system

Abstract

The system according to this embodiment aims to strengthen information sharing and cooperation among local governments and to enable rapid and effective emergency response. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, a sharing unit, and an emergency response unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes and executes the optimal action based on the analysis results obtained by the analysis unit. The sharing unit shares information among local governments. The emergency response unit performs emergency response.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, information sharing and cooperation between local governments are not sufficiently carried out, and there is still room for improvement in emergency response.

[0005] The system according to the embodiment aims to strengthen information sharing and cooperation between local governments and perform emergency response quickly and effectively.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a sharing unit, and an emergency response unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes and executes the optimal action based on the analysis results obtained by the analysis unit. The sharing unit shares information among local governments. The emergency response unit handles emergency situations. [Effects of the Invention]

[0007] The system according to this embodiment can strengthen information sharing and cooperation among local governments, enabling a rapid and effective response to emergencies. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The local government information sharing system according to an embodiment of the present invention is a system that strengthens information sharing, cooperation, and emergency response among local governments by deploying an AI agent in each local government. This local government information sharing system deploys a dedicated AI agent in each local government, and the AI ​​agent collects data in real time and shares information among local governments. Furthermore, the AI ​​agent analyzes the collected data and proposes and executes the optimal action for problem solving. This mechanism strengthens information sharing among local governments, enabling rapid response in the event of a disaster and efficient administrative operations. For example, the local government information sharing system deploys a dedicated AI agent in each local government. The AI ​​agent is installed in each local government and collects data related to the operation of the local government. For example, it collects data such as residents' opinions and requests, and the situation when a disaster occurs. This data is collected in real time by the AI ​​agent. Next, the local government information sharing system analyzes the data collected by the AI ​​agent. Based on the collected data, the AI ​​agent shares information among local governments. For example, when a disaster occurs, it shares information such as the extent of damage and the need for support with other local governments. This enables a rapid response. Furthermore, based on the data collected by the AI ​​agent, the local government information sharing system proposes and executes the optimal action for problem solving. For example, in the event of a disaster, the system proposes and implements actions such as suggesting evacuation routes and distributing relief supplies. Furthermore, in administrative operations, it formulates optimal operational policies based on data analysis. This enables efficient administrative operations. This mechanism strengthens information sharing among local governments, enabling rapid response and efficient administrative operations during disasters. For example, in the event of a disaster, sharing information such as the extent of damage and the need for support with other local governments enables a rapid response. Furthermore, in administrative operations, formulating optimal operational policies based on data analysis enables efficient operations. This improves the quality of life for local residents and realizes a safe and secure society. Thus, the local government information sharing system strengthens information sharing, cooperation, and emergency response among local governments, enabling rapid and efficient administrative operations.

[0029] The local government information sharing system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a sharing unit, and an emergency response unit. The collection unit collects data. The collection unit collects data such as residents' opinions and requests, and the situation at the time of a disaster. The collection unit can collect residents' opinions and requests, for example, through questionnaire surveys. The collection unit can also collect residents' opinions and requests through interviews. Furthermore, the collection unit can also collect residents' opinions and requests through feedback forms. For example, the collection unit can collect the situation at the time of a disaster through on-site surveys. The collection unit can also collect the situation at the time of a disaster through sensor information. Furthermore, the collection unit can also collect the situation at the time of a disaster through reports. The analysis unit analyzes the data collected by the collection unit. The analysis unit shares information among local governments based on the collected data. The analysis unit can share information by standardizing data formats, for example. The analysis unit can also share information by standardizing communication protocols. Furthermore, the analysis unit can share information by implementing security measures. The Proposal Department proposes and executes the optimal action based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can propose and execute the optimal action based on efficiency. The Proposal Department can also propose and execute the optimal action based on effectiveness. Furthermore, the Proposal Department can also propose and execute the optimal action based on cost. The Sharing Department shares information among local governments. For example, the Sharing Department shares information such as the extent of damage and the need for support in the event of a disaster with other local governments. For example, the Sharing Department can collect information on the extent of damage through on-site surveys and share it with other local governments. Furthermore, the Sharing Department can collect information on the extent of damage through sensor data and share it with other local governments. Furthermore, the Sharing Department can collect information on the extent of damage through reports and share it with other local governments. The Emergency Response Department handles emergency response. For example, the Emergency Response Department proposes and executes actions such as proposing evacuation routes and distributing relief supplies in the event of a disaster. For example, when proposing evacuation routes, the Emergency Response Department can propose them based on safety. Furthermore, the Emergency Response Department can also propose evacuation routes based on distance.Furthermore, the emergency response unit can propose evacuation routes based on ease of access. For example, when distributing relief supplies, the emergency response unit can prioritize the distribution of food. It can also prioritize the distribution of water. Furthermore, it can prioritize the distribution of medicines. As a result, the municipal information sharing system according to this embodiment can strengthen information sharing, cooperation, and emergency response among municipalities, enabling swift and efficient administrative operations.

[0030] The data collection department collects data. For example, the data collection department collects data such as residents' opinions and requests, and the situation during a disaster. For example, the data collection department can collect residents' opinions and requests through surveys. It can also collect residents' opinions and requests through interviews. Furthermore, the data collection department can collect residents' opinions and requests through feedback forms. For example, the data collection department can collect information about the situation during a disaster through on-site surveys. It can also collect information about the situation during a disaster through sensor data. Furthermore, the data collection department can collect information about the situation during a disaster through reports. The data collection department can conduct online and paper-based questionnaires to collect residents' opinions and requests. Online questionnaires are distributed through the local government's website and social media, making them easily accessible and responsive for residents. Paper-based questionnaires are distributed at local public facilities and events, allowing residents to fill them out and submit them directly. Interviews are a method in which local government officials directly interact with residents to gather detailed opinions and requests. Interviews are conducted in the form of individual visits or group discussions, allowing for the understanding of residents' specific needs and problems. Feedback forms are provided through local government websites and applications, allowing residents to freely submit their opinions and requests. This enables residents to submit their opinions on a regular basis, and local governments to continuously collect residents' voices. On-site surveys are crucial for gathering information on the situation during a disaster. On-site surveys are conducted by local government officials and volunteers who visit the affected area to directly assess the damage and take photographs and videos. This allows for the rapid collection of accurate damage information. Sensor data is used to understand the situation during a disaster in real time. For example, earthquake sensors, water level sensors, and weather sensors are installed, and the data obtained from these sensors is used to immediately grasp the occurrence and progression of the disaster. Reports are official documents created after a disaster, detailing the damage, countermeasures, and future challenges. Reports are shared with stakeholders inside and outside the local government and are used to improve disaster response and prepare for future disasters.This allows the data collection department to comprehensively gather residents' opinions and requests, as well as information about the situation during disasters, using a variety of means, and contribute to the decision-making and formulation of response measures by local governments.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit shares information between local governments based on the collected data. The analysis unit can share information by, for example, standardizing data formats. It can also share information by standardizing communication protocols. Furthermore, it can share information by implementing security measures. The analysis unit utilizes AI technology to efficiently analyze the collected data. For example, it can use natural language processing technology to automatically classify residents' opinions and requests and extract important keywords and trends. This allows local governments to quickly grasp residents' needs and problems and take appropriate countermeasures. It can also use machine learning algorithms to analyze data during disasters and predict damage and assess risks. This allows local governments to take preventative measures and minimize damage. Standardizing data formats is crucial for sharing information between local governments. The analysis unit converts collected data into a standardized format, enabling smooth information sharing with other local governments and related organizations. For example, using common data formats such as CSV and JSON facilitates data exchange between different systems. Standardizing communication protocols also contributes to the efficiency of information sharing. The analysis department adopts standardized communication protocols, enabling smooth data transmission and reception. For example, using protocols such as HTTP and MQTT allows for real-time data communication, facilitating rapid information sharing. Security measures are essential to ensure the reliability of information sharing. The analysis department implements data encryption, access control, and authentication functions to protect data confidentiality and integrity. This ensures secure information sharing between local governments and prevents unauthorized access and tampering of data. Furthermore, the analysis department utilizes data visualization tools to display collected data in visual formats such as graphs, charts, and maps. This allows local government officials and stakeholders to intuitively understand data trends and patterns, enabling effective decision-making.This allows the analysis unit to perform advanced analysis of the collected data, promote information sharing among local governments, and support the rapid development of appropriate countermeasures.

