relevant laws and regulations during a search. For example, the search unit may prioritize displaying the laws and regulations most relevant to the inquiry. For example, the search unit may briefly display information on less relevant laws and regulations. For example, the search unit adjusts the order of search results based on relevance. This allows for the provision of more appropriate search results by adjusting the order of search results based on the relevance of relevant laws and regulations. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input relevance data of relevant laws and regulations into a generating AI and have the generating AI perform the adjustment of the order of search results.

[0098] The response unit can estimate the client's emotions and adjust the way it expresses its response based on those emotions. For example, if the client is feeling anxious, the response unit can provide a gentle tone of voice to reassure them. For example, if the client is in a hurry, the response unit can provide a concise response that allows for quick answers. For example, if the client is relaxed, the response unit can provide a response that includes detailed explanations. This allows for the provision of more appropriate responses by adjusting the way the response is expressed based on the client's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the response unit may be performed using AI or not. For example, the response unit can input the client's emotion data into the generative AI and have the generative AI adjust the way the response is expressed.

[0099] The response unit can adjust the level of detail in its response based on the importance of the consultation. For example, the response unit provides a detailed response to important consultations. For example, it provides a concise response to general consultations. The response unit adjusts the level of detail in its response based on importance. This allows for the provision of more appropriate responses by adjusting the level of detail in the response based on the importance of the consultation. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input importance data of the consultation into a generating AI and have the generating AI perform the adjustment of the level of detail in the response.

[0100] The response unit can apply different response algorithms depending on the category of the consultation content when providing a response. For example, the response unit can apply a legal-specific response algorithm to consultations concerning legal matters. For example, the response unit can apply a regulatory-specific response algorithm to consultations concerning regulations. For example, the response unit can apply a general-purpose response algorithm to general consultations. By applying different response algorithms depending on the category of the consultation content, it is possible to provide more appropriate answers. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input consultation content category data into a generating AI and have the generating AI execute the application of different response algorithms.

[0101] The response unit can estimate the caller's emotions and adjust the length of the response based on the estimated emotions. For example, if the caller is in a hurry, the response unit will provide a short, to-the-point response. For example, if the caller is relaxed, the response unit will provide a longer response that includes detailed explanations. For example, if the caller is feeling anxious, the response unit will provide a more reassuring response. By adjusting the length of the response based on the caller's emotions, a more appropriate response can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the caller's emotion data into the generative AI and have the generative AI adjust the length of the response.

[0102] The response unit can determine the priority of responses based on when the consultation content was submitted. For example, the response unit will prioritize responses to recently submitted consultation content. For example, the response unit will also adjust the priority of older consultation content according to its importance. For example, the response unit will determine the priority of responses based on the submission date. This allows for the provision of more appropriate responses by determining the priority of responses based on the submission date of the consultation content. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input consultation content submission date data into a generating AI and have the generating AI perform the determination of response priority.

[0103] The response unit can adjust the order of responses based on the relevance of the consultation content when providing answers. For example, the response unit may prioritize providing the most relevant answer to the consultation content. For example, the response unit may also provide a concise answer to less relevant consultation content. The response unit adjusts the order of responses based on relevance. By adjusting the order of responses based on the relevance of the consultation content, more appropriate answers can be provided. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit may input relevance data of the consultation content into a generating AI and have the generating AI perform the adjustment of the order of responses.

[0104] The suggestion unit can estimate the client's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the client is feeling anxious, the suggestion unit can offer suggestions in a gentle tone to provide reassurance. If the client is in a hurry, the suggestion unit can offer concise suggestions that allow for quick responses. If the client is relaxed, the suggestion unit can offer suggestions that include detailed explanations. By adjusting the way it presents suggestions based on the client's emotions, it can provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the client's emotion data into a generative AI and have the generative AI adjust the way it presents its suggestions.

[0105] The proposal department can adjust the level of detail in a proposal based on the importance of past consultation cases and FAQs. For example, the proposal department can provide a detailed proposal based on important consultation cases and FAQs. For example, the proposal department can provide a concise proposal based on general consultation cases and FAQs. For example, the proposal department can adjust the level of detail in a proposal based on importance. This allows for the provision of more appropriate proposals by adjusting the level of detail in proposals based on the importance of past consultation cases and FAQs. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input importance data of past consultation cases and FAQs into a generating AI and have the generating AI perform the adjustment of the level of detail in proposals.

