System

Abstract

To provide a system that efficiently acquires information necessary for local residents and reflects their requests.SOLUTION: A system according to the embodiment comprises a collection unit, a provision unit, a reception unit, and a support unit. The collection unit collects regional information indicating a regional situation. The provision unit provides information to local residents using generative AI based on the information collected by the collection unit. The reception unit receives requests from the local residents. The support unit supports plans to attract facilities based on the requests received by the reception unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there was a problem in that it was difficult for local residents to efficiently obtain the information they needed and have their requests reflected.

[0005] The system according to the embodiment aims to enable local residents to efficiently obtain the information they need and to reflect their requests. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a provision unit, a reception unit, and a support unit. The collection unit collects regional information indicating regional conditions. The provision unit provides information to residents using a generation AI based on the information collected by the collection unit. The reception unit receives requests from residents. The support unit supports facility attraction plans based on the requests received by the reception unit. Effect of the Invention

[0007] The system according to the embodiment enables local residents to efficiently obtain the information they need and to have their requests reflected. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system that uses a generation AI to allow local residents to obtain information such as hospital availability and reservations, transportation operation and congestion, sales information for commercial facilities, and area-limited event information. This system collects information indicating the local situation, and the generation AI provides information to residents based on the collected information, accepts requests from residents, and supports facility attraction plans based on the requests. For example, when collecting information indicating the local situation, detailed data such as hospital availability, transportation operation, sales information for commercial facilities, and area-limited event information is collected using IoT and 5G. Next, the generation AI provides information to residents based on the collected information. The generation AI analyzes the collected data and provides information useful to residents. For example, it can analyze the operation and congestion of transportation and propose optimal travel routes. In addition, it is possible to enrich the lives of residents by providing sales information for commercial facilities and area-limited event information. Furthermore, it accepts requests from residents and supports facility attraction plans based on those requests. For example, if a resident wishes to attract a new commercial facility, the reception department accepts the request, and the support department supports the attraction plan based on the request. This allows local infrastructure to be optimized to meet the needs of residents. This system allows local residents to quickly obtain the information they need, optimizing local infrastructure. For example, by knowing hospital availability in real time, it is possible to respond quickly in emergencies. In addition, by knowing the operation status and congestion of public transportation, efficient travel is possible. Furthermore, by providing information on sales at commercial facilities and local events, it is possible to enrich the lives of residents. This allows local residents to quickly obtain the information they need, optimizing local infrastructure.

[0029] The information provision system according to the embodiment includes a collection unit, a provision unit, a reception unit, and a support unit. The collection unit collects regional information indicating regional conditions. The regional information includes, for example, hospital availability, transportation operation status, sales information at commercial facilities, and regional event information, but is not limited to such examples. The collection unit can collect hospital availability in real time, for example, using IoT or 5G. The collection unit can also collect the operation status and congestion status of transportation and provide it to residents. The collection unit can also collect sales information at commercial facilities and regional event information and provide it to residents. The provision unit uses a generation AI to analyze the collected data and provide information useful to residents. For example, the provision unit can analyze the operation status and congestion status of transportation and propose an optimal travel route. The provision unit can enrich the lives of residents by providing sales information at commercial facilities and regional event information. The generation AI provides information useful to residents using a text generation AI (for example, LLM) or a multimodal generation AI. The reception unit accepts requests from residents. For example, when a resident wishes to attract a new commercial facility, the reception unit can receive the request. The support unit supports the facility attraction plan based on the request received by the reception unit. For example, the support unit can support the facility attraction plan based on the resident's request. In this way, the information providing system according to the embodiment allows local residents to quickly obtain necessary information, and optimizes local infrastructure. For example, by grasping the availability of hospitals in real time, it is possible to respond quickly in an emergency. In addition, by grasping the operation status and congestion status of transportation, efficient movement is possible. Furthermore, by providing sales information of commercial facilities and event information limited to the area, it is possible to enrich the lives of residents.

[0030] The collection unit collects regional information indicating regional conditions. Regional information includes, for example, hospital availability, transportation operation status, sales information at commercial facilities, and regional event information, but is not limited to such examples. The collection unit can collect hospital availability in real time, for example, using IoT or 5G. Specifically, the collection unit cooperates with a hospital reservation system and a bed management system to obtain the current number of available beds and waiting time in real time. In addition, the collection unit can collect the operation status and congestion status of transportation and provide it to residents. The operation status of transportation is obtained from GPS data and a traffic management system, and bus and train delay information, operation suspension information, congestion degree, etc. are grasped in real time. Furthermore, the collection unit can collect sales information of commercial facilities and regional event information and provide it to residents. Sales information of commercial facilities is obtained from the store's POS system and online advertisement, and detailed information such as the start time of the sale, discount rate, and target products is collected. Regional event information is obtained from the event calendar of the local government and SNS, and the date and time, location, and participation conditions of the event are collected. This allows the collection department to collect a wide range of local information from various sources and provide useful information to residents. Furthermore, the collection department can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored on a cloud server so that the provision department and support department can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection department to collect data efficiently and effectively and improve the performance of the entire system.

[0031] The provision unit uses the generation AI to analyze the collected data and provide information useful to residents. For example, the provision unit can analyze the operation status and congestion status of transportation facilities and propose the optimal travel route. Specifically, the generation AI analyzes traffic data and calculates the most efficient travel route based on the current congestion status and delay information. For example, it analyzes the operation status of buses and trains in real time and proposes alternative routes and optimal transfer points to avoid congestion. In addition, the provision unit can enrich the lives of residents by providing sales information of commercial facilities and event information limited to the area. The generation AI provides information useful to residents using text generation AI (e.g., LLM) and multimodal generation AI. For example, based on sales information, it generates personalized sales information taking into account the purchasing history and preferences of residents and notifies them of the sales information. In addition, it analyzes event information limited to the area and recommends events based on the interests and concerns of residents. The generation AI uses natural language processing technology to analyze the collected data and provide information in a format that is easy for residents to understand. For example, traffic information and sales information can be displayed in simple text and graphics so that residents can intuitively understand. This allows the information providing unit to effectively analyze collected data and quickly provide useful information to residents. Furthermore, the information providing unit can collect feedback from residents and continuously improve the accuracy and usefulness of the information provided. For example, when residents rate and comment on the information provided, the generation AI learns from that feedback and reflects it in the information provided from the next time onwards. This allows the information providing unit to always provide the most appropriate information to residents and improve the quality of life in the area.

[0032] The reception department accepts requests from residents. For example, if a resident wishes to attract a new commercial facility, the reception department can accept the request. Specifically, the reception department accepts requests from residents through an online form or a dedicated app. Residents can enter the necessary information into the form and submit the request. The reception department saves the accepted requests in a database and shares them with the support department and other related departments. The reception department also categorizes the contents of the requests and sets priorities to enable efficient responses. For example, requests with high urgency or requests submitted by many residents are given priority. Furthermore, the reception department can notify residents of the acceptance status and progress of the request. For example, an email is sent to confirm that the request has been accepted, and the subsequent progress is periodically reported. This allows residents to understand how their requests are being handled. The reception department can facilitate communication with residents and support prompt and appropriate responses to requests. Furthermore, the reception department can collect feedback from residents and use it to improve the entire system. For example, based on feedback on the process of accepting requests and the speed of response, improvements to the system can be identified and reflected in future responses. This enables the reception department to respond quickly and appropriately to residents' requests and improve the living environment in the area.

