system

Abstract

The system according to this embodiment aims to concretize the consultation content of those considering relocation and to clearly understand the concerns of their partners. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a follow-up unit. The reception unit receives inquiries from people considering relocation. The analysis unit analyzes the content of the inquiries received by the reception unit. The generation unit generates a leaflet based on the content analyzed by the analysis unit. The follow-up unit delivers follow-up messages.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, it is difficult to formulate a specific plan for a person considering migration and to grasp the concerns of their partner, and there are also problems in the PR and consultation responses of local governments.

[0005] The system according to the embodiment aims to materialize the consultation content of a person considering migration and clearly grasp the concerns of their partner.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a follow-up unit. The reception unit receives inquiries from people considering relocation. The analysis unit analyzes the content of the inquiries received by the reception unit. The generation unit generates a leaflet based on the content analyzed by the analysis unit. The follow-up unit delivers follow-up messages. [Effects of the Invention]

[0007] The system according to this embodiment can concretize the consultation content of those considering relocation and clearly understand the concerns of their partners. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The migration support system according to an embodiment of the present invention is a system that utilizes a generation AI to solve problems for prospective migrants and local governments. In this migration support system, prospective migrants can concretize their migration plans and aspirations by consulting with a migration consultation generation AI. Next, the migration consultation generation AI also interviews the prospective migrant's partner to clearly understand their concerns. Furthermore, a concern-alleviating leaflet generation AI creates a leaflet from the responses of the migration consultation generation AI, which can be used to persuade family members. This mechanism makes it easier for prospective migrants to create concrete plans and to clarify their partner's concerns. In addition, local governments can differentiate themselves from other local governments and compensate for the lack of knowledge and consultation skills of their migration staff. For example, a prospective migrant consults with a migration consultation generation AI. At this time, the prospective migrant inputs their hopes and concerns regarding migration. For example, they input specific consultation content such as, "I want to move to a rural area, but I'm worried about work and living environment." This information is input into the generation AI and analyzed. Next, the generation AI analyzes the prospective migrant's consultation content and provides concrete migration plans and advice. For example, it provides information on the destination local government, methods for finding work related to migration, and information on living environment. This allows prospective migrants to create concrete plans. Furthermore, the generating AI also interviews the prospective migrants' partners to clearly understand their concerns. For example, it asks specific questions about the partners' concerns and analyzes their answers. This clarifies the partners' concerns. Next, based on the generating AI's answers, the concern-alleviating leaflet generating AI creates a leaflet. For example, it generates a leaflet that includes specific solutions to the partners' concerns and the benefits of relocating. This makes it easier for prospective migrants to persuade their partners. Finally, the follow-up generating AI operates on the messaging app and delivers follow-up messages. For example, it regularly delivers advice and information tailored to the prospective migrants' inquiries and concerns. This allows prospective migrants to receive continuous support. This system makes it easier for prospective migrants to create concrete plans and clarifies their partners' concerns. In addition, it allows local governments to differentiate themselves from other local governments and compensate for any lack of knowledge or consultation skills among their migration staff.This is expected to promote migration and revitalize local communities. The migration support system can address the challenges faced by prospective migrants and local governments, thereby promoting migration and revitalizing local areas.

[0029] The migration support system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a follow-up unit. The reception unit receives consultations from people considering migration. These consultations may include, for example, hopes and concerns regarding migration, but are not limited to such examples. The reception unit receives, for example, the consultation content entered by the person considering migration. The reception unit can also receive consultation content via voice input from the person considering migration. Furthermore, the reception unit can also receive consultation content using images or videos. For example, the reception unit receives text data entered by the person considering migration. Voice data is converted into text data using speech recognition technology. Image and video data are analyzed using image recognition technology. The analysis unit analyzes the consultation content received by the reception unit. The analysis may be performed using, for example, text analysis, sentiment analysis, or keyword extraction, but is not limited to such examples. For example, the analysis unit analyzes the consultation content using text analysis technology. The analysis unit may also analyze the emotions of the person seeking advice using sentiment analysis technology. Furthermore, the analysis unit may also extract important keywords from the consultation content using keyword extraction technology. For example, text analysis technology analyzes the content of the consultation using natural language processing technology. Sentiment analysis technology estimates emotions from the consultationr's text data. Keyword extraction technology extracts particularly important keywords from the consultation content. The generation unit generates a leaflet based on the content analyzed by the analysis unit. Leaflet generation is performed, for example, based on the use of a template or the degree of customization, but is not limited to such examples. For example, the generation unit generates a leaflet using a template. The generation unit can also customize the content of the leaflet according to the consultation content. The generation unit can also adjust the design of the leaflet. For example, a template is a pre-prepared leaflet template into which content is filled in according to the consultation content. Customization changes the content of the leaflet according to the consultation content. Design adjustment adjusts the layout and colors of the leaflet. The follow-up unit delivers follow-up messages. Follow-up messages are delivered, for example, by email, SMS, notifications, etc., but is not limited to such examples.For example, the follow-up unit can deliver follow-up messages via email. The follow-up unit can also deliver follow-up messages via SMS. Furthermore, the follow-up unit can deliver follow-up messages using the app's notification function. For example, email sends follow-up messages to the prospective migrant's email address. SMS sends follow-up messages to the prospective migrant's mobile phone number. The notification function delivers follow-up messages through the migrant support app. As a result, the migrant support system according to this embodiment can efficiently receive and analyze inquiries from prospective migrants, generate leaflets, and deliver follow-up messages.

[0030] The reception desk accepts consultations from people considering relocation. These consultations may include, but are not limited to, hopes and concerns regarding relocation. The reception desk accepts consultation details entered by the relocation applicants. Furthermore, the reception desk allows applicants to input their consultation details via voice. Additionally, the reception desk accepts consultation details using images and videos. For example, the reception desk accepts text data entered by the relocation applicants. Voice data is converted to text data using speech recognition technology. Image and video data is analyzed using image recognition technology. The reception desk provides multiple interfaces, including text input, voice input, and image and video uploads, to allow relocation applicants to input their consultation details in various ways. Text input allows applicants to enter detailed consultation details using a keyboard, while voice input converts spoken content into text in real time via a microphone. Speech recognition technology uses natural language processing to achieve highly accurate text conversion, accurately understanding the applicant's intentions. Image and video uploads allow applicants to upload photos and videos they have taken to the system, where image recognition technology is used to analyze the content. For example, by uploading photos and videos of the desired relocation site, users can visually check the local environment and facilities. This allows the reception department to respond to the diverse needs of prospective relocators and receive more detailed and accurate consultations. Furthermore, the reception department has a function to automatically categorize the received consultations and distribute them to the appropriate department or person in charge. For example, questions about the desired relocation site can be distributed to the local information department, and questions about housing can be distributed to the real estate department, enabling a quick and appropriate response. In this way, the reception department can efficiently receive consultations from prospective relocators and provide a foundation for appropriate responses.

[0031] The analysis department analyzes the consultation content received by the reception department. Analysis is performed using methods such as text analysis, sentiment analysis, and keyword extraction, but is not limited to these examples. For instance, the analysis department may use text analysis technology to analyze the consultation content. It can also analyze the consultant's emotions using sentiment analysis technology. Furthermore, it can extract important keywords from the consultation content using keyword extraction technology. For example, text analysis technology uses natural language processing technology to analyze the consultation content. Sentiment analysis technology estimates emotions from the consultant's text data. Keyword extraction technology extracts particularly important keywords from the consultation content. The analysis department utilizes the latest AI technology to comprehensively analyze the received consultation content. Text analysis uses natural language processing technology to accurately understand the context and intent of the consultation content. For example, it extracts specific requests and concerns regarding the desired relocation destination and provides appropriate information based on them. Sentiment analysis estimates emotions from the consultant's text data to grasp the consultant's psychological state. This allows for an understanding of the consultant's anxieties and expectations, enabling more appropriate responses. Keyword extraction extracts particularly important keywords from the consultation content to grasp the main points of the consultation. For example, keywords such as "natural environment," "educational facilities," and "medical institutions" are extracted, and relevant information is provided based on them. Furthermore, the analysis department can refer to past consultation data to see examples of responses to similar consultation content. This enables a quick and appropriate response, improving the satisfaction of those seeking advice. Based on these analysis results, the analysis department builds a foundation for providing optimal information to those considering relocation.

[0032] The generation unit generates leaflets based on the content analyzed by the analysis unit. Leaflet generation is performed based on, for example, the use of templates or the degree of customization, but is not limited to these examples. For example, the generation unit generates leaflets using templates. The generation unit can also customize the content of the leaflet according to the consultation content. Furthermore, the generation unit can adjust the design of the leaflet. For example, a template is a pre-prepared leaflet template into which content is filled in according to the consultation content. Customization involves changing the content of the leaflet according to the consultation content. Design adjustment involves adjusting the layout and color scheme of the leaflet. The generation unit has advanced customization capabilities to generate leaflets tailored to the individual needs of prospective migrants. For example, the content of the leaflet is individually adjusted based on the characteristics of the desired migration destination, the prospective migrant's family structure, occupation, hobbies, etc. Templates have a basic structure for providing general information, and individual information is added through customization. For example, maps of the desired migration destination, information on major facilities, and information on local events and community activities can be included. Design adjustment involves adjusting the layout and color scheme of the leaflet to create visually appealing materials. This allows prospective migrants to receive leaflets that are visually easy to understand and contain information that is relevant to them. Furthermore, the generation unit can automate the leaflet generation process and provide leaflets quickly. For example, it can receive analysis results from the analysis unit and automatically fill in the information into a template to generate leaflets in a short time. The generation unit can also regularly update the content of the leaflets to provide the latest information. In this way, the generation unit can always provide prospective migrants with the most up-to-date and optimal information and support their decision-making regarding migration.