[0032] The proposal department proposes and implements the optimal actions based on the analysis results obtained by the analysis department. For example, the proposal department can propose and implement the optimal actions based on efficiency. It can also propose and implement the optimal actions based on effectiveness. Furthermore, it can propose and implement the optimal actions based on cost. The proposal department is responsible for planning and implementing specific measures and projects based on the analysis results. For example, it can propose measures to improve local infrastructure and public services based on residents' opinions and requests. Proposals based on efficiency aim to optimize resource allocation and improve business processes. For example, it can propose measures to optimize garbage collection routes and reduce energy consumption, thereby reducing the operating costs of the local government. Proposals based on effectiveness emphasize measures that improve resident satisfaction and quality of life. For example, it can propose specific measures to improve the convenience of public transportation and enhance medical services, thereby improving the living environment of residents. Proposals based on cost select measures that will have the greatest effect while considering budget constraints. For example, the proposal department can propose low-cost disaster prevention measures and projects that utilize subsidies and grants, enabling the implementation of effective measures within a limited budget. The proposal department monitors the implementation status of the proposed measures and takes improvement measures as needed. For example, it regularly evaluates the progress and results of the measures and identifies problems and challenges. This allows the proposal department to maximize the effectiveness of the measures and contribute to the achievement of the local government's goals. Furthermore, the proposal department emphasizes communication with residents and stakeholders and engages in activities to gain their understanding and cooperation regarding the proposed content. For example, it holds resident briefings and workshops to explain the proposed content in an easy-to-understand manner and reflect the opinions and requests of residents. This allows the proposal department to implement effective measures while gaining the trust and cooperation of residents. As a result, the proposal department can propose and implement optimal actions based on the analysis results, supporting the efficient and effective operation of the local government.

[0033] The sharing function facilitates information sharing among local governments. For example, it shares information such as the extent of damage and the need for support during a disaster with other local governments. The sharing function can, for instance, collect damage data through on-site surveys and share it with other local governments. It can also collect damage data through sensor information and share it with other local governments. Furthermore, it can collect damage data through reports and share it with other local governments. The sharing function provides a platform for information sharing and plays a role in strengthening cooperation between local governments. For example, it can implement a cloud-based information sharing system, allowing each local government to upload and view data in real time. This enables rapid assessment of damage and the need for support during a disaster, allowing for appropriate responses. Damage data collected through on-site surveys is shared in the form of photos, videos, and text reports. This allows other local governments to visually understand the specific extent of the damage and provide necessary support quickly. Sensor information is updated in real time, allowing for immediate understanding of the progress of a disaster and the extent of damage. For example, data from earthquake sensors and water level sensors is automatically transmitted to the cloud system and shared with other local governments. This enables a rapid response and helps prevent the spread of damage. The report is an official document created after a disaster occurs, detailing the extent of the damage, countermeasures, and future challenges. The report is shared with relevant parties inside and outside the local government and is used to improve disaster response and prepare for future disasters. The sharing department also implements security measures for information sharing. For example, it introduces data encryption, access control, and authentication functions to protect the confidentiality and integrity of the data. This ensures that information is shared safely between local governments and prevents unauthorized access and tampering with data. Furthermore, the sharing department holds regular information exchange meetings and workshops to strengthen cooperation between local governments. This allows each local government to share the latest information and technologies and accumulate know-how for disaster response. In this way, the sharing department can promote information sharing among local governments and support a rapid and appropriate response.

[0034] The Emergency Response Department is responsible for responding to emergencies. For example, in the event of a disaster, the Emergency Response Department proposes and implements actions such as suggesting evacuation routes and distributing relief supplies. When suggesting evacuation routes, for example, the Emergency Response Department can make suggestions based on safety. It can also make suggestions based on distance. Furthermore, it can make suggestions based on ease of access. When distributing relief supplies, for example, the Emergency Response Department can prioritize the distribution of food. It can also prioritize the distribution of water. Furthermore, it can also prioritize the distribution of medicine. The Emergency Response Department is responsible for formulating and implementing plans for a rapid and effective response in the event of a disaster. For example, when suggesting evacuation routes, it utilizes a Geographic Information System (GIS) to calculate the optimal evacuation route. Based on information such as terrain, road conditions, and building layouts, the GIS can propose the safest and fastest evacuation route. Since the suggested evacuation routes are updated in real time, the optimal route can always be provided according to the progress of the disaster and road closure information. When distributing relief supplies, an efficient distribution plan will be formulated, taking into account the type and quantity of supplies and the priority of distribution destinations. For example, basic necessities such as food, water, and medicine will be distributed preferentially to support the lives of disaster victims. The distribution of relief supplies requires a flexible response according to the situation and needs of the affected area. The Emergency Response Department will grasp the situation in the affected area in real time and build a logistics network to quickly deliver necessary supplies. This will enable a rapid response to the needs of disaster victims. Furthermore, the Emergency Response Department will also operate evacuation centers and provide support to disaster victims. In operating evacuation centers, they will provide an environment in which disaster victims can stay safely and comfortably by accepting disaster victims, improving living conditions, and providing medical support. In supporting disaster victims, they will provide psychological support, information provision, and assistance in rebuilding lives to support the early recovery of disaster victims' lives. In this way, the Emergency Response Department can respond quickly and effectively when a disaster occurs, protecting the safety and lives of disaster victims.

[0035] The data collection unit can collect data such as residents' opinions and requests, and the situation during a disaster. For example, the data collection unit can collect residents' opinions and requests through questionnaire surveys. For example, the data collection unit can collect residents' opinions and requests through interviews. For example, the data collection unit can collect residents' opinions and requests through feedback forms. For example, the data collection unit can collect the situation during a disaster through on-site surveys. For example, the data collection unit can collect the situation during a disaster through sensor information. For example, the data collection unit can collect the situation during a disaster through reports. In this way, by collecting data such as residents' opinions and requests and the situation during a disaster, information useful for the operation of local governments can be obtained.

[0036] The analysis unit can share information among local governments based on the collected data. For example, the analysis unit can share information by standardizing data formats. It can also share information by standardizing communication protocols. Furthermore, it can share information by implementing security measures. This strengthens cooperation among local governments by sharing information based on the collected data.

[0037] The proposal department can propose and implement the optimal actions for problem solving based on the collected data. For example, the proposal department can propose and implement the optimal actions based on efficiency. For example, the proposal department can propose and implement the optimal actions based on effectiveness. For example, the proposal department can propose and implement the optimal actions based on cost. This enables a swift and effective response by proposing and implementing the optimal actions for problem solving.

[0038] The shared information unit can share information such as the extent of damage and the need for support during a disaster with other municipalities. For example, the shared information unit can collect information on the extent of damage through on-site surveys and share it with other municipalities. For example, the shared information unit can collect information on the extent of damage through sensor data and share it with other municipalities. For example, the shared information unit can collect information on the extent of damage and the need for support during a disaster and share it with other municipalities. This enables a rapid response by sharing information such as the extent of damage and the need for support during a disaster.