[0033] The support department supports the attraction plan for the facility based on the request received by the reception department. For example, the support department can support the attraction plan for a new commercial facility based on the request of the residents. Specifically, the support department analyzes the request from the residents and investigates the needs and market trends of the area. For example, if the residents wish to attract a new supermarket, the support department investigates the demographics and purchasing power of the area and the status of existing commercial facilities to evaluate the possibility of attracting the new supermarket. In addition, the support department cooperates with the operating company of the commercial facility and the local government to materialize the attraction plan. For example, the support department explains the needs and market trends of the area to the operating company of the commercial facility and proposes the merits of the attraction. Furthermore, the support department reports the progress of the attraction plan to the residents and ensures transparency. For example, the support department periodically reports on progress so that the residents can grasp the progress of the plan. In this way, the support department can effectively support the attraction plan for the facility based on the request of the residents and improve the living environment of the area. Furthermore, even after the implementation of the attraction plan, the support department can monitor the operation status of the facility and make improvement proposals as necessary. For example, after a new commercial facility opens, the support department collects information about its use and feedback from residents and proposes improvements to the operating company, helping to ensure the success of the attraction plan and continually improving the living environment in the area.

[0034] The collection unit can collect data on hospital availability, transportation operation status, sales information at commercial facilities, and area-limited event information using IoT or 5G. The collection unit can collect hospital availability in real time using IoT technology, for example. For example, a sensor detects the availability and reservation status of hospital beds and collects data. The collection unit can also collect transportation operation status and congestion status using 5G technology. For example, the transportation operation schedule and delay information are collected in real time and provided to residents. Furthermore, the collection unit can also collect sales information at commercial facilities and area-limited event information. For example, the discount rate and sale period of the commercial facility are collected and provided to residents. As a result, detailed data can be collected efficiently by using IoT or 5G. Part or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data collected by a sensor into AI and have AI perform analysis of the data.

[0035] The providing unit can analyze the collected data and provide information useful to the residents. The providing unit can, for example, analyze the collected data and provide information useful to the residents. For example, the providing unit can analyze the operation status and congestion status of transportation facilities and propose an optimal travel route. The providing unit can also enrich the lives of the residents by providing sales information of commercial facilities and area-limited event information. The generating AI provides information useful to the residents using a text generating AI (for example, LLM) or a multimodal generating AI. For example, the generating AI can analyze the operation status and congestion status of transportation facilities and propose an optimal travel route. The generating AI can also enrich the lives of the residents by providing sales information of commercial facilities and area-limited event information. This allows the collected data to be analyzed to provide information useful to the residents. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the collected data to AI and have the AI ​​execute analysis of the data.

[0036] The reception unit can receive requests from residents. For example, when a resident wishes to attract a new commercial facility, the reception unit can receive the request. For example, the reception unit can allow residents to submit requests through an online form. The reception unit can also allow residents to submit requests through telephone or email. Furthermore, the reception unit can allow residents to submit requests directly at the counter. This allows responses according to local needs to be made by receiving requests from residents. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input requests submitted through an online form into AI and have the AI ​​analyze the requests.

[0037] The support unit can support the attraction plan for the facility based on the request received by the reception unit. The support unit can support the attraction plan for a new commercial facility based on, for example, the request of the residents. For example, the support unit analyzes the location conditions and demand of the commercial facility based on the request of the residents, and proposes an optimal attraction plan. The support unit can also support the installation plan for a public facility based on the request of the residents. For example, the support unit analyzes the installation location and size of the public facility based on the request of the residents, and proposes an optimal installation plan. As a result, the attraction plan for the facility is supported based on the request received by the reception unit, and the regional infrastructure is optimized to meet the needs of the residents. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the request of the residents into AI and have the AI ​​execute the analysis of the attraction plan.

[0038] The collection unit can analyze the past data collection history and select an appropriate collection method. For example, the collection unit identifies the most efficient collection time period from the past data collection history and collects information during that time period. For example, the collection unit adjusts the collection frequency from a specific data source based on the past data collection history. The collection unit can also analyze the past data collection history and apply an algorithm for optimizing the collection method. For example, the collection unit develops an algorithm for selecting an optimal collection method based on the past data collection history. In this way, the optimal collection method can be selected by analyzing the past data collection history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the past data collection history into AI and cause AI to select the optimal collection method.

[0039] The collection unit can adjust the type of data to be collected based on a specific event or season in the region when collecting information. For example, when a specific event in the region is held, the collection unit preferentially collects information related to the event. For example, the collection unit collects information related to festivals and sporting events in the region. The collection unit can also collect specific data for each season (e.g., summer tourist information, winter traffic information). For example, the collection unit collects congestion status and event information at tourist spots in the summer, and collects operation status of transportation facilities and the impact of snow in the winter. Furthermore, the collection unit can adjust the type of data to be collected based on an annual event calendar in the region. For example, the collection unit collects information related to annual events in the region and provides it to residents. As a result, by adjusting the type of data to be collected based on a specific event or season in the region, more relevant information can be collected. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input event information in the region to AI and have the AI ​​adjust the type of data to be collected.

[0040] When collecting information, the collection unit can preferentially collect highly relevant data in consideration of the geographical characteristics of the region. The collection unit, for example, preferentially collects the operation status of transportation based on the geographical characteristics of the region. For example, the collection unit collects the operation status and congestion status of transportation according to the geographical characteristics of the region, such as mountainous areas and urban areas. The collection unit can also preferentially collect sales information of commercial facilities based on the geographical characteristics of the region. For example, the collection unit collects sales information of commercial facilities in urban areas and sales information of shopping malls in suburban areas. Furthermore, the collection unit can also preferentially collect vacancy status of hospitals based on the geographical characteristics of the region. For example, the collection unit collects vacancy status of hospitals based on the distribution and access status of medical facilities in the region. As a result, more useful information can be provided by preferentially collecting highly relevant data in consideration of the geographical characteristics of the region. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data regarding the geographical characteristics of the region to AI and have AI determine the priority order of data to be collected.

[0041] The collection unit can analyze local social media activities and collect related data when collecting information. For example, the collection unit analyzes local social media activities and collects event information. For example, the collection unit analyzes the content of posts on social media and the usage of hashtags to collect local event information. The collection unit can also analyze local social media activities and collect the operation status of transportation. For example, the collection unit collects delay information and congestion status of transportation based on the content of posts on social media. Furthermore, the collection unit can analyze local social media activities and collect sales information of commercial facilities. For example, the collection unit collects sales information and discount information of commercial facilities based on the content of posts on social media. In this way, by analyzing local social media activities, related data can be efficiently collected. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the posted data on social media to AI and cause AI to collect related data.

[0042] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit provides information of high importance (e.g., availability of hospitals) in detail. For example, the providing unit provides the availability and reservation status of hospitals in detail so that residents can respond quickly. The providing unit can also provide information of low importance (e.g., sale information of commercial facilities) in a concise manner. For example, the providing unit provides the discount rate and sale period of commercial facilities in a concise manner so that residents can quickly obtain the necessary information. Furthermore, the providing unit can dynamically adjust the level of detail of the information according to the importance. For example, the providing unit provides information of high urgency in detail and provides information of low urgency in a concise manner. As a result, by adjusting the level of detail of the information provided based on the importance of the information, more appropriate information can be provided. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the importance of the information to AI and cause AI to adjust the level of detail of the information provided.

[0043] The providing unit can apply different providing algorithms depending on the category of information at the time of providing. For example, the providing unit applies an algorithm that is updated in real time to information regarding the availability of hospital beds. For example, the providing unit updates the availability and reservation status of hospital beds in real time and provides the information to residents. The providing unit can also apply a prediction algorithm to information regarding the operation status of transportation. For example, the providing unit predicts the operation schedule and delay information of transportation and provides it to residents. Furthermore, the providing unit can apply an algorithm that is updated periodically to information regarding sales information of commercial facilities. For example, the providing unit regularly updates the discount rate and sale period of the commercial facility and provides it to residents. This makes it possible to provide more appropriate information by applying different providing algorithms depending on the category of information. Part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the category of information to AI and cause AI to apply the providing algorithm.