[0033] The follow-up department delivers follow-up messages. These messages may be delivered via email, SMS, or notifications, but are not limited to these methods. For example, the follow-up department may send follow-up messages via email. It may also send follow-up messages via SMS. Furthermore, the follow-up department may deliver follow-up messages using the app's notification function. For example, email sends follow-up messages to the prospective migrant's email address. SMS sends follow-up messages to the prospective migrant's mobile phone number. The notification function delivers follow-up messages through the migrant support app. The follow-up department delivers regular follow-up messages to provide ongoing support to prospective migrants. For example, after a certain period has passed since a prospective migrant received a leaflet, the follow-up department sends a follow-up message providing additional information or the latest event information. If a prospective migrant has specific questions or concerns, the follow-up department can also send personalized follow-up messages tailored to those concerns. This ensures that prospective migrants always receive the latest information and have their questions and anxieties about migrating addressed. Additionally, the follow-up department can collect feedback from prospective migrants and use it to improve the overall system. For example, follow-up messages can include survey links to collect opinions and feedback from prospective migrants. This allows for continuous improvement of system functionality and services, thereby increasing prospective migrants' satisfaction. The follow-up department can reliably transmit information using multiple communication methods. For example, in addition to email, voice calls and SNS messaging functions can be used in conjunction to ensure important information is delivered reliably. This enables the follow-up department to provide prompt and reliable support to prospective migrants, helping them to successfully relocate.

[0034] The Hearing Department can gather information about the Partner's concerns. For example, the Hearing Department can gather information about the Partner's concerns through an interview format. For example, the Hearing Department can ask the Partner specific questions to clarify their concerns. The Hearing Department can also gather information about the Partner's concerns through a questionnaire format. For example, the Hearing Department can conduct a questionnaire with the Partner to collect their concerns. The Hearing Department can also gather information about the Partner's concerns through online chat. For example, the Hearing Department can gather information about the Partner's concerns through online chat. This allows for a clear understanding of the Partner's concerns. Some or all of the above processes in the Hearing Department may be performed using AI, or not. For example, the Hearing Department can input the Partner's response data into a generating AI and have the generating AI extract the concerns.

[0035] The service provider can provide the generated leaflets. The service provider can provide the leaflets using digital distribution, for example. For example, the service provider can send the generated leaflets to prospective migrants in PDF format. The service provider can also provide the leaflets by postal mail, for example. For example, the service provider can print the generated leaflets and mail them to prospective migrants. The service provider can also display the leaflets within an app, for example. For example, the service provider can display the generated leaflets within a migration support app. This allows the service provider to provide the generated leaflets to users. Some or all of the above processes performed by the service provider may be carried out using AI, for example, or without AI. For example, the service provider can input the data of the generated leaflets into a generation AI and have the generation AI select the method of distributing the leaflets.

[0036] The generation unit can generate the content of follow-up messages. For example, the generation unit can generate the content of follow-up messages using a template. For example, the generation unit can generate follow-up messages based on a pre-prepared template. The generation unit can also customize the content of follow-up messages. For example, the generation unit can change the content of follow-up messages according to the consultation content of the person considering relocation. The generation unit can also adjust the design of follow-up messages. For example, the generation unit can adjust the layout and color scheme of the follow-up messages. This enables the generation of follow-up message content. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input follow-up message template data into a generation AI and have the generation AI perform message generation.

[0037] The reception department can analyze past consultation history and select the most suitable reception method. For example, the reception department can prioritize suggesting consultation methods that the user has used in the past. For example, the reception department can analyze the user's past consultation history and prioritize suggesting consultation methods that the user has used in the past. The reception department can also suggest the most suitable reception time based on the user's past consultation content. For example, the reception department can analyze the user's past consultation content and suggest the most suitable reception time. The reception department can also select the most suitable consultant based on the user's past consultation history. For example, the reception department can analyze the user's past consultation history and select the most suitable consultant. This allows the reception department to select the most suitable reception method based on 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 can input past consultation history data into a generating AI and have the generating AI select the most suitable reception method.

[0038] The reception desk can filter the user's current living situation and areas of interest at the time of reception. For example, the reception desk can prioritize receiving consultations related to the user's current living situation. For example, the reception desk can analyze the user's living situation and prioritize receiving consultations related to that situation. The reception desk can also suggest the most suitable consultation based on the user's areas of interest. For example, the reception desk can analyze the user's areas of interest and suggest the most suitable consultation. The reception desk can also select the most suitable consultant considering the user's living situation and areas of interest. For example, the reception desk can analyze the user's living situation and areas of interest and select the most suitable consultant. This allows the reception desk to suggest the most suitable consultation based on the user's living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0039] The reception desk can prioritize receiving inquiries that are highly relevant to the user, taking into account the user's geographical location information. For example, the reception desk can prioritize receiving inquiries from relevant municipalities based on the user's current location. For example, the reception desk can analyze the user's geographical location information and prioritize receiving inquiries from relevant municipalities based on the current location. The reception desk can also select the most suitable consultant, taking into account the user's geographical location information. For example, the reception desk can analyze the user's geographical location information and select the most suitable consultant. The reception desk can also propose the most suitable consultation content based on the user's geographical location information. For example, the reception desk can analyze the user's geographical location information and propose the most suitable consultation content. This allows the reception desk to propose the most suitable consultation content based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information data into a generating AI and have the generating AI select highly relevant inquiries.

[0040] The reception department can analyze a user's social media activity at the time of reception and accept relevant inquiries. For example, the reception department can prioritize inquiries from municipalities of interest based on the user's social media activity. The reception department can also analyze a user's social media activity and propose the most suitable consultation content. The reception department can also select the most suitable consultant based on the user's social media activity. This allows the reception department to propose the most suitable consultation content based on the user's social media activity. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input the user's social media activity data into a generating AI and have the generating AI select relevant inquiries.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content during the analysis. For example, the analysis unit can perform a detailed analysis for important consultation content. For example, the analysis unit can analyze the importance of the consultation content and perform a detailed analysis for important consultation content. The analysis unit can also perform a standard analysis for general consultation content. For example, the analysis unit can analyze the importance of the consultation content and perform a standard analysis for general consultation content. The analysis unit can also perform a concise analysis for simple consultation content. For example, the analysis unit can analyze the importance of the consultation content and perform a concise analysis for simple consultation content. This allows the analysis results to be provided with the optimal level of detail according to the importance of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the consultation content into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the consultation content during analysis. For example, the analysis unit can apply a specific analysis algorithm to consultation content related to work. For example, the analysis unit can analyze the category of the consultation content and apply a specific analysis algorithm to consultation content related to work. The analysis unit can also apply a different analysis algorithm to consultation content related to living environment. For example, the analysis unit can analyze the category of the consultation content and apply a different analysis algorithm to consultation content related to living environment. The analysis unit can also apply yet another analysis algorithm to consultation content related to education. For example, the analysis unit can analyze the category of the consultation content and apply yet another analysis algorithm to consultation content related to education. This allows the optimal analysis algorithm to be applied according to the category of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the category data of the consultation content into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on the submission date of the consultation content during the analysis. For example, the analysis unit may prioritize the analysis of the most recently submitted consultation content. For example, the analysis unit may analyze the submission date of the consultation content and prioritize the analysis of the most recently submitted consultation content. The analysis unit may also postpone the analysis of older consultation content. For example, the analysis unit may analyze the submission date of the consultation content and postpone the analysis of older consultation content. The analysis unit may also adjust the order of analysis based on the submission date. For example, the analysis unit may analyze the submission date of the consultation content and adjust the order of analysis based on the submission date. This allows analysis to be performed in the optimal order based on the submission date of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit may input the consultation content submission date data into a generating AI and have the generating AI perform the priority determination.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the consultation content during the analysis. For example, the analysis unit can prioritize the analysis of consultation content that is highly relevant. For example, the analysis unit can analyze the relevance of the consultation content and prioritize the analysis of the most relevant consultation content. The analysis unit can also postpone the analysis of consultation content that is less relevant. For example, the analysis unit can analyze the relevance of the consultation content and postpone the analysis of the less relevant consultation content. The analysis unit can also adjust the order of analysis based on the relevance of the consultation content. For example, the analysis unit can analyze the relevance of the consultation content and adjust the order of analysis based on that relevance. This allows the analysis to be performed in the optimal order based on the relevance of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the relevance data of the consultation content into a generating AI and have the generating AI perform the order adjustment.

[0045] The generation unit can adjust the level of detail in a leaflet based on the importance of the consultation content when generating the leaflet. For example, the generation unit can generate a detailed leaflet for important consultation content. For example, the generation unit can analyze the importance of the consultation content and generate a detailed leaflet for important consultation content. The generation unit can also generate a standard leaflet for general consultation content. For example, the generation unit can analyze the importance of the consultation content and generate a standard leaflet for general consultation content. The generation unit can also generate a concise leaflet for simple consultation content. For example, the generation unit can analyze the importance of the consultation content and generate a concise leaflet for simple consultation content. This allows the generation of a leaflet with the optimal level of detail according to the importance of the consultation content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance data of the consultation content into a generation AI and have the generation AI perform the adjustment of the level of detail.

