System

Abstract

An object of a system according to an embodiment is to analyze date in an area and promote regional revitalization.SOLUTION: A system includes an information analysis part, a cashless settlement part, an event optimization part, and a service efficiency improvement part. The information analysis unit collects and analyzes data in an area. The cashless settlement unit provides an electronic settlement service. The event optimization unit analyzes the data of the local event. The service efficiency improving unit analyzes data of the public service and the infrastructure.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been effective in utilizing data within a region to revitalize the region, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze data within a region and promote regional revitalization. [Means for solving the problem]

[0006] The system according to the embodiment includes an information analysis unit, a cashless payment unit, an event optimization unit, and a service efficiency improvement unit. The information analysis unit collects and analyzes data within a region. The cashless payment unit provides electronic payment services. The event optimization unit analyzes data on local events. The service efficiency improvement unit analyzes data on public services and infrastructure. [Effects of the Invention]

[0007] The system according to the embodiment can analyze data within a region and promote regional revitalization. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The regional revitalization system according to an embodiment of the present invention is a system that combines AI technology and electronic payment services to promote regional revitalization in a multifaceted manner. This system aims to improve the efficiency and convenience of various activities within the region through information analysis and optimization and the introduction of cashless payments. As a result, the regional revitalization system can solve various regional problems and revitalize the entire region.

[0029] A regional revitalization system according to an embodiment includes an information analysis unit, a cashless payment unit, an event optimization unit, and a service efficiency improvement unit. The information analysis unit collects and analyzes data within the region. For example, the information analysis unit collects regional climate data and proposes optimal times and methods for agriculture and tourism. The information analysis unit can also analyze regional demographics and plan services for the elderly and events for young people. The information analysis unit can also analyze regional economic data and propose measures to revitalize the regional economy. The cashless payment unit provides electronic payment services. For example, the cashless payment unit can introduce QR code payments or electronic money, enabling shopping and service use without carrying cash. The cashless payment unit can also enable smooth payments at local stores and service businesses. The cashless payment unit can also use electronic payment services to improve the efficiency of transactions within the region. The event optimization unit analyzes data on regional events. For example, the event optimization unit can analyze data on past events and analyze which events were successful. The event optimization unit can also analyze data on local traditional events and festivals and propose new programs that meet the needs of participants. The event optimization unit can also support the planning and management of local events. The service efficiency improvement unit analyzes data on public services and infrastructure. For example, the service efficiency improvement unit can analyze local garbage collection data and propose optimal collection routes. The service efficiency improvement unit can also analyze public transportation operation data and propose operation schedules that match the busy times of day. The service efficiency unit can also propose measures to improve the efficiency of public services within the region. As a result, the local revitalization system according to the embodiment can solve various local problems and revitalize the entire region. For example, optimizing local events can revitalize the local economy, and improving the efficiency of public services can improve the living environment of residents. This is expected to lead to sustainable development throughout the region.

[0030] The information analysis unit analyzes climate data and can propose optimal times and methods for agriculture and tourism. For example, the information analysis unit collects regional climate data and analyzes it using AI. For example, in agriculture, it can propose optimal cultivation times and irrigation schedules based on data such as temperature, precipitation, and wind speed. In the tourism industry, it can also propose optimal tourist seasons and event times based on climate data. This can improve the efficiency of agriculture and tourism.

[0031] The cashless payment unit can introduce QR code payments and electronic money, allowing consumers to shop and use services without carrying cash. For example, the cashless payment unit can introduce QR code payments, allowing consumers to make payments easily using their smartphones. The cashless payment unit can also introduce electronic money, allowing consumers to make payments using their cards or smartphones. The cashless payment unit can also enable smooth payments at local stores and service businesses. This is expected to improve convenience for consumers and revitalize the local economy.

[0032] The event optimization department can analyze data on traditional events and festivals and propose new programs that meet the needs of participants. For example, the event optimization department collects data on local traditional events and festivals and analyzes it using AI. For example, it analyzes what programs were popular based on the number of participants in the past and their feedback. The event optimization department can also propose new programs that meet the needs of participants. For example, it can plan programs that suit the age group and interests of participants. This improves the appeal of local events and increases participant satisfaction.

[0033] The service efficiency improvement unit can analyze garbage collection data and propose optimal collection routes. For example, the service efficiency improvement unit collects local garbage collection data and analyzes it using AI. For example, it can propose optimal collection routes based on data such as collection volume, collection frequency, and collection route. The service efficiency improvement unit can also optimize collection routes by taking traffic conditions and collection efficiency into consideration. This improves the efficiency of garbage collection and the living environment of local residents.

[0034] The service efficiency improvement unit can analyze operation data of public transportation and propose operation schedules that match the time periods when there are many users. For example, the service efficiency improvement unit collects operation data of public transportation and analyzes it using AI. For example, it can propose operation schedules that match the time periods when there are many users based on data such as operation times, number of users, and delay information. The service efficiency improvement unit can also optimize operation schedules by taking into account peak user times and operation costs. This improves the operation efficiency of public transportation and increases convenience for users.

[0035] The information analysis unit can analyze traffic data and propose optimal traffic routes and congestion avoidance measures. For example, the information analysis unit collects traffic data within a region and analyzes it using AI. For example, it can grasp traffic volume and congestion conditions in real time and propose optimal traffic routes. The information analysis unit can also propose congestion avoidance measures. For example, it can propose alternative routes and adjust traffic signals. This can improve traffic efficiency and alleviate congestion.

[0036] The information analysis unit can analyze energy consumption data and propose optimal energy management methods. For example, the information analysis unit collects energy consumption data within a region and analyzes it using AI. For example, based on data such as electricity consumption, gas consumption, and water consumption, the information analysis unit can propose management methods to improve energy efficiency and reduce costs. The information analysis unit can also identify peak energy consumption times and propose measures to promote efficient energy use. This promotes efficient energy use.

[0037] The information analysis unit can analyze crime data and propose crime prevention measures. For example, the information analysis unit collects crime data within a region and analyzes it using AI. For example, based on data such as the number of crimes, types of crimes, and locations of crimes, it can identify crime occurrence patterns and propose prevention measures. The information analysis unit can also propose specific crime prevention measures, such as installing security cameras and increasing patrols. This will improve the safety of the region.

[0038] The cashless payment department can analyze sales data from stores and service businesses and propose optimal promotion strategies. For example, the cashless payment department collects sales data from local stores and service businesses and analyzes it using AI. For example, it can analyze sales trends based on data such as monthly sales, sales by product, and sales by customer, and propose optimal promotion strategies. The cashless payment department can also identify target customers and propose types of advertising. This will increase sales for stores and service businesses and revitalize the local economy.