[0044] The providing unit can determine the priority of the information provided based on the time of collection of the information at the time of providing. The providing unit, for example, provides the latest information with priority. For example, the providing unit provides the latest information, such as the availability of hospitals and the operation status of transportation facilities, with priority, so that residents can respond quickly. The providing unit can also lower the priority of the information collected old. For example, the providing unit lowers the priority of the information collected old, such as sales information of commercial facilities and event information limited to a region. Furthermore, the providing unit can dynamically adjust the order of information provided based on the collection time. For example, the providing unit provides information with a high degree of urgency with priority, and postpones the order of information with a low degree of urgency. As a result, the latest information can be provided with priority by determining the priority of the information provided based on the time of collection of the information. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the time of collection of the information to AI and allow AI to determine the priority of the information provided.

[0045] The providing unit can adjust the order of provision based on the relevance of information at the time of provision. For example, the providing unit preferentially provides information that is most relevant to the user's current situation. For example, the providing unit preferentially provides information that is highly relevant based on the user's current location information and behavior history. The providing unit can also postpone the order of provision of information with low relevance. For example, the providing unit postpones the order of provision of information in which the user has low interest. Furthermore, the providing unit can dynamically adjust the order of provision based on the relevance of information. For example, the providing unit adjusts the order of provision of information based on the user's interests and regional characteristics. As a result, by adjusting the order of provision based on the relevance of information, more relevant information can be preferentially provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance of information to AI and cause AI to execute adjustment of the order of provision.

[0046] The reception unit can analyze the past request reception history and select the optimal reception method. For example, the reception unit identifies the most efficient reception method from the past request reception history and preferentially proposes that method. For example, the reception unit selects a specific reception method (e.g., online form, telephone reception) based on the past request reception history. The reception unit can also analyze the past request reception history and apply an algorithm for optimizing the reception method. For example, the reception unit develops an algorithm for selecting the optimal reception method based on the past request reception history. In this way, the optimal reception method can be selected by analyzing the past request reception history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the past request reception history into AI and have the AI ​​select the optimal reception method.

[0047] The reception unit can perform filtering based on the user's current living situation and field of interest when receiving a request. The reception unit, for example, preferentially receives requests with high relevance based on the user's current living situation. For example, the reception unit preferentially receives requests with high relevance based on the user's family structure and income situation. The reception unit can also filter requests and propose an appropriate reception method based on the user's field of interest. For example, the reception unit filters requests based on the user's hobbies and topics of interest. Furthermore, the reception unit can determine the priority of requests based on the user's living situation and field of interest. For example, the reception unit develops an algorithm for determining the priority of requests based on the user's living situation and field of interest. As a result, by filtering based on the user's current living situation and field of interest, it is possible to preferentially receive requests with high relevance. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data related to the user's living situation and field of interest into AI and cause AI to execute filtering of requests.

[0048] The reception unit can preferentially receive requests with high relevance in consideration of the geographical location information of the user when receiving a request. The reception unit preferentially receives requests with high relevance based on, for example, the user's current location. For example, the reception unit preferentially receives requests related to the current location based on the user's GPS data. The reception unit can also propose an optimal reception method in consideration of the user's geographical location information. For example, the reception unit proposes an optimal reception method (e.g., online form, telephone reception) based on the user's location information. Furthermore, the reception unit can determine the priority of requests based on the user's geographical location information. For example, the reception unit develops an algorithm for determining the priority of requests based on the user's location information. This allows for more appropriate request reception by preferentially receiving requests with high relevance in consideration of the user's geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's location information into AI and have the AI ​​determine the priority of requests.

[0049] The reception unit can analyze the social media activity of the user at the time of receiving the request and receive the related request. The reception unit, for example, analyzes the social media activity of the user and receives the related request with priority. For example, the reception unit analyzes the content of posts on social media and the usage of hashtags, and receives the related request with priority. The reception unit can also suggest the optimal reception method based on the social media activity of the user. For example, the reception unit suggests the optimal reception method (e.g., online form, telephone reception) based on the activity on social media. Furthermore, the reception unit can determine the priority of the request based on the social media activity of the user. For example, the reception unit develops an algorithm for determining the priority of the request based on the activity on social media. This makes it possible to efficiently receive the related request by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the posting data on social media to AI and cause the AI ​​to execute the reception of the related request.

[0050] The support unit can select an appropriate support method by analyzing past support history at the time of support. For example, the support unit identifies the most effective support method from the past support history and preferentially proposes that method. For example, the support unit selects a specific support method (e.g., telephone support, online chat) based on the past support history. The support unit can also analyze the past support history and apply an algorithm for optimizing the support method. For example, the support unit develops an algorithm for selecting the optimal support method based on the past support history. In this way, the optimal support method can be selected by analyzing the past support history. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the past support history into AI and cause the AI ​​to select the optimal support method.

[0051] The support unit can customize the means of support based on the user's current living situation at the time of support. The support unit, for example, proposes the optimal means of support based on the user's current living situation. For example, the support unit proposes the optimal means of support (e.g., telephone support, online chat) based on the user's family structure and income situation. The support unit can also customize the content of support according to the user's living situation. For example, the support unit customizes the content of support based on the user's living situation. Furthermore, the support unit can also determine the priority of support taking into account the user's current living situation. For example, the support unit develops an algorithm that determines the priority of support based on the user's living situation. This enables more appropriate support by customizing the means of support based on the user's current living situation. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input data regarding the user's living situation into AI and have the AI ​​customize the means of support.

[0052] The support unit can select an appropriate support method in consideration of the geographical location information of the user when providing support. The support unit, for example, proposes an optimal support method based on the user's current location. For example, the support unit proposes a support method related to the current location based on the user's GPS data. The support unit can also customize the content of the support in consideration of the user's geographical location information. For example, the support unit customizes the support content based on the user's location information. Furthermore, the support unit can also determine the priority of the support based on the user's geographical location information. For example, the support unit develops an algorithm for determining the priority of the support based on the user's location information. This enables more appropriate support by selecting the optimal support method in consideration of the user's geographical location information. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the user's location information into AI and cause the AI ​​to select the support method.

[0053] The support unit can analyze the user's social media activity and suggest a means of support at the time of support. The support unit, for example, analyzes the user's social media activity and suggests related means of support. For example, the support unit analyzes the content of posts on social media and the usage of hashtags, and suggests related means of support. The support unit can also suggest an optimal support method based on the user's social media activity. For example, the support unit suggests an optimal support method (e.g., telephone support, online chat) based on the activity on social media. Furthermore, the support unit can determine the priority of support based on the user's social media activity. For example, the support unit develops an algorithm for determining the priority of support based on the activity on social media. This makes it possible to efficiently suggest related means of support by analyzing the user's social media activity. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the posting data on social media to AI and have the AI ​​execute the suggestion of support means.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The collection unit can collect local weather information and adjust the priority of other information collection based on the collected weather information. For example, when bad weather is expected, the collection unit prioritizes collection of transportation operation status and hospital vacancy status. The collection unit can also temporarily reduce collection of sales information and event information at commercial facilities based on the weather information. Furthermore, the collection unit can collect weather information in real time and dynamically adjust plans for other information collection based on the collected information. In this way, more relevant information can be collected by adjusting the priority of information collection in consideration of the weather information.

[0056] The collection unit can collect energy consumption data in the region and support the improvement of energy efficiency based on the collected data. For example, the collection unit can collect energy consumption data of homes and commercial facilities in the region and identify areas with low energy efficiency. The collection unit can also analyze the energy consumption data and make suggestions for improving energy efficiency. Furthermore, the collection unit can collect energy consumption data in real time and make dynamic adjustments for improving energy efficiency. In this way, it is possible to contribute to the sustainable development of the region by collecting energy consumption data and supporting the improvement of energy efficiency.

