system

Abstract

The system of the embodiment aims to collect and analyze information on regional promotion coupons and make proposals to non-smartphone users through collaboration. [Solution] A system according to an embodiment includes an information collection unit, an analysis unit, a collaboration unit, an explanation unit, and a proposal unit. The information collection unit collects information. The analysis unit analyzes the information collected by the information collection unit. The collaboration unit collaborates based on the information analyzed by the analysis unit. The explanation unit explains how to use and set up a mobile phone based on the collaboration performed by the collaboration unit. The proposal unit makes suggestions to non-mobile phone users based on the content explained by the explanation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of collecting information and collaborating on regional promotion coupons was complicated, and there were issues with not making sufficient proposals to non-smartphone users.

[0005] The system of the embodiment aims to collect and analyze information on regional promotion coupons and make proposals to non-smartphone users through collaboration. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, a collaboration unit, an explanation unit, and a proposal unit. The information collection unit collects information. The analysis unit analyzes the information collected by the information collection unit. The collaboration unit collaborates based on the information analyzed by the analysis unit. The explanation unit explains how to use and set up the mobile phone based on the collaboration performed by the collaboration unit. The proposal unit makes proposals to non-mobile phone users based on the content explained by the explanation unit. [Effects of the Invention]

[0007] The system according to the embodiment collects and analyzes information on regional promotion coupons and can make proposals to non-smartphone users through collaboration. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A regional revitalization system according to an embodiment of the present invention utilizes a generation AI to collect the start dates and details of electronic regional revitalization vouchers offered by local governments and participate in regional revitalization projects. The generation AI collects the start dates and details of electronic regional revitalization vouchers offered by local governments, participating stores, and telecommunications carrier shops based on the collected information to expand benefits to each company and user. The regional revitalization system also provides assistance with setting up, using, and issuing electronic regional revitalization vouchers at telecommunications carrier shops, including how to use them on smartphones. Furthermore, the system offers smartphone upgrades and suggestions to non-smartphone users who wish to save money. For example, the generation AI collects and analyzes information from each local government's official website and related news articles. For example, the generation AI collects information such as, "Electronic regional revitalization vouchers for City X will be available from X / X, and can be used at X and X stores." The regional revitalization system then collaborates with each local government, participating stores, and telecommunications carrier shops based on the collected information. Specifically, information about electronic regional promotion coupons provided by each local government is provided to participating stores, which then use the information to promote the coupons. Telecommunications carrier shops also explain to users how to use and set up the coupons on their smartphones and assist with issuance. This expands the benefits for both companies and users. Furthermore, the regional promotion system assists with setting up, using, and issuing electronic regional promotion coupons at telecommunications carrier shops, including how to use the coupons on smartphones. For example, staff at telecommunications carrier shops explain to users how to set up and use the electronic regional promotion coupons and support them with the necessary procedures. This allows non-smartphone users to use the electronic regional promotion coupons. The regional promotion system also makes suggestions to non-smartphone users about switching to smartphones and to those who wish to save money. For example, it suggests purchasing a smartphone to users who do not own a smartphone and explains the benefits of using one. Furthermore, it suggests discounts and benefits that can be obtained by using the electronic regional promotion coupons to those who wish to save money. This allows non-smartphone users to use the electronic regional promotion coupons, contributing to regional promotion.This allows the regional revitalization system to use generation AI to collect, analyze, and collaborate on information about each local government's electronic regional revitalization coupons, explain how to use and set them up on smartphones, and make suggestions to non-smartphone users. For example, the regional revitalization system can quickly and accurately collect and analyze information about each local government's electronic regional revitalization coupons, thereby expanding the benefits to each company and user. In addition, by allowing non-smartphone users to use the electronic regional revitalization coupons, it can contribute to regional revitalization.

[0029] A regional revitalization system according to an embodiment includes an information collection unit, an analysis unit, a collaboration unit, an explanation unit, and a proposal unit. The information collection unit uses a generation AI to collect information on the start date and details of each local government's electronic regional revitalization voucher. For example, the information collection unit collects information from each local government's official website and related news articles. The information collection unit can also collect information from unofficial sources such as social media and blogs. The information collection unit can also improve the accuracy of the collected information by referencing past regional revitalization voucher data. The analysis unit uses a generation AI to analyze the collected information and identify the start date and details of the electronic regional revitalization voucher. For example, the analysis unit analyzes the collected information and identifies details of the electronic regional revitalization voucher. The analysis unit can also compare the collected information with past data to identify trends. The analysis unit can also predict the effectiveness of the regional revitalization voucher, taking into account the economic situation and policies of each local government. The collaboration unit collaborates with each local government, participating stores, and telecommunications carrier shops. For example, the Collaboration Department provides participating stores with information about electronic regional promotion coupons provided by local governments, and participating stores use that information to promote their products. The Collaboration Department also works with telecommunications carrier shops to explain how to use and set up the coupons on smartphones and assist with issuance. The Collaboration Department can also collaborate with chambers of commerce and tourism associations. The Explanation Department explains how to use and set up the coupons on smartphones to users. For example, the Explanation Department explains how to use and set up the coupons on smartphones to users and supports them in completing the necessary procedures. The Explanation Department can also customize the explanation content taking into account the user's age group and technical literacy. Furthermore, the Explanation Department can also improve the explanation method based on user feedback. The Proposal Department makes suggestions to non-smartphone users about switching to smartphones and to those who wish to save money. For example, the Proposal Department suggests purchasing a smartphone to users who do not own a smartphone and explains the benefits of using one. The Proposal Department can also suggest discounts and benefits that can be obtained by using the electronic regional promotion coupons to those who wish to save money. Furthermore, the suggestion unit can also customize the suggestions taking into account the user's living situation and areas of interest.As a result, the regional development system according to the embodiment can automate and efficiently carry out a series of processes from information collection to analysis, collaboration, explanation, and proposal.

[0030] The information collection unit can collect information from the official websites and news articles of each local government. For example, the information collection unit collects information about electronic regional promotion coupons from the official websites of each local government. For example, the information collection unit accesses the official websites of the local governments to collect information about the start date of the electronic regional promotion coupons and the stores where they can be used. The information collection unit can also collect information from related news articles. For example, the information collection unit collects the latest information about the electronic regional promotion coupons from online news sites and newspaper articles. This allows for the collection of information from official sources to obtain reliable data. Some or all of the above-described processing in the information collection unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the information collection unit can automatically collect information from the official websites and news articles of each local government using a generation AI.

[0031] The analysis unit can analyze the collected information and identify the start date and content of the electronic regional promotion voucher. The analysis unit can, for example, analyze the collected information and identify the start date and content of the electronic regional promotion voucher. For example, the analysis unit can analyze the collected information and identify the start date of the electronic regional promotion voucher and information on stores where the electronic regional promotion voucher can be used. The analysis unit can also identify details of the electronic regional promotion voucher based on the collected information. For example, the analysis unit can analyze the collected information and identify the terms of use and benefit content of the electronic regional promotion voucher. In this way, by analyzing the collected information, details of the electronic regional promotion voucher can be identified. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can automatically analyze the collected information using a generation AI and identify the start date and content of the electronic regional promotion voucher.

[0032] The collaboration unit can collaborate with local governments, participating stores, and telecommunications carrier shops. For example, the collaboration unit provides participating stores with information about electronic regional promotion coupons provided by local governments, and the participating stores use that information to conduct promotional campaigns. For example, the collaboration unit provides participating stores with information about electronic regional promotion coupons provided by local governments, and the participating stores use that information to conduct discount campaigns. The collaboration unit can also collaborate with telecommunications carrier shops to explain how to use and set up the coupons on smartphones and assist with issuance. For example, the collaboration unit can collaborate with telecommunications carrier shops to explain how to use and set up the coupons on smartphones and support the issuance process for electronic regional promotion coupons. This can enhance the effectiveness of regional promotion through collaboration between local governments, participating stores, and telecommunications carrier shops. Some or all of the above-described processing in the collaboration unit can be performed using or without the generation AI. For example, the collaboration unit can use the generation AI to automatically coordinate collaboration with local governments, participating stores, and telecommunications carrier shops.

[0033] The explanation unit can explain to the user how to use and set up the smartphone. For example, the explanation unit can explain to the user how to use and set up the smartphone and support the necessary procedures. For example, the explanation unit can explain to the user how to set up the electronic regional promotion coupon on the smartphone and support the setting procedure. The explanation unit can also explain to the user how to use the electronic regional promotion coupon on the smartphone and support the usage procedure. This makes it easier for the user to understand how to use and set up the smartphone. Some or all of the above-mentioned processing in the explanation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the explanation unit can use a generation AI to automatically explain how to use and set up the smartphone.

[0034] The suggestion unit can make suggestions to non-smartphone users about switching to smartphones and suggestions to users who wish to save money. For example, the suggestion unit can suggest to users who do not own a smartphone that they purchase a smartphone and explain the benefits of using a smartphone. For example, the suggestion unit can suggest to users who do not own a smartphone that they purchase a smartphone and explain the discounts and benefits that can be obtained by using a smartphone. The suggestion unit can also suggest discounts and benefits that can be obtained by using electronic regional promotion coupons to users who wish to save money. For example, the suggestion unit can suggest discounts and benefits that can be obtained by using electronic regional promotion coupons to users who wish to save money, and encourage their use. This can promote the use of electronic regional promotion coupons even among non-smartphone users. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can automatically make smartphone suggestions to non-smartphone users and suggestions to users who wish to save money using a generation AI.

[0035] The information collection unit can collect information from unofficial sources such as social media and blogs, in addition to the official websites and related news articles of each local government. For example, the generation AI collects posts about each local government's electronic regional promotion coupons from social media such as Twitter (registered trademark) and Facebook (registered trademark). For example, the generation AI collects posts about each local government's electronic regional promotion coupons from social media such as Twitter and Facebook. The information collection unit can also collect user opinions and reviews about the electronic regional promotion coupons from local blogs and forums. For example, the generation AI collects user opinions and reviews about the electronic regional promotion coupons from local blogs and forums. The information collection unit can also collect and analyze videos about the electronic regional promotion coupons from video platforms such as YouTube (registered trademark). For example, the generation AI collects and analyzes videos about the electronic regional promotion coupons from video platforms such as YouTube. By collecting information from unofficial sources, more diverse information can be obtained. Some or all of the above-described processing in the information collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the information collection unit may use the generation AI to automatically collect information from unofficial information sources such as social media and blogs.

[0036] When collecting information, the information collection unit can improve the accuracy of the collected information by referring to data on past regional promotion coupons issued by each local government. For example, the information collection unit can improve accuracy by having the generation AI refer to past regional promotion coupon issuance data and compare it with current information. For example, the information collection unit can improve accuracy by having the generation AI refer to past regional promotion coupon issuance data and compare it with current information. The information collection unit can also analyze past regional promotion coupon usage data and reflect it in current information collection. For example, the information collection unit can analyze past regional promotion coupon usage data and reflect it in current information collection. The information collection unit can also evaluate the effectiveness of past regional promotion coupons and use it in current information collection. For example, the information collection unit can evaluate the effectiveness of past regional promotion coupons and use it in current information collection. By doing so, the accuracy of the collected information can be improved by referring to past data. Some or all of the above-described processing in the information collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the information collection department can use the generation AI to automatically refer to data on past regional promotion coupons to improve the accuracy of the information it collects.