[0039] The cashless payment department can analyze electronic payment data, predict consumer purchasing behavior, and optimize inventory management. For example, the cashless payment department collects electronic payment data and analyzes it using AI. For example, it can predict consumer purchasing behavior based on data such as payment amount, payment date and time, and the store used. The cashless payment department can also understand consumer needs based on past purchase history and purchase frequency, and optimize inventory management. This makes inventory management more efficient and enables quicker response to consumer needs.

[0040] The cashless payment department can introduce a point system that uses electronic payments to promote consumption within the local area. For example, the cashless payment department can introduce a point system that uses electronic payments to allow consumers to earn points when shopping or using services. The cashless payment department can also suggest ways to award and use points. For example, it can run a campaign that awards more points when using specific stores or services. This promotes consumption within the local area and revitalizes the local economy.

[0041] The cashless payment department can analyze electronic payment data and provide a dashboard that visualizes economic activity within a region. The cashless payment department, for example, collects electronic payment data and analyzes it using AI. For example, it can provide a dashboard that visualizes economic activity within a region based on data such as sales data, consumer trends, and economic indicators. The cashless payment department can also propose visual display methods to make economic trends easier to understand. This visualizes economic activity within a region, making it easier to understand economic trends.

[0042] The event optimization unit can use AI to analyze past event data and propose optimal event schedules. For example, the event optimization unit collects past event data and analyzes it using AI. For example, it analyzes what types of events were successful based on data such as the number of participants, the date and time of the event, and the program content. The event optimization unit can also propose optimal event schedules based on the needs of participants and past success stories. This optimizes the event schedule and improves participant satisfaction.

[0043] The event optimization unit can analyze feedback data from event participants and propose improvements for the next event. For example, the event optimization unit collects feedback data from event participants and analyzes it using AI. For example, it identifies participant satisfaction and areas for improvement based on data such as survey results and social media posts. The event optimization unit can also propose improvements for the next event. For example, it can improve the program content and management methods based on participant feedback. This improves the quality of the event and increases participant satisfaction.

[0044] The event optimization unit uses AI to centrally manage event information within a region and can propose optimal promotion strategies. The event optimization unit, for example, collects event information within a region and centrally manages it using AI. For example, it can centrally manage event schedules, participant information, feedback data, etc., and propose optimal promotion strategies. The event optimization unit can also identify target customers and propose types of advertising. This centralizes event information and optimizes promotion strategies.

[0045] The event optimization unit can analyze live streaming data and propose measures to meet the needs of online participants. For example, the event optimization unit collects live streaming data of an event and analyzes it using AI. For example, the event optimization unit identifies the needs of online participants based on data such as the number of viewers, viewing time, and comment content. The event optimization unit can also propose measures to meet the needs of online participants. For example, a real-time feedback system can be built to reflect the opinions of online participants. This allows measures to be provided that meet the needs of online participants, improving their satisfaction.

[0046] The service efficiency improvement unit can analyze medical data and propose optimal allocation of medical resources. For example, the service efficiency improvement unit collects medical data within a region and analyzes it using AI. For example, it can optimize the allocation of medical resources based on data such as patients' medical records, test results, and treatment history. The service efficiency improvement unit can also propose allocation of medical resources taking into account the urgency of the patient and the utilization status of resources. This optimizes the allocation of medical resources and improves the quality of medical services.

[0047] The service efficiency improvement unit can analyze operation data of public transportation and propose optimal operation schedules. For example, the service efficiency improvement unit collects operation data of public transportation and analyzes it using AI. For example, it can propose optimal operation schedules based on data such as operation times, number of users, and delay information. The service efficiency improvement unit can also optimize operation schedules by taking into account peak times for users and operation costs. This optimizes public transportation operation schedules and improves user convenience.

[0048] The Service Efficiency Department can analyze educational data and propose optimal educational programs. For example, the Service Efficiency Department collects educational data within the region and analyzes it using AI. For example, the Service Efficiency Department can propose optimal educational programs based on data such as student grades, attendance, and learning history. The Service Efficiency Department can also optimize educational programs by taking into account students' learning needs and educational goals. This optimizes educational programs and improves students' learning effectiveness.

[0049] The Service Efficiency Department can analyze energy consumption data and propose optimal energy management methods. For example, the Service Efficiency Department collects energy consumption data within a region and analyzes it using AI. For example, based on data such as electricity consumption, gas consumption, and water consumption, the Service Efficiency Department can propose management methods to improve energy efficiency and reduce costs. The Service Efficiency Department can also identify peak energy consumption times and propose measures to promote efficient energy use. This promotes efficient energy use.

[0050] The information analysis unit can use AI to analyze tourist movement data and propose optimal tourist routes. For example, the information analysis unit collects tourist movement data and analyzes it using AI. For example, based on data such as travel routes, travel times, and destinations, the information analysis unit can identify tourist movement patterns and propose optimal tourist routes. The information analysis unit can also optimize tourist routes by taking into account the popularity of tourist spots and shortening travel times. This makes tourist movement more efficient and improves the tourist experience.

[0051] The information analysis unit can analyze congestion data at tourist destinations and propose ways to avoid congestion. For example, the information analysis unit collects congestion data at tourist destinations and analyzes it using AI. For example, based on data such as the number of visitors, length of stay, and busy times, it can identify congestion patterns and propose ways to avoid congestion. The information analysis unit can also adjust visit times and propose alternative routes. This reduces congestion at tourist destinations and improves tourist satisfaction.

[0052] The information analysis unit can use AI to analyze word-of-mouth data on tourist destinations and propose optimal promotion strategies. For example, the information analysis unit collects word-of-mouth data on tourist destinations and analyzes it using AI. For example, based on data such as online reviews and social media posts, the information analysis unit can identify the rating and popularity of tourist destinations and propose optimal promotion strategies. The information analysis unit can also identify target customers and propose types of advertising. This optimizes the promotion strategy of tourist destinations and promotes the attraction of tourists.

[0053] The information analysis unit can analyze environmental data on tourist destinations and propose sustainable tourism plans. For example, the information analysis unit collects environmental data on tourist destinations and analyzes it using AI. For example, based on data such as temperature, precipitation, and wind speed, it can propose tourism plans that take into account environmental protection and the sustainability of the local economy. The information analysis unit can also propose measures to minimize the environmental impact of tourist destinations. This promotes sustainable management of tourist destinations and helps protect the environment.

[0054] The information analysis unit can use AI to analyze crop growth data and propose optimal cultivation methods. For example, the information analysis unit collects crop growth data and analyzes it using AI. For example, it can propose optimal cultivation methods based on data such as growth rate, pest and disease occurrence, and weather conditions. The information analysis unit can also optimize cultivation techniques by taking soil and weather conditions into consideration. This optimizes crop cultivation methods and improves yields.