[0046] The generation unit can apply different generation algorithms depending on the category of the consultation content when generating leaflets. For example, the generation unit can apply a specific generation algorithm to consultations related to work. For example, the generation unit analyzes the category of the consultation content and applies a specific generation algorithm to consultations related to work. The generation unit can also apply a different generation algorithm to consultations related to living environment. For example, the generation unit analyzes the category of the consultation content and applies a different generation algorithm to consultations related to living environment. The generation unit can also apply yet another generation algorithm to consultations related to education. For example, the generation unit analyzes the category of the consultation content and applies yet another generation algorithm to consultations related to education. This allows the optimal generation algorithm to be applied according to the category of the consultation content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category data of the consultation content into a generation AI and have the generation AI execute the application of the generation algorithm.

[0047] The generation unit can determine the priority of leaflets based on the submission date of the consultation content when generating leaflets. For example, the generation unit can prioritize reflecting the most recently submitted consultation content in the leaflets. For example, the generation unit can analyze the submission date of the consultation content and prioritize reflecting the most recently submitted consultation content in the leaflets. The generation unit can also postpone older consultation content. For example, the generation unit can analyze the submission date of the consultation content and postpone older consultation content. The generation unit can also adjust the leaflet generation order based on the submission date. For example, the generation unit can analyze the submission date of the consultation content and adjust the leaflet generation order based on the submission date. This allows leaflets to be generated in the optimal order based on the submission date of the consultation content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the consultation content submission date data into a generation AI and have the generation AI perform the priority determination.

[0048] The generation unit can adjust the order of leaflets based on the relevance of the consultation content when generating them. For example, the generation unit can prioritize reflecting highly relevant consultation content in the leaflets. For example, the generation unit can analyze the relevance of the consultation content and prioritize reflecting highly relevant consultation content in the leaflets. The generation unit can also postpone less relevant consultation content. For example, the generation unit can analyze the relevance of the consultation content and postpone less relevant consultation content. The generation unit can also adjust the order in which the leaflets are generated based on the relevance of the consultation content. For example, the generation unit can analyze the relevance of the consultation content and adjust the order in which the leaflets are generated based on that relevance. This makes it possible to generate leaflets in the optimal order based on the relevance of the consultation content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the relevance data of the consultation content into a generation AI and have the generation AI perform the order adjustment.

[0049] The follow-up unit can select the most appropriate message by referring to past follow-up history when delivering follow-up messages. For example, the follow-up unit can select the most appropriate message based on the user's past follow-up history. For example, the follow-up unit can analyze the user's past follow-up history and select the most appropriate message. The follow-up unit can also prioritize sending messages that are highly relevant based on the user's past follow-up history. For example, the follow-up unit can analyze the user's past follow-up history and prioritize sending messages that are highly relevant. The follow-up unit can also analyze the user's past follow-up history and send messages at the optimal time. For example, the follow-up unit can analyze the user's past follow-up history and send messages at the optimal time. This allows the system to provide the most appropriate message based on past follow-up history. Some or all of the above processing in the follow-up unit may be performed using AI, or not. For example, the follow-up unit can input past follow-up history data into a generating AI and have the generating AI select the most appropriate message.

[0050] The follow-up unit can select the most appropriate message when delivering follow-up messages, taking into account the user's geographical location information. For example, the follow-up unit can send relevant follow-up messages based on the user's current location. For example, the follow-up unit can analyze the user's geographical location information and send relevant follow-up messages based on the current location. The follow-up unit can also customize the most appropriate follow-up message, taking into account the user's geographical location information. For example, the follow-up unit can analyze the user's geographical location information and customize the most appropriate follow-up message. The follow-up unit can also send follow-up messages at the optimal timing based on the user's geographical location information. For example, the follow-up unit can analyze the user's geographical location information and send follow-up messages at the optimal timing. This allows the system to provide optimal follow-up messages based on the user's geographical location information. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input the user's geographical location data into a generating AI and have the generating AI select the most appropriate message.

[0051] The follow-up unit can analyze the user's social media activity and suggest message content when delivering follow-up messages. For example, the follow-up unit can send follow-up messages containing content of interest based on the user's social media activity. The follow-up unit can also analyze the user's social media activity and customize the optimal follow-up message. The follow-up unit can also send follow-up messages at the optimal time based on the user's social media activity. This allows the system to provide optimal follow-up messages based on the user's social media activity. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input the user's social media activity data into a generating AI and have the generating AI suggest message content.

[0052] The interviewing unit can select the most appropriate questions during an interview by referring to past interview history. For example, the interviewing unit can select the most appropriate questions based on the user's past interview history. For example, the interviewing unit can analyze the user's past interview history and select the most appropriate questions. The interviewing unit can also prioritize asking questions that are highly relevant based on the user's past interview history. For example, the interviewing unit can analyze the user's past interview history and prioritize asking questions that are highly relevant. The interviewing unit can also analyze the user's past interview history and ask questions at the optimal time. For example, the interviewing unit can analyze the user's past interview history and ask questions at the optimal time. This allows the interviewing unit to ask the most appropriate questions based on past interview history. Some or all of the above processing in the interviewing unit may be performed using AI, or not. For example, the interviewing unit can input past interview history data into a generating AI and have the generating AI select the most appropriate questions.

[0053] The interviewing unit can select the most appropriate questions during the interview, taking into account the user's geographical location. For example, the interviewing unit can prioritize relevant questions based on the user's current location. For example, the interviewing unit can analyze the user's geographical location and prioritize relevant questions based on the current location. The interviewing unit can also customize the most appropriate questions, taking into account the user's geographical location. For example, the interviewing unit can analyze the user's geographical location and customize the most appropriate questions. The interviewing unit can also ask questions at the optimal time based on the user's geographical location. For example, the interviewing unit can analyze the user's geographical location and ask questions at the optimal time. This allows for the asking of questions that are optimal based on the user's geographical location. Some or all of the above processing in the interviewing unit may be performed using AI, or not. For example, the interviewing unit can input the user's geographical location data into a generating AI and have the generating AI select the most appropriate questions.

[0054] The delivery unit can select the optimal delivery method by referring to past delivery history when providing leaflets. For example, the delivery unit can select the optimal delivery method based on the user's past delivery history. For example, the delivery unit can analyze the user's past delivery history and select the optimal delivery method. The delivery unit can also prioritize providing leaflets that are highly relevant based on the user's past delivery history. For example, the delivery unit can analyze the user's past delivery history and prioritize providing leaflets that are highly relevant. The delivery unit can also analyze the user's past delivery history and provide leaflets at the optimal timing. For example, the delivery unit can analyze the user's past delivery history and provide leaflets at the optimal timing. This allows leaflets to be provided in the most optimal way based on past delivery history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input past delivery history data into a generating AI and have the generating AI select the optimal delivery method.

[0055] The distribution unit can select the optimal distribution method when providing leaflets, taking into account the user's geographical location information. For example, the distribution unit can prioritize providing relevant leaflets based on the user's current location. For example, the distribution unit can analyze the user's geographical location information and prioritize providing relevant leaflets based on the current location. The distribution unit can also customize the optimal leaflets, taking into account the user's geographical location information. For example, the distribution unit can analyze the user's geographical location information and customize the optimal leaflets. The distribution unit can also provide leaflets at the optimal timing based on the user's geographical location information. For example, the distribution unit can analyze the user's geographical location information and provide leaflets at the optimal timing. This allows the distribution unit to provide leaflets in the most optimal way based on the user's geographical location information. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal distribution method.

[0056] The service provider can provide the most suitable leaflet by analyzing the user's social media activity when providing the leaflet. For example, the service provider can provide a leaflet containing content of interest based on the user's social media activity. For example, the service provider can analyze the user's social media activity and provide a leaflet containing content of interest. The service provider can also analyze the user's social media activity and customize the most suitable leaflet. For example, the service provider can analyze the user's social media activity and customize the most suitable leaflet. The service provider can also provide the leaflet at the optimal time based on the user's social media activity. For example, the service provider can analyze the user's social media activity and provide the leaflet at the optimal time. This allows the service provider to provide the most suitable leaflet based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing the most suitable leaflet.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The reception desk can suggest the most suitable consultation method based on the user's past consultation history. For example, if the user has previously consulted via text, a similar method will be suggested. Furthermore, if the user prefers voice consultation, voice consultation can be prioritized. Additionally, if the user has previously consulted using images or videos, a similar method can be suggested. This allows the reception desk to suggest the most suitable consultation method based on the user's past consultation history. 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 past consultation history data into a generating AI and have the generating AI suggest the most suitable consultation method.

[0059] The distribution unit can adjust the method of delivering leaflets considering the user's geographical location. For example, if the user lives in an urban area, digital delivery may be prioritized. If the user lives in a rural area, postal delivery may be prioritized. Furthermore, if the user is on the move, in-app display may be prioritized. This allows the leaflets to be delivered in the most optimal way based on the user's geographical location. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal delivery method.

[0060] The follow-up unit can select the most appropriate message based on the user's past follow-up history. For example, it can analyze the content of messages the user has received in the past and prioritize sending highly relevant messages. It can also send messages in the most appropriate format based on the message format the user has preferred in the past. Furthermore, it can send messages at the optimal timing based on the user's past follow-up history. This allows the system to provide the most appropriate message based on past follow-up history. Some or all of the above processes in the follow-up unit may be performed using AI, for example, or not. For example, the follow-up unit can input past follow-up history data into a generating AI and have the generating AI select the most appropriate message.