[0057] The providing unit can analyze the user's past behavior history and customize the information to be provided. For example, the providing unit can suggest an optimal travel route based on the user's past movement history. The providing unit can also provide related sale information based on the user's past purchase history. Furthermore, the providing unit can provide event information of interest based on the user's past event participation history. In this way, by analyzing the user's past behavior history and customizing the information to be provided, more relevant information can be provided.

[0058] The collection unit can collect local environmental data and support environmental protection activities based on the collected data. For example, the collection unit can collect local air quality data and water quality data and evaluate the effectiveness of the environmental protection activities. The collection unit can also analyze the environmental data and suggest improvements to the environmental protection activities. Furthermore, the collection unit can collect environmental data in real time and dynamically adjust the environmental protection activities. In this way, it is possible to contribute to the sustainable development of the region by collecting environmental data and supporting environmental protection activities.

[0059] The providing unit can determine the priority of the information to be provided based on the user's current location information. For example, the providing unit preferentially provides information related to the user's current location. For example, the providing unit provides traffic information and event information related to the current location based on the user's GPS data. The providing unit can also suggest an optimal travel route based on the user's location information. Furthermore, the providing unit can provide sales information of nearby commercial facilities based on the user's location information. In this way, by determining the priority of the information to be provided based on the user's current location information, more relevant information can be provided.

[0060] The support department can collect local economic data and support the revitalization of the local economy based on the collected data. For example, the support department collects sales data and employment data from local commercial facilities to grasp the current state of the economy. The support department can also analyze the economic data and make proposals for revitalizing the local economy. Furthermore, the support department can collect economic data in real time and make dynamic adjustments for economic revitalization. In this way, by collecting economic data and supporting the revitalization of the local economy, it is possible to contribute to the sustainable development of the region.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects local information that indicates the local situation. Local information includes hospital vacancies, transportation operation status, sales information at commercial facilities, and local event information. The collection unit can collect this information in real time using IoT and 5G. Step 2: The provider uses the generation AI to analyze the collected data and provide useful information to residents. For example, it can analyze the operation status and congestion of public transport to suggest optimal travel routes, or provide information on sales at commercial facilities and events limited to the area. Step 3: The reception department accepts requests from residents. For example, if a resident wishes to attract a new commercial facility, the request can be accepted. Step 4: The support department supports the attraction plan for the facility based on the request received by the reception department. For example, the support department can support the attraction plan for a new commercial facility based on the request of residents.

[0063] (Example 2) The system according to the embodiment of the present invention is a system that uses a generation AI to allow local residents to obtain information such as hospital availability and reservations, transportation operation and congestion, sales information for commercial facilities, and area-limited event information. This system collects information indicating the local situation, and the generation AI provides information to residents based on the collected information, accepts requests from residents, and supports facility attraction plans based on the requests. For example, when collecting information indicating the local situation, detailed data such as hospital availability, transportation operation, sales information for commercial facilities, and area-limited event information is collected using IoT and 5G. Next, the generation AI provides information to residents based on the collected information. The generation AI analyzes the collected data and provides information useful to residents. For example, it can analyze the operation and congestion of transportation and propose optimal travel routes. In addition, it is possible to enrich the lives of residents by providing sales information for commercial facilities and area-limited event information. Furthermore, it accepts requests from residents and supports facility attraction plans based on those requests. For example, if a resident wishes to attract a new commercial facility, the reception department accepts the request, and the support department supports the attraction plan based on the request. This allows local infrastructure to be optimized to meet the needs of residents. This system allows local residents to quickly obtain the information they need, optimizing local infrastructure. For example, by knowing hospital availability in real time, it is possible to respond quickly in emergencies. In addition, by knowing the operation status and congestion of public transportation, efficient travel is possible. Furthermore, by providing information on sales at commercial facilities and local events, it is possible to enrich the lives of residents. This allows local residents to quickly obtain the information they need, optimizing local infrastructure.

[0064] The information provision system according to the embodiment includes a collection unit, a provision unit, a reception unit, and a support unit. The collection unit collects regional information indicating regional conditions. The regional information includes, for example, hospital availability, transportation operation status, sales information at commercial facilities, and regional event information, but is not limited to such examples. The collection unit can collect hospital availability in real time, for example, using IoT or 5G. The collection unit can also collect the operation status and congestion status of transportation and provide it to residents. The collection unit can also collect sales information at commercial facilities and regional event information and provide it to residents. The provision unit uses a generation AI to analyze the collected data and provide information useful to residents. For example, the provision unit can analyze the operation status and congestion status of transportation and propose an optimal travel route. The provision unit can enrich the lives of residents by providing sales information at commercial facilities and regional event information. The generation AI provides information useful to residents using a text generation AI (for example, LLM) or a multimodal generation AI. The reception unit accepts requests from residents. For example, when a resident wishes to attract a new commercial facility, the reception unit can receive the request. The support unit supports the facility attraction plan based on the request received by the reception unit. For example, the support unit can support the facility attraction plan based on the resident's request. In this way, the information providing system according to the embodiment allows local residents to quickly obtain necessary information, and optimizes local infrastructure. For example, by grasping the availability of hospitals in real time, it is possible to respond quickly in an emergency. In addition, by grasping the operation status and congestion status of transportation, efficient movement is possible. Furthermore, by providing sales information of commercial facilities and event information limited to the area, it is possible to enrich the lives of residents.

[0065] The collection unit collects regional information indicating regional conditions. Regional information includes, for example, hospital availability, transportation operation status, sales information at commercial facilities, and regional event information, but is not limited to such examples. The collection unit can collect hospital availability in real time, for example, using IoT or 5G. Specifically, the collection unit cooperates with a hospital reservation system and a bed management system to obtain the current number of available beds and waiting time in real time. In addition, the collection unit can collect the operation status and congestion status of transportation and provide it to residents. The operation status of transportation is obtained from GPS data and a traffic management system, and bus and train delay information, operation suspension information, congestion degree, etc. are grasped in real time. Furthermore, the collection unit can collect sales information of commercial facilities and regional event information and provide it to residents. Sales information of commercial facilities is obtained from the store's POS system and online advertisement, and detailed information such as the start time of the sale, discount rate, and target products is collected. Regional event information is obtained from the event calendar of the local government and SNS, and the date and time, location, and participation conditions of the event are collected. This allows the collection department to collect a wide range of local information from various sources and provide useful information to residents. Furthermore, the collection department can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored on a cloud server so that the provision department and support department can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection department to collect data efficiently and effectively and improve the performance of the entire system.

[0066] The provision unit uses the generation AI to analyze the collected data and provide information useful to residents. For example, the provision unit can analyze the operation status and congestion status of transportation facilities and propose the optimal travel route. Specifically, the generation AI analyzes traffic data and calculates the most efficient travel route based on the current congestion status and delay information. For example, it analyzes the operation status of buses and trains in real time and proposes alternative routes and optimal transfer points to avoid congestion. In addition, the provision unit can enrich the lives of residents by providing sales information of commercial facilities and event information limited to the area. The generation AI provides information useful to residents using text generation AI (e.g., LLM) and multimodal generation AI. For example, based on sales information, it generates personalized sales information taking into account the purchasing history and preferences of residents and notifies them of the sales information. In addition, it analyzes event information limited to the area and recommends events based on the interests and concerns of residents. The generation AI uses natural language processing technology to analyze the collected data and provide information in a format that is easy for residents to understand. For example, traffic information and sales information can be displayed in simple text and graphics so that residents can intuitively understand. This allows the information providing unit to effectively analyze collected data and quickly provide useful information to residents. Furthermore, the information providing unit can collect feedback from residents and continuously improve the accuracy and usefulness of the information provided. For example, when residents rate and comment on the information provided, the generation AI learns from that feedback and reflects it in the information provided from the next time onwards. This allows the information providing unit to always provide the most appropriate information to residents and improve the quality of life in the area.