[0037] When collecting information, the information collection unit can filter information by taking into account the geographical characteristics and demographics of each municipality. For example, the generation AI of the information collection unit takes into account the geographical characteristics of each municipality and collects information according to characteristics such as mountainous areas and urban areas. For example, the generation AI of the information collection unit takes into account the geographical characteristics of each municipality and collects information according to characteristics such as mountainous areas and urban areas. The information collection unit can also take into account the demographics of each municipality and collect information according to areas with a large elderly population and areas with a large young population. For example, the generation AI of the information collection unit takes into account the demographics of each municipality and collects information according to areas with a large elderly population and areas with a large young population. The information collection unit can also take into account the economic situation of each municipality and collect information according to economically struggling areas and prosperous areas. For example, the generation AI of the information collection unit takes into account the economic situation of each municipality and collects information according to economically struggling areas and prosperous areas. This allows for more appropriate information to be collected by taking into account geographical characteristics and demographics. Some or all of the above-described processing in the information collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the information collection department can use generative AI to automatically take into account the geographical characteristics and demographics of each municipality and filter the information.

[0038] When collecting information, the information collection unit can collect information from local chambers of commerce and tourism associations in addition to the official websites and related news articles of each local government. For example, the generation AI collects information about regional promotion coupons from the websites of the chambers of commerce of each local government. For example, the generation AI collects information about regional promotion coupons from the websites of the chambers of commerce of each local government. The information collection unit can also collect information about regional promotion coupons from the websites of the tourism associations of each local government. For example, the information collection unit can collect information about regional promotion coupons from the websites of the tourism associations of each local government. The information collection unit can also collect information about regional promotion coupons from newsletters and reports of the chambers of commerce and tourism associations of each local government. For example, the information collection unit can collect information about regional promotion coupons from newsletters and reports of the chambers of commerce and tourism associations of each local government. This allows the generation AI to obtain more diverse information by also collecting information from the chambers of commerce and tourism associations of each local government. Some or all of the above-described processing in the information collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the information collection unit may use the generation AI to automatically collect information from the chamber of commerce and tourism association of each local government.

[0039] When collecting information, the information collection unit can refer to the event calendar of each local government to identify the period during which the regional promotion coupons can be used. For example, the generation AI of the information collection unit can identify the period during which the regional promotion coupons can be used from the event calendar of the official website of each local government. For example, the information collection unit can identify the period during which the regional promotion coupons can be used from the event calendar of the official website of each local government. The information collection unit can also collect local event information and identify the period during which the regional promotion coupons can be used. For example, the information collection unit can collect local event information and identify the period during which the regional promotion coupons can be used. The information collection unit can also collect local event information and identify the period during which the regional promotion coupons can be used from the event calendar of the tourism association of each local government. For example, the information collection unit can collect local event information and identify the period during which the regional promotion coupons can be used from the event calendar of the tourism association of each local government. This makes it possible to accurately identify the period during which the regional promotion coupons can be used by referring to the event calendar. Some or all of the above-mentioned processing in the information collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the information collection department can use generation AI to automatically refer to each local government's event calendar and identify the period during which regional promotion coupons can be used.

[0040] The information collection unit can predict the scale of issuance of regional promotion coupons by referring to the budget information of each local government when collecting information. For example, the generation AI in the information collection unit predicts the scale of issuance of regional promotion coupons from the budget report of each local government. For example, the information collection unit predicts the scale of issuance of regional promotion coupons from the budget report of each local government. The information collection unit can also predict the scale of issuance of regional promotion coupons by analyzing the financial situation of each local government. For example, the information collection unit can predict the scale of issuance of regional promotion coupons by analyzing the financial situation of each local government. The information collection unit can also predict the scale of issuance of current regional promotion coupons by referring to past budget data. For example, the information collection unit can predict the scale of issuance of current regional promotion coupons by referring to past budget data. In this way, the scale of issuance of regional promotion coupons can be accurately predicted by referring to budget information. Some or all of the above-mentioned processing in the information collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the information collection department can use generation AI to automatically refer to each local government's budget information and predict the scale of regional promotion coupons to be issued.

[0041] When analyzing the collected information, the analysis unit can compare it with past data to identify trends. For example, the generation AI compares past regional promotion coupon data with current data to identify trends. For example, the analysis unit compares past regional promotion coupon data with current data to identify trends. The analysis unit can also compare past usage data with current usage data to identify user trends. For example, the analysis unit compares past usage data with current usage data to identify user trends. The analysis unit can also compare past promotion data with current promotion data to identify effective promotion methods. For example, the analysis unit compares past promotion data with current promotion data to identify effective promotion methods. This allows current trends to be identified by comparing with past data. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to automatically compare past data with current data to identify trends.

[0042] During analysis, the analysis unit can predict the effects of the regional promotion coupons by taking into account the economic situation and policies of each local government. For example, the generation AI analyzes the economic situation of each local government and predicts the effects of the regional promotion coupons. For example, the analysis unit analyzes the economic situation of each local government and predicts the effects of the regional promotion coupons. The analysis unit can also predict the effects of the regional promotion coupons by taking into account the policies of each local government. For example, the analysis unit can predict the effects of the regional promotion coupons by taking into account the policies of each local government. The analysis unit can also predict the effects of the regional promotion coupons by having the generation AI refer to past economic data and policy data. For example, the analysis unit can predict the effects of the regional promotion coupons by having the generation AI refer to past economic data and policy data. This allows the effects of the regional promotion coupons to be accurately predicted by taking into account the economic situation and policies. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to automatically consider the economic situation and policies of each local government and predict the effects of the regional promotion coupons.

[0043] During analysis, the analysis unit can evaluate the reliability of the collected information and prioritize analyzing highly reliable information. For example, the analysis unit has the generation AI evaluate the reliability of the information source and prioritize analyzing highly reliable information. For example, the analysis unit has the generation AI evaluate the reliability of the information source and prioritize analyzing highly reliable information. The analysis unit can also have the generation AI compare the information with past data to identify highly reliable information. For example, the analysis unit has the generation AI compare the information with past data to identify highly reliable information. The analysis unit can also have the generation AI confirm the source of the information and prioritize analyzing highly reliable information. For example, the analysis unit has the generation AI confirm the source of the information and prioritize analyzing highly reliable information. This prioritizes analyzing highly reliable information, thereby improving the accuracy of the analysis results. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to automatically evaluate the reliability of the collected information and prioritize analyzing highly reliable information.

[0044] During analysis, the analysis unit can visualize the collected information in cooperation with a geographic information system (GIS). For example, the analysis unit can link the information collected by the generation AI with a GIS and visualize it on a map. For example, the analysis unit can link the information collected by the generation AI with a GIS and visualize it on a map. The analysis unit can also link the information of each local government with a GIS and visualize information for each region. For example, the analysis unit can link the information of each local government with a GIS and visualize information for each region. The analysis unit can also link the information of stores used by the generation AI with a GIS and visualize information for each store. For example, the analysis unit can link the information of stores used by the generation AI with a GIS and visualize information for each store. In this way, by linking with a geographic information system, information can be displayed in a visually easy-to-understand manner. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to automatically link the collected information with a GIS and visualize it.

[0045] During analysis, the analysis unit can refer to the demographic data of each local government to identify the target demographic for the regional promotion coupons. For example, the generation AI can refer to the demographic data of each local government to identify the target demographic for the regional promotion coupons. For example, the analysis unit can refer to the demographic data of each local government to identify the target demographic for the regional promotion coupons. The analysis unit can also have the generation AI identify target demographics based on whether the area has a high elderly population or a high young population. For example, the analysis unit can have the generation AI identify target demographics based on whether the area has a high elderly population or a high young population. The analysis unit can also have the generation AI analyze the demographic data of each local government to identify the optimal target demographic. For example, the analysis unit can have the generation AI analyze the demographic data of each local government to identify the optimal target demographic. This allows the optimal target demographic for the regional promotion coupons to be identified by referring to the demographic data. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to automatically refer to the demographic data of each local government to identify the target demographic for the regional promotion coupons.

[0046] During analysis, the analysis unit can refer to the tourism data of each local government and propose measures to promote the use of regional promotion coupons. For example, the generation AI of the analysis unit can refer to the tourism data of each local government and propose measures to promote the use of regional promotion coupons. For example, the analysis unit can refer to the tourism data of each local government and propose measures to promote the use of regional promotion coupons. The analysis unit can also analyze tourist spot data and propose measures to promote the use of regional promotion coupons. For example, the analysis unit can analyze tourist spot data and propose measures to promote the use of regional promotion coupons. The analysis unit can also analyze tourist trends and propose measures to promote the use of regional promotion coupons. For example, the analysis unit can analyze tourist trends and propose measures to promote the use of regional promotion coupons. In this way, by referring to the tourism data, measures to promote the use of regional promotion coupons can be effectively proposed. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to automatically refer to the tourism data of each local government and propose measures to promote the use of regional promotion coupons.

[0047] When collaborating with each local government, participating store, and telecommunications carrier shop, the collaboration department can select the optimal collaboration method by referring to past collaboration cases. For example, the collaboration department has the generation AI refer to past collaboration cases and select the optimal collaboration method. For example, the collaboration department has the generation AI refer to past collaboration cases and select the optimal collaboration method. The collaboration department can also analyze past successful cases and select the optimal collaboration method. For example, the collaboration department has the generation AI analyze past successful cases and select the optimal collaboration method. The collaboration department can also analyze past unsuccessful cases and select the optimal collaboration method. For example, the collaboration department has the generation AI analyze past unsuccessful cases and select the optimal collaboration method. In this way, the optimal collaboration method can be selected by referring to past collaboration cases. Some or all of the above-mentioned processing in the collaboration department may be performed using the generation AI, or may be performed without using the generation AI. For example, the collaboration department can automatically refer to past collaboration cases and select the optimal collaboration method using the generation AI.

[0048] During collaboration, the collaboration department can customize the collaboration content taking into account the regional characteristics and commercial environment of each local government. For example, the collaboration department customizes the collaboration content by taking into account the regional characteristics of each local government using a generation AI. For example, the collaboration department customizes the collaboration content by taking into account the regional characteristics of each local government using a generation AI. The collaboration department can also optimize the collaboration content by analyzing the commercial environment of each local government using a generation AI. For example, the collaboration department optimizes the collaboration content by analyzing the commercial environment of each local government using a generation AI. The collaboration department can also customize the collaboration content by proposing promotion methods that suit the regional characteristics. For example, the collaboration department customizes the collaboration content by proposing promotion methods that suit the regional characteristics. This allows the collaboration content to be optimized by taking into account the regional characteristics and commercial environment. Some or all of the above-mentioned processing in the collaboration department may be performed using a generation AI, or may be performed without using a generation AI. For example, the collaboration department can customize the collaboration content by automatically taking into account the regional characteristics and commercial environment of each local government using a generation AI.

[0049] At the time of collaboration, the collaboration department can clarify the purpose of the collaboration by taking into account the policies and goals of each local government. For example, the collaboration department has the generation AI refer to the policies of each local government to clarify the purpose of the collaboration. For example, the collaboration department has the generation AI refer to the policies of each local government to clarify the purpose of the collaboration. The collaboration department can also set the purpose of the collaboration by taking into account the goals of each local government. For example, the collaboration department has the generation AI refer to the policies of each local government to clarify the purpose of the collaboration. The collaboration department can also clarify the purpose of the collaboration by analyzing past policy data. For example, the collaboration department has the generation AI analyze past policy data to clarify the purpose of the collaboration. In this way, the purpose of the collaboration can be clearly set by taking into account the policies and goals. Some or all of the above-mentioned processing in the collaboration department may be performed using the generation AI, or may be performed without using the generation AI. For example, the collaboration department can use the generation AI to automatically consider the policies and goals of each local government to clarify the purpose of the collaboration.