[0106] The proposal unit can apply different proposal algorithms depending on the category of the consultation content when making a proposal. For example, the proposal unit can apply a legal-specific proposal algorithm to consultations concerning legal matters. For example, the proposal unit can apply a regulatory-specific proposal algorithm to consultations concerning regulations. For example, the proposal unit can apply a general-purpose proposal algorithm to general consultations. By applying different proposal algorithms depending on the category of the consultation content, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input consultation content category data into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0107] The suggestion unit can estimate the client's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the client is in a hurry, the suggestion unit will provide a short, to-the-point suggestion. If the client is relaxed, the suggestion unit will provide a longer suggestion with detailed explanations. If the client is feeling anxious, the suggestion unit will provide a more reassuring suggestion. By adjusting the length of the suggestion based on the client's emotions, it is possible to provide a more appropriate suggestion. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the client's emotion data into the generative AI and have the generative AI adjust the length of the suggestion.

[0108] The proposal department can determine the priority of proposals based on the submission dates of past consultation cases and FAQs. For example, the proposal department will prioritize recently submitted consultation cases and FAQs. The proposal department will also adjust the priority of older consultation cases and FAQs according to their importance. The proposal department will determine the priority of proposals based on the submission date. This allows for the provision of more appropriate proposals by determining the priority of proposals based on the submission dates of past consultation cases and FAQs. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on the submission dates of past consultation cases and FAQs into a generating AI and have the generating AI perform the determination of proposal priorities.

[0109] The proposal unit can adjust the order of proposals based on the relevance of past consultation cases and FAQs. For example, the proposal unit will prioritize proposing consultation cases and FAQs that are most relevant to the consultation content. For example, the proposal unit will also provide concise proposals for consultation cases and FAQs that are less relevant. The proposal unit will adjust the order of proposals based on relevance. This allows for the provision of more appropriate proposals by adjusting the order of proposals based on the relevance of past consultation cases and FAQs. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input relevance data of past consultation cases and FAQs into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0110] The adjustment unit can estimate the client's emotions and adjust the adjustment method based on the estimated emotions. For example, if the client is feeling anxious, the adjustment unit can provide an adjustment method in a gentle tone to provide reassurance. For example, if the client is in a hurry, the adjustment unit can provide a concise method that allows for quick adjustment. For example, if the client is relaxed, the adjustment unit can provide an adjustment method that includes detailed explanations. This allows for more appropriate adjustments by adjusting the adjustment method based on the client's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input the client's emotion data into the generative AI and have the generative AI perform the adjustment of the adjustment method.

[0111] The adjustment unit can adjust the level of detail of the adjustments based on the importance of each department's schedule during the adjustment process. For example, the adjustment unit prioritizes adjusting the schedules of important departments. For example, the adjustment unit also adjusts the level of detail of the adjustments for general departments according to their importance. For example, the adjustment unit adjusts the level of detail of the adjustments based on their importance. This allows for more appropriate adjustments by adjusting the level of detail of the adjustments based on the importance of each department's schedule. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input importance data for each department's schedule into a generating AI and have the generating AI perform the adjustment of the level of detail of the adjustments.

[0112] The adjustment unit can apply different adjustment algorithms depending on the category of the consultation content during the adjustment process. For example, the adjustment unit applies a legal adjustment algorithm to consultations concerning legal matters. For example, the adjustment unit applies a regulatory adjustment algorithm to consultations concerning regulations. For example, the adjustment unit applies a general adjustment algorithm to general consultations. By applying different adjustment algorithms depending on the category of the consultation content, more appropriate adjustments become possible. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input consultation content category data into a generating AI and have the generating AI execute the application of different adjustment algorithms.

[0113] The adjustment unit can estimate the client's emotions and determine the priority of adjustments based on the estimated emotions. For example, if the client feels urgency, the adjustment unit will prioritize important adjustments. For example, if the client is relaxed, the adjustment unit will perform adjustments with normal priority. For example, if the client is feeling anxious, the adjustment unit will perform adjustments quickly to provide reassurance. This allows for more appropriate adjustments by determining the priority of adjustments based on the client's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input the client's emotion data into the generative AI and have the generative AI determine the priority of adjustments.