[0061] The analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content. For example, a detailed analysis is performed for important consultation content. A standard analysis can be performed for general consultation content. Furthermore, a concise analysis can be performed for simple consultation content. This allows the analysis results to be provided with the optimal level of detail according to the importance of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the consultation content into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0062] The generation unit can apply different generation algorithms depending on the category of the consultation content when generating leaflets. For example, a specific generation algorithm can be applied to consultations related to work. Another generation algorithm can be applied to consultations related to living environment. Furthermore, yet another generation algorithm can be applied to consultations related to education. This allows the optimal generation algorithm to be applied according to the category of the consultation content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category data of the consultation content into a generation AI and have the generation AI execute the application of the generation algorithm.

[0063] The reception department can prioritize receiving inquiries that are highly relevant to the user, taking into account the user's geographical location. For example, it can prioritize receiving inquiries from relevant local governments based on the user's current location. It can also select the most suitable consultant, taking into account the user's geographical location. Furthermore, it can suggest the most suitable consultation content based on the user's geographical location. This allows for the suggestion of the most suitable consultation content based on the user's geographical location. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the user's geographical location data into a generating AI and have the generating AI select highly relevant inquiries.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk receives inquiries from prospective migrants. These inquiries may include, but are not limited to, hopes and concerns regarding relocation. The reception desk receives inquiries entered by prospective migrants, for example. The reception desk can also receive inquiries by voice. Furthermore, the reception desk can receive inquiries by images and videos. For example, the reception desk receives text data entered by prospective migrants. Voice data is converted into text data using speech recognition technology. Image and video data is analyzed using image recognition technology. Step 2: The analysis unit analyzes the consultation content received by the reception unit. The analysis is performed using methods such as text analysis, sentiment analysis, and keyword extraction, but is not limited to these examples. For example, the analysis unit may use text analysis technology to analyze the consultation content. The analysis unit may also use sentiment analysis technology to analyze the feelings of the person seeking advice. The analysis unit may also use keyword extraction technology to extract important keywords from the consultation content. For example, text analysis technology uses natural language processing technology to analyze the consultation content. Sentiment analysis technology estimates feelings from the person seeking advice's text data. Keyword extraction technology extracts particularly important keywords from the consultation content. Step 3: The generation unit generates a leaflet based on the content analyzed by the analysis unit. Leaflet generation is performed based on, for example, the use of a template or the degree of customization, but is not limited to such examples. For example, the generation unit generates a leaflet using a template. The generation unit can also customize the content of the leaflet according to the consultation content. The generation unit can also adjust the design of the leaflet. For example, a template is a pre-prepared leaflet template into which content is filled in according to the consultation content. Customization involves changing the content of the leaflet according to the consultation content. Design adjustment involves adjusting the layout and color scheme of the leaflet. Step 4: The follow-up department delivers follow-up messages. Follow-up messages may be delivered by methods such as email, SMS, or notifications, but are not limited to these examples. For example, the follow-up department may deliver follow-up messages via email. The follow-up department may also deliver follow-up messages via SMS. The follow-up department may also deliver follow-up messages using the app's notification function. For example, email sends follow-up messages to the prospective migrant's email address. SMS sends follow-up messages to the prospective migrant's mobile phone number. The notification function delivers follow-up messages through the migrant support app.

[0066] (Example of form 2) The migration support system according to an embodiment of the present invention is a system that utilizes a generation AI to solve problems for prospective migrants and local governments. In this migration support system, prospective migrants can concretize their migration plans and aspirations by consulting with a migration consultation generation AI. Next, the migration consultation generation AI also interviews the prospective migrant's partner to clearly understand their concerns. Furthermore, a concern-alleviating leaflet generation AI creates a leaflet from the responses of the migration consultation generation AI, which can be used to persuade family members. This mechanism makes it easier for prospective migrants to create concrete plans and to clarify their partner's concerns. In addition, local governments can differentiate themselves from other local governments and compensate for the lack of knowledge and consultation skills of their migration staff. For example, a prospective migrant consults with a migration consultation generation AI. At this time, the prospective migrant inputs their hopes and concerns regarding migration. For example, they input specific consultation content such as, "I want to move to a rural area, but I'm worried about work and living environment." This information is input into the generation AI and analyzed. Next, the generation AI analyzes the prospective migrant's consultation content and provides concrete migration plans and advice. For example, it provides information on the destination local government, methods for finding work related to migration, and information on living environment. This allows prospective migrants to create concrete plans. Furthermore, the generating AI also interviews the prospective migrants' partners to clearly understand their concerns. For example, it asks specific questions about the partners' concerns and analyzes their answers. This clarifies the partners' concerns. Next, based on the generating AI's answers, the concern-alleviating leaflet generating AI creates a leaflet. For example, it generates a leaflet that includes specific solutions to the partners' concerns and the benefits of relocating. This makes it easier for prospective migrants to persuade their partners. Finally, the follow-up generating AI operates on the messaging app and delivers follow-up messages. For example, it regularly delivers advice and information tailored to the prospective migrants' inquiries and concerns. This allows prospective migrants to receive continuous support. This system makes it easier for prospective migrants to create concrete plans and clarifies their partners' concerns. In addition, it allows local governments to differentiate themselves from other local governments and compensate for any lack of knowledge or consultation skills among their migration staff.This is expected to promote migration and revitalize local communities. The migration support system can address the challenges faced by prospective migrants and local governments, thereby promoting migration and revitalizing local areas.

[0067] The migration support system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a follow-up unit. The reception unit receives consultations from people considering migration. These consultations may include, for example, hopes and concerns regarding migration, but are not limited to such examples. The reception unit receives, for example, the consultation content entered by the person considering migration. The reception unit can also receive consultation content via voice input from the person considering migration. Furthermore, the reception unit can also receive consultation content using images or videos. For example, the reception unit receives text data entered by the person considering migration. Voice data is converted into text data using speech recognition technology. Image and video data are analyzed using image recognition technology. The analysis unit analyzes the consultation content received by the reception unit. The analysis may be performed using, for example, text analysis, sentiment analysis, or keyword extraction, but is not limited to such examples. For example, the analysis unit analyzes the consultation content using text analysis technology. The analysis unit may also analyze the emotions of the person seeking advice using sentiment analysis technology. Furthermore, the analysis unit may also extract important keywords from the consultation content using keyword extraction technology. For example, text analysis technology analyzes the content of the consultation using natural language processing technology. Sentiment analysis technology estimates emotions from the consultationr's text data. Keyword extraction technology extracts particularly important keywords from the consultation content. The generation unit generates a leaflet based on the content analyzed by the analysis unit. Leaflet generation is performed, for example, based on the use of a template or the degree of customization, but is not limited to such examples. For example, the generation unit generates a leaflet using a template. The generation unit can also customize the content of the leaflet according to the consultation content. The generation unit can also adjust the design of the leaflet. For example, a template is a pre-prepared leaflet template into which content is filled in according to the consultation content. Customization changes the content of the leaflet according to the consultation content. Design adjustment adjusts the layout and colors of the leaflet. The follow-up unit delivers follow-up messages. Follow-up messages are delivered, for example, by email, SMS, notifications, etc., but is not limited to such examples.For example, the follow-up unit can deliver follow-up messages via email. The follow-up unit can also deliver follow-up messages via SMS. Furthermore, the follow-up unit can deliver follow-up messages using the app's notification function. For example, email sends follow-up messages to the prospective migrant's email address. SMS sends follow-up messages to the prospective migrant's mobile phone number. The notification function delivers follow-up messages through the migrant support app. As a result, the migrant support system according to this embodiment can efficiently receive and analyze inquiries from prospective migrants, generate leaflets, and deliver follow-up messages.

[0068] The reception desk accepts consultations from people considering relocation. These consultations may include, but are not limited to, hopes and concerns regarding relocation. The reception desk accepts consultation details entered by the relocation applicants. Furthermore, the reception desk allows applicants to input their consultation details via voice. Additionally, the reception desk accepts consultation details using images and videos. For example, the reception desk accepts text data entered by the relocation applicants. Voice data is converted to text data using speech recognition technology. Image and video data is analyzed using image recognition technology. The reception desk provides multiple interfaces, including text input, voice input, and image and video uploads, to allow relocation applicants to input their consultation details in various ways. Text input allows applicants to enter detailed consultation details using a keyboard, while voice input converts spoken content into text in real time via a microphone. Speech recognition technology uses natural language processing to achieve highly accurate text conversion, accurately understanding the applicant's intentions. Image and video uploads allow applicants to upload photos and videos they have taken to the system, where image recognition technology is used to analyze the content. For example, by uploading photos and videos of the desired relocation site, users can visually check the local environment and facilities. This allows the reception department to respond to the diverse needs of prospective relocators and receive more detailed and accurate consultations. Furthermore, the reception department has a function to automatically categorize the received consultations and distribute them to the appropriate department or person in charge. For example, questions about the desired relocation site can be distributed to the local information department, and questions about housing can be distributed to the real estate department, enabling a quick and appropriate response. In this way, the reception department can efficiently receive consultations from prospective relocators and provide a foundation for appropriate responses.