[0050] During collaboration, the collaboration department can also collaborate with the chambers of commerce and tourism associations of each local government. In the collaboration department, for example, the generation AI collaborates with the chambers of commerce and tourism associations of each local government to coordinate the collaboration content. For example, in the collaboration department, the generation AI collaborates with the chambers of commerce and tourism associations of each local government to coordinate the collaboration content. In addition, in the collaboration department, the generation AI can collaborate with the tourism associations of each local government to optimize the collaboration content. For example, in the collaboration department, the generation AI collaborates with the tourism associations of each local government to optimize the collaboration content. In addition, in the collaboration department, the generation AI can customize the collaboration content based on information from the chambers of commerce and tourism associations. For example, in the collaboration department, the generation AI customizes the collaboration content based on information from the chambers of commerce and tourism associations. This makes it possible to enhance the effectiveness of collaboration by collaborating with chambers of commerce and tourism associations. Some or all of the above-mentioned processing in the collaboration department may be performed using the generation AI, or may be performed without using the generation AI. For example, the Collaboration Department can use generative AI to automatically connect with local government chambers of commerce and tourism associations and collaborate with them.

[0051] During collaboration, the collaboration department can monitor the usage status of each local government's regional promotion coupons in real time and adjust the collaboration content. For example, the collaboration department can have the generation AI monitor the usage status of the regional promotion coupons in real time and adjust the collaboration content. For example, the collaboration department can have the generation AI monitor the usage status of the regional promotion coupons in real time and adjust the collaboration content. The collaboration department can also have the generation AI analyze the usage status data and optimize the collaboration content. For example, the collaboration department can have the generation AI analyze the usage status data and optimize the collaboration content. The collaboration department can also quickly adjust the collaboration content in response to changes in the usage status. For example, the collaboration department can quickly adjust the collaboration content in response to changes in the usage status. In this way, the collaboration content can be quickly adjusted by monitoring the usage status in real time. Some or all of the above-mentioned processing in the collaboration department may be performed using the generation AI, or may be performed without using the generation AI. For example, the collaboration department can use the generation AI to automatically monitor the usage status of each local government's regional promotion coupons and adjust the collaboration content.

[0052] During collaboration, the collaboration department can jointly implement a campaign to promote the use of regional promotion coupons for each local government. For example, the collaboration department has the generation AI collaborate with each local government to plan a campaign to promote the use of regional promotion coupons. For example, the collaboration department has the generation AI collaborate with each local government to plan a campaign to promote the use of regional promotion coupons. The collaboration department can also have the generation AI collaborate with participating stores to implement a campaign to promote the use of regional promotion coupons. For example, the collaboration department has the generation AI collaborate with participating stores to implement a campaign to promote the use of regional promotion coupons. The collaboration department can also have the generation AI collaborate with a telecommunications carrier's shop to jointly implement a campaign to promote the use of regional promotion coupons. For example, the collaboration department has the generation AI collaborate with a telecommunications carrier's shop to jointly implement a campaign to promote the use of regional promotion coupons. In this way, by jointly implementing a promotion campaign, it is possible to promote the use of regional promotion coupons. Some or all of the above-mentioned processing in the collaboration department may be performed using the generation AI, or may be performed without using the generation AI. For example, the Collaboration Department can use the generation AI to automatically connect with local governments, participating stores, and telecommunications carrier shops to jointly implement campaigns to promote the use of regional promotion coupons.

[0053] When explaining how to use or set up a smartphone, the explanation unit can select the optimal explanation method by referring to past explanation history. For example, the explanation unit has the generation AI refer to past explanation history and select the optimal explanation method. For example, the explanation unit has the generation AI refer to past explanation history and select the optimal explanation method. The explanation unit can also have the generation AI analyze past success cases and select the optimal explanation method. For example, the explanation unit has the generation AI analyze past success cases and select the optimal explanation method. The explanation unit can also have the generation AI analyze past failure cases and select the optimal explanation method. For example, the explanation unit has the generation AI analyze past failure cases and select the optimal explanation method. In this way, the optimal explanation method can be selected by referring to the past explanation history. Some or all of the above-mentioned processing in the explanation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the explanation unit can use the generation AI to automatically refer to past explanation history and select the optimal explanation method.

[0054] The explanation unit can customize the explanation content by taking into account the user's age group and technical literacy when providing the explanation. For example, the generation AI can provide appropriate explanation content by taking into account the user's age group. For example, the explanation unit can provide appropriate explanation content by taking into account the user's age group. The explanation unit can also analyze the user's technical literacy by having the generation AI analyze the user's technical literacy and provide appropriate explanation content. For example, the explanation unit can analyze the user's technical literacy by having the generation AI analyze the user's technical literacy and provide appropriate explanation content. The explanation unit can also provide appropriate explanation content by referring to the user's past usage history. For example, the explanation unit can provide appropriate explanation content by referring to the user's past usage history. This makes it possible to provide appropriate explanation content for the user by taking into account the age group and technical literacy. Some or all of the above-mentioned processing in the explanation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the explanation unit can use the generation AI to automatically consider the user's age group and technical literacy to customize the explanation content.

[0055] The explanation unit can improve the explanation method by reflecting user feedback during explanation. In the explanation unit, for example, the generation AI collects user feedback and improves the explanation method. For example, in the explanation unit, the generation AI collects user feedback and improves the explanation method. In addition, the explanation unit can also have the generation AI analyze past feedback and optimize the explanation method. For example, in the explanation unit, the generation AI analyzes past feedback and optimizes the explanation method. In addition, the explanation unit can have the generation AI reflect feedback in real time and adjust the explanation method. For example, in the explanation unit, the generation AI reflects feedback in real time and adjusts the explanation method. In this way, the explanation method can be continuously improved by reflecting feedback. Some or all of the above-mentioned processing in the explanation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the explanation unit can automatically collect user feedback and improve the explanation method using the generation AI.

[0056] When providing an explanation, the explanation unit can select the optimal explanation method by taking into account the user's device information. For example, the explanation unit can have the generation AI refer to the user's device information and provide the optimal explanation method. For example, the explanation unit can have the generation AI refer to the user's device information and provide the optimal explanation method. The explanation unit can also have the generation AI consider the screen size of the user's device and provide an appropriate explanation method. For example, the explanation unit can have the generation AI analyze the functions of the user's device and provide the optimal explanation method. For example, the explanation unit can analyze the functions of the user's device and provide the optimal explanation method. In this way, the optimal explanation method for the user can be provided by taking the device information into account. Some or all of the above-mentioned processing in the explanation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the explanation unit can use the generation AI to automatically refer to the user's device information and select the optimal explanation method.

[0057] The explanation unit can make the explanation content multilingual according to the user's language setting when providing the explanation. For example, the generation AI refers to the user's language setting and provides the explanation in an appropriate language. For example, the explanation unit refers to the user's language setting and provides the explanation in an appropriate language. The explanation unit can also provide multilingual explanations so that the user can select one. For example, the explanation unit can provide multilingual explanations so that the user can select one. The explanation unit can also provide multilingual explanations so that the generation AI refers to the user's past language setting and provides the explanation in an appropriate language. For example, the explanation unit refers to the user's past language setting and provides the explanation in an appropriate language. This makes it possible to provide multilingual explanations in a language that is easy for the user to understand. Some or all of the above-described processing in the explanation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the explanation unit can use the generation AI to automatically refer to the user's language setting and provide the explanation content in multiple languages.

[0058] The explanation unit can improve accessibility by taking into account the user's visual or hearing impairment when providing explanations. For example, the explanation unit can provide an audio explanation by taking into account the user's visual impairment using a generation AI. For example, the explanation unit can provide an audio explanation by taking into account the user's visual impairment using a generation AI. The explanation unit can also provide a text explanation by taking into account the user's hearing impairment using a generation AI. For example, the explanation unit can provide a text explanation by taking into account the user's hearing impairment using a generation AI. The explanation unit can also provide accessibility features according to the user's disability. For example, the explanation unit can provide accessibility features according to the user's disability. This improves accessibility by taking into account visual or hearing impairments, making it possible to accommodate a wider range of users. Some or all of the above-described processing in the explanation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the explanation unit can use a generation AI to automatically take into account the user's visual or hearing impairment to improve accessibility.

[0059] When making smartphone conversion suggestions to non-smartphone users, the suggestion unit can select the optimal suggestion method by referring to past suggestion history. In the suggestion unit, for example, the generation AI refers to past suggestion history and selects the optimal suggestion method. For example, in the suggestion unit, the generation AI refers to past suggestion history and selects the optimal suggestion method. In addition, the suggestion unit can also analyze past successful cases and select the optimal suggestion method. For example, in the suggestion unit, the generation AI analyzes past successful cases and selects the optimal suggestion method. In addition, the suggestion unit can analyze past unsuccessful cases and select the optimal suggestion method. For example, in the suggestion unit, the generation AI analyzes past unsuccessful cases and selects the optimal suggestion method. In this way, the optimal suggestion method can be selected by referring to the past suggestion history. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can automatically refer to past suggestion history and select the optimal suggestion method using the generation AI.

[0060] When making a suggestion, the suggestion unit can customize the suggestion content by taking into account the living situation and areas of interest of the non-smartphone user. For example, the suggestion unit uses a generation AI to consider the living situation of the non-smartphone user and provide appropriate suggestion content. For example, the suggestion unit uses a generation AI to consider the living situation of the non-smartphone user and provide appropriate suggestion content. The suggestion unit can also analyze the areas of interest of the non-smartphone user and provide appropriate suggestion content. For example, the suggestion unit can analyze the areas of interest of the non-smartphone user and provide appropriate suggestion content. The suggestion unit can also provide appropriate suggestion content by referencing the non-smartphone user's past usage history. For example, the suggestion unit uses a generation AI to refer to the non-smartphone user's past usage history and provide appropriate suggestion content. This allows suggestion content appropriate for non-smartphone users to be provided by taking into account their living situation and areas of interest. Some or all of the above-described processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can use the generation AI to automatically consider the living situation and areas of interest of the non-smartphone user and customize the suggestion content.

[0061] The suggestion unit can improve the suggestion method by reflecting feedback from non-smartphone users when making a suggestion. For example, the suggestion unit has the generation AI collect feedback from non-smartphone users and improve the suggestion method. For example, the suggestion unit has the generation AI collect feedback from non-smartphone users and improve the suggestion method. The suggestion unit can also have the generation AI analyze past feedback and optimize the suggestion method. For example, the suggestion unit has the generation AI analyze past feedback and optimize the suggestion method. The suggestion unit can also have the generation AI reflect feedback in real time and adjust the suggestion method. For example, the suggestion unit has the generation AI reflect feedback in real time and adjust the suggestion method. In this way, the suggestion method can be continuously improved by reflecting feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can use the generation AI to automatically collect feedback from non-smartphone users and improve the suggestion method.

[0062] When making a proposal, the proposal unit can select the optimal proposal method by taking into account the geographical location information of the non-smartphone user. For example, the proposal unit can provide the optimal proposal method by having the generation AI refer to the geographical location information of the non-smartphone user. For example, the proposal unit can provide the optimal proposal method by having the generation AI refer to the geographical location information of the non-smartphone user. The proposal unit can also provide an appropriate proposal method by taking into account the characteristics of the area where the non-smartphone user lives. For example, the proposal unit can provide an appropriate proposal method by having the generation AI analyze the geographical location information of the non-smartphone user. For example, the proposal unit can provide the optimal proposal method by taking into account the geographical location information of the non-smartphone user. Some or all of the above-described processing in the proposal unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the proposal unit can automatically refer to the geographical location information of the non-smartphone user and select the optimal proposal method.