[0039] The Emergency Response Department can propose and implement actions such as suggesting evacuation routes and distributing relief supplies in the event of a disaster. For example, when proposing evacuation routes, the Emergency Response Department can make suggestions based on safety. For example, the Emergency Response Department can make suggestions based on distance. For example, the Emergency Response Department can make suggestions based on accessibility. For example, when distributing relief supplies, the Emergency Response Department can prioritize the distribution of food. For example, the Emergency Response Department can prioritize the distribution of water. For example, the Emergency Response Department can prioritize the distribution of medicine. This enables a swift and appropriate response in the event of a disaster.

[0040] The data collection department can analyze residents' past opinions and requests and select the most suitable data collection method. For example, the department can analyze opinions and requests previously submitted by residents and collect data in the form of questionnaires. For example, the department can collect detailed data in the form of interviews based on residents' past requests. For example, the department can analyze trends in residents' opinions and efficiently collect data using online forms. In this way, by analyzing residents' past opinions and requests, the department can select the most suitable data collection method.

[0041] The data collection unit can filter data based on residents' current living situations and areas of interest during the data collection process. For example, the unit can consider residents' living situations and collect data specifically for families with young children. For example, the unit can analyze residents' areas of interest and prioritize the collection of data from residents interested in environmental issues. For example, the unit can collect data on services for the elderly based on residents' living situations. By filtering data based on residents' living situations and areas of interest, more relevant data can be collected.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of residents during data collection. For example, the data collection unit can prioritize the collection of data from disaster-stricken areas based on the geographical location information of residents. For example, the data collection unit can prioritize the collection of data from areas experiencing traffic congestion by considering the geographical location information of residents. For example, the data collection unit can prioritize the collection of data regarding the usage of public facilities based on the geographical location information of residents. In this way, by considering the geographical location information of residents, highly relevant data can be prioritized.

[0043] The data collection unit can analyze residents' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze residents' social media activity and collect data on trending topics. For example, the data collection unit can also collect data on local events based on residents' social media activity. For example, the data collection unit can analyze residents' social media activity and collect data on residents' interests. In this way, relevant data can be collected by analyzing residents' social media activity.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can perform an analysis with an appropriate level of detail on data of moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a disaster analysis algorithm to disaster data. For example, the analysis unit can apply an opinion analysis algorithm to resident opinion data. For example, the analysis unit can apply an administration analysis algorithm to administrative operation data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved.

[0046] The analysis unit can determine the priority of analysis based on the data collection date. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can postpone the analysis of older data. For example, the analysis unit can perform analysis with an appropriate priority on data of moderate recency. This allows for efficient analysis by determining the priority of analysis based on the data collection date.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, it can prioritize the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. For example, it can analyze data with moderate relevance in an appropriate order. By adjusting the order of analysis based on the relevance of the data, efficient analysis becomes possible.

[0048] The proposal department can adjust the level of detail in its proposals based on the importance of the problem. For example, it can provide detailed proposals for high-priority problems. For example, it can provide simplified proposals for low-priority problems. For example, it can provide proposals with an appropriate level of detail for medium-priority problems. This allows for more efficient proposals by adjusting the level of detail based on the importance of the problem.

[0049] The proposal department can apply different proposal algorithms depending on the category of the problem when making a proposal. For example, the proposal department can apply a disaster response algorithm to disaster response. For example, the proposal department can apply an administrative proposal algorithm to administrative management. For example, the proposal department can apply an opinion proposal algorithm to residents' opinions. By applying the appropriate proposal algorithm according to the category of the problem, the accuracy of the proposal is improved.

[0050] The proposal department can determine the priority of proposals based on when the problem occurred. For example, the proposal department can prioritize proposals for recently occurring problems. For example, the proposal department can postpone proposals for older problems. For example, the proposal department can give appropriate priority to proposals for problems of moderate relevance. This allows for more efficient proposals by determining the priority of proposals based on when the problem occurred.

[0051] The proposal department can adjust the order of proposals based on the relevance of the issues. For example, it can prioritize proposals for highly relevant issues. For example, it can postpone proposals for less relevant issues. For example, it can propose proposals for moderately relevant issues in an appropriate order. By adjusting the order of proposals based on the relevance of the issues, efficient proposals become possible.

[0052] The sharing function can adjust the level of detail shared based on the importance of the data during information sharing. For example, it can share detailed information for highly important data. For example, it can share simplified information for less important data. For example, it can share information with an appropriate level of detail for moderately important data. This allows for efficient information sharing by adjusting the level of detail based on the importance of the data.

[0053] The sharing function can apply different sharing algorithms depending on the data category when sharing information. For example, it can apply a disaster sharing algorithm to disaster data. For example, it can apply an opinion sharing algorithm to resident opinion data. For example, it can apply an administration sharing algorithm to administrative operation data. By applying the appropriate sharing algorithm according to the data category, the accuracy of information sharing is improved.

[0054] The sharing function can adjust the order of information sharing based on when the data was collected. For example, it can prioritize sharing the most recent data. It can also postpone sharing older data. For example, it can share data of moderate recency in an appropriate order. By adjusting the order of sharing based on when the data was collected, efficient information sharing becomes possible.

[0055] The sharing function can adjust the order of information sharing based on the relevance of the data. For example, it can prioritize sharing information with highly relevant data. It can also postpone sharing information with less relevant data. For example, it can share information with moderately relevant data in an appropriate order. This allows for efficient information sharing by adjusting the order of sharing based on the relevance of the data.

[0056] The emergency response department can select the optimal response method by referring to past emergency response data during an emergency. For example, the emergency response department can propose the optimal evacuation route by referring to past disaster response data. For example, the emergency response department can select the optimal medical support method by referring to past emergency medical response data. For example, the emergency response department can select the optimal method of distributing emergency supplies by referring to past emergency supply distribution data. In this way, the optimal response method can be selected by referring to past emergency response data.

[0057] The emergency response unit can customize its response measures based on the current living conditions of residents during an emergency. For example, the emergency response unit can provide evacuation support for the elderly, taking into account the living conditions of residents. For example, the emergency response unit can provide support for families with young children, taking into account the living conditions of residents. For example, the emergency response unit can provide support for people with disabilities, taking into account the living conditions of residents. By customizing response measures based on the living conditions of residents, a more appropriate emergency response becomes possible.

[0058] The Emergency Response Department can select the optimal response method during an emergency by considering the geographical location information of residents. For example, the Emergency Response Department can propose the optimal evacuation route to residents in a disaster-stricken area based on their geographical location information. For example, the Emergency Response Department can also select the optimal response method for residents in areas experiencing traffic congestion by considering their geographical location information. For example, the Emergency Response Department can also select a response method based on the usage status of public facilities based on the geographical location information of residents. In this way, the optimal response method can be selected by considering the geographical location information of residents.

[0059] The emergency response department can analyze residents' social media activity and propose response measures during emergencies. For example, the emergency response department can analyze residents' social media activity and propose response measures based on trending topics. For example, the emergency response department can propose response measures related to local events based on residents' social media activity. For example, the emergency response department can analyze residents' social media activity and propose response measures based on residents' interests. In this way, by analyzing residents' social media activity, more appropriate response measures can be proposed.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The local government information sharing system can also be equipped with a prediction unit. This unit can predict future disasters and problems based on collected data. For example, it can analyze past disaster data to predict future disaster risks. It can also analyze trends in residents' opinions and requests to predict future challenges in administrative operations. Furthermore, it can predict natural disasters based on weather and earthquake data, enabling proactive measures. This allows the prediction unit to identify future risks in advance and respond quickly and appropriately.