[0067] The reception department accepts requests from residents. For example, if a resident wishes to attract a new commercial facility, the reception department can accept the request. Specifically, the reception department accepts requests from residents through an online form or a dedicated app. Residents can enter the necessary information into the form and submit the request. The reception department saves the accepted requests in a database and shares them with the support department and other related departments. The reception department also categorizes the contents of the requests and sets priorities to enable efficient responses. For example, requests with high urgency or requests submitted by many residents are given priority. Furthermore, the reception department can notify residents of the acceptance status and progress of the request. For example, an email is sent to confirm that the request has been accepted, and the subsequent progress is periodically reported. This allows residents to understand how their requests are being handled. The reception department can facilitate communication with residents and support prompt and appropriate responses to requests. Furthermore, the reception department can collect feedback from residents and use it to improve the entire system. For example, based on feedback on the process of accepting requests and the speed of response, improvements to the system can be identified and reflected in future responses. This enables the reception department to respond quickly and appropriately to residents' requests and improve the living environment in the area.

[0068] The support department supports the attraction plan for the facility based on the request received by the reception department. For example, the support department can support the attraction plan for a new commercial facility based on the request of the residents. Specifically, the support department analyzes the request from the residents and investigates the needs and market trends of the area. For example, if the residents wish to attract a new supermarket, the support department investigates the demographics and purchasing power of the area and the status of existing commercial facilities to evaluate the possibility of attracting the new supermarket. In addition, the support department cooperates with the operating company of the commercial facility and the local government to materialize the attraction plan. For example, the support department explains the needs and market trends of the area to the operating company of the commercial facility and proposes the merits of the attraction. Furthermore, the support department reports the progress of the attraction plan to the residents and ensures transparency. For example, the support department periodically reports on progress so that the residents can grasp the progress of the plan. In this way, the support department can effectively support the attraction plan for the facility based on the request of the residents and improve the living environment of the area. Furthermore, even after the implementation of the attraction plan, the support department can monitor the operation status of the facility and make improvement proposals as necessary. For example, after a new commercial facility opens, the support department collects information about its use and feedback from residents and proposes improvements to the operating company, helping to ensure the success of the attraction plan and continually improving the living environment in the area.

[0069] The collection unit can collect data on hospital availability, transportation operation status, sales information at commercial facilities, and area-limited event information using IoT or 5G. The collection unit can collect hospital availability in real time using IoT technology, for example. For example, a sensor detects the availability and reservation status of hospital beds and collects data. The collection unit can also collect transportation operation status and congestion status using 5G technology. For example, the transportation operation schedule and delay information are collected in real time and provided to residents. Furthermore, the collection unit can also collect sales information at commercial facilities and area-limited event information. For example, the discount rate and sale period of the commercial facility are collected and provided to residents. As a result, detailed data can be collected efficiently by using IoT or 5G. Part or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data collected by a sensor into AI and have AI perform analysis of the data.

[0070] The providing unit can analyze the collected data and provide information useful to the residents. The providing unit can, for example, analyze the collected data and provide information useful to the residents. For example, the providing unit can analyze the operation status and congestion status of transportation facilities and propose an optimal travel route. The providing unit can also enrich the lives of the residents by providing sales information of commercial facilities and area-limited event information. The generating AI provides information useful to the residents using a text generating AI (for example, LLM) or a multimodal generating AI. For example, the generating AI can analyze the operation status and congestion status of transportation facilities and propose an optimal travel route. The generating AI can also enrich the lives of the residents by providing sales information of commercial facilities and area-limited event information. This allows the collected data to be analyzed to provide information useful to the residents. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the collected data to AI and have the AI ​​execute analysis of the data.

[0071] The reception unit can receive requests from residents. For example, when a resident wishes to attract a new commercial facility, the reception unit can receive the request. For example, the reception unit can allow residents to submit requests through an online form. The reception unit can also allow residents to submit requests through telephone or email. Furthermore, the reception unit can allow residents to submit requests directly at the counter. This allows responses according to local needs to be made by receiving requests from residents. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input requests submitted through an online form into AI and have the AI ​​analyze the requests.

[0072] The support unit can support the attraction plan for the facility based on the request received by the reception unit. The support unit can support the attraction plan for a new commercial facility based on, for example, the request of the residents. For example, the support unit analyzes the location conditions and demand of the commercial facility based on the request of the residents, and proposes an optimal attraction plan. The support unit can also support the installation plan for a public facility based on the request of the residents. For example, the support unit analyzes the installation location and size of the public facility based on the request of the residents, and proposes an optimal installation plan. As a result, the attraction plan for the facility is supported based on the request received by the reception unit, and the regional infrastructure is optimized to meet the needs of the residents. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the request of the residents into AI and have the AI ​​execute the analysis of the attraction plan.

[0073] The collection unit can estimate the user's emotion and adjust the timing of information collection based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit reduces the frequency of information collection and collects only important information. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, the collection unit can increase the frequency of information collection and collect detailed information when the user is relaxed. For example, the collection unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, when the user is in a hurry, the collection unit can collect information in real time and provide it quickly. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This enables more appropriate information collection by adjusting the timing of information collection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0074] The collection unit can analyze the past data collection history and select an appropriate collection method. For example, the collection unit identifies the most efficient collection time period from the past data collection history and collects information during that time period. For example, the collection unit adjusts the collection frequency from a specific data source based on the past data collection history. The collection unit can also analyze the past data collection history and apply an algorithm for optimizing the collection method. For example, the collection unit develops an algorithm for selecting an optimal collection method based on the past data collection history. In this way, the optimal collection method can be selected by analyzing the past data collection history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the past data collection history into AI and cause AI to select the optimal collection method.

[0075] The collection unit can adjust the type of data to be collected based on a specific event or season in the region when collecting information. For example, when a specific event in the region is held, the collection unit preferentially collects information related to the event. For example, the collection unit collects information related to festivals and sporting events in the region. The collection unit can also collect specific data for each season (e.g., summer tourist information, winter traffic information). For example, the collection unit collects congestion status and event information at tourist spots in the summer, and collects operation status of transportation facilities and the impact of snow in the winter. Furthermore, the collection unit can adjust the type of data to be collected based on an annual event calendar in the region. For example, the collection unit collects information related to annual events in the region and provides it to residents. As a result, by adjusting the type of data to be collected based on a specific event or season in the region, more relevant information can be collected. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input event information in the region to AI and have the AI ​​adjust the type of data to be collected.

[0076] The collection unit can estimate the user's emotion and determine the priority of information to be collected based on the estimated user's emotion. For example, when the user feels anxious, the collection unit preferentially collects information that gives a sense of security (e.g., availability of hospitals). For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, when the user is excited, the collection unit can also preferentially collect entertainment information (e.g., event information). For example, the collection unit records the user's voice and estimates the emotion using a voice analysis technology. In addition, when the user is tired, the collection unit can also preferentially collect information that can relax the user (e.g., sale information at a commercial facility). For example, the collection unit collects the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows more appropriate information to be collected by determining the priority of information to be collected according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0077] When collecting information, the collection unit can preferentially collect highly relevant data in consideration of the geographical characteristics of the region. The collection unit, for example, preferentially collects the operation status of transportation based on the geographical characteristics of the region. For example, the collection unit collects the operation status and congestion status of transportation according to the geographical characteristics of the region, such as mountainous areas and urban areas. The collection unit can also preferentially collect sales information of commercial facilities based on the geographical characteristics of the region. For example, the collection unit collects sales information of commercial facilities in urban areas and sales information of shopping malls in suburban areas. Furthermore, the collection unit can also preferentially collect vacancy status of hospitals based on the geographical characteristics of the region. For example, the collection unit collects vacancy status of hospitals based on the distribution and access status of medical facilities in the region. As a result, more useful information can be provided by preferentially collecting highly relevant data in consideration of the geographical characteristics of the region. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data regarding the geographical characteristics of the region to AI and have AI determine the priority order of data to be collected.