[0063] When making a proposal, the suggestion unit can analyze the social media activity of the non-smartphone user and make a relevant proposal. For example, the suggestion unit uses a generation AI to analyze the social media activity of the non-smartphone user and make a relevant proposal. For example, the suggestion unit uses a generation AI to analyze the social media activity of the non-smartphone user and make a relevant proposal. The suggestion unit can also make an appropriate proposal by having the generation AI refer to the content posted by the non-smartphone user. For example, the suggestion unit can make an appropriate proposal by having the generation AI refer to the content posted by the non-smartphone user. The suggestion unit can also make a relevant proposal by having the generation AI refer to the activity of the non-smartphone user's friends. For example, the suggestion unit can make a relevant proposal by having the generation AI refer to the activity of the non-smartphone user's friends. In this way, relevant suggestions can be made by analyzing social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can use the generation AI to automatically analyze the social media activity of the non-smartphone user and make a relevant proposal.

[0064] When making a proposal, the suggestion unit can customize the proposal content by reflecting past feedback from non-smartphone users. For example, the suggestion unit customizes the proposal content by having the generation AI refer to past feedback from non-smartphone users. For example, the suggestion unit customizes the proposal content by having the generation AI refer to past feedback from non-smartphone users. The suggestion unit can also analyze past feedback and provide optimal proposal content by having the generation AI analyze past feedback. For example, the suggestion unit can analyze past feedback and provide optimal proposal content by having the generation AI analyze past feedback. The suggestion unit can also adjust the proposal content by having the generation AI reflect feedback in real time. For example, the suggestion unit can optimize the proposal content by reflecting past feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can automatically refer to past feedback from non-smartphone users and customize the proposal content by using the generation AI.

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

[0066] The regional development system can further include a tourist information provider that provides information on local specialties and tourist spots. The tourist information provider uses generative AI to collect information on tourist spots and specialties of each local government and provide it to the user. For example, the tourist information provider collects information on tourist spots from the websites of each local government's tourism association and provides it to the user. The tourist information provider can also collect reviews and photos of tourist spots from unofficial sources such as social media and blogs and provide them to the user. Furthermore, the tourist information provider can provide information on nearby tourist spots and specialties based on the user's current location. This allows the regional development system to promote regional development by utilizing the region's tourist resources.

[0067] When analyzing the collected information, the analysis unit can adjust the analysis results by taking into account the climate data of each local government. For example, the analysis unit uses the generation AI to refer to the climate data of each local government and provide analysis results according to the climate conditions. The analysis unit can also predict the period during which regional promotion coupons can be used and their effectiveness based on the climate data. Furthermore, the analysis unit can also suggest appropriate promotion methods to users by taking into account the climate data. In this way, by taking into account the climate data, more accurate analysis results can be provided.

[0068] The explanation section can customize the explanation method according to the user's learning style when explaining how to use and set up the smartphone. For example, it can provide explanations using diagrams and videos to visual learners. It can also provide audio guides to auditory learners. It can also provide interactive tutorials to hands-on learners. This makes it possible to provide explanations that are easy to understand by providing explanation methods that suit the user's learning style.

[0069] The regional development system may further include an event information provider that provides regional event information. The event information provider uses generative AI to collect event information from each local government and provide it to the user. For example, the event information provider may collect event information from the official websites of each local government and provide it to the user. The event information provider may also collect event reviews and photos from unofficial sources such as social media and blogs and provide them to the user. Furthermore, the event information provider may provide nearby event information based on the user's current location. This allows the regional development system to promote regional development by utilizing regional event information.

[0070] When analyzing collected information, the analysis unit can adjust the analysis results by taking into account the educational data of each local government. For example, the analysis unit has the generation AI refer to the educational data of each local government and provide analysis results according to the educational level. The analysis unit can also predict the usable period and effectiveness of regional promotion coupons based on the educational data. Furthermore, it can also suggest appropriate promotion methods to users by taking the educational data into consideration. In this way, by taking the educational data into consideration, it is possible to provide more accurate analysis results.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The information gathering unit uses the generation AI to collect the launch date and details of each local government's electronic regional promotion coupons. For example, the information gathering unit collects information from each local government's official website and related news articles. The information gathering unit can also collect information from unofficial sources such as social media and blogs. Furthermore, the information gathering unit can refer to data on past regional promotion coupons to improve the accuracy of the information it collects. Step 2: The analysis unit uses the generation AI to analyze the collected information and identify the start date and content of the electronic regional promotion coupons. For example, the analysis unit analyzes the collected information and identifies the details of the electronic regional promotion coupons. The analysis unit can also compare the data with past data to identify trends. Furthermore, the analysis unit can predict the effectiveness of the regional promotion coupons, taking into account the economic situation and policies of each local government. Step 3: The Collaboration Department collaborates with each local government, participating stores, and telecommunications carrier shops. For example, the Collaboration Department provides participating stores with information about the electronic regional promotion coupons provided by each local government, and the participating stores use that information to promote themselves. The Collaboration Department also works with telecommunications carrier shops to explain to users how to use and set up the coupons on their smartphones and assist with issuance. The Collaboration Department can also collaborate with chambers of commerce and tourism associations. Step 4: The explanation unit explains to the user how to use and set up the smartphone. For example, the explanation unit explains to the user how to use and set up the smartphone and supports the user in completing the necessary procedures. The explanation unit can also customize the explanation content taking into account the user's age group and technical literacy. Furthermore, the explanation unit can improve the explanation method by reflecting user feedback. Step 5: The proposal unit makes proposals to non-smartphone users to switch to smartphones and to those who wish to save money. For example, the proposal unit suggests purchasing a smartphone to a user who does not own a smartphone and explains the benefits of using a smartphone. The proposal unit also suggests discounts and benefits that can be obtained by using electronic regional promotion coupons to those who wish to save money. Furthermore, the proposal unit can customize the content of the proposals taking into account the user's living situation and areas of interest.

[0073] (Example 2) A regional revitalization system according to an embodiment of the present invention utilizes a generation AI to collect the start dates and details of electronic regional revitalization vouchers offered by local governments and participate in regional revitalization projects. The generation AI collects the start dates and details of electronic regional revitalization vouchers offered by local governments, participating stores, and telecommunications carrier shops based on the collected information to expand benefits to each company and user. The regional revitalization system also provides assistance with setting up, using, and issuing electronic regional revitalization vouchers at telecommunications carrier shops, including how to use them on smartphones. Furthermore, the system offers smartphone upgrades and suggestions to non-smartphone users who wish to save money. For example, the generation AI collects and analyzes information from each local government's official website and related news articles. For example, the generation AI collects information such as, "Electronic regional revitalization vouchers for City X will be available from X / X, and can be used at X and X stores." The regional revitalization system then collaborates with each local government, participating stores, and telecommunications carrier shops based on the collected information. Specifically, information about electronic regional promotion coupons provided by each local government is provided to participating stores, which then use the information to promote the coupons. Telecommunications carrier shops also explain to users how to use and set up the coupons on their smartphones and assist with issuance. This expands the benefits for both companies and users. Furthermore, the regional promotion system assists with setting up, using, and issuing electronic regional promotion coupons at telecommunications carrier shops, including how to use the coupons on smartphones. For example, staff at telecommunications carrier shops explain to users how to set up and use the electronic regional promotion coupons and support them with the necessary procedures. This allows non-smartphone users to use the electronic regional promotion coupons. The regional promotion system also makes suggestions to non-smartphone users about switching to smartphones and to those who wish to save money. For example, it suggests purchasing a smartphone to users who do not own a smartphone and explains the benefits of using one. Furthermore, it suggests discounts and benefits that can be obtained by using the electronic regional promotion coupons to those who wish to save money. This allows non-smartphone users to use the electronic regional promotion coupons, contributing to regional promotion.This allows the regional revitalization system to use generation AI to collect, analyze, and collaborate on information about each local government's electronic regional revitalization coupons, explain how to use and set them up on smartphones, and make suggestions to non-smartphone users. For example, the regional revitalization system can quickly and accurately collect and analyze information about each local government's electronic regional revitalization coupons, thereby expanding the benefits to each company and user. In addition, by allowing non-smartphone users to use the electronic regional revitalization coupons, it can contribute to regional revitalization.

[0074] A regional revitalization system according to an embodiment includes an information collection unit, an analysis unit, a collaboration unit, an explanation unit, and a proposal unit. The information collection unit uses a generation AI to collect information on the start date and details of each local government's electronic regional revitalization voucher. For example, the information collection unit collects information from each local government's official website and related news articles. The information collection unit can also collect information from unofficial sources such as social media and blogs. The information collection unit can also improve the accuracy of the collected information by referencing past regional revitalization voucher data. The analysis unit uses a generation AI to analyze the collected information and identify the start date and details of the electronic regional revitalization voucher. For example, the analysis unit analyzes the collected information and identifies details of the electronic regional revitalization voucher. The analysis unit can also compare the collected information with past data to identify trends. The analysis unit can also predict the effectiveness of the regional revitalization voucher, taking into account the economic situation and policies of each local government. The collaboration unit collaborates with each local government, participating stores, and telecommunications carrier shops. For example, the Collaboration Department provides participating stores with information about electronic regional promotion coupons provided by local governments, and participating stores use that information to promote their products. The Collaboration Department also works with telecommunications carrier shops to explain how to use and set up the coupons on smartphones and assist with issuance. The Collaboration Department can also collaborate with chambers of commerce and tourism associations. The Explanation Department explains how to use and set up the coupons on smartphones to users. For example, the Explanation Department explains how to use and set up the coupons on smartphones to users and supports them in completing the necessary procedures. The Explanation Department can also customize the explanation content taking into account the user's age group and technical literacy. Furthermore, the Explanation Department can also improve the explanation method based on user feedback. The Proposal Department makes suggestions to non-smartphone users about switching to smartphones and to those who wish to save money. For example, the Proposal Department suggests purchasing a smartphone to users who do not own a smartphone and explains the benefits of using one. The Proposal Department can also suggest discounts and benefits that can be obtained by using the electronic regional promotion coupons to those who wish to save money. Furthermore, the suggestion unit can also customize the suggestions taking into account the user's living situation and areas of interest.As a result, the regional development system according to the embodiment can automate and efficiently carry out a series of processes from information collection to analysis, collaboration, explanation, and proposal.

[0075] The information collection unit can collect information from the official websites and news articles of each local government. For example, the information collection unit collects information about electronic regional promotion coupons from the official websites of each local government. For example, the information collection unit accesses the official websites of the local governments to collect information about the start date of the electronic regional promotion coupons and the stores where they can be used. The information collection unit can also collect information from related news articles. For example, the information collection unit collects the latest information about the electronic regional promotion coupons from online news sites and newspaper articles. This allows for the collection of information from official sources to obtain reliable data. Some or all of the above-described processing in the information collection unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the information collection unit can automatically collect information from the official websites and news articles of each local government using a generation AI.

[0076] The analysis unit can analyze the collected information and identify the start date and content of the electronic regional promotion voucher. The analysis unit can, for example, analyze the collected information and identify the start date and content of the electronic regional promotion voucher. For example, the analysis unit can analyze the collected information and identify the start date of the electronic regional promotion voucher and information on stores where the electronic regional promotion voucher can be used. The analysis unit can also identify details of the electronic regional promotion voucher based on the collected information. For example, the analysis unit can analyze the collected information and identify the terms of use and benefit content of the electronic regional promotion voucher. In this way, by analyzing the collected information, details of the electronic regional promotion voucher can be identified. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can automatically analyze the collected information using a generation AI and identify the start date and content of the electronic regional promotion voucher.