[0055] The information analysis unit can analyze weather data and propose optimal irrigation schedules. For example, the information analysis unit collects weather data and analyzes it using AI. For example, it can propose optimal irrigation schedules based on data such as temperature, precipitation, and wind speed. The information analysis unit can also optimize irrigation schedules by taking into account soil moisture and the growth stage of crops. This optimizes irrigation schedules and promotes efficient use of water resources.

[0056] The information analysis department can use AI to analyze market data for agricultural products and propose optimal sales strategies. For example, the information analysis department collects market data for agricultural products and analyzes it using AI. For example, it can propose optimal sales strategies based on data such as price trends, demand forecasts, and competitive analysis. The information analysis department can also identify target markets, set prices, and propose promotion methods. This optimizes agricultural product sales strategies and increases profits.

[0057] The information analysis unit can analyze agricultural machinery operation data and propose optimal maintenance schedules. For example, the information analysis unit collects agricultural machinery operation data and analyzes it using AI. For example, it can propose optimal maintenance schedules based on data such as operating hours, fuel consumption, and failure history. The information analysis unit can also optimize maintenance schedules by taking operating hours and failure history into consideration. This optimizes agricultural machinery maintenance and enables efficient operation.

[0058] The information analysis unit can use AI to analyze a patient's health data and propose the optimal treatment method. For example, the information analysis unit collects a patient's health data and analyzes it using AI. For example, the information analysis unit can propose the optimal treatment method based on data such as medical records, test results, and treatment history. The information analysis unit can also optimize the treatment method by taking into account the patient's medical history and the latest medical guidelines. This allows the optimal treatment method to be proposed based on the patient's health data, improving the effectiveness of treatment.

[0059] The information analysis unit can analyze the medical institution's operational data and propose optimal resource allocation. For example, the information analysis unit collects the medical institution's operational data and analyzes it using AI. For example, resource allocation can be optimized based on data such as the number of patients, staff working hours, and equipment operating status. The information analysis unit can also propose resource allocation taking into account the urgency of patients and resource usage status. This optimizes the medical institution's resource allocation and improves operational efficiency.

[0060] The information analysis unit can use AI to analyze medical data and propose optimal preventive medical care plans. For example, the information analysis unit collects medical data and analyzes it using AI. For example, it can optimize preventive medical care plans based on data such as the patient's medical records, test results, and treatment history. The information analysis unit can also propose preventive medical care plans taking into account the patient's health risks and the latest medical guidelines. This optimizes preventive medical care plans and improves health management.

[0061] The information analysis unit can analyze the medical institution's operational data and propose optimal staff schedules. For example, the information analysis unit collects the medical institution's operational data and analyzes it using AI. For example, it can propose optimal staff schedules based on data such as the number of patients, staff working hours, and equipment operating status. The information analysis unit can also optimize staff schedules by taking into account the urgency of patients and staff working hours. This optimizes staff schedules and improves the operational efficiency of medical institutions.

[0062] The information analysis unit can use AI to analyze disaster risk data and propose optimal evacuation routes. For example, the information analysis unit collects disaster risk data and analyzes it using AI. For example, it can propose optimal evacuation routes based on data such as earthquake risk, flood risk, and fire risk. The information analysis unit can also optimize evacuation routes by taking into account the location of evacuation shelters and traffic conditions. This optimizes evacuation routes in the event of a disaster and improves safety.

[0063] The information analysis unit can analyze past disaster data and propose optimal disaster prevention measures. For example, the information analysis unit collects past disaster data and analyzes it using AI. For example, it can propose optimal disaster prevention measures based on data such as the date and time of the disaster, the extent of the damage, and countermeasures. The information analysis unit can also optimize disaster prevention measures by taking into account past disaster data and the latest disaster prevention technology. This allows disaster prevention measures to be optimized based on past disaster data, improving disaster preparedness.

[0064] The information analysis unit can use AI to analyze communication data during a disaster and propose the optimal method of transmitting information. For example, the information analysis unit collects communication data during a disaster and analyzes it using AI. For example, it can propose the optimal method of transmitting information based on data such as communication delays, communication interruptions, and communication volume. The information analysis unit can also optimize the method of transmitting information by taking into account the selection of communication means and the priority of information. This optimizes the method of transmitting information during a disaster, enabling fast and accurate information transmission.

[0065] The information analysis unit can analyze logistics data during disasters and propose optimal supply routes. For example, the information analysis unit collects logistics data during disasters and analyzes it using AI. For example, it can propose optimal supply routes based on data such as logistics delays, disruptions, and logistics volume. The information analysis unit can also optimize supply routes by taking into account the selection of logistics methods and supply priorities. This optimizes supply routes during disasters, enabling rapid supply of supplies.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The regional revitalization system can also include a cultural resource provider that digitizes the region's cultural resources and provides them to tourists and residents. For example, it could create a digital archive of the region's historical buildings and traditional crafts and make them available online. The cultural resource provider could also live-stream traditional local events and festivals, allowing people to participate from afar. The cultural resource provider could also provide quizzes and games related to the region's culture, providing content that allows people to learn while having fun. This will help widely publicize the region's cultural resources and increase its appeal.

[0068] The Information Analysis Department can also analyze health data of local residents and propose health promotion programs. For example, it can collect data on residents' exercise habits and dietary habits and provide individually customized health promotion programs. The Information Analysis Department can also propose holding local health events and workshops. Furthermore, the Information Analysis Department can work with local medical institutions to monitor the health status of residents and detect health risks early. This is expected to improve the health of local residents and reduce medical costs.

[0069] The cashless payment department can also analyze sales data from local stores and service businesses to propose optimal promotion strategies. For example, it can use sales data to propose discount campaigns tailored to specific times of day or days of the week. The cashless payment department can also analyze customers' purchasing history to provide individually customized promotions. Furthermore, the cashless payment department can introduce a point system that links local stores together to promote consumption throughout the region. This will revitalize the local economy and increase store sales.

[0070] The Event Optimization Department can also analyze data from local sporting events and propose optimal sporting programs. For example, it can identify popular sports and competitions based on past participant data and feedback, and plan new programs. The Event Optimization Department can also analyze the usage of local sporting facilities and propose efficient facility management. Furthermore, the Event Optimization Department can propose promotion strategies for local sporting events and implement measures to increase participation. This will revitalize local sporting activities and promote the health of residents.