[0078] The collection unit can analyze local social media activities and collect related data when collecting information. For example, the collection unit analyzes local social media activities and collects event information. For example, the collection unit analyzes the content of posts on social media and the usage of hashtags to collect local event information. The collection unit can also analyze local social media activities and collect the operation status of transportation. For example, the collection unit collects delay information and congestion status of transportation based on the content of posts on social media. Furthermore, the collection unit can analyze local social media activities and collect sales information of commercial facilities. For example, the collection unit collects sales information and discount information of commercial facilities based on the content of posts on social media. In this way, by analyzing local social media activities, related data can be efficiently collected. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the posted data on social media to AI and cause AI to collect related data.

[0079] The providing unit can estimate the user's emotion and adjust the expression method of information provision based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides a simple and highly visible expression method. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, when the user is relaxed, the providing unit can also provide an expression method including detailed information. For example, the providing unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, when the user is in a hurry, the providing unit can also provide an expression method that covers the main points. For example, the providing unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This makes it possible to provide more appropriate information by adjusting the expression method of information provision according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a user taken by a camera to the generating AI, and cause the generating AI to estimate the user's emotions.

[0080] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit provides information of high importance (e.g., availability of hospitals) in detail. For example, the providing unit provides the availability and reservation status of hospitals in detail so that residents can respond quickly. The providing unit can also provide information of low importance (e.g., sale information of commercial facilities) in a concise manner. For example, the providing unit provides the discount rate and sale period of commercial facilities in a concise manner so that residents can quickly obtain the necessary information. Furthermore, the providing unit can dynamically adjust the level of detail of the information according to the importance. For example, the providing unit provides information of high urgency in detail and provides information of low urgency in a concise manner. As a result, by adjusting the level of detail of the information provided based on the importance of the information, more appropriate information can be provided. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the importance of the information to AI and cause AI to adjust the level of detail of the information provided.

[0081] The providing unit can apply different providing algorithms depending on the category of information at the time of providing. For example, the providing unit applies an algorithm that is updated in real time to information regarding the availability of hospital beds. For example, the providing unit updates the availability and reservation status of hospital beds in real time and provides the information to residents. The providing unit can also apply a prediction algorithm to information regarding the operation status of transportation. For example, the providing unit predicts the operation schedule and delay information of transportation and provides it to residents. Furthermore, the providing unit can apply an algorithm that is updated periodically to information regarding sales information of commercial facilities. For example, the providing unit regularly updates the discount rate and sale period of the commercial facility and provides it to residents. This makes it possible to provide more appropriate information by applying different providing algorithms depending on the category of information. Part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the category of information to AI and cause AI to apply the providing algorithm.

[0082] The providing unit can estimate the user's emotion and adjust the length of information provided based on the estimated user's emotion. For example, when the user is in a hurry, the providing unit provides short, to-the-point information. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, when the user is relaxed, the providing unit can provide longer information including a detailed explanation. For example, the providing unit records the user's voice and estimates the emotion using a voice analysis technique. Furthermore, when the user is excited, the providing unit can provide information with a visually stimulating effect. For example, the providing unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the length of information provided to be adjusted according to the user's emotion, thereby making it possible to provide more appropriate information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a user taken by a camera to the generating AI, and cause the generating AI to estimate the user's emotions.

[0083] The providing unit can determine the priority of the information provided based on the time of collection of the information at the time of providing. The providing unit, for example, provides the latest information with priority. For example, the providing unit provides the latest information, such as the availability of hospitals and the operation status of transportation facilities, with priority, so that residents can respond quickly. The providing unit can also lower the priority of the information collected old. For example, the providing unit lowers the priority of the information collected old, such as sales information of commercial facilities and event information limited to a region. Furthermore, the providing unit can dynamically adjust the order of information provided based on the collection time. For example, the providing unit provides information with a high degree of urgency with priority, and postpones the order of information with a low degree of urgency. As a result, the latest information can be provided with priority by determining the priority of the information provided based on the time of collection of the information. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the time of collection of the information to AI and allow AI to determine the priority of the information provided.

[0084] The providing unit can adjust the order of provision based on the relevance of information at the time of provision. For example, the providing unit preferentially provides information that is most relevant to the user's current situation. For example, the providing unit preferentially provides information that is highly relevant based on the user's current location information and behavior history. The providing unit can also postpone the order of provision of information with low relevance. For example, the providing unit postpones the order of provision of information in which the user has low interest. Furthermore, the providing unit can dynamically adjust the order of provision based on the relevance of information. For example, the providing unit adjusts the order of provision of information based on the user's interests and regional characteristics. As a result, by adjusting the order of provision based on the relevance of information, more relevant information can be preferentially provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance of information to AI and cause AI to execute adjustment of the order of provision.

[0085] The reception unit can estimate the user's emotion and adjust the request reception method based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit provides a simple interface and minimizes the input procedure. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Also, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, the reception unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and quickly accept the request. For example, the reception unit collects the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the request reception method to be adjusted according to the user's emotion, making it possible to more appropriately accept the request. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0086] The reception unit can analyze the past request reception history and select the optimal reception method. For example, the reception unit identifies the most efficient reception method from the past request reception history and preferentially proposes that method. For example, the reception unit selects a specific reception method (e.g., online form, telephone reception) based on the past request reception history. The reception unit can also analyze the past request reception history and apply an algorithm for optimizing the reception method. For example, the reception unit develops an algorithm for selecting the optimal reception method based on the past request reception history. In this way, the optimal reception method can be selected by analyzing the past request reception history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the past request reception history into AI and have the AI ​​select the optimal reception method.

[0087] The reception unit can perform filtering based on the user's current living situation and field of interest when receiving a request. The reception unit, for example, preferentially receives requests with high relevance based on the user's current living situation. For example, the reception unit preferentially receives requests with high relevance based on the user's family structure and income situation. The reception unit can also filter requests and propose an appropriate reception method based on the user's field of interest. For example, the reception unit filters requests based on the user's hobbies and topics of interest. Furthermore, the reception unit can determine the priority of requests based on the user's living situation and field of interest. For example, the reception unit develops an algorithm for determining the priority of requests based on the user's living situation and field of interest. As a result, by filtering based on the user's current living situation and field of interest, it is possible to preferentially receive requests with high relevance. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data related to the user's living situation and field of interest into AI and cause AI to execute filtering of requests.

[0088] The reception unit can estimate the user's emotion and determine the priority of requests to be received based on the estimated user's emotion. For example, when the user feels anxious, the reception unit preferentially receives requests that provide a sense of security. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, when the user is excited, the reception unit can also preferentially receive requests related to entertainment. For example, the reception unit records the user's voice and estimates the emotion using a voice analysis technique. In addition, when the user is tired, the reception unit can also preferentially receive requests that allow the user to relax. For example, the reception unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows for more appropriate request reception by determining the priority of requests according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0089] The reception unit can preferentially receive requests with high relevance in consideration of the geographical location information of the user when receiving a request. The reception unit preferentially receives requests with high relevance based on, for example, the user's current location. For example, the reception unit preferentially receives requests related to the current location based on the user's GPS data. The reception unit can also propose an optimal reception method in consideration of the user's geographical location information. For example, the reception unit proposes an optimal reception method (e.g., online form, telephone reception) based on the user's location information. Furthermore, the reception unit can determine the priority of requests based on the user's geographical location information. For example, the reception unit develops an algorithm for determining the priority of requests based on the user's location information. This allows for more appropriate request reception by preferentially receiving requests with high relevance in consideration of the user's geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's location information into AI and have the AI ​​determine the priority of requests.