[0077] The collaboration unit can collaborate with local governments, participating stores, and telecommunications carrier shops. For example, the collaboration unit provides participating stores with information about electronic regional promotion coupons provided by local governments, and the participating stores use that information to conduct promotional campaigns. For example, the collaboration unit provides participating stores with information about electronic regional promotion coupons provided by local governments, and the participating stores use that information to conduct discount campaigns. The collaboration unit can also collaborate with telecommunications carrier shops to explain how to use and set up the coupons on smartphones and assist with issuance. For example, the collaboration unit can collaborate with telecommunications carrier shops to explain how to use and set up the coupons on smartphones and support the issuance process for electronic regional promotion coupons. This can enhance the effectiveness of regional promotion through collaboration between local governments, participating stores, and telecommunications carrier shops. Some or all of the above-described processing in the collaboration unit can be performed using or without the generation AI. For example, the collaboration unit can use the generation AI to automatically coordinate collaboration with local governments, participating stores, and telecommunications carrier shops.

[0078] The explanation unit can explain to the user how to use and set up the smartphone. For example, the explanation unit can explain to the user how to use and set up the smartphone and support the necessary procedures. For example, the explanation unit can explain to the user how to set up the electronic regional promotion coupon on the smartphone and support the setting procedure. The explanation unit can also explain to the user how to use the electronic regional promotion coupon on the smartphone and support the usage procedure. This makes it easier for the user to understand how to use and set up the smartphone. Some or all of the above-mentioned processing in the explanation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the explanation unit can use a generation AI to automatically explain how to use and set up the smartphone.

[0079] The suggestion unit can make suggestions to non-smartphone users about switching to smartphones and suggestions to users who wish to save money. For example, the suggestion unit can suggest to users who do not own a smartphone that they purchase a smartphone and explain the benefits of using a smartphone. For example, the suggestion unit can suggest to users who do not own a smartphone that they purchase a smartphone and explain the discounts and benefits that can be obtained by using a smartphone. The suggestion unit can also suggest discounts and benefits that can be obtained by using electronic regional promotion coupons to users who wish to save money. For example, the suggestion unit can suggest discounts and benefits that can be obtained by using electronic regional promotion coupons to users who wish to save money, and encourage their use. This can promote the use of electronic regional promotion coupons even among non-smartphone users. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can automatically make smartphone suggestions to non-smartphone users and suggestions to users who wish to save money using a generation AI.

[0080] The information collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, when the user is feeling stressed, the information collection unit causes the generation AI to reduce the frequency of information collection, thereby reducing the user's burden. For example, when the user is feeling stressed, the information collection unit causes the generation AI to reduce the frequency of information collection, thereby reducing the user's burden. Furthermore, when the user is relaxed, the information collection unit can also cause the generation AI to increase the frequency of information collection and provide the latest information. For example, when the user is relaxed, the information collection unit can cause the generation AI to increase the frequency of information collection and provide the latest information. Furthermore, when the user is in a hurry, the information collection unit can also cause the generation AI to quickly collect information and provide it immediately. For example, when the user is in a hurry, the information collection unit can cause the generation AI to quickly collect information and provide it immediately. This allows the user's burden to be reduced by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the information collection unit may use the generation AI to estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

[0081] The information collection unit can collect information from unofficial sources such as social media and blogs, in addition to the official websites and related news articles of each local government. For example, the generation AI collects posts about each local government's electronic regional promotion vouchers from social media such as Twitter and Facebook. For example, the generation AI collects posts about each local government's electronic regional promotion vouchers from social media such as Twitter and Facebook. The information collection unit can also collect user opinions and reviews about the electronic regional promotion vouchers from local blogs and forums. For example, the generation AI collects user opinions and reviews about the electronic regional promotion vouchers from local blogs and forums. The information collection unit can also collect and analyze videos about the electronic regional promotion vouchers from video platforms such as YouTube. For example, the generation AI collects and analyzes videos about the electronic regional promotion vouchers from video platforms such as YouTube. By collecting information from unofficial sources, more diverse information can be obtained. Some or all of the above-described processing in the information collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the information collection unit may use the generation AI to automatically collect information from unofficial information sources such as social media and blogs.

[0082] When collecting information, the information collection unit can improve the accuracy of the collected information by referring to data on past regional promotion coupons issued by each local government. For example, the information collection unit can improve accuracy by having the generation AI refer to past regional promotion coupon issuance data and compare it with current information. For example, the information collection unit can improve accuracy by having the generation AI refer to past regional promotion coupon issuance data and compare it with current information. The information collection unit can also analyze past regional promotion coupon usage data and reflect it in current information collection. For example, the information collection unit can analyze past regional promotion coupon usage data and reflect it in current information collection. The information collection unit can also evaluate the effectiveness of past regional promotion coupons and use it in current information collection. For example, the information collection unit can evaluate the effectiveness of past regional promotion coupons and use it in current information collection. By doing so, the accuracy of the collected information can be improved by referring to past data. Some or all of the above-described processing in the information collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the information collection department can use the generation AI to automatically refer to data on past regional promotion coupons to improve the accuracy of the information it collects.

[0083] When collecting information, the information collection unit can filter information by taking into account the geographical characteristics and demographics of each municipality. For example, the generation AI of the information collection unit takes into account the geographical characteristics of each municipality and collects information according to characteristics such as mountainous areas and urban areas. For example, the generation AI of the information collection unit takes into account the geographical characteristics of each municipality and collects information according to characteristics such as mountainous areas and urban areas. The information collection unit can also take into account the demographics of each municipality and collect information according to areas with a large elderly population and areas with a large young population. For example, the generation AI of the information collection unit takes into account the demographics of each municipality and collects information according to areas with a large elderly population and areas with a large young population. The information collection unit can also take into account the economic situation of each municipality and collect information according to economically struggling areas and prosperous areas. For example, the generation AI of the information collection unit takes into account the economic situation of each municipality and collects information according to economically struggling areas and prosperous areas. This allows for more appropriate information to be collected by taking into account geographical characteristics and demographics. Some or all of the above-described processing in the information collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the information collection department can use generative AI to automatically take into account the geographical characteristics and demographics of each municipality and filter the information.

[0084] The information collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the information collection unit causes the generation AI to prioritize collecting only important information. For example, when the user is feeling stressed, the information collection unit causes the generation AI to prioritize collecting only important information. Furthermore, when the user is relaxed, the information collection unit can also cause the generation AI to prioritize collecting detailed information. For example, when the user is relaxed, the information collection unit causes the generation AI to prioritize collecting detailed information. Furthermore, when the user is in a hurry, the information collection unit can also prioritize collecting information that the generation AI can collect quickly. For example, when the user is in a hurry, the information collection unit prioritizes collecting information that the generation AI can collect quickly. In this way, by determining the priority of information according to the user's emotions, important information can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the information collection unit may use the generation AI to estimate the user's emotions and determine the priority of the information to be collected based on the estimated user emotions.

[0085] When collecting information, the information collection unit can collect information from local chambers of commerce and tourism associations in addition to the official websites and related news articles of each local government. For example, the generation AI collects information about regional promotion coupons from the websites of the chambers of commerce of each local government. For example, the generation AI collects information about regional promotion coupons from the websites of the chambers of commerce of each local government. The information collection unit can also collect information about regional promotion coupons from the websites of the tourism associations of each local government. For example, the information collection unit can collect information about regional promotion coupons from the websites of the tourism associations of each local government. The information collection unit can also collect information about regional promotion coupons from newsletters and reports of the chambers of commerce and tourism associations of each local government. For example, the information collection unit can collect information about regional promotion coupons from newsletters and reports of the chambers of commerce and tourism associations of each local government. This allows the generation AI to obtain more diverse information by also collecting information from the chambers of commerce and tourism associations of each local government. Some or all of the above-described processing in the information collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the information collection unit may use the generation AI to automatically collect information from the chamber of commerce and tourism association of each local government.

[0086] When collecting information, the information collection unit can refer to the event calendar of each local government to identify the period during which the regional promotion coupons can be used. For example, the generation AI of the information collection unit can identify the period during which the regional promotion coupons can be used from the event calendar of the official website of each local government. For example, the information collection unit can identify the period during which the regional promotion coupons can be used from the event calendar of the official website of each local government. The information collection unit can also collect local event information and identify the period during which the regional promotion coupons can be used. For example, the information collection unit can collect local event information and identify the period during which the regional promotion coupons can be used. The information collection unit can also collect local event information and identify the period during which the regional promotion coupons can be used from the event calendar of the tourism association of each local government. For example, the information collection unit can collect local event information and identify the period during which the regional promotion coupons can be used from the event calendar of the tourism association of each local government. This makes it possible to accurately identify the period during which the regional promotion coupons can be used by referring to the event calendar. Some or all of the above-mentioned processing in the information collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the information collection department can use generation AI to automatically refer to each local government's event calendar and identify the period during which regional promotion coupons can be used.

[0087] The information collection unit can predict the scale of issuance of regional promotion coupons by referring to the budget information of each local government when collecting information. For example, the generation AI in the information collection unit predicts the scale of issuance of regional promotion coupons from the budget report of each local government. For example, the information collection unit predicts the scale of issuance of regional promotion coupons from the budget report of each local government. The information collection unit can also predict the scale of issuance of regional promotion coupons by analyzing the financial situation of each local government. For example, the information collection unit can predict the scale of issuance of regional promotion coupons by analyzing the financial situation of each local government. The information collection unit can also predict the scale of issuance of current regional promotion coupons by referring to past budget data. For example, the information collection unit can predict the scale of issuance of current regional promotion coupons by referring to past budget data. In this way, the scale of issuance of regional promotion coupons can be accurately predicted by referring to budget information. Some or all of the above-mentioned processing in the information collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the information collection department can use generation AI to automatically refer to each local government's budget information and predict the scale of regional promotion coupons to be issued.

[0088] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method including detailed information. For example, if the user is relaxed, the analysis unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, if the user is in a hurry, the analysis unit provides a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotions, thereby enabling a display that is easy for the user to view. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can use the generation AI to estimate the user's emotions and adjust the way the analysis results are displayed based on the estimated user emotions.

[0089] When analyzing the collected information, the analysis unit can compare it with past data to identify trends. For example, the generation AI compares past regional promotion coupon data with current data to identify trends. For example, the analysis unit compares past regional promotion coupon data with current data to identify trends. The analysis unit can also compare past usage data with current usage data to identify user trends. For example, the analysis unit compares past usage data with current usage data to identify user trends. The analysis unit can also compare past promotion data with current promotion data to identify effective promotion methods. For example, the analysis unit compares past promotion data with current promotion data to identify effective promotion methods. This allows current trends to be identified by comparing with past data. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to automatically compare past data with current data to identify trends.

[0090] During analysis, the analysis unit can predict the effects of the regional promotion coupons by taking into account the economic situation and policies of each local government. For example, the generation AI analyzes the economic situation of each local government and predicts the effects of the regional promotion coupons. For example, the analysis unit analyzes the economic situation of each local government and predicts the effects of the regional promotion coupons. The analysis unit can also predict the effects of the regional promotion coupons by taking into account the policies of each local government. For example, the analysis unit can predict the effects of the regional promotion coupons by taking into account the policies of each local government. The analysis unit can also predict the effects of the regional promotion coupons by having the generation AI refer to past economic data and policy data. For example, the analysis unit can predict the effects of the regional promotion coupons by having the generation AI refer to past economic data and policy data. This allows the effects of the regional promotion coupons to be accurately predicted by taking into account the economic situation and policies. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to automatically consider the economic situation and policies of each local government and predict the effects of the regional promotion coupons.