[0114] The coordination unit can determine the priority of adjustments based on the submission timing of each department's schedules during the adjustment process. For example, the coordination unit may prioritize recently submitted schedules. For example, the coordination unit may also adjust the priority of older schedules according to their importance. For example, the coordination unit may determine the priority of adjustments based on the submission timing. This allows for more appropriate adjustments by determining the priority of adjustments based on the submission timing of each department's schedules. Some or all of the above processes in the coordination unit may be performed using AI, for example, or not using AI. For example, the coordination unit may input the submission timing data of each department's schedules into a generating AI and have the generating AI perform the determination of the adjustment priority.

[0115] The coordination unit can adjust the order of adjustments based on the relevance of each department's schedule during the adjustment process. For example, the coordination unit prioritizes adjusting the schedule of the department most relevant to the consultation content. The coordination unit also makes concise adjustments to the schedules of less relevant departments. The coordination unit adjusts the order of adjustments based on relevance. This allows for more appropriate adjustments by adjusting the order of adjustments based on the relevance of each department's schedule. Some or all of the above-described processes in the coordination unit may be performed using AI, for example, or without AI. For example, the coordination unit can input the relevance data of each department's schedule into a generating AI and have the generating AI perform the adjustment of the order of adjustments.

[0116] The improvement unit can estimate the client's emotions and adjust the method of suggesting improvements based on the estimated emotions. For example, if the client is feeling anxious, the improvement unit will offer suggestions in a gentle tone to provide reassurance. For example, if the client is in a hurry, the improvement unit will offer a concise method that allows for quick suggestions. For example, if the client is relaxed, the improvement unit will offer suggestions that include detailed explanations. By adjusting the method of suggesting improvements based on the client's emotions, more appropriate suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the improvement unit may be performed using AI or not using AI. For example, the improvement unit can input the client's emotion data into the generative AI and have the generative AI adjust the method of suggesting improvements.

[0117] The improvement unit can adjust the level of detail of suggestions based on the importance of past feedback data when proposing improvements. For example, the improvement unit can provide detailed suggestions based on important feedback data. For example, the improvement unit can provide concise suggestions based on general feedback data. For example, the improvement unit can adjust the level of detail of suggestions based on importance. This allows for more appropriate suggestions by adjusting the level of detail of suggestions based on the importance of past feedback data. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input importance data of past feedback data into a generating AI and have the generating AI perform the adjustment of the level of detail of suggestions.

[0118] The improvement unit can apply different suggestion algorithms depending on the category of the feedback data when proposing improvements. For example, the improvement unit can apply a legal-specific suggestion algorithm to legal feedback data. For example, it can apply a regulatory-specific suggestion algorithm to regulatory feedback data. For example, it can apply a general-purpose suggestion algorithm to general feedback data. By applying different suggestion algorithms depending on the category of the feedback data, more appropriate suggestions can be made. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the category data of the feedback data into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0119] The improvement unit can estimate the client's emotions and determine the priority of improvements based on the estimated emotions. For example, if the client feels a sense of urgency, the improvement unit will prioritize suggesting important improvements. For example, if the client is relaxed, the improvement unit will suggest improvements in the usual order of priority. For example, if the client is feeling anxious, the improvement unit will quickly suggest improvements to provide reassurance. This allows for more appropriate suggestions by prioritizing improvements based on the client's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the improvement unit may be performed using AI or not. For example, the improvement unit can input the client's emotion data into a generative AI and have the generative AI determine the priority of improvements.

[0120] The improvement department can prioritize suggestions based on the timing of feedback data submission when proposing improvements. For example, the improvement department may prioritize recently submitted feedback data. The improvement department may also adjust the priority of older feedback data according to its importance. The improvement department may prioritize suggestions based on the submission date. This allows for more appropriate suggestions by prioritizing suggestions based on the timing of feedback data submission. Some or all of the above processes in the improvement department may be performed using AI, for example, or not using AI. For example, the improvement department may input feedback data submission timing data into a generating AI and have the generating AI perform the determination of suggestion priorities.

[0121] The improvement unit can adjust the order of suggestions based on the relevance of the feedback data when proposing improvements. For example, the improvement unit will prioritize suggesting the feedback data most relevant to the consultation. For example, the improvement unit will also provide concise suggestions for less relevant feedback data. The improvement unit will adjust the order of suggestions based on relevance. By adjusting the order of suggestions based on the relevance of the feedback data, more appropriate suggestions can be made. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the relevance data of the feedback data into a generating AI and have the generating AI perform the adjustment of the order of suggestions.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The reception desk can select the most suitable reception method by referring to the caller's past consultation history. For example, it can prioritize suggesting the consultation method the caller has frequently used in the past. It can also automatically display relevant reception options based on the caller's past consultation content. Furthermore, it can predict and suggest the reception method the caller will use at a specific time of day based on their past consultation history. This enables efficient reception by selecting the most suitable reception method based on the caller's past consultation history.