[0071] The Service Efficiency Department can also analyze local educational data and propose optimal educational programs. For example, it can provide individually customized learning plans based on students' grade data and attendance records. The Service Efficiency Department can also work with local educational institutions to propose teacher training programs. Furthermore, the Service Efficiency Department can propose holding local educational events and workshops and implement measures to improve the quality of education. This will improve the level of education in the region and increase student learning effectiveness.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The Information Analysis Department collects and analyzes data within the region. For example, the Information Analysis Department collects local climate data and proposes the best times and methods for agriculture and tourism. The Information Analysis Department can also analyze local demographics and plan services for the elderly and events for young people. The Information Analysis Department can also analyze local economic data and propose measures to revitalize the local economy. Step 2: The cashless payment department provides electronic payment services. For example, the cashless payment department introduces QR code payments and electronic money, allowing people to shop and use services without carrying cash. The cashless payment department can also make payments at local stores and service businesses smoother. The cashless payment department can also use electronic payment services to make transactions within the local area more efficient. Step 3: The Event Optimization Department analyzes data on local events. For example, the Event Optimization Department analyzes data on past events and analyzes what types of events were successful. The Event Optimization Department can also analyze data on local traditional events and festivals and propose new programs that meet the needs of participants. The Event Optimization Department can also support the planning and management of local events. Step 4: The Service Efficiency Department analyzes data on public services and infrastructure. For example, the Service Efficiency Department analyzes local garbage collection data and proposes optimal collection routes. The Service Efficiency Department can also analyze public transportation operation data and propose operation schedules that match the times when there are many users. The Service Efficiency Department can also propose measures to improve the efficiency of public services within the region.

[0074] (Example 2) The regional revitalization system according to an embodiment of the present invention is a system that combines AI technology and electronic payment services to promote regional revitalization in a multifaceted manner. This system aims to improve the efficiency and convenience of various activities within the region through information analysis and optimization and the introduction of cashless payments. As a result, the regional revitalization system can solve various regional problems and revitalize the entire region.

[0075] A regional revitalization system according to an embodiment includes an information analysis unit, a cashless payment unit, an event optimization unit, and a service efficiency improvement unit. The information analysis unit collects and analyzes data within the region. For example, the information analysis unit collects regional climate data and proposes optimal times and methods for agriculture and tourism. The information analysis unit can also analyze regional demographics and plan services for the elderly and events for young people. The information analysis unit can also analyze regional economic data and propose measures to revitalize the regional economy. The cashless payment unit provides electronic payment services. For example, the cashless payment unit can introduce QR code payments or electronic money, enabling shopping and service use without carrying cash. The cashless payment unit can also enable smooth payments at local stores and service businesses. The cashless payment unit can also use electronic payment services to improve the efficiency of transactions within the region. The event optimization unit analyzes data on regional events. For example, the event optimization unit can analyze data on past events and analyze which events were successful. The event optimization unit can also analyze data on local traditional events and festivals and propose new programs that meet the needs of participants. The event optimization unit can also support the planning and management of local events. The service efficiency improvement unit analyzes data on public services and infrastructure. For example, the service efficiency improvement unit can analyze local garbage collection data and propose optimal collection routes. The service efficiency improvement unit can also analyze public transportation operation data and propose operation schedules that match the busy times of day. The service efficiency unit can also propose measures to improve the efficiency of public services within the region. As a result, the local revitalization system according to the embodiment can solve various local problems and revitalize the entire region. For example, optimizing local events can revitalize the local economy, and improving the efficiency of public services can improve the living environment of residents. This is expected to lead to sustainable development throughout the region.

[0076] The information analysis unit analyzes climate data and can propose optimal times and methods for agriculture and tourism. For example, the information analysis unit collects regional climate data and analyzes it using AI. For example, in agriculture, it can propose optimal cultivation times and irrigation schedules based on data such as temperature, precipitation, and wind speed. In the tourism industry, it can also propose optimal tourist seasons and event times based on climate data. This can improve the efficiency of agriculture and tourism.

[0077] The cashless payment unit can introduce QR code payments and electronic money, allowing consumers to shop and use services without carrying cash. For example, the cashless payment unit can introduce QR code payments, allowing consumers to make payments easily using their smartphones. The cashless payment unit can also introduce electronic money, allowing consumers to make payments using their cards or smartphones. The cashless payment unit can also enable smooth payments at local stores and service businesses. This is expected to improve convenience for consumers and revitalize the local economy.

[0078] The event optimization department can analyze data on traditional events and festivals and propose new programs that meet the needs of participants. For example, the event optimization department collects data on local traditional events and festivals and analyzes it using AI. For example, it analyzes what programs were popular based on the number of participants in the past and their feedback. The event optimization department can also propose new programs that meet the needs of participants. For example, it can plan programs that suit the age group and interests of participants. This improves the appeal of local events and increases participant satisfaction.

[0079] The service efficiency improvement unit can analyze garbage collection data and propose optimal collection routes. For example, the service efficiency improvement unit collects local garbage collection data and analyzes it using AI. For example, it can propose optimal collection routes based on data such as collection volume, collection frequency, and collection route. The service efficiency improvement unit can also optimize collection routes by taking traffic conditions and collection efficiency into consideration. This improves the efficiency of garbage collection and the living environment of local residents.

[0080] The service efficiency improvement unit can analyze operation data of public transportation and propose operation schedules that match the time periods when there are many users. For example, the service efficiency improvement unit collects operation data of public transportation and analyzes it using AI. For example, it can propose operation schedules that match the time periods when there are many users based on data such as operation times, number of users, and delay information. The service efficiency improvement unit can also optimize operation schedules by taking into account peak user times and operation costs. This improves the operation efficiency of public transportation and increases convenience for users.

[0081] The information analysis unit analyzes social media posts by local residents, ascertaining local needs and trends in real time and reflecting them in policies. For example, the information analysis unit collects social media posts by local residents and performs text analysis using AI. For example, it extracts specific keywords and hashtags to identify local interests and issues. The information analysis unit can also ascertain local needs and trends in real time and reflect them in policies. For example, new services and events can be planned based on the opinions of local residents. This allows the needs and trends of local residents to be quickly identified and appropriate policies to be implemented.

[0082] The information analysis unit can analyze traffic data and propose optimal traffic routes and congestion avoidance measures. For example, the information analysis unit collects traffic data within a region and analyzes it using AI. For example, it can grasp traffic volume and congestion conditions in real time and propose optimal traffic routes. The information analysis unit can also propose congestion avoidance measures. For example, it can propose alternative routes and adjust traffic signals. This can improve traffic efficiency and alleviate congestion.

[0083] The information analysis unit can use the emotion estimation function to analyze the emotions of local residents and propose measures to elicit positive emotions. The information analysis unit, for example, collects social media posts and survey results from local residents and uses the emotion estimation function to understand their emotional state. For example, it analyzes posts with positive emotions and identifies the factors behind them. The information analysis unit can also propose measures to elicit positive emotions. For example, it can hold local events and promote community activities. This improves the satisfaction of local residents and promotes the revitalization of the area.