[0069] The analysis department analyzes the consultation content received by the reception department. Analysis is performed using methods such as text analysis, sentiment analysis, and keyword extraction, but is not limited to these examples. For instance, the analysis department may use text analysis technology to analyze the consultation content. It can also analyze the consultant's emotions using sentiment analysis technology. Furthermore, it can extract important keywords from the consultation content using keyword extraction technology. For example, text analysis technology uses natural language processing technology to analyze the consultation content. Sentiment analysis technology estimates emotions from the consultant's text data. Keyword extraction technology extracts particularly important keywords from the consultation content. The analysis department utilizes the latest AI technology to comprehensively analyze the received consultation content. Text analysis uses natural language processing technology to accurately understand the context and intent of the consultation content. For example, it extracts specific requests and concerns regarding the desired relocation destination and provides appropriate information based on them. Sentiment analysis estimates emotions from the consultant's text data to grasp the consultant's psychological state. This allows for an understanding of the consultant's anxieties and expectations, enabling more appropriate responses. Keyword extraction extracts particularly important keywords from the consultation content to grasp the main points of the consultation. For example, keywords such as "natural environment," "educational facilities," and "medical institutions" are extracted, and relevant information is provided based on them. Furthermore, the analysis department can refer to past consultation data to see examples of responses to similar consultation content. This enables a quick and appropriate response, improving the satisfaction of those seeking advice. Based on these analysis results, the analysis department builds a foundation for providing optimal information to those considering relocation.

[0070] The generation unit generates leaflets based on the content analyzed by the analysis unit. Leaflet generation is performed based on, for example, the use of templates or the degree of customization, but is not limited to these examples. For example, the generation unit generates leaflets using templates. The generation unit can also customize the content of the leaflet according to the consultation content. Furthermore, the generation unit can adjust the design of the leaflet. For example, a template is a pre-prepared leaflet template into which content is filled in according to the consultation content. Customization involves changing the content of the leaflet according to the consultation content. Design adjustment involves adjusting the layout and color scheme of the leaflet. The generation unit has advanced customization capabilities to generate leaflets tailored to the individual needs of prospective migrants. For example, the content of the leaflet is individually adjusted based on the characteristics of the desired migration destination, the prospective migrant's family structure, occupation, hobbies, etc. Templates have a basic structure for providing general information, and individual information is added through customization. For example, maps of the desired migration destination, information on major facilities, and information on local events and community activities can be included. Design adjustment involves adjusting the layout and color scheme of the leaflet to create visually appealing materials. This allows prospective migrants to receive leaflets that are visually easy to understand and contain information that is relevant to them. Furthermore, the generation unit can automate the leaflet generation process and provide leaflets quickly. For example, it can receive analysis results from the analysis unit and automatically fill in the information into a template to generate leaflets in a short time. The generation unit can also regularly update the content of the leaflets to provide the latest information. In this way, the generation unit can always provide prospective migrants with the most up-to-date and optimal information and support their decision-making regarding migration.

[0071] The follow-up department delivers follow-up messages. These messages may be delivered via email, SMS, or notifications, but are not limited to these methods. For example, the follow-up department may send follow-up messages via email. It may also send follow-up messages via SMS. Furthermore, the follow-up department may deliver follow-up messages using the app's notification function. For example, email sends follow-up messages to the prospective migrant's email address. SMS sends follow-up messages to the prospective migrant's mobile phone number. The notification function delivers follow-up messages through the migrant support app. The follow-up department delivers regular follow-up messages to provide ongoing support to prospective migrants. For example, after a certain period has passed since a prospective migrant received a leaflet, the follow-up department sends a follow-up message providing additional information or the latest event information. If a prospective migrant has specific questions or concerns, the follow-up department can also send personalized follow-up messages tailored to those concerns. This ensures that prospective migrants always receive the latest information and have their questions and anxieties about migrating addressed. Additionally, the follow-up department can collect feedback from prospective migrants and use it to improve the overall system. For example, follow-up messages can include survey links to collect opinions and feedback from prospective migrants. This allows for continuous improvement of system functionality and services, thereby increasing prospective migrants' satisfaction. The follow-up department can reliably transmit information using multiple communication methods. For example, in addition to email, voice calls and SNS messaging functions can be used in conjunction to ensure important information is delivered reliably. This enables the follow-up department to provide prompt and reliable support to prospective migrants, helping them to successfully relocate.

[0072] The Hearing Department can gather information about the Partner's concerns. For example, the Hearing Department can gather information about the Partner's concerns through an interview format. For example, the Hearing Department can ask the Partner specific questions to clarify their concerns. The Hearing Department can also gather information about the Partner's concerns through a questionnaire format. For example, the Hearing Department can conduct a questionnaire with the Partner to collect their concerns. The Hearing Department can also gather information about the Partner's concerns through online chat. For example, the Hearing Department can gather information about the Partner's concerns through online chat. This allows for a clear understanding of the Partner's concerns. Some or all of the above processes in the Hearing Department may be performed using AI, or not. For example, the Hearing Department can input the Partner's response data into a generating AI and have the generating AI extract the concerns.

[0073] The service provider can provide the generated leaflets. The service provider can provide the leaflets using digital distribution, for example. For example, the service provider can send the generated leaflets to prospective migrants in PDF format. The service provider can also provide the leaflets by postal mail, for example. For example, the service provider can print the generated leaflets and mail them to prospective migrants. The service provider can also display the leaflets within an app, for example. For example, the service provider can display the generated leaflets within a migration support app. This allows the service provider to provide the generated leaflets to users. Some or all of the above processes performed by the service provider may be carried out using AI, for example, or without AI. For example, the service provider can input the data of the generated leaflets into a generation AI and have the generation AI select the method of distributing the leaflets.

[0074] The generation unit can generate the content of follow-up messages. For example, the generation unit can generate the content of follow-up messages using a template. For example, the generation unit can generate follow-up messages based on a pre-prepared template. The generation unit can also customize the content of follow-up messages. For example, the generation unit can change the content of follow-up messages according to the consultation content of the person considering relocation. The generation unit can also adjust the design of follow-up messages. For example, the generation unit can adjust the layout and color scheme of the follow-up messages. This enables the generation of follow-up message content. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input follow-up message template data into a generation AI and have the generation AI perform message generation.

[0075] The reception desk can estimate the user's emotions and adjust the timing of consultation based on the estimated emotions. For example, if the user is feeling stressed, the reception desk will schedule a consultation during a time when the user can relax. For example, the reception desk will analyze the user's emotions and, if they are feeling stressed, will schedule a consultation during a time when they can relax, such as at night or on weekends. The reception desk can also schedule a consultation immediately if the user is relaxed. For example, the reception desk will analyze the user's emotions and, if relaxed, will schedule a consultation immediately. The reception desk can also schedule a consultation quickly if the user is in a hurry. For example, the reception desk will analyze the user's emotions and, if urgent, will schedule a consultation quickly. This allows for consultations to be scheduled at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0076] The reception department can analyze past consultation history and select the most suitable reception method. For example, the reception department can prioritize suggesting consultation methods that the user has used in the past. For example, the reception department can analyze the user's past consultation history and prioritize suggesting consultation methods that the user has used in the past. The reception department can also suggest the most suitable reception time based on the user's past consultation content. For example, the reception department can analyze the user's past consultation content and suggest the most suitable reception time. The reception department can also select the most suitable consultant based on the user's past consultation history. For example, the reception department can analyze the user's past consultation history and select the most suitable consultant. This allows the reception department to select the most suitable reception method based on 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 can input past consultation history data into a generating AI and have the generating AI select the most suitable reception method.

[0077] The reception desk can filter the user's current living situation and areas of interest at the time of reception. For example, the reception desk can prioritize receiving consultations related to the user's current living situation. For example, the reception desk can analyze the user's living situation and prioritize receiving consultations related to that situation. The reception desk can also suggest the most suitable consultation based on the user's areas of interest. For example, the reception desk can analyze the user's areas of interest and suggest the most suitable consultation. The reception desk can also select the most suitable consultant considering the user's living situation and areas of interest. For example, the reception desk can analyze the user's living situation and areas of interest and select the most suitable consultant. This allows the reception desk to suggest the most suitable consultation based on the user's living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0078] The reception desk can estimate the user's emotions and determine the priority of consultations to accept based on the estimated emotions. For example, if the user is feeling stressed, the reception desk will prioritize accepting the consultation. For example, the reception desk will analyze the user's emotions and, if they are feeling stressed, will prioritize accepting the consultation. The reception desk can also accept consultations with the normal priority if the user is relaxed. For example, the reception desk will analyze the user's emotions and, if they are relaxed, will prioritize accepting the consultation. The reception desk can also accept consultations quickly if the user is in a hurry. For example, the reception desk will analyze the user's emotions and, if they are in a hurry, will accept the consultation quickly. This allows for the prioritization of consultations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 without AI. For example, the reception desk can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0079] The reception desk can prioritize receiving inquiries that are highly relevant to the user, taking into account the user's geographical location information. For example, the reception desk can prioritize receiving inquiries from relevant municipalities based on the user's current location. For example, the reception desk can analyze the user's geographical location information and prioritize receiving inquiries from relevant municipalities based on the current location. The reception desk can also select the most suitable consultant, taking into account the user's geographical location information. For example, the reception desk can analyze the user's geographical location information and select the most suitable consultant. The reception desk can also propose the most suitable consultation content based on the user's geographical location information. For example, the reception desk can analyze the user's geographical location information and propose the most suitable consultation content. This allows the reception desk to propose the most suitable consultation content based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information data into a generating AI and have the generating AI select highly relevant inquiries.