[0091] During analysis, the analysis unit can evaluate the reliability of the collected information and prioritize analyzing highly reliable information. For example, the analysis unit has the generation AI evaluate the reliability of the information source and prioritize analyzing highly reliable information. For example, the analysis unit has the generation AI evaluate the reliability of the information source and prioritize analyzing highly reliable information. The analysis unit can also have the generation AI compare the information with past data to identify highly reliable information. For example, the analysis unit has the generation AI compare the information with past data to identify highly reliable information. The analysis unit can also have the generation AI confirm the source of the information and prioritize analyzing highly reliable information. For example, the analysis unit has the generation AI confirm the source of the information and prioritize analyzing highly reliable information. This prioritizes analyzing highly reliable information, thereby improving the accuracy of the analysis results. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to automatically evaluate the reliability of the collected information and prioritize analyzing highly reliable information.

[0092] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, when the user is feeling stressed, the analysis unit causes the generation AI to prioritize displaying only important analysis results. For example, when the user is feeling stressed, the analysis unit causes the generation AI to prioritize displaying only important analysis results. Furthermore, when the user is relaxed, the analysis unit can also cause the generation AI to prioritize displaying detailed analysis results. For example, when the user is relaxed, the analysis unit causes the generation AI to prioritize displaying detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can also cause the generation AI to quickly display analysis results. For example, when the user is in a hurry, the analysis unit causes the generation AI to quickly display analysis results. Thus, by prioritizing the analysis results according to the user's emotions, important information can be displayed preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit may use a generation AI to estimate a user's emotion and determine the priority of the analysis results based on the estimated user's emotion.

[0093] During analysis, the analysis unit can visualize the collected information in cooperation with a geographic information system (GIS). For example, the analysis unit can link the information collected by the generation AI with a GIS and visualize it on a map. For example, the analysis unit can link the information collected by the generation AI with a GIS and visualize it on a map. The analysis unit can also link the information of each local government with a GIS and visualize information for each region. For example, the analysis unit can link the information of each local government with a GIS and visualize information for each region. The analysis unit can also link the information of stores used by the generation AI with a GIS and visualize information for each store. For example, the analysis unit can link the information of stores used by the generation AI with a GIS and visualize information for each store. In this way, by linking with a geographic information system, information can be displayed in a visually easy-to-understand manner. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to automatically link the collected information with a GIS and visualize it.

[0094] During analysis, the analysis unit can refer to the demographic data of each local government to identify the target demographic for the regional promotion coupons. For example, the generation AI can refer to the demographic data of each local government to identify the target demographic for the regional promotion coupons. For example, the analysis unit can refer to the demographic data of each local government to identify the target demographic for the regional promotion coupons. The analysis unit can also have the generation AI identify target demographics based on whether the area has a high elderly population or a high young population. For example, the analysis unit can have the generation AI identify target demographics based on whether the area has a high elderly population or a high young population. The analysis unit can also have the generation AI analyze the demographic data of each local government to identify the optimal target demographic. For example, the analysis unit can have the generation AI analyze the demographic data of each local government to identify the optimal target demographic. This allows the optimal target demographic for the regional promotion coupons to be identified by referring to the demographic data. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to automatically refer to the demographic data of each local government to identify the target demographic for the regional promotion coupons.

[0095] During analysis, the analysis unit can refer to the tourism data of each local government and propose measures to promote the use of regional promotion coupons. For example, the generation AI of the analysis unit can refer to the tourism data of each local government and propose measures to promote the use of regional promotion coupons. For example, the analysis unit can refer to the tourism data of each local government and propose measures to promote the use of regional promotion coupons. The analysis unit can also analyze tourist spot data and propose measures to promote the use of regional promotion coupons. For example, the analysis unit can analyze tourist spot data and propose measures to promote the use of regional promotion coupons. The analysis unit can also analyze tourist trends and propose measures to promote the use of regional promotion coupons. For example, the analysis unit can analyze tourist trends and propose measures to promote the use of regional promotion coupons. In this way, by referring to the tourism data, measures to promote the use of regional promotion coupons can be effectively proposed. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to automatically refer to the tourism data of each local government and propose measures to promote the use of regional promotion coupons.

[0096] The collaboration unit can estimate the user's emotions and adjust the way the collaboration proceeds based on the estimated user's emotions. For example, if the user is nervous, the collaboration unit can slowly proceed with the collaboration. For example, if the user is nervous, the collaboration unit can slowly proceed with the collaboration. The collaboration unit can also smoothly proceed with the collaboration if the user is relaxed. For example, if the user is relaxed, the collaboration unit can smoothly proceed with the collaboration. The collaboration unit can also quickly proceed with the collaboration if the user is in a hurry. For example, if the user is in a hurry, the collaboration unit can quickly proceed with the collaboration. This improves the efficiency of collaboration by adjusting the way the collaboration proceeds based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collaboration unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collaboration unit may use the generation AI to estimate the user's emotions and adjust the way the collaboration proceeds based on the estimated user emotions.

[0097] When collaborating with each local government, participating store, and telecommunications carrier shop, the collaboration department can select the optimal collaboration method by referring to past collaboration cases. For example, the collaboration department has the generation AI refer to past collaboration cases and select the optimal collaboration method. For example, the collaboration department has the generation AI refer to past collaboration cases and select the optimal collaboration method. The collaboration department can also analyze past successful cases and select the optimal collaboration method. For example, the collaboration department has the generation AI analyze past successful cases and select the optimal collaboration method. The collaboration department can also analyze past unsuccessful cases and select the optimal collaboration method. For example, the collaboration department has the generation AI analyze past unsuccessful cases and select the optimal collaboration method. In this way, the optimal collaboration method can be selected by referring to past collaboration cases. Some or all of the above-mentioned processing in the collaboration department may be performed using the generation AI, or may be performed without using the generation AI. For example, the collaboration department can automatically refer to past collaboration cases and select the optimal collaboration method using the generation AI.

[0098] During collaboration, the collaboration department can customize the collaboration content taking into account the regional characteristics and commercial environment of each local government. For example, the collaboration department customizes the collaboration content by taking into account the regional characteristics of each local government using a generation AI. For example, the collaboration department customizes the collaboration content by taking into account the regional characteristics of each local government using a generation AI. The collaboration department can also optimize the collaboration content by analyzing the commercial environment of each local government using a generation AI. For example, the collaboration department optimizes the collaboration content by analyzing the commercial environment of each local government using a generation AI. The collaboration department can also customize the collaboration content by proposing promotion methods that suit the regional characteristics. For example, the collaboration department customizes the collaboration content by proposing promotion methods that suit the regional characteristics. This allows the collaboration content to be optimized by taking into account the regional characteristics and commercial environment. Some or all of the above-mentioned processing in the collaboration department may be performed using a generation AI, or may be performed without using a generation AI. For example, the collaboration department can customize the collaboration content by automatically taking into account the regional characteristics and commercial environment of each local government using a generation AI.

[0099] At the time of collaboration, the collaboration department can clarify the purpose of the collaboration by taking into account the policies and goals of each local government. For example, the collaboration department has the generation AI refer to the policies of each local government to clarify the purpose of the collaboration. For example, the collaboration department has the generation AI refer to the policies of each local government to clarify the purpose of the collaboration. The collaboration department can also set the purpose of the collaboration by taking into account the goals of each local government. For example, the collaboration department has the generation AI refer to the policies of each local government to clarify the purpose of the collaboration. The collaboration department can also clarify the purpose of the collaboration by analyzing past policy data. For example, the collaboration department has the generation AI analyze past policy data to clarify the purpose of the collaboration. In this way, the purpose of the collaboration can be clearly set by taking into account the policies and goals. Some or all of the above-mentioned processing in the collaboration department may be performed using the generation AI, or may be performed without using the generation AI. For example, the collaboration department can use the generation AI to automatically consider the policies and goals of each local government to clarify the purpose of the collaboration.

[0100] The collaboration unit can estimate the user's emotions and determine collaboration priorities based on the estimated user emotions. For example, if the user is feeling stressed, the collaboration unit prioritizes only important collaborations. For example, if the user is feeling stressed, the collaboration unit prioritizes only important collaborations. Furthermore, if the user is relaxed, the collaboration unit can prioritize detailed collaborations. For example, if the user is relaxed, the collaboration unit prioritizes detailed collaborations. Furthermore, if the user is in a hurry, the collaboration unit can prioritize collaborations that need to be progressed quickly. For example, if the user is in a hurry, the collaboration unit prioritizes collaborations that need to be progressed quickly. In this way, by determining collaboration priorities according to the user's emotions, important collaborations can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collaboration unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collaboration unit may use the generation AI to estimate the user's emotions and determine collaboration priorities based on the estimated user emotions.

[0101] During collaboration, the collaboration department can also collaborate with the chambers of commerce and tourism associations of each local government. In the collaboration department, for example, the generation AI collaborates with the chambers of commerce and tourism associations of each local government to coordinate the collaboration content. For example, in the collaboration department, the generation AI collaborates with the chambers of commerce and tourism associations of each local government to coordinate the collaboration content. In addition, in the collaboration department, the generation AI can collaborate with the tourism associations of each local government to optimize the collaboration content. For example, in the collaboration department, the generation AI collaborates with the tourism associations of each local government to optimize the collaboration content. In addition, in the collaboration department, the generation AI can customize the collaboration content based on information from the chambers of commerce and tourism associations. For example, in the collaboration department, the generation AI customizes the collaboration content based on information from the chambers of commerce and tourism associations. This makes it possible to enhance the effectiveness of collaboration by collaborating with chambers of commerce and tourism associations. Some or all of the above-mentioned processing in the collaboration department may be performed using the generation AI, or may be performed without using the generation AI. For example, the Collaboration Department can use generative AI to automatically connect with local government chambers of commerce and tourism associations and collaborate with them.

[0102] During collaboration, the collaboration department can monitor the usage status of each local government's regional promotion coupons in real time and adjust the collaboration content. For example, the collaboration department can have the generation AI monitor the usage status of the regional promotion coupons in real time and adjust the collaboration content. For example, the collaboration department can have the generation AI monitor the usage status of the regional promotion coupons in real time and adjust the collaboration content. The collaboration department can also have the generation AI analyze the usage status data and optimize the collaboration content. For example, the collaboration department can have the generation AI analyze the usage status data and optimize the collaboration content. The collaboration department can also quickly adjust the collaboration content in response to changes in the usage status. For example, the collaboration department can quickly adjust the collaboration content in response to changes in the usage status. In this way, the collaboration content can be quickly adjusted by monitoring the usage status in real time. Some or all of the above-mentioned processing in the collaboration department may be performed using the generation AI, or may be performed without using the generation AI. For example, the collaboration department can use the generation AI to automatically monitor the usage status of each local government's regional promotion coupons and adjust the collaboration content.

[0103] During collaboration, the collaboration department can jointly implement a campaign to promote the use of regional promotion coupons for each local government. For example, the collaboration department has the generation AI collaborate with each local government to plan a campaign to promote the use of regional promotion coupons. For example, the collaboration department has the generation AI collaborate with each local government to plan a campaign to promote the use of regional promotion coupons. The collaboration department can also have the generation AI collaborate with participating stores to implement a campaign to promote the use of regional promotion coupons. For example, the collaboration department has the generation AI collaborate with participating stores to implement a campaign to promote the use of regional promotion coupons. The collaboration department can also have the generation AI collaborate with a telecommunications carrier's shop to jointly implement a campaign to promote the use of regional promotion coupons. For example, the collaboration department has the generation AI collaborate with a telecommunications carrier's shop to jointly implement a campaign to promote the use of regional promotion coupons. In this way, by jointly implementing a promotion campaign, it is possible to promote the use of regional promotion coupons. Some or all of the above-mentioned processing in the collaboration department may be performed using the generation AI, or may be performed without using the generation AI. For example, the Collaboration Department can use the generation AI to automatically connect with local governments, participating stores, and telecommunications carrier shops to jointly implement campaigns to promote the use of regional promotion coupons.