[0084] The information analysis unit can analyze energy consumption data and propose optimal energy management methods. For example, the information analysis unit collects energy consumption data within a region and analyzes it using AI. For example, based on data such as electricity consumption, gas consumption, and water consumption, the information analysis unit can propose management methods to improve energy efficiency and reduce costs. The information analysis unit can also identify peak energy consumption times and propose measures to promote efficient energy use. This promotes efficient energy use.

[0085] The information analysis unit can analyze crime data and propose crime prevention measures. For example, the information analysis unit collects crime data within a region and analyzes it using AI. For example, based on data such as the number of crimes, types of crimes, and locations of crimes, it can identify crime occurrence patterns and propose prevention measures. The information analysis unit can also propose specific crime prevention measures, such as installing security cameras and increasing patrols. This will improve the safety of the region.

[0086] The information analysis unit can use the emotion estimation function to analyze tourists' emotions and propose measures to maximize the appeal of tourist destinations. For example, the information analysis unit collects tourist surveys and social media posts and uses the emotion estimation function to understand their emotional state. For example, it analyzes posts that express positive emotions toward tourist destinations and identifies the factors behind them. The information analysis unit can also propose measures to maximize the appeal of tourist destinations. For example, it can improve tourist facilities or hold events. This improves the appeal of tourist destinations and increases tourist satisfaction.

[0087] The cashless payment department can analyze sales data from stores and service businesses and propose optimal promotion strategies. For example, the cashless payment department collects sales data from local stores and service businesses and analyzes it using AI. For example, it can analyze sales trends based on data such as monthly sales, sales by product, and sales by customer, and propose optimal promotion strategies. The cashless payment department can also identify target customers and propose types of advertising. This will increase sales for stores and service businesses and revitalize the local economy.

[0088] The cashless payment department can analyze electronic payment data, predict consumer purchasing behavior, and optimize inventory management. For example, the cashless payment department collects electronic payment data and analyzes it using AI. For example, it can predict consumer purchasing behavior based on data such as payment amount, payment date and time, and the store used. The cashless payment department can also understand consumer needs based on past purchase history and purchase frequency, and optimize inventory management. This makes inventory management more efficient and enables quicker response to consumer needs.

[0089] The cashless payment unit can use the emotion estimation function to analyze consumers' emotions when making a purchase and propose measures to increase their purchasing motivation. For example, the cashless payment unit collects consumer survey results and social media posts and uses the emotion estimation function to understand their emotional state. For example, it analyzes posts that show positive emotions when making a purchase and identifies the factors behind them. The cashless payment unit can also propose measures to increase purchasing motivation. For example, it can offer discount campaigns or point rewards. This increases consumers' purchasing motivation and increases sales.

[0090] The cashless payment department can introduce a point system that uses electronic payments to promote consumption within the local area. For example, the cashless payment department can introduce a point system that uses electronic payments to allow consumers to earn points when shopping or using services. The cashless payment department can also suggest ways to award and use points. For example, it can run a campaign that awards more points when using specific stores or services. This promotes consumption within the local area and revitalizes the local economy.

[0091] The cashless payment department can analyze electronic payment data and provide a dashboard that visualizes economic activity within a region. The cashless payment department, for example, collects electronic payment data and analyzes it using AI. For example, it can provide a dashboard that visualizes economic activity within a region based on data such as sales data, consumer trends, and economic indicators. The cashless payment department can also propose visual display methods to make economic trends easier to understand. This visualizes economic activity within a region, making it easier to understand economic trends.

[0092] The cashless payment unit can use the emotion estimation function to provide customized promotions based on the consumer's emotions. For example, the cashless payment unit collects consumer survey results and social media posts and uses the emotion estimation function to understand the consumer's emotional state. For example, it can provide individually customized promotions based on the consumer's purchase history and emotional data. The cashless payment unit can also propose promotion strategies based on the consumer's emotions. This allows the promotions to be provided based on the consumer's emotions, increasing their willingness to purchase.

[0093] The event optimization unit can use AI to analyze past event data and propose optimal event schedules. For example, the event optimization unit collects past event data and analyzes it using AI. For example, it analyzes what types of events were successful based on data such as the number of participants, the date and time of the event, and the program content. The event optimization unit can also propose optimal event schedules based on the needs of participants and past success stories. This optimizes the event schedule and improves participant satisfaction.

[0094] The event optimization unit can analyze feedback data from event participants and propose improvements for the next event. For example, the event optimization unit collects feedback data from event participants and analyzes it using AI. For example, it identifies participant satisfaction and areas for improvement based on data such as survey results and social media posts. The event optimization unit can also propose improvements for the next event. For example, it can improve the program content and management methods based on participant feedback. This improves the quality of the event and increases participant satisfaction.

[0095] The event optimization unit can use the emotion estimation function to analyze the emotions of event participants in real time and reflect the results in event management. For example, the event optimization unit collects the facial expressions and voices of event participants using a camera or microphone, and analyzes their emotions in real time using the emotion estimation function. For example, it can calculate an emotion score based on changes in facial expressions and tone of voice and reflect this in event management. The event optimization unit can also build a real-time feedback system and notify management staff of emotion data. This allows event management to be optimized in real time and improves participant satisfaction.

[0096] The event optimization unit uses AI to centrally manage event information within a region and can propose optimal promotion strategies. The event optimization unit, for example, collects event information within a region and centrally manages it using AI. For example, it can centrally manage event schedules, participant information, feedback data, etc., and propose optimal promotion strategies. The event optimization unit can also identify target customers and propose types of advertising. This centralizes event information and optimizes promotion strategies.

[0097] The event optimization unit can analyze live streaming data and propose measures to meet the needs of online participants. For example, the event optimization unit collects live streaming data of an event and analyzes it using AI. For example, the event optimization unit identifies the needs of online participants based on data such as the number of viewers, viewing time, and comment content. The event optimization unit can also propose measures to meet the needs of online participants. For example, a real-time feedback system can be built to reflect the opinions of online participants. This allows measures to be provided that meet the needs of online participants, improving their satisfaction.

[0098] The event optimization unit can use the emotion estimation function to analyze the emotions of online participants and adjust the event content in real time. For example, the event optimization unit collects the facial expressions and voices of online participants using a camera or microphone and analyzes their emotions in real time using the emotion estimation function. For example, the event optimization unit can calculate an emotion score based on changes in facial expressions and tone of voice and adjust the event content in real time. The event optimization unit can also build a real-time feedback system and notify the event staff of the emotion data. This allows the content of the online event to be optimized in real time and improves participant satisfaction.

[0099] The service efficiency improvement unit can analyze medical data and propose optimal allocation of medical resources. For example, the service efficiency improvement unit collects medical data within a region and analyzes it using AI. For example, it can optimize the allocation of medical resources based on data such as patients' medical records, test results, and treatment history. The service efficiency improvement unit can also propose allocation of medical resources taking into account the urgency of the patient and the utilization status of resources. This optimizes the allocation of medical resources and improves the quality of medical services.