[0124] The search function can retrieve relevant laws and regulations in real time based on the content of the inquiry. For example, it can use a search engine to search for relevant laws and regulations. It is also possible to search for relevant laws and regulations using the real-time update function of the database. Furthermore, it can also use natural language processing technology to search for relevant laws and regulations. As a result, by searching for relevant laws and regulations in real time based on the content of the inquiry, a quick response is possible.

[0125] The proposal department can automatically search for relevant past consultation cases and FAQs based on the content of the consultation. For example, it can search for consultation cases from the past year. It can also search for FAQs related to specific categories. Furthermore, it can use a search algorithm to automatically search for past consultation cases and FAQs. This enables efficient proposals by automatically searching for relevant past consultation cases and FAQs based on the content of the consultation.

[0126] The coordination department can manage the schedules of each department in the city hall in real time and automatically adjust and book the optimal consultation time. For example, it can adjust the optimal consultation time based on the time requested by the person seeking consultation. It can also adjust the optimal consultation time based on the availability of each department. Furthermore, it can manage the schedules of each department in real time using a schedule management system. As a result, by managing the schedules of each department in the city hall in real time and automatically adjusting and booking the optimal consultation time, the waiting time for those seeking consultation can be minimized.

[0127] The improvement department can automatically collect feedback from clients and propose areas for service improvement. For example, feedback can be collected using surveys. It is also possible to collect feedback through direct interviews. Furthermore, feedback can be collected automatically using feedback collection tools. This allows for continuous improvement of service quality by automatically collecting client feedback and proposing areas for service improvement.

[0128] The reception desk can estimate the caller's emotions and adjust the reception process based on those estimates. For example, if the caller is feeling anxious, it can provide a gentle voice guidance to reassure them. If the caller is in a hurry, it can provide a concise interface that allows for quick input. Furthermore, if the caller is relaxed, it can provide an interface with detailed explanations and carefully receive the caller's concerns. By adjusting the reception process based on the caller's emotions, more appropriate responses can be provided.

[0129] The search function can estimate the user's emotions and adjust how search results are displayed based on that estimation. For example, if the user is feeling anxious, simple and easy-to-read search results can be displayed. If the user is relaxed, search results containing more detailed information can be displayed. Furthermore, if the user is in a hurry, concise search results can be displayed. By adjusting how search results are displayed based on the user's emotions, more appropriate results can be provided.

[0130] The response function can estimate the caller's emotions and adjust the way it expresses its response based on those emotions. For example, if the caller is feeling anxious, it can provide a gentle tone of voice to reassure them. If the caller is in a hurry, it can provide a concise response that allows for quick answers. Furthermore, if the caller is relaxed, it can provide a response that includes detailed explanations. In this way, by adjusting the expression of the response based on the caller's emotions, it can provide more appropriate answers.

[0131] The proposal function can estimate the client's emotions and adjust the way it presents its proposals based on those emotions. For example, if the client is feeling anxious, it can offer a gentle tone of voice to provide reassurance. If the client is in a hurry, it can offer concise and quick suggestions. Furthermore, if the client is relaxed, it can offer suggestions that include detailed explanations. By adjusting the presentation of proposals based on the client's emotions, it can provide more appropriate suggestions.