[0104] The explanation unit can estimate the user's emotions and adjust the way the explanation is expressed based on the estimated user's emotions. For example, if the user is nervous, the explanation unit can provide the explanation in a calm tone. For example, if the user is nervous, the explanation unit can provide the explanation in a calm tone. Furthermore, if the user is relaxed, the explanation unit can provide the explanation in a bright tone. For example, if the user is relaxed, the explanation unit can provide the explanation in a bright tone. Furthermore, if the user is in a hurry, the explanation unit can provide a concise and quick explanation. For example, if the user is in a hurry, the explanation unit can provide a concise and quick explanation. This allows the explanation to be adjusted according to the user's emotions, making it possible to provide an explanation that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the explanation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the explanation unit can use a generation AI to estimate the user's emotions and adjust the way the explanation is expressed based on the estimated user's emotions.

[0105] When explaining how to use or set up a smartphone, the explanation unit can select the optimal explanation method by referring to past explanation history. For example, the explanation unit has the generation AI refer to past explanation history and select the optimal explanation method. For example, the explanation unit has the generation AI refer to past explanation history and select the optimal explanation method. The explanation unit can also have the generation AI analyze past success cases and select the optimal explanation method. For example, the explanation unit has the generation AI analyze past success cases and select the optimal explanation method. The explanation unit can also have the generation AI analyze past failure cases and select the optimal explanation method. For example, the explanation unit has the generation AI analyze past failure cases and select the optimal explanation method. In this way, the optimal explanation method can be selected by referring to the past explanation history. Some or all of the above-mentioned processing in the explanation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the explanation unit can use the generation AI to automatically refer to past explanation history and select the optimal explanation method.

[0106] The explanation unit can customize the explanation content by taking into account the user's age group and technical literacy when providing the explanation. For example, the generation AI can provide appropriate explanation content by taking into account the user's age group. For example, the explanation unit can provide appropriate explanation content by taking into account the user's age group. The explanation unit can also analyze the user's technical literacy by having the generation AI analyze the user's technical literacy and provide appropriate explanation content. For example, the explanation unit can analyze the user's technical literacy by having the generation AI analyze the user's technical literacy and provide appropriate explanation content. The explanation unit can also provide appropriate explanation content by referring to the user's past usage history. For example, the explanation unit can provide appropriate explanation content by referring to the user's past usage history. This makes it possible to provide appropriate explanation content for the user by taking into account the age group and technical literacy. Some or all of the above-mentioned processing in the explanation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the explanation unit can use the generation AI to automatically consider the user's age group and technical literacy to customize the explanation content.

[0107] The explanation unit can improve the explanation method by reflecting user feedback during explanation. In the explanation unit, for example, the generation AI collects user feedback and improves the explanation method. For example, in the explanation unit, the generation AI collects user feedback and improves the explanation method. In addition, the explanation unit can also have the generation AI analyze past feedback and optimize the explanation method. For example, in the explanation unit, the generation AI analyzes past feedback and optimizes the explanation method. In addition, the explanation unit can have the generation AI reflect feedback in real time and adjust the explanation method. For example, in the explanation unit, the generation AI reflects feedback in real time and adjusts the explanation method. In this way, the explanation method can be continuously improved by reflecting feedback. Some or all of the above-mentioned processing in the explanation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the explanation unit can automatically collect user feedback and improve the explanation method using the generation AI.

[0108] The explanation unit can estimate the user's emotions and determine the priority of explanations based on the estimated user's emotions. For example, when the user is stressed, the explanation unit prioritizes only important explanations. For example, when the user is stressed, the explanation unit prioritizes only important explanations. Furthermore, when the user is relaxed, the explanation unit can prioritize detailed explanations. For example, when the user is relaxed, the explanation unit prioritizes detailed explanations. Furthermore, when the user is in a hurry, the explanation unit can quickly provide explanations. For example, when the user is in a hurry, the explanation unit quickly provides explanations. In this way, by determining the priority of explanations according to the user's emotions, important explanations can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the explanation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the explanation unit can use a generation AI to estimate the user's emotions and determine the priority of explanations based on the estimated user's emotions.

[0109] When providing an explanation, the explanation unit can select the optimal explanation method by taking into account the user's device information. For example, the explanation unit can have the generation AI refer to the user's device information and provide the optimal explanation method. For example, the explanation unit can have the generation AI refer to the user's device information and provide the optimal explanation method. The explanation unit can also have the generation AI consider the screen size of the user's device and provide an appropriate explanation method. For example, the explanation unit can have the generation AI analyze the functions of the user's device and provide the optimal explanation method. For example, the explanation unit can analyze the functions of the user's device and provide the optimal explanation method. In this way, the optimal explanation method for the user can be provided by taking the device information into account. Some or all of the above-mentioned processing in the explanation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the explanation unit can use the generation AI to automatically refer to the user's device information and select the optimal explanation method.

[0110] The explanation unit can make the explanation content multilingual according to the user's language setting when providing the explanation. For example, the generation AI refers to the user's language setting and provides the explanation in an appropriate language. For example, the explanation unit refers to the user's language setting and provides the explanation in an appropriate language. The explanation unit can also provide multilingual explanations so that the user can select one. For example, the explanation unit can provide multilingual explanations so that the user can select one. The explanation unit can also provide multilingual explanations so that the generation AI refers to the user's past language setting and provides the explanation in an appropriate language. For example, the explanation unit refers to the user's past language setting and provides the explanation in an appropriate language. This makes it possible to provide multilingual explanations in a language that is easy for the user to understand. Some or all of the above-described processing in the explanation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the explanation unit can use the generation AI to automatically refer to the user's language setting and provide the explanation content in multiple languages.

[0111] The explanation unit can improve accessibility by taking into account the user's visual or hearing impairment when providing explanations. For example, the explanation unit can provide an audio explanation by taking into account the user's visual impairment using a generation AI. For example, the explanation unit can provide an audio explanation by taking into account the user's visual impairment using a generation AI. The explanation unit can also provide a text explanation by taking into account the user's hearing impairment using a generation AI. For example, the explanation unit can provide a text explanation by taking into account the user's hearing impairment using a generation AI. The explanation unit can also provide accessibility features according to the user's disability. For example, the explanation unit can provide accessibility features according to the user's disability. This improves accessibility by taking into account visual or hearing impairments, making it possible to accommodate a wider range of users. Some or all of the above-described processing in the explanation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the explanation unit can use a generation AI to automatically take into account the user's visual or hearing impairment to improve accessibility.

[0112] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is expressed based on the estimated user's emotions. For example, if the user is nervous, the suggestion unit makes the suggestion in a calm tone. For example, if the user is nervous, the suggestion unit makes the suggestion in a calm tone. Furthermore, if the user is relaxed, the suggestion unit can make the suggestion in a bright tone. For example, if the user is relaxed, the suggestion unit can make the suggestion in a bright tone. Furthermore, if the user is in a hurry, the suggestion unit can make a concise and quick suggestion. For example, if the user is in a hurry, the suggestion unit makes a concise and quick suggestion. This allows the suggestion to be easily understood by adjusting the way the suggestion is expressed based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can use a generation AI to estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions.

[0113] When making smartphone conversion suggestions to non-smartphone users, the suggestion unit can select the optimal suggestion method by referring to past suggestion history. In the suggestion unit, for example, the generation AI refers to past suggestion history and selects the optimal suggestion method. For example, in the suggestion unit, the generation AI refers to past suggestion history and selects the optimal suggestion method. In addition, the suggestion unit can also analyze past successful cases and select the optimal suggestion method. For example, in the suggestion unit, the generation AI analyzes past successful cases and selects the optimal suggestion method. In addition, the suggestion unit can analyze past unsuccessful cases and select the optimal suggestion method. For example, in the suggestion unit, the generation AI analyzes past unsuccessful cases and selects the optimal suggestion method. In this way, the optimal suggestion method can be selected by referring to the past suggestion history. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can automatically refer to past suggestion history and select the optimal suggestion method using the generation AI.

[0114] When making a suggestion, the suggestion unit can customize the suggestion content by taking into account the living situation and areas of interest of the non-smartphone user. For example, the suggestion unit uses a generation AI to consider the living situation of the non-smartphone user and provide appropriate suggestion content. For example, the suggestion unit uses a generation AI to consider the living situation of the non-smartphone user and provide appropriate suggestion content. The suggestion unit can also analyze the areas of interest of the non-smartphone user and provide appropriate suggestion content. For example, the suggestion unit can analyze the areas of interest of the non-smartphone user and provide appropriate suggestion content. The suggestion unit can also provide appropriate suggestion content by referencing the non-smartphone user's past usage history. For example, the suggestion unit uses a generation AI to refer to the non-smartphone user's past usage history and provide appropriate suggestion content. This allows suggestion content appropriate for non-smartphone users to be provided by taking into account their living situation and areas of interest. Some or all of the above-described processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can use the generation AI to automatically consider the living situation and areas of interest of the non-smartphone user and customize the suggestion content.

[0115] The suggestion unit can improve the suggestion method by reflecting feedback from non-smartphone users when making a suggestion. For example, the suggestion unit has the generation AI collect feedback from non-smartphone users and improve the suggestion method. For example, the suggestion unit has the generation AI collect feedback from non-smartphone users and improve the suggestion method. The suggestion unit can also have the generation AI analyze past feedback and optimize the suggestion method. For example, the suggestion unit has the generation AI analyze past feedback and optimize the suggestion method. The suggestion unit can also have the generation AI reflect feedback in real time and adjust the suggestion method. For example, the suggestion unit has the generation AI reflect feedback in real time and adjust the suggestion method. In this way, the suggestion method can be continuously improved by reflecting feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can use the generation AI to automatically collect feedback from non-smartphone users and improve the suggestion method.

[0116] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user's emotions. For example, when the user is feeling stressed, the suggestion unit prioritizes only important suggestions. For example, when the user is feeling stressed, the suggestion unit prioritizes only important suggestions. Furthermore, when the user is relaxed, the suggestion unit can prioritize detailed suggestions. For example, when the user is relaxed, the suggestion unit prioritizes detailed suggestions. Furthermore, when the user is in a hurry, the suggestion unit can quickly make suggestions. For example, when the user is in a hurry, the suggestion unit quickly makes suggestions. In this way, by determining the priority of suggestions according to the user's emotions, important suggestions can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can use a generation AI to estimate the user's emotions and determine the priority of suggestions based on the estimated user's emotions.

[0117] When making a proposal, the proposal unit can select the optimal proposal method by taking into account the geographical location information of the non-smartphone user. For example, the proposal unit can provide the optimal proposal method by having the generation AI refer to the geographical location information of the non-smartphone user. For example, the proposal unit can provide the optimal proposal method by having the generation AI refer to the geographical location information of the non-smartphone user. The proposal unit can also provide an appropriate proposal method by taking into account the characteristics of the area where the non-smartphone user lives. For example, the proposal unit can provide an appropriate proposal method by having the generation AI analyze the geographical location information of the non-smartphone user. For example, the proposal unit can provide the optimal proposal method by taking into account the geographical location information of the non-smartphone user. Some or all of the above-described processing in the proposal unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the proposal unit can automatically refer to the geographical location information of the non-smartphone user and select the optimal proposal method.