[0062] The local government information sharing system can also include the Ministry of Education. Based on the collected data, the Ministry of Education can create and provide educational programs for residents. For example, it can provide educational programs on evacuation methods and disaster prevention measures. It can also provide programs on how to use administrative services and on local history and culture. Furthermore, it can provide educational programs on residents' health management and environmental protection. Through this, the Ministry of Education can improve residents' knowledge and awareness, and ensure the safety and security of the community.

[0063] The local government information sharing system can also include a feedback section. This feedback section can collect feedback from residents and reflect it in the operation of the local government. For example, it can provide feedback on the status of responses to opinions and requests submitted by residents. It can also provide feedback on the results of surveys conducted with resident participation. Furthermore, it can provide feedback on residents' satisfaction with the administrative services they have used. In this way, the feedback section can realize administrative operations that reflect residents' opinions and requests, thereby improving resident satisfaction.

[0064] The local government information sharing system can also include a collaboration department. This department can strengthen cooperation with other local governments, private companies, and NPOs. For example, in the event of a disaster, it can collaborate with other local governments to procure and distribute relief supplies. It can also collaborate with private companies to revitalize the local economy. Furthermore, it can collaborate with NPOs to enhance local welfare services. In this way, the collaboration department strengthens cooperation between local governments and with other organizations, contributing to the resolution of challenges in local communities.

[0065] The local government information sharing system can also include an evaluation unit. This unit can assess the effectiveness of local government policies and services based on collected data. For example, it can evaluate the effectiveness of disaster countermeasures and propose areas for improvement. It can also evaluate the utilization of administrative services and make suggestions for improving service quality. Furthermore, it can evaluate resident satisfaction and propose policies that meet resident needs. This allows the evaluation unit to objectively assess the effectiveness of local government policies and services and use the results for improvement.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects data. For example, they collect data such as residents' opinions and requests, and the situation during disasters. Data collection methods include questionnaires, interviews, feedback forms, field surveys, sensor data, and reports. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it shares information by standardizing data formats and communication protocols and implementing security measures. Step 3: The proposal department proposes and executes the optimal action based on the analysis results obtained by the analysis department. For example, it proposes and executes the optimal action based on efficiency, effectiveness, and cost. Step 4: Sharing involves sharing information among local governments. For example, information such as the extent of damage and the need for support during a disaster is shared with other local governments. Methods of collection include on-site surveys, sensor data, and reports. Step 5: The Emergency Response Department will carry out emergency response. For example, in the event of a disaster, they will propose and implement actions such as suggesting evacuation routes and distributing relief supplies. Criteria for suggesting evacuation routes include safety, distance, and accessibility, while criteria for distributing relief supplies include food, water, and medicine.

[0068] (Example of form 2) The local government information sharing system according to an embodiment of the present invention is a system that strengthens information sharing, cooperation, and emergency response among local governments by deploying an AI agent in each local government. This local government information sharing system deploys a dedicated AI agent in each local government, and the AI ​​agent collects data in real time and shares information among local governments. Furthermore, the AI ​​agent analyzes the collected data and proposes and executes the optimal action for problem solving. This mechanism strengthens information sharing among local governments, enabling rapid response in the event of a disaster and efficient administrative operations. For example, the local government information sharing system deploys a dedicated AI agent in each local government. The AI ​​agent is installed in each local government and collects data related to the operation of the local government. For example, it collects data such as residents' opinions and requests, and the situation when a disaster occurs. This data is collected in real time by the AI ​​agent. Next, the local government information sharing system analyzes the data collected by the AI ​​agent. Based on the collected data, the AI ​​agent shares information among local governments. For example, when a disaster occurs, it shares information such as the extent of damage and the need for support with other local governments. This enables a rapid response. Furthermore, based on the data collected by the AI ​​agent, the local government information sharing system proposes and executes the optimal action for problem solving. For example, in the event of a disaster, the system proposes and implements actions such as suggesting evacuation routes and distributing relief supplies. Furthermore, in administrative operations, it formulates optimal operational policies based on data analysis. This enables efficient administrative operations. This mechanism strengthens information sharing among local governments, enabling rapid response and efficient administrative operations during disasters. For example, in the event of a disaster, sharing information such as the extent of damage and the need for support with other local governments enables a rapid response. Furthermore, in administrative operations, formulating optimal operational policies based on data analysis enables efficient operations. This improves the quality of life for local residents and realizes a safe and secure society. Thus, the local government information sharing system strengthens information sharing, cooperation, and emergency response among local governments, enabling rapid and efficient administrative operations.

[0069] The local government information sharing system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a sharing unit, and an emergency response unit. The collection unit collects data. The collection unit collects data such as residents' opinions and requests, and the situation at the time of a disaster. The collection unit can collect residents' opinions and requests, for example, through questionnaire surveys. The collection unit can also collect residents' opinions and requests through interviews. Furthermore, the collection unit can also collect residents' opinions and requests through feedback forms. For example, the collection unit can collect the situation at the time of a disaster through on-site surveys. The collection unit can also collect the situation at the time of a disaster through sensor information. Furthermore, the collection unit can also collect the situation at the time of a disaster through reports. The analysis unit analyzes the data collected by the collection unit. The analysis unit shares information among local governments based on the collected data. The analysis unit can share information by standardizing data formats, for example. The analysis unit can also share information by standardizing communication protocols. Furthermore, the analysis unit can share information by implementing security measures. The Proposal Department proposes and executes the optimal action based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can propose and execute the optimal action based on efficiency. The Proposal Department can also propose and execute the optimal action based on effectiveness. Furthermore, the Proposal Department can also propose and execute the optimal action based on cost. The Sharing Department shares information among local governments. For example, the Sharing Department shares information such as the extent of damage and the need for support in the event of a disaster with other local governments. For example, the Sharing Department can collect information on the extent of damage through on-site surveys and share it with other local governments. Furthermore, the Sharing Department can collect information on the extent of damage through sensor data and share it with other local governments. Furthermore, the Sharing Department can collect information on the extent of damage through reports and share it with other local governments. The Emergency Response Department handles emergency response. For example, the Emergency Response Department proposes and executes actions such as proposing evacuation routes and distributing relief supplies in the event of a disaster. For example, when proposing evacuation routes, the Emergency Response Department can propose them based on safety. Furthermore, the Emergency Response Department can also propose evacuation routes based on distance.Furthermore, the emergency response unit can propose evacuation routes based on ease of access. For example, when distributing relief supplies, the emergency response unit can prioritize the distribution of food. It can also prioritize the distribution of water. Furthermore, it can prioritize the distribution of medicines. As a result, the municipal information sharing system according to this embodiment can strengthen information sharing, cooperation, and emergency response among municipalities, enabling swift and efficient administrative operations.

[0070] The data collection department collects data. For example, the data collection department collects data such as residents' opinions and requests, and the situation during a disaster. For example, the data collection department can collect residents' opinions and requests through surveys. It can also collect residents' opinions and requests through interviews. Furthermore, the data collection department can collect residents' opinions and requests through feedback forms. For example, the data collection department can collect information about the situation during a disaster through on-site surveys. It can also collect information about the situation during a disaster through sensor data. Furthermore, the data collection department can collect information about the situation during a disaster through reports. The data collection department can conduct online and paper-based questionnaires to collect residents' opinions and requests. Online questionnaires are distributed through the local government's website and social media, making them easily accessible and responsive for residents. Paper-based questionnaires are distributed at local public facilities and events, allowing residents to fill them out and submit them directly. Interviews are a method in which local government officials directly interact with residents to gather detailed opinions and requests. Interviews are conducted in the form of individual visits or group discussions, allowing for the understanding of residents' specific needs and problems. Feedback forms are provided through local government websites and applications, allowing residents to freely submit their opinions and requests. This enables residents to submit their opinions on a regular basis, and local governments to continuously collect residents' voices. On-site surveys are crucial for gathering information on the situation during a disaster. On-site surveys are conducted by local government officials and volunteers who visit the affected area to directly assess the damage and take photographs and videos. This allows for the rapid collection of accurate damage information. Sensor data is used to understand the situation during a disaster in real time. For example, earthquake sensors, water level sensors, and weather sensors are installed, and the data obtained from these sensors is used to immediately grasp the occurrence and progression of the disaster. Reports are official documents created after a disaster, detailing the damage, countermeasures, and future challenges. Reports are shared with stakeholders inside and outside the local government and are used to improve disaster response and prepare for future disasters.This allows the data collection department to comprehensively gather residents' opinions and requests, as well as information about the situation during disasters, using a variety of means, and contribute to the decision-making and formulation of response measures by local governments.