[0080] The reception department can analyze a user's social media activity at the time of reception and accept relevant inquiries. For example, the reception department can prioritize inquiries from municipalities of interest based on the user's social media activity. The reception department can also analyze a user's social media activity and propose the most suitable consultation content. The reception department can also select the most suitable consultant based on the user's social media activity. This allows the reception department to propose the most suitable consultation content based on the user's social media activity. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input the user's social media activity data into a generating AI and have the generating AI select relevant inquiries.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the analysis unit analyzes the user's emotions and finds them relaxed, it can provide detailed analysis results. The analysis unit can also provide concise analysis results if the user is stressed. For example, if the analysis unit analyzes the user's emotions and finds them stressed, it can provide concise analysis results. The analysis unit can also provide quick analysis results if the user is in a hurry. For example, if the analysis unit analyzes the user's emotions and finds them in a hurry, it can provide quick analysis results. This allows the analysis results to be presented in the most appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content during the analysis. For example, the analysis unit can perform a detailed analysis for important consultation content. For example, the analysis unit can analyze the importance of the consultation content and perform a detailed analysis for important consultation content. The analysis unit can also perform a standard analysis for general consultation content. For example, the analysis unit can analyze the importance of the consultation content and perform a standard analysis for general consultation content. The analysis unit can also perform a concise analysis for simple consultation content. For example, the analysis unit can analyze the importance of the consultation content and perform a concise analysis for simple consultation content. This allows the analysis results to be provided with the optimal level of detail according to the importance of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the consultation content into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0083] The analysis unit can apply different analysis algorithms depending on the category of the consultation content during analysis. For example, the analysis unit can apply a specific analysis algorithm to consultation content related to work. For example, the analysis unit can analyze the category of the consultation content and apply a specific analysis algorithm to consultation content related to work. The analysis unit can also apply a different analysis algorithm to consultation content related to living environment. For example, the analysis unit can analyze the category of the consultation content and apply a different analysis algorithm to consultation content related to living environment. The analysis unit can also apply yet another analysis algorithm to consultation content related to education. For example, the analysis unit can analyze the category of the consultation content and apply yet another analysis algorithm to consultation content related to education. This allows the optimal analysis algorithm to be applied according to the category of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the category data of the consultation content into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the analysis unit analyzes the user's emotions and is relaxed, it can provide detailed analysis results. The analysis unit can also provide concise analysis results if the user is stressed. For example, if the analysis unit analyzes the user's emotions and is stressed, it can provide concise analysis results. The analysis unit can also provide quick analysis results if the user is in a hurry. For example, if the analysis unit analyzes the user's emotions and is in a hurry, it can provide quick analysis results. This allows for the provision of analysis results of the optimal length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0085] The analysis unit can determine the priority of analysis based on the submission date of the consultation content during the analysis. For example, the analysis unit may prioritize the analysis of the most recently submitted consultation content. For example, the analysis unit may analyze the submission date of the consultation content and prioritize the analysis of the most recently submitted consultation content. The analysis unit may also postpone the analysis of older consultation content. For example, the analysis unit may analyze the submission date of the consultation content and postpone the analysis of older consultation content. The analysis unit may also adjust the order of analysis based on the submission date. For example, the analysis unit may analyze the submission date of the consultation content and adjust the order of analysis based on the submission date. This allows analysis to be performed in the optimal order based on the submission date of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit may input the consultation content submission date data into a generating AI and have the generating AI perform the priority determination.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the consultation content during the analysis. For example, the analysis unit can prioritize the analysis of consultation content that is highly relevant. For example, the analysis unit can analyze the relevance of the consultation content and prioritize the analysis of the most relevant consultation content. The analysis unit can also postpone the analysis of consultation content that is less relevant. For example, the analysis unit can analyze the relevance of the consultation content and postpone the analysis of the less relevant consultation content. The analysis unit can also adjust the order of analysis based on the relevance of the consultation content. For example, the analysis unit can analyze the relevance of the consultation content and adjust the order of analysis based on that relevance. This allows the analysis to be performed in the optimal order based on the relevance of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the relevance data of the consultation content into a generating AI and have the generating AI perform the order adjustment.

[0087] The generation unit can estimate the user's emotions and adjust the presentation of the leaflet based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed leaflet. For example, the generation unit can analyze the user's emotions and, if relaxed, generate a detailed leaflet. The generation unit can also generate a concise leaflet if the user is stressed. For example, the generation unit can analyze the user's emotions and, if stressed, generate a concise leaflet. The generation unit can also quickly generate a leaflet if the user is in a hurry. For example, the generation unit can analyze the user's emotions and, if in a hurry, quickly generate a leaflet. This allows the generation of a leaflet to be presented in the most appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, 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 generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0088] The generation unit can adjust the level of detail in a leaflet based on the importance of the consultation content when generating the leaflet. For example, the generation unit can generate a detailed leaflet for important consultation content. For example, the generation unit can analyze the importance of the consultation content and generate a detailed leaflet for important consultation content. The generation unit can also generate a standard leaflet for general consultation content. For example, the generation unit can analyze the importance of the consultation content and generate a standard leaflet for general consultation content. The generation unit can also generate a concise leaflet for simple consultation content. For example, the generation unit can analyze the importance of the consultation content and generate a concise leaflet for simple consultation content. This allows the generation of a leaflet with the optimal level of detail according to the importance of the consultation content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance data of the consultation content into a generation AI and have the generation AI perform the adjustment of the level of detail.

[0089] The generation unit can apply different generation algorithms depending on the category of the consultation content when generating leaflets. For example, the generation unit can apply a specific generation algorithm to consultations related to work. For example, the generation unit analyzes the category of the consultation content and applies a specific generation algorithm to consultations related to work. The generation unit can also apply a different generation algorithm to consultations related to living environment. For example, the generation unit analyzes the category of the consultation content and applies a different generation algorithm to consultations related to living environment. The generation unit can also apply yet another generation algorithm to consultations related to education. For example, the generation unit analyzes the category of the consultation content and applies yet another generation algorithm to consultations related to education. This allows the optimal generation algorithm to be applied according to the category of the consultation content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category data of the consultation content into a generation AI and have the generation AI execute the application of the generation algorithm.

[0090] The generation unit can estimate the user's emotions and adjust the length of the leaflet based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed leaflet. For example, the generation unit can analyze the user's emotions and, if relaxed, generate a detailed leaflet. The generation unit can also generate a concise leaflet if the user is stressed. For example, the generation unit can analyze the user's emotions and, if stressed, generate a concise leaflet. The generation unit can also quickly generate a leaflet if the user is in a hurry. For example, the generation unit can analyze the user's emotions and, if in a hurry, quickly generate a leaflet. This allows for the generation of leaflets of the optimal length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, 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 generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0091] The generation unit can determine the priority of leaflets based on the submission date of the consultation content when generating leaflets. For example, the generation unit can prioritize reflecting the most recently submitted consultation content in the leaflets. For example, the generation unit can analyze the submission date of the consultation content and prioritize reflecting the most recently submitted consultation content in the leaflets. The generation unit can also postpone older consultation content. For example, the generation unit can analyze the submission date of the consultation content and postpone older consultation content. The generation unit can also adjust the leaflet generation order based on the submission date. For example, the generation unit can analyze the submission date of the consultation content and adjust the leaflet generation order based on the submission date. This allows leaflets to be generated in the optimal order based on the submission date of the consultation content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the consultation content submission date data into a generation AI and have the generation AI perform the priority determination.

[0092] The generation unit can adjust the order of leaflets based on the relevance of the consultation content when generating them. For example, the generation unit can prioritize reflecting highly relevant consultation content in the leaflets. For example, the generation unit can analyze the relevance of the consultation content and prioritize reflecting highly relevant consultation content in the leaflets. The generation unit can also postpone less relevant consultation content. For example, the generation unit can analyze the relevance of the consultation content and postpone less relevant consultation content. The generation unit can also adjust the order in which the leaflets are generated based on the relevance of the consultation content. For example, the generation unit can analyze the relevance of the consultation content and adjust the order in which the leaflets are generated based on that relevance. This makes it possible to generate leaflets in the optimal order based on the relevance of the consultation content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the relevance data of the consultation content into a generation AI and have the generation AI perform the order adjustment.

[0093] The follow-up unit can estimate the user's emotions and adjust the content of follow-up messages based on the estimated emotions. For example, if the user is relaxed, the follow-up unit can send a detailed follow-up message. For example, the follow-up unit can analyze the user's emotions and send a detailed follow-up message if the user is relaxed. The follow-up unit can also send a concise follow-up message if the user is stressed. For example, the follow-up unit can analyze the user's emotions and send a concise follow-up message if the user is stressed. The follow-up unit can also send a quick follow-up message if the user is in a hurry. For example, the follow-up unit can analyze the user's emotions and send a quick follow-up message if the user is in a hurry. This allows for the provision of follow-up messages with optimal content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, 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 follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0094] The follow-up unit can select the most appropriate message by referring to past follow-up history when delivering follow-up messages. For example, the follow-up unit can select the most appropriate message based on the user's past follow-up history. For example, the follow-up unit can analyze the user's past follow-up history and select the most appropriate message. The follow-up unit can also prioritize sending messages that are highly relevant based on the user's past follow-up history. For example, the follow-up unit can analyze the user's past follow-up history and prioritize sending messages that are highly relevant. The follow-up unit can also analyze the user's past follow-up history and send messages at the optimal time. For example, the follow-up unit can analyze the user's past follow-up history and send messages at the optimal time. This allows the system to provide the most appropriate message based on past follow-up history. Some or all of the above processing in the follow-up unit may be performed using AI, or not. For example, the follow-up unit can input past follow-up history data into a generating AI and have the generating AI select the most appropriate message.