[0132] The adjustment unit can estimate the client's emotions and adjust the adjustment method based on those emotions. For example, if the client is feeling anxious, it can provide an adjustment method in a gentle tone to reassure them. If the client is in a hurry, it can provide a concise method that allows for quick adjustment. Furthermore, if the client is relaxed, it can provide an adjustment method that includes detailed explanations. By adjusting the adjustment method based on the client's emotions, more appropriate adjustments can be made.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The reception desk receives inquiries from residents. These inquiries include legal advice, advice on administrative procedures, and advice on daily life. The reception desk accepts voice input and uploads of images and documents. Step 2: The search unit searches for relevant laws and regulations based on the consultation content received by the reception unit. The search unit uses search engines and real-time database update functions to search for relevant laws and regulations in real time. Step 3: The response unit provides immediate answers based on the laws and regulations searched by the search unit. The response unit provides real-time answers within seconds. Step 4: The proposal department searches for relevant past consultation cases and FAQs based on the consultation content received by the reception department, and makes proposals based on the past consultation cases and FAQs found. The proposal department searches for consultation cases from the past year and FAQs related to specific categories, and makes proposals based on these references. Step 5: The Coordination Department manages the schedules of each department at the city hall based on the consultation content received by the Reception Department, and adjusts and reserves the optimal consultation time. The Coordination Department adjusts the optimal consultation time based on the consultationer's preferred time and the availability of each department, and manages each department's schedule in real time using the schedule management system. Step 6: The Improvement Department collects feedback from callers based on the content of the consultations received by the Reception Department and proposes ways to improve the service. The Improvement Department collects feedback automatically using surveys, direct interviews, and feedback collection tools, and conducts regular data collection.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0137] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the reception unit, search unit, response unit, proposal unit, coordination unit, and improvement unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and accepts voice input and uploads of images and documents. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for relevant laws and regulations. The response unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides an immediate response based on the search results. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for past consultation cases and FAQs and makes proposals. The coordination unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the schedules of each department of the city hall and adjusts and reserves the optimal consultation time. The improvement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects feedback from consultants and proposes improvements to the service. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the reception unit, search unit, response unit, proposal unit, coordination unit, and improvement unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and accepts voice input and uploads of images and documents. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for relevant laws and regulations. The response unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides an immediate response based on the search results. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for past consultation cases and FAQs and makes proposals. The coordination unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the schedules of each department of the city hall and adjusts and reserves the optimal consultation time. The improvement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects feedback from the consultant and proposes improvements to the service. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0163] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0164] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0165] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0167] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0169] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0170] Each of the multiple elements described above, including the reception unit, search unit, response unit, proposal unit, coordination unit, and improvement unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and accepts voice input and uploads of images and documents. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for relevant laws and regulations. The response unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides immediate answers based on search results. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for past consultation cases and FAQs and makes proposals. The coordination unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the schedules of each department of the city hall and adjusts and reserves the optimal consultation time. The improvement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects feedback from consultants and proposes improvements to the service. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0174] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0175] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0177] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0178] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0179] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0180] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0181] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0182] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0183] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0184] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0185] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0186] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0187] Each of the multiple elements described above, including the reception unit, search unit, response unit, proposal unit, coordination unit, and improvement unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and accepts voice input and uploads of images and documents. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for relevant laws and regulations. The response unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides immediate answers based on search results. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for past consultation cases and FAQs and makes proposals. The coordination unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the schedules of each department of the city hall and adjusts and reserves the optimal consultation time. The improvement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects feedback from consultants and proposes improvements to the service. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0188] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0189] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0190] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0191] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0192] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0193] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0194] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0195] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0196] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0197] 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.