[0071] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit shares information between local governments based on the collected data. The analysis unit can share information by, for example, standardizing data formats. It can also share information by standardizing communication protocols. Furthermore, it can share information by implementing security measures. The analysis unit utilizes AI technology to efficiently analyze the collected data. For example, it can use natural language processing technology to automatically classify residents' opinions and requests and extract important keywords and trends. This allows local governments to quickly grasp residents' needs and problems and take appropriate countermeasures. It can also use machine learning algorithms to analyze data during disasters and predict damage and assess risks. This allows local governments to take preventative measures and minimize damage. Standardizing data formats is crucial for sharing information between local governments. The analysis unit converts collected data into a standardized format, enabling smooth information sharing with other local governments and related organizations. For example, using common data formats such as CSV and JSON facilitates data exchange between different systems. Standardizing communication protocols also contributes to the efficiency of information sharing. The analysis department adopts standardized communication protocols, enabling smooth data transmission and reception. For example, using protocols such as HTTP and MQTT allows for real-time data communication, facilitating rapid information sharing. Security measures are essential to ensure the reliability of information sharing. The analysis department implements data encryption, access control, and authentication functions to protect data confidentiality and integrity. This ensures secure information sharing between local governments and prevents unauthorized access and tampering of data. Furthermore, the analysis department utilizes data visualization tools to display collected data in visual formats such as graphs, charts, and maps. This allows local government officials and stakeholders to intuitively understand data trends and patterns, enabling effective decision-making.This allows the analysis unit to perform advanced analysis of the collected data, promote information sharing among local governments, and support the rapid development of appropriate countermeasures.

[0072] The proposal department proposes and implements the optimal actions based on the analysis results obtained by the analysis department. For example, the proposal department can propose and implement the optimal actions based on efficiency. It can also propose and implement the optimal actions based on effectiveness. Furthermore, it can propose and implement the optimal actions based on cost. The proposal department is responsible for planning and implementing specific measures and projects based on the analysis results. For example, it can propose measures to improve local infrastructure and public services based on residents' opinions and requests. Proposals based on efficiency aim to optimize resource allocation and improve business processes. For example, it can propose measures to optimize garbage collection routes and reduce energy consumption, thereby reducing the operating costs of the local government. Proposals based on effectiveness emphasize measures that improve resident satisfaction and quality of life. For example, it can propose specific measures to improve the convenience of public transportation and enhance medical services, thereby improving the living environment of residents. Proposals based on cost select measures that will have the greatest effect while considering budget constraints. For example, the proposal department can propose low-cost disaster prevention measures and projects that utilize subsidies and grants, enabling the implementation of effective measures within a limited budget. The proposal department monitors the implementation status of the proposed measures and takes improvement measures as needed. For example, it regularly evaluates the progress and results of the measures and identifies problems and challenges. This allows the proposal department to maximize the effectiveness of the measures and contribute to the achievement of the local government's goals. Furthermore, the proposal department emphasizes communication with residents and stakeholders and engages in activities to gain their understanding and cooperation regarding the proposed content. For example, it holds resident briefings and workshops to explain the proposed content in an easy-to-understand manner and reflect the opinions and requests of residents. This allows the proposal department to implement effective measures while gaining the trust and cooperation of residents. As a result, the proposal department can propose and implement optimal actions based on the analysis results, supporting the efficient and effective operation of the local government.

[0073] The sharing function facilitates information sharing among local governments. For example, it shares information such as the extent of damage and the need for support during a disaster with other local governments. The sharing function can, for instance, collect damage data through on-site surveys and share it with other local governments. It can also collect damage data through sensor information and share it with other local governments. Furthermore, it can collect damage data through reports and share it with other local governments. The sharing function provides a platform for information sharing and plays a role in strengthening cooperation between local governments. For example, it can implement a cloud-based information sharing system, allowing each local government to upload and view data in real time. This enables rapid assessment of damage and the need for support during a disaster, allowing for appropriate responses. Damage data collected through on-site surveys is shared in the form of photos, videos, and text reports. This allows other local governments to visually understand the specific extent of the damage and provide necessary support quickly. Sensor information is updated in real time, allowing for immediate understanding of the progress of a disaster and the extent of damage. For example, data from earthquake sensors and water level sensors is automatically transmitted to the cloud system and shared with other local governments. This enables a rapid response and helps prevent the spread of damage. The report is an official document created after a disaster occurs, detailing the extent of the damage, countermeasures, and future challenges. The report is shared with relevant parties inside and outside the local government and is used to improve disaster response and prepare for future disasters. The sharing department also implements security measures for information sharing. For example, it introduces data encryption, access control, and authentication functions to protect the confidentiality and integrity of the data. This ensures that information is shared safely between local governments and prevents unauthorized access and tampering with data. Furthermore, the sharing department holds regular information exchange meetings and workshops to strengthen cooperation between local governments. This allows each local government to share the latest information and technologies and accumulate know-how for disaster response. In this way, the sharing department can promote information sharing among local governments and support a rapid and appropriate response.

[0074] The Emergency Response Department is responsible for responding to emergencies. For example, in the event of a disaster, the Emergency Response Department proposes and implements actions such as suggesting evacuation routes and distributing relief supplies. When suggesting evacuation routes, for example, the Emergency Response Department can make suggestions based on safety. It can also make suggestions based on distance. Furthermore, it can make suggestions based on ease of access. When distributing relief supplies, for example, the Emergency Response Department can prioritize the distribution of food. It can also prioritize the distribution of water. Furthermore, it can also prioritize the distribution of medicine. The Emergency Response Department is responsible for formulating and implementing plans for a rapid and effective response in the event of a disaster. For example, when suggesting evacuation routes, it utilizes a Geographic Information System (GIS) to calculate the optimal evacuation route. Based on information such as terrain, road conditions, and building layouts, the GIS can propose the safest and fastest evacuation route. Since the suggested evacuation routes are updated in real time, the optimal route can always be provided according to the progress of the disaster and road closure information. When distributing relief supplies, an efficient distribution plan will be formulated, taking into account the type and quantity of supplies and the priority of distribution destinations. For example, basic necessities such as food, water, and medicine will be distributed preferentially to support the lives of disaster victims. The distribution of relief supplies requires a flexible response according to the situation and needs of the affected area. The Emergency Response Department will grasp the situation in the affected area in real time and build a logistics network to quickly deliver necessary supplies. This will enable a rapid response to the needs of disaster victims. Furthermore, the Emergency Response Department will also operate evacuation centers and provide support to disaster victims. In operating evacuation centers, they will provide an environment in which disaster victims can stay safely and comfortably by accepting disaster victims, improving living conditions, and providing medical support. In supporting disaster victims, they will provide psychological support, information provision, and assistance in rebuilding lives to support the early recovery of disaster victims' lives. In this way, the Emergency Response Department can respond quickly and effectively when a disaster occurs, protecting the safety and lives of disaster victims.