[0118] When making a proposal, the suggestion unit can analyze the social media activity of the non-smartphone user and make a relevant proposal. For example, the suggestion unit uses a generation AI to analyze the social media activity of the non-smartphone user and make a relevant proposal. For example, the suggestion unit uses a generation AI to analyze the social media activity of the non-smartphone user and make a relevant proposal. The suggestion unit can also make an appropriate proposal by having the generation AI refer to the content posted by the non-smartphone user. For example, the suggestion unit can make an appropriate proposal by having the generation AI refer to the content posted by the non-smartphone user. The suggestion unit can also make a relevant proposal by having the generation AI refer to the activity of the non-smartphone user's friends. For example, the suggestion unit can make a relevant proposal by having the generation AI refer to the activity of the non-smartphone user's friends. In this way, relevant suggestions can be made by analyzing social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can use the generation AI to automatically analyze the social media activity of the non-smartphone user and make a relevant proposal.

[0119] When making a proposal, the suggestion unit can customize the proposal content by reflecting past feedback from non-smartphone users. For example, the suggestion unit customizes the proposal content by having the generation AI refer to past feedback from non-smartphone users. For example, the suggestion unit customizes the proposal content by having the generation AI refer to past feedback from non-smartphone users. The suggestion unit can also analyze past feedback and provide optimal proposal content by having the generation AI analyze past feedback. For example, the suggestion unit can analyze past feedback and provide optimal proposal content by having the generation AI analyze past feedback. The suggestion unit can also adjust the proposal content by having the generation AI reflect feedback in real time. For example, the suggestion unit can optimize the proposal content by reflecting past feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can automatically refer to past feedback from non-smartphone users and customize the proposal content by using the generation AI. === Hard Collateral 1-1 === Each of the multiple elements, including the information collection unit, analysis unit, collaboration unit, explanation unit, and proposal unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the information collection unit collects information from official local government websites and news articles using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit analyzes the collected information using the specific processing unit 290 of the data processing device 12 and identifies the start date and content of the electronic regional promotion coupons. The collaboration unit collaborates with local governments, participating stores, and telecommunications carrier shops using the specific processing unit 290 of the data processing device 12. The explanation unit explains how to use and set up the smart device on a smartphone to the user using the control unit 46A of the smart device 14. The proposal unit makes smartphone conversion suggestions to non-smartphone users and suggestions to those who wish to save money using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the information collection unit, analysis unit, collaboration unit, explanation unit, and proposal unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the information collection unit collects information from official websites and news articles of each local government using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and identifies the start date and content of the electronic regional promotion coupon. The collaboration unit collaborates with each local government, participating stores, and telecommunications carrier shops using the specific processing unit 290 of the data processing device 12. The explanation unit explains how to use and set up the smart glasses on a smartphone to the user using the control unit 46A of the smart glasses 214. The proposal unit makes smartphone conversion suggestions to non-smartphone users and suggestions to those who wish to save money using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the information collection unit, analysis unit, collaboration unit, explanation unit, and proposal unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the information collection unit collects information from official local government websites and news articles using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and identifies the start date and content of the electronic regional promotion coupons. The collaboration unit collaborates with local governments, participating stores, and telecommunications carrier shops using the specific processing unit 290 of the data processing device 12. The explanation unit explains how to use and set up the smartphone to the user using the control unit 46A of the headset terminal 314. The proposal unit makes smartphone conversion suggestions to non-smartphone users and suggestions to those wishing to save money using the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the information collection unit, analysis unit, collaboration unit, explanation unit, and proposal unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the information collection unit collects information from official websites and news articles of each local government using the camera 42 and communication I / F 44 of the robot 414. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and identifies the start date and content of the electronic regional promotion coupon. The collaboration unit collaborates with each local government, participating stores, and telecommunications carrier shops using the specific processing unit 290 of the data processing device 12. The explanation unit explains how to use and set up the smartphone to the user using the control unit 46A of the robot 414. The proposal unit makes smartphone conversion suggestions to non-smartphone users and suggestions to those who wish to save money using the control unit 46A of the robot 414.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The regional development system can further include a tourist information provider that provides information on local specialties and tourist spots. The tourist information provider uses generative AI to collect information on tourist spots and specialties of each local government and provide it to the user. For example, the tourist information provider collects information on tourist spots from the websites of each local government's tourism association and provides it to the user. The tourist information provider can also collect reviews and photos of tourist spots from unofficial sources such as social media and blogs and provide them to the user. Furthermore, the tourist information provider can provide information on nearby tourist spots and specialties based on the user's current location. This allows the regional development system to promote regional development by utilizing the region's tourist resources.

[0122] The information collecting unit can estimate the user's emotions and evaluate the reliability of information based on the estimated user's emotions. For example, if the user is feeling anxious, the information collecting unit can preferentially collect information from highly reliable information sources. Also, if the user is relaxed, the information collecting unit can collect information from a wide range of information sources. Furthermore, if the user is in a hurry, the information collecting unit can preferentially collect information that can be collected quickly. This makes it possible to evaluate the reliability of information according to the user's emotions and provide appropriate information.

[0123] When analyzing the collected information, the analysis unit can adjust the analysis results by taking into account the climate data of each local government. For example, the analysis unit uses the generation AI to refer to the climate data of each local government and provide analysis results according to the climate conditions. The analysis unit can also predict the period during which regional promotion coupons can be used and their effectiveness based on the climate data. Furthermore, the analysis unit can also suggest appropriate promotion methods to users by taking into account the climate data. In this way, by taking into account the climate data, more accurate analysis results can be provided.

[0124] The collaboration unit can estimate the user's emotions and adjust the speed of collaboration based on the estimated user's emotions. For example, if the user is feeling stressed, the collaboration unit can proceed slowly. If the user is relaxed, the collaboration unit can proceed smoothly. Furthermore, if the user is in a hurry, the collaboration unit can proceed quickly. In this way, the efficiency of collaboration can be improved by adjusting the speed of collaboration according to the user's emotions.

[0125] The explanation section can customize the explanation method according to the user's learning style when explaining how to use and set up the smartphone. For example, it can provide explanations using diagrams and videos to visual learners. It can also provide audio guides to auditory learners. It can also provide interactive tutorials to hands-on learners. This makes it possible to provide explanations that are easy to understand by providing explanation methods that suit the user's learning style.

[0126] When making smartphone usage suggestions to non-smartphone users, the suggestion unit can estimate the user's emotions and adjust the timing of the suggestions based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can delay the timing of the suggestions. Also, if the user is relaxed, the suggestion unit can also advance the timing of the suggestions. Furthermore, if the user is in a hurry, the suggestion unit can make suggestions quickly. This makes it possible to make effective suggestions by adjusting the timing of the suggestions according to the user's emotions.

[0127] The regional development system may further include an event information provider that provides regional event information. The event information provider uses generative AI to collect event information from each local government and provide it to the user. For example, the event information provider may collect event information from the official websites of each local government and provide it to the user. The event information provider may also collect event reviews and photos from unofficial sources such as social media and blogs and provide them to the user. Furthermore, the event information provider may provide nearby event information based on the user's current location. This allows the regional development system to promote regional development by utilizing regional event information.

[0128] The information collection unit can estimate the user's emotions and adjust the information collection method based on the estimated user's emotions. For example, if the user is feeling stressed, the information collection unit can reduce the frequency of information collection to reduce the user's burden. Also, if the user is relaxed, the information collection unit can increase the frequency of information collection to provide the latest information. Furthermore, if the user is in a hurry, the information collection unit can quickly collect information and provide it immediately. In this way, the burden on the user can be reduced by adjusting the information collection method according to the user's emotions.

[0129] When analyzing collected information, the analysis unit can adjust the analysis results by taking into account the educational data of each local government. For example, the analysis unit has the generation AI refer to the educational data of each local government and provide analysis results according to the educational level. The analysis unit can also predict the usable period and effectiveness of regional promotion coupons based on the educational data. Furthermore, it can also suggest appropriate promotion methods to users by taking the educational data into consideration. In this way, by taking the educational data into consideration, it is possible to provide more accurate analysis results.

[0130] The collaboration unit can estimate the user's emotions and determine the priority of collaboration based on the estimated user's emotions. For example, if the user is feeling stressed, the collaboration unit can prioritize only important collaborations. Also, if the user is relaxed, the collaboration unit can prioritize detailed collaboration content. Furthermore, if the user is in a hurry, the collaboration unit can prioritize collaborations that need to be completed quickly. In this way, by determining the priority of collaborations according to the user's emotions, it is possible to prioritize important collaborations.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The information gathering unit uses the generation AI to collect the launch date and details of each local government's electronic regional promotion coupons. For example, the information gathering unit collects information from each local government's official website and related news articles. The information gathering unit can also collect information from unofficial sources such as social media and blogs. Furthermore, the information gathering unit can refer to data on past regional promotion coupons to improve the accuracy of the information it collects. Step 2: The analysis unit uses the generation AI to analyze the collected information and identify the start date and content of the electronic regional promotion coupons. For example, the analysis unit analyzes the collected information and identifies the details of the electronic regional promotion coupons. The analysis unit can also compare the data with past data to identify trends. Furthermore, the analysis unit can predict the effectiveness of the regional promotion coupons, taking into account the economic situation and policies of each local government. Step 3: The Collaboration Department collaborates with each local government, participating stores, and telecommunications carrier shops. For example, the Collaboration Department provides participating stores with information about the electronic regional promotion coupons provided by each local government, and the participating stores use that information to promote themselves. The Collaboration Department also works with telecommunications carrier shops to explain to users how to use and set up the coupons on their smartphones and assist with issuance. The Collaboration Department can also collaborate with chambers of commerce and tourism associations. Step 4: The explanation unit explains to the user how to use and set up the smartphone. For example, the explanation unit explains to the user how to use and set up the smartphone and supports the user in completing the necessary procedures. The explanation unit can also customize the explanation content taking into account the user's age group and technical literacy. Furthermore, the explanation unit can improve the explanation method by reflecting user feedback. Step 5: The proposal unit makes proposals to non-smartphone users to switch to smartphones and to those who wish to save money. For example, the proposal unit suggests purchasing a smartphone to a user who does not own a smartphone and explains the benefits of using a smartphone. The proposal unit also suggests discounts and benefits that can be obtained by using electronic regional promotion coupons to those who wish to save money. Furthermore, the proposal unit can customize the content of the proposals taking into account the user's living situation and areas of interest.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

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

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] 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.

[0196] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information collection unit that collects information; an analysis unit that analyzes the information collected by the information collection unit; a collaboration unit that performs collaboration based on the information analyzed by the analysis unit; an explanation unit that explains how to use and set up the mobile phone based on the collaboration performed by the collaboration unit; a suggestion unit that makes suggestions to non-users of the mobile phone based on the content explained by the explanation unit; Equipped with A system characterized by:

2. The information collecting unit Gather information from official local government websites and news articles 2. The system of claim 1.

3. The analysis unit Analyze the collected information and identify the launch date and content of the electronic regional promotion coupons 2. The system of claim 1.

4. The Collaboration Department: Collaborate with local governments, participating stores, and telecommunications carrier shops 2. The system of claim 1.

5. The explanation section Explain to users how to use and set up their smartphones 2. The system of claim 1.

6. The proposal unit Propose smartphone adoption to non-smartphone users and make proposals to those who wish to save money 2. The system of claim 1.

7. The information collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

8. The information collecting unit In addition to the official websites of each local government and related news articles, gather information from unofficial sources such as social media and blogs.

2. The system of claim 1.

9. The information collecting unit When collecting information, refer to the data of each local government's past regional promotion coupons to improve the accuracy of the information collected.

2. The system of claim 1.

10. The information collecting unit When collecting information, filter it taking into account the geographical characteristics and demographics of each municipality.

2. The system of claim 1.

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