[0100] The service efficiency improvement unit can analyze operation data of public transportation and propose optimal operation schedules. For example, the service efficiency improvement unit collects operation data of public transportation and analyzes it using AI. For example, it can propose optimal operation schedules based on data such as operation times, number of users, and delay information. The service efficiency improvement unit can also optimize operation schedules by taking into account peak times for users and operation costs. This optimizes public transportation operation schedules and improves user convenience.

[0101] The service efficiency improvement unit can use the emotion estimation function to analyze the emotions of public service users and propose measures to improve the quality of the service. For example, the service efficiency improvement unit collects survey results and social media posts from public service users and uses the emotion estimation function to understand their emotional state. For example, it analyzes posts that show positive emotions when using public services and identifies the factors behind them. The service efficiency improvement unit can also propose measures to improve the quality of the service. For example, it can improve the service or train staff. This improves the quality of public services and increases user satisfaction.

[0102] The Service Efficiency Department can analyze educational data and propose optimal educational programs. For example, the Service Efficiency Department collects educational data within the region and analyzes it using AI. For example, the Service Efficiency Department can propose optimal educational programs based on data such as student grades, attendance, and learning history. The Service Efficiency Department can also optimize educational programs by taking into account students' learning needs and educational goals. This optimizes educational programs and improves students' learning effectiveness.

[0103] The Service Efficiency Department can analyze energy consumption data and propose optimal energy management methods. For example, the Service Efficiency Department collects energy consumption data within a region and analyzes it using AI. For example, based on data such as electricity consumption, gas consumption, and water consumption, the Service Efficiency Department can propose management methods to improve energy efficiency and reduce costs. The Service Efficiency Department can also identify peak energy consumption times and propose measures to promote efficient energy use. This promotes efficient energy use.

[0104] The service efficiency improvement unit can use the emotion estimation function to analyze the emotions of educational program participants and optimize the program content. For example, the service efficiency improvement unit collects questionnaire surveys and social media posts from educational program participants and uses the emotion estimation function to understand their emotional state. For example, it analyzes posts that show positive emotions when participating in an educational program and identifies the factors behind them. The service efficiency improvement unit can also propose measures to optimize the program content. For example, it can improve the program content and progress method based on participant feedback. This optimizes the content of the educational program and increases participant satisfaction.

[0105] The information analysis unit can use AI to analyze tourist movement data and propose optimal tourist routes. For example, the information analysis unit collects tourist movement data and analyzes it using AI. For example, based on data such as travel routes, travel times, and destinations, the information analysis unit can identify tourist movement patterns and propose optimal tourist routes. The information analysis unit can also optimize tourist routes by taking into account the popularity of tourist spots and shortening travel times. This makes tourist movement more efficient and improves the tourist experience.

[0106] The information analysis unit can analyze congestion data at tourist destinations and propose ways to avoid congestion. For example, the information analysis unit collects congestion data at tourist destinations and analyzes it using AI. For example, based on data such as the number of visitors, length of stay, and busy times, it can identify congestion patterns and propose ways to avoid congestion. The information analysis unit can also adjust visit times and propose alternative routes. This reduces congestion at tourist destinations and improves tourist satisfaction.

[0107] The information analysis unit can use the emotion estimation function to analyze tourists' emotions and propose measures to maximize the appeal of tourist destinations. For example, the information analysis unit collects tourist surveys and social media posts and uses the emotion estimation function to understand their emotional state. For example, it analyzes posts that express positive emotions toward tourist destinations and identifies the factors behind them. The information analysis unit can also propose measures to maximize the appeal of tourist destinations. For example, it can improve tourist facilities or hold events. This improves the appeal of tourist destinations and increases tourist satisfaction.

[0108] The information analysis unit can use AI to analyze word-of-mouth data on tourist destinations and propose optimal promotion strategies. For example, the information analysis unit collects word-of-mouth data on tourist destinations and analyzes it using AI. For example, based on data such as online reviews and social media posts, the information analysis unit can identify the rating and popularity of tourist destinations and propose optimal promotion strategies. The information analysis unit can also identify target customers and propose types of advertising. This optimizes the promotion strategy of tourist destinations and promotes the attraction of tourists.

[0109] The information analysis unit can analyze environmental data on tourist destinations and propose sustainable tourism plans. For example, the information analysis unit collects environmental data on tourist destinations and analyzes it using AI. For example, based on data such as temperature, precipitation, and wind speed, it can propose tourism plans that take into account environmental protection and the sustainability of the local economy. The information analysis unit can also propose measures to minimize the environmental impact of tourist destinations. This promotes sustainable management of tourist destinations and helps protect the environment.

[0110] The information analysis unit can use the emotion estimation function to provide a customized sightseeing plan based on the tourist's emotions. The information analysis unit, for example, collects tourist survey results and social media posts and uses the emotion estimation function to understand the tourist's emotional state. For example, it can provide an individually customized sightseeing plan based on the tourist's interests, preferences, past visit history, etc. The information analysis unit can also suggest sightseeing routes and spots according to the tourist's emotions. This allows the tourist to be provided with a customized sightseeing plan based on their emotions, improving their satisfaction.

[0111] The information analysis unit can use AI to analyze crop growth data and propose optimal cultivation methods. For example, the information analysis unit collects crop growth data and analyzes it using AI. For example, it can propose optimal cultivation methods based on data such as growth rate, pest and disease occurrence, and weather conditions. The information analysis unit can also optimize cultivation techniques by taking soil and weather conditions into consideration. This optimizes crop cultivation methods and improves yields.

[0112] The information analysis unit can analyze weather data and propose optimal irrigation schedules. For example, the information analysis unit collects weather data and analyzes it using AI. For example, it can propose optimal irrigation schedules based on data such as temperature, precipitation, and wind speed. The information analysis unit can also optimize irrigation schedules by taking into account soil moisture and the growth stage of crops. This optimizes irrigation schedules and promotes efficient use of water resources.

[0113] The information analysis unit can use the emotion estimation function to analyze farmers' emotions and propose measures to improve the efficiency of agricultural work. For example, the information analysis unit collects farmers' survey results and social media posts and uses the emotion estimation function to understand their emotional state. For example, it analyzes posts that show positive emotions during agricultural work and identifies the causes. The information analysis unit can also propose measures to improve the efficiency of agricultural work. For example, it can automate work and reduce working hours. This improves the efficiency of agricultural work and increases farmers' satisfaction.

[0114] The information analysis department can use AI to analyze market data for agricultural products and propose optimal sales strategies. For example, the information analysis department collects market data for agricultural products and analyzes it using AI. For example, it can propose optimal sales strategies based on data such as price trends, demand forecasts, and competitive analysis. The information analysis department can also identify target markets, set prices, and propose promotion methods. This optimizes agricultural product sales strategies and increases profits.