[0198] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0199] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0200] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0201] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0202] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0203] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0204] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0205] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0206] (Note 1) A reception desk that receives inquiries from residents, A search unit that searches for relevant laws and regulations based on the content of the consultation received by the aforementioned reception unit, A response unit that immediately provides an answer based on the laws and regulations searched by the aforementioned search unit, The proposal department searches for relevant past consultation cases and FAQs based on the content of the consultation received by the reception department, The proposal department makes proposals based on past consultation cases and FAQs searched by the aforementioned proposal department, Based on the consultation details received by the aforementioned reception department, the coordination department manages the schedules of each department in the city hall and adjusts and reserves the most suitable consultation time. The system includes an improvement unit that collects feedback from clients based on the content of their consultations received by the reception unit and proposes ways to improve the service. A system characterized by the following features. (Note 2) The aforementioned reception unit is It accepts voice input and uploads of images and documents. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned search unit, Search for relevant laws and regulations in real time based on the content of your inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, The system automatically searches for relevant past consultation cases and FAQs based on the content of the consultation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The adjustment unit is, The system manages the schedules of each department at the city hall in real time and automatically adjusts and reserves the optimal consultation time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned improvement unit is, The system automatically collects feedback from clients and suggests ways to improve the service. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate the client's emotions and adjust the reception process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Upon receiving a request, the most appropriate registration method will be selected by referring to the applicant's past consultation history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is During the application process, filtering will be performed based on the applicant's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is We estimate the client's emotions and prioritize the types of consultations we accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a call, we will prioritize accepting calls with the most relevant content, taking into account the caller's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is Upon receiving a call, the system analyzes the caller's social media activity and receives relevant information about their consultation. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned search unit, The system estimates the feelings of the person seeking advice and adjusts how search results are displayed based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, When you search, adjust the level of detail in the search results based on the importance of the relevant laws and regulations. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, When searching, different search algorithms are applied depending on the category of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, The system estimates the client's emotions and prioritizes search results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, When you search, search results are prioritized based on when relevant laws and regulations came into effect. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned search unit, When you search, the order of search results will be adjusted based on the relevance of relevant laws and regulations. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned response section is, We estimate the client's emotions and adjust the way we express our response based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned response section is, When responding, adjust the level of detail in your response based on the importance of the issue. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned response section is, When responding, different response algorithms are applied depending on the category of the inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned response section is, The system estimates the client's emotions and adjusts the length of the response based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned response section is, When responding, we will prioritize responses based on when the inquiry was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned response section is, When responding, adjust the order of your answers based on the relevance of the topics you are asking about. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, We estimate the client's emotions and adjust the way we express our proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of past consultation cases and FAQs. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, We estimate the client's emotions and adjust the length of the proposal based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When submitting a proposal, we will prioritize it based on past consultation cases and the timing of FAQ submissions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making proposals, adjust the order of suggestions based on the relevance of past consultation cases and FAQs. The system described in Appendix 1, characterized by the features described herein. (Note 31) The adjustment unit is, We estimate the client's emotions and adjust the adjustment method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The adjustment unit is, During the coordination process, adjust the level of detail based on the importance of each department's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 33) The adjustment unit is, During the adjustment process, different adjustment algorithms are applied depending on the category of the consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 34) The adjustment unit is, We estimate the client's emotions and determine the priority of adjustments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The adjustment unit is, During the coordination process, the priority of the coordination will be determined based on when each department submits its schedule. The system described in Appendix 1, characterized by the features described herein. (Note 36) The adjustment unit is, During the coordination process, the order of adjustments will be adjusted based on the relevance of each department's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned improvement unit is, We estimate the client's emotions and adjust the method of suggesting improvements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned improvement unit is, When proposing improvements, adjust the level of detail in the suggestions based on the importance of past feedback data. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned improvement unit is, When suggesting improvements, different suggestion algorithms are applied depending on the category of the feedback data. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned improvement unit is, We estimate the client's emotions and determine the priority of areas for improvement based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned improvement unit is, When proposing improvements, prioritize the suggestions based on when the feedback data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned improvement unit is, When proposing improvements, adjust the order of suggestions based on the relevance of the feedback data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives inquiries from residents, A search unit that searches for relevant laws and regulations based on the content of the consultation received by the aforementioned reception unit, A response unit that immediately provides an answer based on the laws and regulations searched by the aforementioned search unit, The proposal department searches for relevant past consultation cases and FAQs based on the content of the consultation received by the reception department, The proposal department makes proposals based on past consultation cases and FAQs searched by the aforementioned proposal department, Based on the consultation details received by the aforementioned reception department, the coordination department manages the schedules of each department in the city hall and adjusts and reserves the most suitable consultation time. The system includes an improvement unit that collects feedback from clients based on the content of their consultations received by the reception unit and proposes ways to improve the service. A system characterized by the following features.

2. The aforementioned reception unit is It accepts voice input and uploads of images and documents. The system according to feature 1.

3. The aforementioned search unit, Search for relevant laws and regulations in real time based on the content of your inquiry. The system according to feature 1.

4. The aforementioned proposal section is, The system automatically searches for relevant past consultation cases and FAQs based on the content of the consultation. The system according to feature 1.

5. The adjustment unit is, The system manages the schedules of each department at the city hall in real time and automatically adjusts and reserves the optimal consultation time. The system according to feature 1.

6. The aforementioned improvement unit is, The system automatically collects feedback from clients and suggests ways to improve the service. The system according to feature 1.

7. The aforementioned reception unit is We estimate the client's emotions and adjust the reception process based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Upon receiving a request, the most appropriate registration method will be selected by referring to the applicant's past consultation history. The system according to feature 1.

Images