[0095] The follow-up unit can estimate the user's emotions and determine the priority of follow-up messages based on the estimated emotions. For example, if the user is stressed, the follow-up unit will prioritize sending follow-up messages. For example, the follow-up unit will analyze the user's emotions and, if stressed, will prioritize sending follow-up messages. The follow-up unit can also send follow-up messages with normal priority if the user is relaxed. For example, the follow-up unit will analyze the user's emotions and, if relaxed, will send follow-up messages with normal priority. The follow-up unit can also send follow-up messages quickly if the user is in a hurry. For example, the follow-up unit will analyze the user's emotions and, if in a hurry, will send follow-up messages quickly. This allows the priority of follow-up messages to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processes in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0096] The follow-up unit can select the most appropriate message when delivering follow-up messages, taking into account the user's geographical location information. For example, the follow-up unit can send relevant follow-up messages based on the user's current location. For example, the follow-up unit can analyze the user's geographical location information and send relevant follow-up messages based on the current location. The follow-up unit can also customize the most appropriate follow-up message, taking into account the user's geographical location information. For example, the follow-up unit can analyze the user's geographical location information and customize the most appropriate follow-up message. The follow-up unit can also send follow-up messages at the optimal timing based on the user's geographical location information. For example, the follow-up unit can analyze the user's geographical location information and send follow-up messages at the optimal timing. This allows the system to provide optimal follow-up messages based on the user's geographical location information. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input the user's geographical location data into a generating AI and have the generating AI select the most appropriate message.

[0097] The follow-up unit can analyze the user's social media activity and suggest message content when delivering follow-up messages. For example, the follow-up unit can send follow-up messages containing content of interest based on the user's social media activity. The follow-up unit can also analyze the user's social media activity and customize the optimal follow-up message. The follow-up unit can also send follow-up messages at the optimal time based on the user's social media activity. This allows the system to provide optimal follow-up messages based on the user's social media activity. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input the user's social media activity data into a generating AI and have the generating AI suggest message content.

[0098] The interviewing unit can estimate the user's emotions and adjust the interview method based on the estimated emotions. For example, if the user is relaxed, the interviewing unit can conduct a detailed interview. For example, if the interviewing unit analyzes the user's emotions and conducts a detailed interview if the user is relaxed. The interviewing unit can also conduct a concise interview if the user is stressed. For example, if the interviewing unit analyzes the user's emotions and conducts a concise interview if the user is stressed. The interviewing unit can also conduct a rapid interview if the user is in a hurry. For example, if the interviewing unit analyzes the user's emotions and conducts a rapid interview if the user is in a hurry. This allows the interview to be conducted in the most optimal way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the hearing unit may be performed using AI, for example, or without AI. For example, the hearing unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0099] The interviewing unit can select the most appropriate questions during an interview by referring to past interview history. For example, the interviewing unit can select the most appropriate questions based on the user's past interview history. For example, the interviewing unit can analyze the user's past interview history and select the most appropriate questions. The interviewing unit can also prioritize asking questions that are highly relevant based on the user's past interview history. For example, the interviewing unit can analyze the user's past interview history and prioritize asking questions that are highly relevant. The interviewing unit can also analyze the user's past interview history and ask questions at the optimal time. For example, the interviewing unit can analyze the user's past interview history and ask questions at the optimal time. This allows the interviewing unit to ask the most appropriate questions based on past interview history. Some or all of the above processing in the interviewing unit may be performed using AI, or not. For example, the interviewing unit can input past interview history data into a generating AI and have the generating AI select the most appropriate questions.

[0100] The interviewing unit can estimate the user's emotions and determine the priority of interviews based on the estimated emotions. For example, if the user is feeling stressed, the interviewing unit will prioritize the interview. For example, the interviewing unit will analyze the user's emotions and, if stressed, will prioritize the interview. The interviewing unit can also conduct interviews with the normal priority if the user is relaxed. For example, the interviewing unit will analyze the user's emotions and, if relaxed, will prioritize the interview. The interviewing unit can also conduct interviews quickly if the user is in a hurry. For example, the interviewing unit will analyze the user's emotions and, if in a hurry, will conduct interviews quickly. This allows the interviewing unit to determine the priority of interviews according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the hearing unit may be performed using AI, for example, or without AI. For example, the hearing unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0101] The interviewing unit can select the most appropriate questions during the interview, taking into account the user's geographical location. For example, the interviewing unit can prioritize relevant questions based on the user's current location. For example, the interviewing unit can analyze the user's geographical location and prioritize relevant questions based on the current location. The interviewing unit can also customize the most appropriate questions, taking into account the user's geographical location. For example, the interviewing unit can analyze the user's geographical location and customize the most appropriate questions. The interviewing unit can also ask questions at the optimal time based on the user's geographical location. For example, the interviewing unit can analyze the user's geographical location and ask questions at the optimal time. This allows for the asking of questions that are optimal based on the user's geographical location. Some or all of the above processing in the interviewing unit may be performed using AI, or not. For example, the interviewing unit can input the user's geographical location data into a generating AI and have the generating AI select the most appropriate questions.

[0102] The delivery unit can estimate the user's emotions and adjust the method of delivering the leaflet based on the estimated emotions. For example, if the user is relaxed, the delivery unit can provide a detailed leaflet. For example, the delivery unit can analyze the user's emotions and provide a detailed leaflet if the user is relaxed. The delivery unit can also provide a concise leaflet if the user is stressed. For example, the delivery unit can analyze the user's emotions and provide a concise leaflet if the user is stressed. The delivery unit can also provide a leaflet quickly if the user is in a hurry. For example, the delivery unit can analyze the user's emotions and provide a leaflet quickly if the user is in a hurry. This allows the leaflet to be delivered in the most optimal way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The 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 delivery unit may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0103] The delivery unit can select the optimal delivery method by referring to past delivery history when providing leaflets. For example, the delivery unit can select the optimal delivery method based on the user's past delivery history. For example, the delivery unit can analyze the user's past delivery history and select the optimal delivery method. The delivery unit can also prioritize providing leaflets that are highly relevant based on the user's past delivery history. For example, the delivery unit can analyze the user's past delivery history and prioritize providing leaflets that are highly relevant. The delivery unit can also analyze the user's past delivery history and provide leaflets at the optimal timing. For example, the delivery unit can analyze the user's past delivery history and provide leaflets at the optimal timing. This allows leaflets to be provided in the most optimal way based on past delivery history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input past delivery history data into a generating AI and have the generating AI select the optimal delivery method.

[0104] The distribution unit can estimate the user's emotions and determine the priority of leaflet distribution based on the estimated emotions. For example, if the user is feeling stressed, the distribution unit will prioritize providing leaflets. For example, the distribution unit will analyze the user's emotions and, if they are feeling stressed, will prioritize providing leaflets. The distribution unit can also provide leaflets with normal priority if the user is relaxed. For example, the distribution unit will analyze the user's emotions and, if they are relaxed, will provide leaflets with normal priority. The distribution unit can also provide leaflets quickly if the user is in a hurry. For example, the distribution unit will analyze the user's emotions and, if they are in a hurry, will provide leaflets quickly. This allows the distribution unit to determine the priority of leaflet distribution according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The 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 distribution unit may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0105] The distribution unit can select the optimal distribution method when providing leaflets, taking into account the user's geographical location information. For example, the distribution unit can prioritize providing relevant leaflets based on the user's current location. For example, the distribution unit can analyze the user's geographical location information and prioritize providing relevant leaflets based on the current location. The distribution unit can also customize the optimal leaflets, taking into account the user's geographical location information. For example, the distribution unit can analyze the user's geographical location information and customize the optimal leaflets. The distribution unit can also provide leaflets at the optimal timing based on the user's geographical location information. For example, the distribution unit can analyze the user's geographical location information and provide leaflets at the optimal timing. This allows the distribution unit to provide leaflets in the most optimal way based on the user's geographical location information. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal distribution method.

[0106] The service provider can provide the most suitable leaflet by analyzing the user's social media activity when providing the leaflet. For example, the service provider can provide a leaflet containing content of interest based on the user's social media activity. For example, the service provider can analyze the user's social media activity and provide a leaflet containing content of interest. The service provider can also analyze the user's social media activity and customize the most suitable leaflet. For example, the service provider can analyze the user's social media activity and customize the most suitable leaflet. The service provider can also provide the leaflet at the optimal time based on the user's social media activity. For example, the service provider can analyze the user's social media activity and provide the leaflet at the optimal time. This allows the service provider to provide the most suitable leaflet based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing the most suitable leaflet.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The reception desk can suggest the most suitable consultation method based on the user's past consultation history. For example, if the user has previously consulted via text, a similar method will be suggested. Furthermore, if the user prefers voice consultation, voice consultation can be prioritized. Additionally, if the user has previously consulted using images or videos, a similar method can be suggested. This allows the reception desk to suggest the most suitable consultation method based on the user's past consultation history. 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 past consultation history data into a generating AI and have the generating AI suggest the most suitable consultation method.

[0109] The interviewing unit can estimate the user's emotions and adjust the interview method based on the estimated emotions. For example, if the user is relaxed, a detailed interview can be conducted. If the user is stressed, a concise interview can be conducted. Furthermore, if the user is in a hurry, a rapid interview can be conducted. This allows the interview to be conducted in the most optimal way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0110] The distribution unit can adjust the method of delivering leaflets considering the user's geographical location. For example, if the user lives in an urban area, digital delivery may be prioritized. If the user lives in a rural area, postal delivery may be prioritized. Furthermore, if the user is on the move, in-app display may be prioritized. This allows the leaflets to be delivered in the most optimal way based on the user's geographical location. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal delivery method.