[0115] The information analysis unit can analyze agricultural machinery operation data and propose optimal maintenance schedules. For example, the information analysis unit collects agricultural machinery operation data and analyzes it using AI. For example, it can propose optimal maintenance schedules based on data such as operating hours, fuel consumption, and failure history. The information analysis unit can also optimize maintenance schedules by taking operating hours and failure history into consideration. This optimizes agricultural machinery maintenance and enables efficient operation.

[0116] The information analysis unit can use the emotion estimation function to provide a customized agricultural produce sales plan based on the consumer's emotions. The information analysis unit, for example, collects consumer survey results and social media posts and uses the emotion estimation function to understand the consumer's emotional state. For example, it can provide an individually customized agricultural produce sales plan based on the consumer's preferences, purchase history, seasonal trends, etc. The information analysis unit can also propose a sales strategy according to the consumer's emotions. This allows the agricultural produce sales plan to be provided in accordance with the consumer's emotions, improving satisfaction.

[0117] The information analysis unit can use AI to analyze a patient's health data and propose the optimal treatment method. For example, the information analysis unit collects a patient's health data and analyzes it using AI. For example, the information analysis unit can propose the optimal treatment method based on data such as medical records, test results, and treatment history. The information analysis unit can also optimize the treatment method by taking into account the patient's medical history and the latest medical guidelines. This allows the optimal treatment method to be proposed based on the patient's health data, improving the effectiveness of treatment.

[0118] The information analysis unit can analyze the medical institution's operational data and propose optimal resource allocation. For example, the information analysis unit collects the medical institution's operational data and analyzes it using AI. For example, resource allocation can be optimized based on data such as the number of patients, staff working hours, and equipment operating status. The information analysis unit can also propose resource allocation taking into account the urgency of patients and resource usage status. This optimizes the medical institution's resource allocation and improves operational efficiency.

[0119] The information analysis unit can use the emotion estimation function to analyze patients' emotions and propose measures to improve the quality of treatment. For example, the information analysis unit collects patient questionnaire surveys and social media posts and uses the emotion estimation function to understand their emotional state. For example, it analyzes posts that show positive emotions during treatment and identifies the factors behind them. The information analysis unit can also propose measures to improve the quality of treatment. For example, it can improve the treatment process or train staff. This improves the quality of treatment based on the patient's emotions and increases satisfaction.

[0120] The information analysis unit can use AI to analyze medical data and propose optimal preventive medical care plans. For example, the information analysis unit collects medical data and analyzes it using AI. For example, it can optimize preventive medical care plans based on data such as the patient's medical records, test results, and treatment history. The information analysis unit can also propose preventive medical care plans taking into account the patient's health risks and the latest medical guidelines. This optimizes preventive medical care plans and improves health management.

[0121] The information analysis unit can analyze the medical institution's operational data and propose optimal staff schedules. For example, the information analysis unit collects the medical institution's operational data and analyzes it using AI. For example, it can propose optimal staff schedules based on data such as the number of patients, staff working hours, and equipment operating status. The information analysis unit can also optimize staff schedules by taking into account the urgency of patients and staff working hours. This optimizes staff schedules and improves the operational efficiency of medical institutions.

[0122] The information analysis unit can use the emotion estimation function to analyze the emotions of medical staff and propose measures to provide a comfortable working environment. For example, the information analysis unit collects survey results and social media posts from medical staff and uses the emotion estimation function to understand their emotional state. For example, it analyzes posts that show positive emotions while at work and identifies the factors behind them. The information analysis unit can also propose measures to provide a comfortable working environment. For example, it can adjust working hours or improve employee benefits. This provides a comfortable working environment based on the emotions of medical staff and improves satisfaction.

[0123] The information analysis unit can use AI to analyze disaster risk data and propose optimal evacuation routes. For example, the information analysis unit collects disaster risk data and analyzes it using AI. For example, it can propose optimal evacuation routes based on data such as earthquake risk, flood risk, and fire risk. The information analysis unit can also optimize evacuation routes by taking into account the location of evacuation shelters and traffic conditions. This optimizes evacuation routes in the event of a disaster and improves safety.

[0124] The information analysis unit can analyze past disaster data and propose optimal disaster prevention measures. For example, the information analysis unit collects past disaster data and analyzes it using AI. For example, it can propose optimal disaster prevention measures based on data such as the date and time of the disaster, the extent of the damage, and countermeasures. The information analysis unit can also optimize disaster prevention measures by taking into account past disaster data and the latest disaster prevention technology. This allows disaster prevention measures to be optimized based on past disaster data, improving disaster preparedness.

[0125] The information analysis unit can use the emotion estimation function to analyze the emotions of disaster victims and propose measures to provide prompt assistance. The information analysis unit, for example, collects survey results and social media posts from disaster victims and uses the emotion estimation function to understand their emotional state. For example, it analyzes posts that show positive emotions during a disaster and identifies the factors behind them. The information analysis unit can also propose measures to provide prompt assistance. For example, it can distribute relief supplies and set up evacuation shelters. This allows for prompt assistance to be provided based on the emotions of disaster victims, improving satisfaction.

[0126] The information analysis unit can use AI to analyze communication data during a disaster and propose the optimal method of transmitting information. For example, the information analysis unit collects communication data during a disaster and analyzes it using AI. For example, it can propose the optimal method of transmitting information based on data such as communication delays, communication interruptions, and communication volume. The information analysis unit can also optimize the method of transmitting information by taking into account the selection of communication means and the priority of information. This optimizes the method of transmitting information during a disaster, enabling fast and accurate information transmission.

[0127] The information analysis unit can analyze logistics data during disasters and propose optimal supply routes. For example, the information analysis unit collects logistics data during disasters and analyzes it using AI. For example, it can propose optimal supply routes based on data such as logistics delays, disruptions, and logistics volume. The information analysis unit can also optimize supply routes by taking into account the selection of logistics methods and supply priorities. This optimizes supply routes during disasters, enabling rapid supply of supplies.

[0128] The information analysis unit can use the emotion estimation function to analyze the emotions of supporters and propose measures to promote effective support activities. The information analysis unit, for example, collects supporter surveys and social media posts and uses the emotion estimation function to understand their emotional state. For example, it analyzes posts that show positive emotions during support activities and identifies the factors behind them. The information analysis unit can also propose measures to promote effective support activities. For example, it can plan supporter training and support activities. This promotes effective support activities based on the supporter's emotions and improves satisfaction.