[0075] The data collection unit can collect data such as residents' opinions and requests, and the situation during a disaster. For example, the data collection unit can collect residents' opinions and requests through questionnaire surveys. For example, the data collection unit can collect residents' opinions and requests through interviews. For example, the data collection unit can collect residents' opinions and requests through feedback forms. For example, the data collection unit can collect the situation during a disaster through on-site surveys. For example, the data collection unit can collect the situation during a disaster through sensor information. For example, the data collection unit can collect the situation during a disaster through reports. In this way, by collecting data such as residents' opinions and requests and the situation during a disaster, information useful for the operation of local governments can be obtained.

[0076] The analysis unit can share information among local governments based on the collected data. For example, the analysis unit can share information by standardizing data formats. It can also share information by standardizing communication protocols. Furthermore, it can share information by implementing security measures. This strengthens cooperation among local governments by sharing information based on the collected data.

[0077] The proposal department can propose and implement the optimal actions for problem solving based on the collected data. For example, the proposal department can propose and implement the optimal actions based on efficiency. For example, the proposal department can propose and implement the optimal actions based on effectiveness. For example, the proposal department can propose and implement the optimal actions based on cost. This enables a swift and effective response by proposing and implementing the optimal actions for problem solving.

[0078] The shared information unit can share information such as the extent of damage and the need for support during a disaster with other municipalities. For example, the shared information unit can collect information on the extent of damage through on-site surveys and share it with other municipalities. For example, the shared information unit can collect information on the extent of damage through sensor data and share it with other municipalities. For example, the shared information unit can collect information on the extent of damage and the need for support during a disaster and share it with other municipalities. This enables a rapid response by sharing information such as the extent of damage and the need for support during a disaster.

[0079] The Emergency Response Department can propose and implement actions such as suggesting evacuation routes and distributing relief supplies in the event of a disaster. For example, when proposing evacuation routes, the Emergency Response Department can make suggestions based on safety. For example, the Emergency Response Department can make suggestions based on distance. For example, the Emergency Response Department can make suggestions based on accessibility. For example, when distributing relief supplies, the Emergency Response Department can prioritize the distribution of food. For example, the Emergency Response Department can prioritize the distribution of water. For example, the Emergency Response Department can prioritize the distribution of medicine. This enables a swift and appropriate response in the event of a disaster.

[0080] The data collection unit can estimate residents' emotions and adjust the timing of data collection based on the estimated emotions. For example, if residents are feeling anxious, the data collection unit can immediately begin data collection and respond quickly. For example, if residents are relaxed, the data collection unit can perform regular data collection and accumulate long-term data. For example, if residents are feeling angry, the data collection unit can refrain from collecting data until their emotions have calmed down and then resume collection later. This allows for more appropriate data collection by adjusting the timing of data collection according to residents' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The data collection department can analyze residents' past opinions and requests and select the most suitable data collection method. For example, the department can analyze opinions and requests previously submitted by residents and collect data in the form of questionnaires. For example, the department can collect detailed data in the form of interviews based on residents' past requests. For example, the department can analyze trends in residents' opinions and efficiently collect data using online forms. In this way, by analyzing residents' past opinions and requests, the department can select the most suitable data collection method.

[0082] The data collection unit can filter data based on residents' current living situations and areas of interest during the data collection process. For example, the unit can consider residents' living situations and collect data specifically for families with young children. For example, the unit can analyze residents' areas of interest and prioritize the collection of data from residents interested in environmental issues. For example, the unit can collect data on services for the elderly based on residents' living situations. By filtering data based on residents' living situations and areas of interest, more relevant data can be collected.

[0083] The data collection unit can estimate residents' emotions and prioritize the data to be collected based on those estimated emotions. For example, if residents are feeling anxious, the unit can prioritize collecting urgent data. For example, if residents are relaxed, the unit can prioritize collecting long-term data. For example, if residents are angry, the unit can prioritize collecting emotion-related data. This allows for the collection of more important data by prioritizing data according to residents' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of residents during data collection. For example, the data collection unit can prioritize the collection of data from disaster-stricken areas based on the geographical location information of residents. For example, the data collection unit can prioritize the collection of data from areas experiencing traffic congestion by considering the geographical location information of residents. For example, the data collection unit can prioritize the collection of data regarding the usage of public facilities based on the geographical location information of residents. In this way, by considering the geographical location information of residents, highly relevant data can be prioritized.

[0085] The data collection unit can analyze residents' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze residents' social media activity and collect data on trending topics. For example, the data collection unit can also collect data on local events based on residents' social media activity. For example, the data collection unit can analyze residents' social media activity and collect data on residents' interests. In this way, relevant data can be collected by analyzing residents' social media activity.

[0086] The analysis unit can estimate the emotions of residents and adjust the presentation of the analysis based on the estimated emotions. For example, if a resident is feeling anxious, the analysis unit can use a simple and easy-to-understand presentation. For example, if a resident is relaxed, the analysis unit can also use a presentation that includes detailed data. For example, if a resident is angry, the analysis unit can also use an emotionally sensitive presentation. By adjusting the presentation of the analysis according to the emotions of the residents, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can perform an analysis with an appropriate level of detail on data of moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible.

[0088] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a disaster analysis algorithm to disaster data. For example, the analysis unit can apply an opinion analysis algorithm to resident opinion data. For example, the analysis unit can apply an administration analysis algorithm to administrative operation data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved.

[0089] The analysis unit can estimate the emotions of residents and adjust the length of the analysis based on the estimated emotions. For example, if a resident is feeling anxious, the analysis unit can perform a short, concise analysis. For example, if a resident is relaxed, the analysis unit can perform a detailed analysis. For example, if a resident is feeling angry, the analysis unit can perform a short, emotion-sensitive analysis. By adjusting the length of the analysis according to the emotions of the residents, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The analysis unit can determine the priority of analysis based on the data collection date. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can postpone the analysis of older data. For example, the analysis unit can perform analysis with an appropriate priority on data of moderate recency. This allows for efficient analysis by determining the priority of analysis based on the data collection date.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, it can prioritize the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. For example, it can analyze data with moderate relevance in an appropriate order. By adjusting the order of analysis based on the relevance of the data, efficient analysis becomes possible.

[0092] The proposal function can estimate the residents' emotions and adjust the way the proposal is expressed based on those estimated emotions. For example, if a resident is feeling anxious, the proposal function can use a simple and easy-to-understand expression. For example, if a resident is relaxed, the proposal function can use an expression that includes detailed information. For example, if a resident is angry, the proposal function can use an expression that is sensitive to the resident's emotions. By adjusting the expression of the proposal according to the resident's emotions, more appropriate proposals can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The proposal department can adjust the level of detail in its proposals based on the importance of the problem. For example, it can provide detailed proposals for high-priority problems. For example, it can provide simplified proposals for low-priority problems. For example, it can provide proposals with an appropriate level of detail for medium-priority problems. This allows for more efficient proposals by adjusting the level of detail based on the importance of the problem.

[0094] The proposal department can apply different proposal algorithms depending on the category of the problem when making a proposal. For example, the proposal department can apply a disaster response algorithm to disaster response. For example, the proposal department can apply an administrative proposal algorithm to administrative management. For example, the proposal department can apply an opinion proposal algorithm to residents' opinions. By applying the appropriate proposal algorithm according to the category of the problem, the accuracy of the proposal is improved.