[0090] The reception unit can analyze the social media activity of the user at the time of receiving the request and receive the related request. The reception unit, for example, analyzes the social media activity of the user and receives the related request with priority. For example, the reception unit analyzes the content of posts on social media and the usage of hashtags, and receives the related request with priority. The reception unit can also suggest the optimal reception method based on the social media activity of the user. For example, the reception unit suggests the optimal reception method (e.g., online form, telephone reception) based on the activity on social media. Furthermore, the reception unit can determine the priority of the request based on the social media activity of the user. For example, the reception unit develops an algorithm for determining the priority of the request based on the activity on social media. This makes it possible to efficiently receive the related request by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the posting data on social media to AI and cause the AI ​​to execute the reception of the related request.

[0091] The support unit can estimate the user's emotion and adjust the method of support based on the estimated user's emotion. For example, when the user is nervous, the support unit provides support in a calm voice. For example, the support unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, when the user is relaxed, the support unit can also provide support in a bright voice. For example, the support unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, when the user is in a hurry, the support unit can provide quick and concise support. For example, the support unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This enables more appropriate support by adjusting the method of support according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit may input image data of the user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0092] The support unit can select an appropriate support method by analyzing past support history at the time of support. For example, the support unit identifies the most effective support method from the past support history and preferentially proposes that method. For example, the support unit selects a specific support method (e.g., telephone support, online chat) based on the past support history. The support unit can also analyze the past support history and apply an algorithm for optimizing the support method. For example, the support unit develops an algorithm for selecting the optimal support method based on the past support history. In this way, the optimal support method can be selected by analyzing the past support history. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the past support history into AI and cause the AI ​​to select the optimal support method.

[0093] The support unit can customize the means of support based on the user's current living situation at the time of support. The support unit, for example, proposes the optimal means of support based on the user's current living situation. For example, the support unit proposes the optimal means of support (e.g., telephone support, online chat) based on the user's family structure and income situation. The support unit can also customize the content of support according to the user's living situation. For example, the support unit customizes the content of support based on the user's living situation. Furthermore, the support unit can also determine the priority of support taking into account the user's current living situation. For example, the support unit develops an algorithm that determines the priority of support based on the user's living situation. This enables more appropriate support by customizing the means of support based on the user's current living situation. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input data regarding the user's living situation into AI and have the AI ​​customize the means of support.

[0094] The support unit can estimate the user's emotion and determine the priority of the support based on the estimated user's emotion. For example, when the user feels anxious, the support unit prioritizes support that gives a sense of security. For example, the support unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, when the user is excited, the support unit can also prioritize entertainment-related support. For example, the support unit records the user's voice and estimates the emotion using a voice analysis technology. In addition, when the user is tired, the support unit can also prioritize support that allows the user to relax. For example, the support unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This enables more appropriate support by determining the priority of support according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit may input image data of the user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0095] The support unit can select an appropriate support method in consideration of the geographical location information of the user when providing support. The support unit, for example, proposes an optimal support method based on the user's current location. For example, the support unit proposes a support method related to the current location based on the user's GPS data. The support unit can also customize the content of the support in consideration of the user's geographical location information. For example, the support unit customizes the support content based on the user's location information. Furthermore, the support unit can also determine the priority of the support based on the user's geographical location information. For example, the support unit develops an algorithm for determining the priority of the support based on the user's location information. This enables more appropriate support by selecting the optimal support method in consideration of the user's geographical location information. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the user's location information into AI and cause the AI ​​to select the support method.

[0096] The support unit can analyze the user's social media activity and suggest a means of support at the time of support. The support unit, for example, analyzes the user's social media activity and suggests related means of support. For example, the support unit analyzes the content of posts on social media and the usage of hashtags, and suggests related means of support. The support unit can also suggest an optimal support method based on the user's social media activity. For example, the support unit suggests an optimal support method (e.g., telephone support, online chat) based on the activity on social media. Furthermore, the support unit can determine the priority of support based on the user's social media activity. For example, the support unit develops an algorithm for determining the priority of support based on the activity on social media. This makes it possible to efficiently suggest related means of support by analyzing the user's social media activity. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the posting data on social media to AI and have the AI ​​execute the suggestion of support means.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The providing unit can estimate the user's emotions and adjust the timing of information provision based on the estimated user's emotions. For example, when the user is feeling stressed, the frequency of information provision is reduced and only important information is provided. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Also, when the user is relaxed, the frequency of information provision can be increased and detailed information can be provided. For example, the providing unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, when the user is in a hurry, information can be provided in real time and a quick response can be made. For example, the providing unit collects the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This makes it possible to provide more appropriate information by adjusting the timing of information provision according to the user's emotions.

[0099] The collection unit can collect local weather information and adjust the priority of other information collection based on the collected weather information. For example, when bad weather is expected, the collection unit prioritizes collection of transportation operation status and hospital vacancy status. The collection unit can also temporarily reduce collection of sales information and event information at commercial facilities based on the weather information. Furthermore, the collection unit can collect weather information in real time and dynamically adjust plans for other information collection based on the collected information. In this way, more relevant information can be collected by adjusting the priority of information collection in consideration of the weather information.

[0100] The providing unit can estimate the user's emotion and adjust the format of information provision based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides information in a simple and highly visible format. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, when the user is relaxed, the information can be provided in a format including detailed information. For example, the providing unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, when the user is in a hurry, the information can be provided in a concise format that focuses on the main points. For example, the providing unit collects the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This makes it possible to provide more appropriate information by adjusting the format of information provision according to the user's emotion.

[0101] The reception unit can estimate the user's emotion and adjust the method of receiving the request based on the estimated user's emotion. For example, when the user is feeling stressed, a simple interface is provided to minimize the input procedure. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Also, when the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. For example, the reception unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, when the user is in a hurry, voice input can be prioritized so that the request can be received quickly. For example, the reception unit collects the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the request reception method to be adjusted according to the user's emotion, making it possible to receive the request more appropriately.

[0102] The support unit can estimate the user's emotion and adjust the method of support based on the estimated user's emotion. For example, if the user is nervous, the support unit provides support in a calm voice. For example, the support unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Also, if the user is relaxed, the support unit can provide support in a bright voice. For example, the support unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, if the user is in a hurry, the support unit can provide quick and concise support. For example, the support unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows more appropriate support by adjusting the method of support according to the user's emotion.

[0103] The collection unit can collect energy consumption data in the region and support the improvement of energy efficiency based on the collected data. For example, the collection unit can collect energy consumption data of homes and commercial facilities in the region and identify areas with low energy efficiency. The collection unit can also analyze the energy consumption data and make suggestions for improving energy efficiency. Furthermore, the collection unit can collect energy consumption data in real time and make dynamic adjustments for improving energy efficiency. In this way, it is possible to contribute to the sustainable development of the region by collecting energy consumption data and supporting the improvement of energy efficiency.

[0104] The providing unit can analyze the user's past behavior history and customize the information to be provided. For example, the providing unit can suggest an optimal travel route based on the user's past movement history. The providing unit can also provide related sale information based on the user's past purchase history. Furthermore, the providing unit can provide event information of interest based on the user's past event participation history. In this way, by analyzing the user's past behavior history and customizing the information to be provided, more relevant information can be provided.

[0105] The collection unit can collect local environmental data and support environmental protection activities based on the collected data. For example, the collection unit can collect local air quality data and water quality data and evaluate the effectiveness of the environmental protection activities. The collection unit can also analyze the environmental data and suggest improvements to the environmental protection activities. Furthermore, the collection unit can collect environmental data in real time and dynamically adjust the environmental protection activities. In this way, it is possible to contribute to the sustainable development of the region by collecting environmental data and supporting environmental protection activities.