[0129] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0130] The regional revitalization system can also include a cultural resource provider that digitizes the region's cultural resources and provides them to tourists and residents. For example, it could create a digital archive of the region's historical buildings and traditional crafts and make them available online. The cultural resource provider could also live-stream traditional local events and festivals, allowing people to participate from afar. The cultural resource provider could also provide quizzes and games related to the region's culture, providing content that allows people to learn while having fun. This will help widely publicize the region's cultural resources and increase its appeal.

[0131] The Information Analysis Department can also analyze health data of local residents and propose health promotion programs. For example, it can collect data on residents' exercise habits and dietary habits and provide individually customized health promotion programs. The Information Analysis Department can also propose holding local health events and workshops. Furthermore, the Information Analysis Department can work with local medical institutions to monitor the health status of residents and detect health risks early. This is expected to improve the health of local residents and reduce medical costs.

[0132] The cashless payment department can also analyze sales data from local stores and service businesses to propose optimal promotion strategies. For example, it can use sales data to propose discount campaigns tailored to specific times of day or days of the week. The cashless payment department can also analyze customers' purchasing history to provide individually customized promotions. Furthermore, the cashless payment department can introduce a point system that links local stores together to promote consumption throughout the region. This will revitalize the local economy and increase store sales.

[0133] The Event Optimization Department can also analyze data from local sporting events and propose optimal sporting programs. For example, it can identify popular sports and competitions based on past participant data and feedback, and plan new programs. The Event Optimization Department can also analyze the usage of local sporting facilities and propose efficient facility management. Furthermore, the Event Optimization Department can propose promotion strategies for local sporting events and implement measures to increase participation. This will revitalize local sporting activities and promote the health of residents.

[0134] The Service Efficiency Department can also analyze local educational data and propose optimal educational programs. For example, it can provide individually customized learning plans based on students' grade data and attendance records. The Service Efficiency Department can also work with local educational institutions to propose teacher training programs. Furthermore, the Service Efficiency Department can propose holding local educational events and workshops and implement measures to improve the quality of education. This will improve the level of education in the region and increase student learning effectiveness.

[0135] The information analysis unit can use the emotion estimation function to analyze the emotions of local residents and propose measures to elicit positive emotions. For example, it can collect social media posts and survey results from local residents to understand their emotional state. It can analyze posts with positive emotions and identify the factors behind them. The information analysis unit can also propose measures to elicit positive emotions. For example, it can hold local events and promote community activities. This will improve the satisfaction of local residents and promote the revitalization of the area.

[0136] The cashless payment department can use the emotion estimation function to analyze consumers' emotions when making a purchase and propose measures to increase their purchasing motivation. For example, it can collect consumer surveys and social media posts to understand their emotional state. It can analyze posts that show positive emotions when making a purchase and identify the factors behind them. The cashless payment department can also propose measures to increase purchasing motivation. For example, it can offer discount campaigns or point rewards. This can increase consumers' purchasing motivation and increase sales.

[0137] The event optimization unit can use the emotion estimation function to analyze the emotions of event participants in real time and reflect this in event management. For example, it can collect the facial expressions and voices of event participants using a camera or microphone to understand their emotional state. It can calculate an emotion score based on changes in facial expressions and tone of voice and reflect this in event management. The event optimization unit can also build a real-time feedback system to notify event management staff of emotion data. This allows event management to be optimized in real time and improves participant satisfaction.

[0138] The service efficiency improvement unit can use the emotion estimation function to analyze the emotions of public service users and propose measures to improve the quality of the service. For example, it can collect questionnaire surveys and social media posts from public service users to understand their emotional state. It can analyze posts that show positive emotions when using public services and identify the factors behind them. The service efficiency improvement unit can also propose measures to improve the quality of the service. For example, it can improve the service or provide staff training. This improves the quality of public services and increases user satisfaction.

[0139] The information analysis unit can use the emotion estimation function to analyze tourists' emotions and propose measures to maximize the appeal of tourist destinations. For example, it can collect tourist surveys and social media posts to understand their emotional state. It can analyze posts with positive emotions toward tourist destinations and identify the factors behind them. The information analysis unit can also propose measures to maximize the appeal of tourist destinations. For example, it can improve tourist facilities and hold events. This will improve the appeal of tourist destinations and increase tourist satisfaction.

[0140] The processing flow of the second embodiment will be briefly explained below.

[0141] Step 1: The Information Analysis Department collects and analyzes data within the region. For example, the Information Analysis Department collects local climate data and proposes the best times and methods for agriculture and tourism. The Information Analysis Department can also analyze local demographics and plan services for the elderly and events for young people. The Information Analysis Department can also analyze local economic data and propose measures to revitalize the local economy. Step 2: The cashless payment department provides electronic payment services. For example, the cashless payment department introduces QR code payments and electronic money, allowing people to shop and use services without carrying cash. The cashless payment department can also make payments at local stores and service businesses smoother. The cashless payment department can also use electronic payment services to make transactions within the local area more efficient. Step 3: The Event Optimization Department analyzes data on local events. For example, the Event Optimization Department analyzes data on past events and analyzes what types of events were successful. The Event Optimization Department can also analyze data on local traditional events and festivals and propose new programs that meet the needs of participants. The Event Optimization Department can also support the planning and management of local events. Step 4: The Service Efficiency Department analyzes data on public services and infrastructure. For example, the Service Efficiency Department analyzes local garbage collection data and proposes optimal collection routes. The Service Efficiency Department can also analyze public transportation operation data and propose operation schedules that match the times when there are many users. The Service Efficiency Department can also propose measures to improve the efficiency of public services within the region.

[0142] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0146] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] 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.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] 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.

[0154] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0155] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0161] 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.

[0162] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] 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.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0167] 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.

[0168] 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.

[0169] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0170] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0176] 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.

[0177] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0178] 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.

[0179] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0180] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0181] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0182] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0183] 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.

[0184] 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.

[0185] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0186] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0187] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0188] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0189] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0190] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0191] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0192] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0193] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0194] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0195] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0196] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0197] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0198] 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.

[0199] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0200] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0201] 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.

[0202] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0203] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0204] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0205] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0206] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0207] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0208] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0209] 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. The Information Analysis Department collects and analyzes data within the region, The cashless payment department provides electronic payment services, an event optimization unit that analyzes data on local events; A service efficiency department that analyzes data on public services and infrastructure. A system characterized by:

2. The information analysis unit Analyzing climate data to suggest optimal times and methods for agriculture and tourism 2. The system of claim 1.

3. The cashless payment unit Introducing QR code payments and electronic money, allowing users to shop and use services without carrying cash.

2. The system of claim 1.

4. The event optimization unit Analyze data from traditional events and festivals and propose new programs that meet the needs of participants 2. The system of claim 1.

5. The information analysis unit Analyze the social media posts of local residents, grasp the needs and trends of the area in real time, and reflect them in policies.

2. The system of claim 1.

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