[0095] The proposal unit can estimate the residents' emotions and adjust the length of the proposal based on the estimated emotions. For example, if a resident is feeling anxious, the proposal unit can make a short, to-the-point proposal. For example, if a resident is relaxed, the proposal unit can make a detailed proposal. For example, if a resident is feeling angry, the proposal unit can make a short, emotion-sensitive proposal. By adjusting the length of the proposal according to the resident's emotions, more appropriate proposals can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The proposal department can determine the priority of proposals based on when the problem occurred. For example, the proposal department can prioritize proposals for recently occurring problems. For example, the proposal department can postpone proposals for older problems. For example, the proposal department can give appropriate priority to proposals for problems of moderate relevance. This allows for more efficient proposals by determining the priority of proposals based on when the problem occurred.

[0097] The proposal department can adjust the order of proposals based on the relevance of the issues. For example, it can prioritize proposals for highly relevant issues. For example, it can postpone proposals for less relevant issues. For example, it can propose proposals for moderately relevant issues in an appropriate order. By adjusting the order of proposals based on the relevance of the issues, efficient proposals become possible.

[0098] The shared section can estimate residents' emotions and adjust the method of information sharing based on the estimated emotions. For example, if residents are feeling anxious, the shared section can use a simple and easy-to-understand method of information sharing. For example, if residents are relaxed, the shared section can use a method of sharing information that includes detailed information. For example, if residents are feeling angry, the shared section can use an emotionally sensitive method of information sharing. This allows for more appropriate information sharing by adjusting the method of information sharing according to residents' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The sharing function can adjust the level of detail shared based on the importance of the data during information sharing. For example, it can share detailed information for highly important data. For example, it can share simplified information for less important data. For example, it can share information with an appropriate level of detail for moderately important data. This allows for efficient information sharing by adjusting the level of detail based on the importance of the data.

[0100] The sharing function can apply different sharing algorithms depending on the data category when sharing information. For example, it can apply a disaster sharing algorithm to disaster data. For example, it can apply an opinion sharing algorithm to resident opinion data. For example, it can apply an administration sharing algorithm to administrative operation data. By applying the appropriate sharing algorithm according to the data category, the accuracy of information sharing is improved.

[0101] The shared system can estimate residents' emotions and determine the priority of information sharing based on those estimated emotions. For example, if residents are feeling anxious, the system can prioritize sharing urgent information. For example, if residents are relaxed, the system can prioritize sharing long-term information. For example, if residents are angry, the system can prioritize sharing information related to those emotions. This allows for the prioritization of information sharing according to residents' emotions, ensuring that more important information is shared first. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The sharing function can adjust the order of information sharing based on when the data was collected. For example, it can prioritize sharing the most recent data. It can also postpone sharing older data. For example, it can share data of moderate recency in an appropriate order. By adjusting the order of sharing based on when the data was collected, efficient information sharing becomes possible.

[0103] The sharing function can adjust the order of information sharing based on the relevance of the data. For example, it can prioritize sharing information with highly relevant data. It can also postpone sharing information with less relevant data. For example, it can share information with moderately relevant data in an appropriate order. This allows for efficient information sharing by adjusting the order of sharing based on the relevance of the data.

[0104] The emergency response unit can estimate the emotions of residents and adjust its emergency response methods based on those estimated emotions. For example, if residents are feeling anxious, the emergency response unit can use a quick and simple response method. For example, if residents are relaxed, the emergency response unit can use a response method that includes detailed information. For example, if residents are angry, the emergency response unit can use an emotion-sensitive response method. By adjusting emergency response methods according to residents' emotions, more appropriate emergency responses become possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The emergency response department can select the optimal response method by referring to past emergency response data during an emergency. For example, the emergency response department can propose the optimal evacuation route by referring to past disaster response data. For example, the emergency response department can select the optimal medical support method by referring to past emergency medical response data. For example, the emergency response department can select the optimal method of distributing emergency supplies by referring to past emergency supply distribution data. In this way, the optimal response method can be selected by referring to past emergency response data.

[0106] The emergency response unit can customize its response measures based on the current living conditions of residents during an emergency. For example, the emergency response unit can provide evacuation support for the elderly, taking into account the living conditions of residents. For example, the emergency response unit can provide support for families with young children, taking into account the living conditions of residents. For example, the emergency response unit can provide support for people with disabilities, taking into account the living conditions of residents. By customizing response measures based on the living conditions of residents, a more appropriate emergency response becomes possible.

[0107] The emergency response unit can estimate residents' emotions and determine the priority of emergency responses based on those estimated emotions. For example, if residents are feeling anxious, the emergency response unit can prioritize high-priority responses. For example, if residents are relaxed, the emergency response unit can also prioritize long-term responses. For example, if residents are feeling angry, the emergency response unit can also prioritize responses related to those emotions. This allows for prioritizing more important responses by determining the priority of emergency responses according to residents' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The Emergency Response Department can select the optimal response method during an emergency by considering the geographical location information of residents. For example, the Emergency Response Department can propose the optimal evacuation route to residents in a disaster-stricken area based on their geographical location information. For example, the Emergency Response Department can also select the optimal response method for residents in areas experiencing traffic congestion by considering their geographical location information. For example, the Emergency Response Department can also select a response method based on the usage status of public facilities based on the geographical location information of residents. In this way, the optimal response method can be selected by considering the geographical location information of residents.

[0109] The emergency response department can analyze residents' social media activity and propose response measures during emergencies. For example, the emergency response department can analyze residents' social media activity and propose response measures based on trending topics. For example, the emergency response department can propose response measures related to local events based on residents' social media activity. For example, the emergency response department can analyze residents' social media activity and propose response measures based on residents' interests. In this way, by analyzing residents' social media activity, more appropriate response measures can be proposed.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The local government information sharing system can also be equipped with a prediction unit. This unit can predict future disasters and problems based on collected data. For example, it can analyze past disaster data to predict future disaster risks. It can also analyze trends in residents' opinions and requests to predict future challenges in administrative operations. Furthermore, it can predict natural disasters based on weather and earthquake data, enabling proactive measures. This allows the prediction unit to identify future risks in advance and respond quickly and appropriately.

[0112] The local government information sharing system can also include the Ministry of Education. Based on the collected data, the Ministry of Education can create and provide educational programs for residents. For example, it can provide educational programs on evacuation methods and disaster prevention measures. It can also provide programs on how to use administrative services and on local history and culture. Furthermore, it can provide educational programs on residents' health management and environmental protection. Through this, the Ministry of Education can improve residents' knowledge and awareness, and ensure the safety and security of the community.

[0113] The local government information sharing system can also include a feedback section. This feedback section can collect feedback from residents and reflect it in the operation of the local government. For example, it can provide feedback on the status of responses to opinions and requests submitted by residents. It can also provide feedback on the results of surveys conducted with resident participation. Furthermore, it can provide feedback on residents' satisfaction with the administrative services they have used. In this way, the feedback section can realize administrative operations that reflect residents' opinions and requests, thereby improving resident satisfaction.

[0114] The local government information sharing system can also include a collaboration department. This department can strengthen cooperation with other local governments, private companies, and NPOs. For example, in the event of a disaster, it can collaborate with other local governments to procure and distribute relief supplies. It can also collaborate with private companies to revitalize the local economy. Furthermore, it can collaborate with NPOs to enhance local welfare services. In this way, the collaboration department strengthens cooperation between local governments and with other organizations, contributing to the resolution of challenges in local communities.

[0115] The local government information sharing system can also include an evaluation unit. This unit can assess the effectiveness of local government policies and services based on collected data. For example, it can evaluate the effectiveness of disaster countermeasures and propose areas for improvement. It can also evaluate the utilization of administrative services and make suggestions for improving service quality. Furthermore, it can evaluate resident satisfaction and propose policies that meet resident needs. This allows the evaluation unit to objectively assess the effectiveness of local government policies and services and use the results for improvement.