[0111] The generation unit can estimate the user's emotions and adjust the presentation of the leaflet based on the estimated emotions. For example, if the user is relaxed, it can generate a detailed leaflet. If the user is stressed, it can generate a concise leaflet. Furthermore, if the user is in a hurry, it can generate a leaflet quickly. This allows for the generation of leaflets with the most appropriate presentation depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI 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 generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0112] The follow-up unit can select the most appropriate message based on the user's past follow-up history. For example, it can analyze the content of messages the user has received in the past and prioritize sending highly relevant messages. It can also send messages in the most appropriate format based on the message format the user has preferred in the past. Furthermore, it can send messages at the optimal timing based on the user's past follow-up history. This allows the system to provide the most appropriate message based on past follow-up history. Some or all of the above processes in the follow-up unit may be performed using AI, for example, or not. For example, the follow-up unit can input past follow-up history data into a generating AI and have the generating AI select the most appropriate message.

[0113] The reception desk can estimate the user's emotions and adjust the timing of consultation based on the estimated emotions. For example, if the user is feeling stressed, consultation can be scheduled for a time when they can relax. If the user is relaxed, consultation can be scheduled immediately. Furthermore, if the user is in a hurry, consultation can be scheduled quickly. This allows for consultation to be scheduled at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0114] The analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content. For example, a detailed analysis is performed for important consultation content. A standard analysis can be performed for general consultation content. Furthermore, a concise analysis can be performed for simple consultation content. This allows the analysis results to be provided with the optimal level of detail according to the importance of the consultation content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the consultation content into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0115] The generation unit can apply different generation algorithms depending on the category of the consultation content when generating leaflets. For example, a specific generation algorithm can be applied to consultations related to work. Another generation algorithm can be applied to consultations related to living environment. Furthermore, yet another generation algorithm can be applied to consultations related to education. This allows the optimal generation algorithm to be applied according to the category of the consultation content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category data of the consultation content into a generation AI and have the generation AI execute the application of the generation algorithm.

[0116] The follow-up unit can estimate the user's emotions and adjust the content of follow-up messages based on the estimated emotions. For example, if the user is relaxed, a detailed follow-up message can be sent. If the user is stressed, a concise follow-up message can be sent. Furthermore, if the user is in a hurry, a follow-up message can be sent quickly. This allows for the provision of follow-up messages with optimal content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 follow-up unit may be performed using AI, or not using AI. For example, the follow-up unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0117] The reception department can prioritize receiving inquiries that are highly relevant to the user, taking into account the user's geographical location. For example, it can prioritize receiving inquiries from relevant local governments based on the user's current location. It can also select the most suitable consultant, taking into account the user's geographical location. Furthermore, it can suggest the most suitable consultation content based on the user's geographical location. This allows for the suggestion of the most suitable consultation content based on the user's geographical location. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the user's geographical location data into a generating AI and have the generating AI select highly relevant inquiries.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The reception desk receives inquiries from prospective migrants. These inquiries may include, but are not limited to, hopes and concerns regarding relocation. The reception desk receives inquiries entered by prospective migrants, for example. The reception desk can also receive inquiries by voice. Furthermore, the reception desk can receive inquiries by images and videos. For example, the reception desk receives text data entered by prospective migrants. Voice data is converted into text data using speech recognition technology. Image and video data is analyzed using image recognition technology. Step 2: The analysis unit analyzes the consultation content received by the reception unit. The analysis is performed using methods such as text analysis, sentiment analysis, and keyword extraction, but is not limited to these examples. For example, the analysis unit may use text analysis technology to analyze the consultation content. The analysis unit may also use sentiment analysis technology to analyze the feelings of the person seeking advice. The analysis unit may also use keyword extraction technology to extract important keywords from the consultation content. For example, text analysis technology uses natural language processing technology to analyze the consultation content. Sentiment analysis technology estimates feelings from the person seeking advice's text data. Keyword extraction technology extracts particularly important keywords from the consultation content. Step 3: The generation unit generates a leaflet based on the content analyzed by the analysis unit. Leaflet generation is performed based on, for example, the use of a template or the degree of customization, but is not limited to such examples. For example, the generation unit generates a leaflet using a template. The generation unit can also customize the content of the leaflet according to the consultation content. The generation unit can also adjust the design of the leaflet. For example, a template is a pre-prepared leaflet template into which content is filled in according to the consultation content. Customization involves changing the content of the leaflet according to the consultation content. Design adjustment involves adjusting the layout and color scheme of the leaflet. Step 4: The follow-up department delivers follow-up messages. Follow-up messages may be delivered by methods such as email, SMS, or notifications, but are not limited to these examples. For example, the follow-up department may deliver follow-up messages via email. The follow-up department may also deliver follow-up messages via SMS. The follow-up department may also deliver follow-up messages using the app's notification function. For example, email sends follow-up messages to the prospective migrant's email address. SMS sends follow-up messages to the prospective migrant's mobile phone number. The notification function delivers follow-up messages through the migrant support app.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, follow-up unit, hearing unit, and provision unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives inquiries from prospective migrants. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received inquiries. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a leaflet based on the analysis results. The follow-up unit is implemented by the control unit 46A of the smart device 14 and delivers follow-up messages. The hearing unit is implemented by the control unit 46A of the smart device 14 and hears the concerns of partners. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated leaflet. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, follow-up unit, hearing unit, and provision unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives inquiries from prospective migrants. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received inquiries. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a leaflet based on the analysis results. The follow-up unit is implemented by the control unit 46A of the smart glasses 214 and delivers follow-up messages. The hearing unit is implemented by the control unit 46A of the smart glasses 214 and hears the concerns of partners. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated leaflet. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, follow-up unit, hearing unit, and provision unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives inquiries from prospective migrants. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received inquiries. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a leaflet based on the analysis results. The follow-up unit is implemented by the control unit 46A of the headset terminal 314 and delivers follow-up messages. The hearing unit is implemented by the control unit 46A of the headset terminal 314 and hears the concerns of partners. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated leaflet. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, follow-up unit, hearing unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives inquiries from prospective migrants. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received inquiries. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a leaflet based on the analysis results. The follow-up unit is implemented by the control unit 46A of the robot 414 and delivers follow-up messages. The hearing unit is implemented by the control unit 46A of the robot 414 and hears the concerns of partners. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated leaflet. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] (Note 1) A reception desk that accepts consultations from people considering relocating, An analysis unit analyzes the content of consultations received by the reception unit, A generation unit that generates a leaflet based on the content analyzed by the analysis unit, It includes a follow-up unit that delivers follow-up messages. A system characterized by the following features. (Note 2) It includes a hearing department to gather information on partners' concerns. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a supply unit that provides the generated leaflets. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a generation unit that generates the content of follow-up messages. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of consultations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is We analyze past consultation history and select the most suitable method of receiving inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is During registration, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of inquiries to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When a user submits a request, the system prioritizes accepting inquiries that are highly relevant to their situation, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Upon receiving a request, the system analyzes the user's social media activity and accepts related inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the consultation content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is We estimate the user's emotions and adjust the wording of the leaflet based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating the leaflet, the level of detail in the leaflet is adjusted based on the importance of the consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating leaflets, different generation algorithms are applied depending on the category of the consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is The system estimates the user's emotions and adjusts the length of the leaflet based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating leaflets, the priority of the leaflets is determined based on when the consultation content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating leaflets, the order of the leaflets is adjusted based on the relevance of the consultation topics. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned follow-up unit is, It estimates the user's emotions and adjusts the content of follow-up messages based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned follow-up unit is, When sending follow-up messages, the system selects the most appropriate message by referring to past follow-up history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned follow-up unit is, It estimates the user's emotions and prioritizes follow-up messages based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned follow-up unit is, When sending follow-up messages, the system selects the most appropriate message by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned follow-up unit is, When sending follow-up messages, we analyze the user's social media activity to suggest message content. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned hearing section is, We estimate the user's emotions and adjust the interview method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned hearing section is, During the interview, we will select the most appropriate questions by referring to past interview history. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned hearing section is, The system estimates the user's emotions and determines the priority of interviews based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned hearing section is, During the interview, select the most appropriate questions while considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned supply unit is, We estimate the user's emotions and adjust the way the leaflet is delivered based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing leaflets, the optimal method of delivery is selected by referring to past distribution history. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned supply unit is, The system estimates the user's emotions and determines the priority of leaflet distribution based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing leaflets, the optimal delivery method will be selected considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing leaflets, we analyze users' social media activity to deliver the most suitable leaflets. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0192] 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 accepts consultations from people considering relocating, An analysis unit analyzes the content of consultations received by the reception unit, A generation unit that generates a leaflet based on the content analyzed by the analysis unit, It includes a follow-up unit that delivers follow-up messages. A system characterized by the following features.

2. It includes a hearing department to gather information on partners' concerns. The system according to feature 1.

3. It includes a supply unit that provides the generated leaflets. The system according to feature 1.

4. It includes a generation unit that generates the content of follow-up messages. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of consultations based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is We analyze past consultation history and select the most suitable method of receiving inquiries. The system according to feature 1.

7. The aforementioned reception unit is During registration, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of inquiries to be accepted based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When a user submits a request, the system prioritizes accepting inquiries that are highly relevant to their situation, taking into account their geographical location. The system according to feature 1.

10. The aforementioned reception unit is Upon receiving a request, the system analyzes the user's social media activity and accepts related inquiries. The system according to feature 1.

Images