[0106] The providing unit can determine the priority of the information to be provided based on the user's current location information. For example, the providing unit preferentially provides information related to the user's current location. For example, the providing unit provides traffic information and event information related to the current location based on the user's GPS data. The providing unit can also suggest an optimal travel route based on the user's location information. Furthermore, the providing unit can provide sales information of nearby commercial facilities based on the user's location information. In this way, by determining the priority of the information to be provided based on the user's current location information, more relevant information can be provided.

[0107] The support department can collect local economic data and support the revitalization of the local economy based on the collected data. For example, the support department collects sales data and employment data from local commercial facilities to grasp the current state of the economy. The support department can also analyze the economic data and make proposals for revitalizing the local economy. Furthermore, the support department can collect economic data in real time and make dynamic adjustments for economic revitalization. In this way, by collecting economic data and supporting the revitalization of the local economy, it is possible to contribute to the sustainable development of the region.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The collection unit collects local information that indicates the local situation. Local information includes hospital vacancies, transportation operation status, sales information at commercial facilities, and local event information. The collection unit can collect this information in real time using IoT and 5G. Step 2: The provider uses the generation AI to analyze the collected data and provide useful information to residents. For example, it can analyze the operation status and congestion of public transport to suggest optimal travel routes, or provide information on sales at commercial facilities and events limited to the area. Step 3: The reception department accepts requests from residents. For example, if a resident wishes to attract a new commercial facility, the request can be accepted. Step 4: The support department supports the attraction plan for the facility based on the request received by the reception department. For example, the support department can support the attraction plan for a new commercial facility based on the request of residents.

[0110] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0111] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] For example, the collection unit can collect local information using the camera 42 and microphone 38B of the smart device 14. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data and provides information useful to residents. The reception unit is realized, for example, by the control unit 46A of the smart device 14, and receives requests from residents. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and supports the facility attraction plan based on the requests of residents. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0117] 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.

[0118] 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 the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] 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 performed in a secure state.

[0121] Fig. 4 shows an example of 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.

[0122] 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.

[0123] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0127] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] For example, the collection unit can collect local information using the camera 42 and microphone 238 of the smart glasses 214. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data and provides information useful to residents. The reception unit is realized, for example, by the control unit 46A of the smart glasses 214, and receives requests from residents. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and supports the facility attraction plan based on the requests of residents. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 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.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0133] 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.

[0134] 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 the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] 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 performed in a secure state.

[0137] 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.

[0138] 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.

[0139] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0141] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] 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 voice indicating a user input for 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.

[0143] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] For example, the collection unit can collect local information using the camera 42 and microphone 238 of the headset terminal 314. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data and provides information useful to residents. The reception unit is realized, for example, by the control unit 46A of the headset terminal 314, and receives requests from residents. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and supports the facility attraction plan based on the requests of residents. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0149] 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.

[0150] 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 the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] 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 performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0154] 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.

[0155] 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.

[0156] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0158] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0159] 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 a voice indicating a user input for 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.

[0160] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0161] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0162] For example, the collection unit can collect local information using the camera 42 and microphone 238 of the robot 414. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data and provides information useful to residents. The reception unit is realized, for example, by the control unit 46A of the robot 414, and receives requests from residents. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and supports the facility attraction plan based on the requests of residents. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0170] 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.

[0171] 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 Universal Serial Bus (USB) 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.

[0172] In addition, 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 upon request from the data processing device 12.

[0173] 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.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0176] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) a collection unit for collecting regional information indicating regional conditions; A providing unit that provides information to residents using a generation AI based on the information collected by the collecting unit; A reception unit that receives requests from the residents; A support unit that supports a facility attraction plan based on the request received by the reception unit. A system characterized by: (Appendix 2) The collecting unit includes: Using IoT or 5G, data on hospital vacancies, transportation status, sales at commercial facilities, and local event information will be collected. 2. The system of claim 1, (Appendix 3) The providing unit is Analyze the collected data and provide useful information to residents 2. The system of claim 1, (Appendix 4) The reception unit is Accepting residents' requests 2. The system of claim 1, (Appendix 5) The support unit includes: Support the attraction plan for facilities based on requests received by the reception department 2. The system of claim 1, (Appendix 6) The collecting unit includes: To estimate a user's emotion and adjust the timing of information collection based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Analyze past data collection history and select appropriate collection methods 2. The system of claim 1, (Appendix 8) The collecting unit includes: When collecting information, tailor the type of data you collect based on specific events or seasons in your area. 2. The system of claim 1, (Appendix 9) The collecting unit includes: To estimate a user's emotion and determine the priority of information to be collected based on the estimated user's emotion. 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting information, prioritize collecting data that is highly relevant, taking into account the geographical characteristics of the region. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting information, analyze local social media activity and collect relevant data 2. The system of claim 1, (Appendix 12) The providing unit is Estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions. 2. The system of claim 1, (Appendix 13) The providing unit is When providing information, adjust the level of detail provided based on the importance of the information 2. The system of claim 1, (Appendix 14) The providing unit is When serving, different serving algorithms are applied depending on the category of information 2. The system of claim 1, (Appendix 15) The providing unit is To estimate a user's emotion and adjust the length of information provided based on the estimated user's emotion. 2. The system of claim 1, (Appendix 16) The providing unit is At the time of provision, the provision will be prioritized based on when the information was collected. 2. The system of claim 1, (Appendix 17) The providing unit is When serving, adjust the order of serving based on the relevance of the information 2. The system of claim 1, (Appendix 18) The reception unit is To estimate a user's emotion and adjust a method of accepting a request based on the estimated user's emotion. 2. The system of claim 1, (Appendix 19) The reception unit is Analyze past request reception history and select the optimal reception method 2. The system of claim 1, (Appendix 20) The reception unit is When receiving requests, filter them based on the user's current life situation and areas of interest 2. The system of claim 1, (Appendix 21) The reception unit is Estimate the user's emotions and prioritize requests based on the estimated user's emotions. 2. The system of claim 1, (Appendix 22) The reception unit is When accepting requests, the system takes into account the user's geographic location information and prioritizes requests that are highly relevant. 2. The system of claim 1, (Appendix 23) The reception unit is When receiving a request, analyze the user's social media activity and receive related requests. 2. The system of claim 1, (Appendix 24) The support unit includes: Estimating a user's emotion and adjusting a method of assistance based on the estimated user's emotion 2. The system of claim 1, (Appendix 25) The support unit includes: When providing support, analyze past support history to select the most appropriate support method 2. The system of claim 1, (Appendix 26) The support unit includes: At the time of assistance, customize assistance measures based on the user's current life situation 2. The system of claim 1, (Appendix 27) The support unit includes: Estimating a user's emotions and determining priority of assistance based on the estimated user's emotions 2. The system of claim 1, (Appendix 28) The support unit includes: When providing assistance, the system selects the appropriate assistance method taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 29) The support unit includes: When providing assistance, the system analyzes the user's social media activity to suggest ways to provide assistance. 2. The system of claim 1, [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit for collecting regional information indicating regional conditions; A providing unit that provides information to residents by a generation AI based on the information collected by the collecting unit; A reception unit that receives requests from the residents; A support unit that supports a facility attraction plan based on the request received by the reception unit. A system characterized by:

2. The collecting unit includes: Using IoT or 5G, data on hospital vacancies, transportation status, sales at commercial facilities, and local event information will be collected.

2. The system of claim 1 .

3. The providing unit is Analyze the collected data and provide useful information to residents 2. The system of claim 1 .

4. The collecting unit includes: To estimate a user's emotion and adjust the timing of information collection based on the estimated user's emotion.

2. The system of claim 1 .

5. The collecting unit includes: Analyze past data collection history and select appropriate collection methods 2. The system of claim 1 .

6. The collecting unit includes: When collecting information, tailor the type of data you collect based on specific events or seasons in your area.

2. The system of claim 1 .

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