[0116] The data collection unit can estimate residents' emotions and adjust data collection methods based on those estimates. For example, if residents are feeling anxious, face-to-face interviews can be avoided, and online questionnaires can be conducted instead. Conversely, if residents are relaxed, detailed interviews can be conducted to gain deeper insights. Furthermore, if residents are feeling angry, emotionally sensitive questions can be used for data collection. By adjusting data collection methods according to residents' emotions, more accurate and reliable data can be collected.

[0117] The analysis unit can estimate residents' emotions and adjust the presentation method of the analysis results based on those estimated emotions. For example, if residents are feeling anxious, the analysis results can be presented using simple and easy-to-understand graphs and charts. If residents are relaxed, a report including detailed data and statistical information can be provided. Furthermore, if residents are feeling angry, the analysis results can be presented using emotionally sensitive language and expressions. By adjusting the presentation method of the analysis results according to residents' emotions, more effective information transmission becomes possible.

[0118] The proposal department can estimate residents' emotions and adjust the content of proposals based on those estimates. For example, if residents are feeling anxious, the department can make proposals that provide a sense of security. If residents are relaxed, the department can make proactive proposals to motivate them. Furthermore, if residents are feeling angry, the department can make proposals that are sensitive to their feelings to alleviate their dissatisfaction. By adjusting proposals according to residents' emotions, more effective proposals can be made.

[0119] The shared area can estimate residents' emotions and adjust the timing of information sharing based on those estimates. For example, if residents are feeling anxious, information can be shared quickly to provide reassurance. If residents are relaxed, regular information sharing can be provided to maintain their interest. Furthermore, if residents are feeling angry, information sharing can be withheld until their emotions have calmed down, and then resumed later. This allows for more appropriate information sharing by adjusting the timing of information sharing according to residents' emotions.

[0120] The emergency response unit can estimate residents' emotions and determine the priority of emergency responses based on those estimates. For example, if residents are feeling anxious, they can prioritize high-priority responses. If residents are relaxed, they can prioritize long-term responses. Furthermore, if residents are feeling angry, they can prioritize responses related to those emotions. By determining the priority of emergency responses according to residents' emotions, more important responses can be prioritized.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects data. For example, they collect data such as residents' opinions and requests, and the situation during disasters. Data collection methods include questionnaires, interviews, feedback forms, field surveys, sensor data, and reports. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it shares information by standardizing data formats and communication protocols and implementing security measures. Step 3: The proposal department proposes and executes the optimal action based on the analysis results obtained by the analysis department. For example, it proposes and executes the optimal action based on efficiency, effectiveness, and cost. Step 4: Sharing involves sharing information among local governments. For example, information such as the extent of damage and the need for support during a disaster is shared with other local governments. Methods of collection include on-site surveys, sensor data, and reports. Step 5: The Emergency Response Department will carry out emergency response. For example, in the event of a disaster, they will propose and implement actions such as suggesting evacuation routes and distributing relief supplies. Criteria for suggesting evacuation routes include safety, distance, and accessibility, while criteria for distributing relief supplies include food, water, and medicine.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, sharing unit, and emergency response unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data such as residents' opinions and requests and the situation during a disaster using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 and shares the information among local governments. The proposal unit proposes and executes the optimal action based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The sharing unit shares information such as the extent of damage and the need for support during a disaster with other local governments using, for example, the control unit 46A of the smart device 14. The emergency response unit proposes and executes actions such as suggesting evacuation routes and distributing relief supplies during a disaster using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, sharing unit, and emergency response unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect data such as residents' opinions and requests, and the situation during a disaster. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 and shares the information among local governments. The proposal unit proposes and executes the optimal action based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The sharing unit shares information such as the extent of damage and the need for support during a disaster with other local governments using, for example, the control unit 46A of the smart glasses 214. The emergency response unit proposes and executes actions such as suggesting evacuation routes and distributing relief supplies during a disaster using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, sharing unit, and emergency response unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect data such as residents' opinions and requests, and the situation during a disaster. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 and shares the information among local governments. The proposal unit proposes and executes the optimal action based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The sharing unit shares information such as the extent of damage and the need for support during a disaster with other local governments using, for example, the control unit 46A of the headset terminal 314. The emergency response unit proposes and executes actions such as suggesting evacuation routes and distributing relief supplies during a disaster using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, sharing unit, and emergency response unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect data such as residents' opinions and requests, and the situation during a disaster. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 and shares the information among local governments. The proposal unit proposes and executes the optimal action based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The sharing unit shares information such as the extent of damage and the need for support during a disaster with other local governments using, for example, the control unit 46A of the robot 414. The emergency response unit proposes and executes actions such as suggesting evacuation routes and distributing relief supplies during a disaster using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit proposes and executes the optimal action based on the analysis results obtained by the aforementioned analysis unit, A sharing section for sharing information between local governments, It includes an emergency response unit that handles emergencies. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as residents' opinions and requests, and the situation during disasters. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, information will be shared among local governments. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the collected data, we propose and execute the optimal actions to solve the problem. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned shared portion is, Share information with other municipalities regarding the extent of damage and the need for support during a disaster. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned emergency response unit, In the event of a disaster, we propose and implement actions such as suggesting evacuation routes and distributing relief supplies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate residents' sentiments and adjust the timing of data collection based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We will analyze residents' past opinions and requests and select the most suitable data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the residents' current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate the sentiments of residents and prioritize the data to collect based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location information of residents. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, analyze residents' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the residents' emotions and adjust the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the residents' sentiments and adjusts the length of the analysis based on the estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, We estimate the residents' feelings and adjust the way the proposal is expressed based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, reduce the level of detail in the proposal based on the importance of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the category of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, Estimate the residents' sentiments and adjust the length of the proposal based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on when the problem occurred. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the issues. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned shared portion is, Estimate residents' sentiments and adjust information sharing methods based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned shared portion is, When sharing information, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned shared portion is, When sharing information, different sharing algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned shared portion is, Estimate residents' sentiments and determine the priority of information sharing based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned shared portion is, When sharing information, adjust the order of sharing based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned shared portion is, When sharing information, adjust the sharing order based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned emergency response unit, Estimate residents' sentiments and adjust emergency response methods based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned emergency response unit, During an emergency, the optimal response method is selected by referring to past emergency response data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned emergency response unit, During emergency response, customize response measures based on the current living conditions of residents. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned emergency response unit, The system estimates residents' sentiments and determines the priority of emergency response based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned emergency response unit, During emergency response, the most appropriate response method will be selected considering the geographical location information of residents. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned emergency response unit, During emergencies, we analyze residents' social media activity and propose appropriate response measures. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes and executes the optimal action, A sharing section for sharing information between local governments, It includes an emergency response unit that handles emergencies. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as residents' opinions and requests, and the situation during disasters. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, information will be shared among local governments. The system according to feature 1.

4. The aforementioned proposal section is, Based on the collected data, we propose and execute the optimal actions to solve the problem. The system according to feature 1.

5. The aforementioned shared portion is, Share information with other municipalities regarding the extent of damage and the need for support during a disaster. The system according to feature 1.

6. The aforementioned emergency response unit, In the event of a disaster, we propose and implement actions such as suggesting evacuation routes and distributing relief supplies. The system according to feature 1.

7. The aforementioned collection unit is We estimate residents' sentiments and adjust the timing of data collection based on those estimated sentiments. The system according to feature 1.

8. The aforementioned collection unit is We will analyze residents' past opinions and requests and select the most suitable data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on the residents' current living situation and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is We estimate the sentiments of residents and prioritize the data to collect based on those estimated sentiments. The system according to feature 1.

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