system

Abstract

The system according to this embodiment aims to establish a rapid support system for natural disasters, wars, and conflicts. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects satellite observation data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes support systems and material provision based on the data analyzed by the analysis unit. The provision unit aggregates information from each organization in the cloud and provides the optimal route and support solution.

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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] The system according to the embodiment aims to establish a prompt support system for natural disasters, wars, and conflicts. <00000,29>

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a provision unit. The data collection unit collects satellite observation data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes support systems and material provision based on the data analyzed by the analysis unit. The provision unit aggregates information from each organization in the cloud and provides the optimal route and support solution. [Effects of the Invention]

[0007] The system according to this embodiment can establish a rapid support system for natural disasters, wars, and conflicts. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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 disaster response system according to an embodiment of the present invention is a system that provides tools and solutions for mitigating damage caused by natural disasters, wars, conflicts, etc., and for building a rapid support system. This disaster response system utilizes satellite observation and AI image and pattern analysis to grasp the disaster situation in real time. Next, big data analysis is used to quickly establish support systems, provide supplies, and coordinate with private companies. Furthermore, IoT technology is used to aggregate crustal change, water level, and climate data in the cloud and perform real-time analysis with AI. This allows for monitoring and prediction of earthquakes, floods, typhoons, wildfires, droughts, refugees, conflicts, etc. In addition, information from various organizations is aggregated in the cloud to build a system that provides optimal routes and support solutions. Finally, disaster-resilient infrastructure is used to provide disaster response services on a global scale. By building this common platform, new solutions that do not succumb to disasters will be provided, contributing to people's well-being. For example, rapid support when a disaster occurs will protect many lives and properties. Also, prior preparation will minimize damage caused by disasters. This will stabilize people's lives and increase their happiness. This allows disaster response systems to mitigate damage from natural disasters, wars, conflicts, and other sources, and to establish a rapid support system.

[0029] The disaster response system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects satellite observation data. The collection unit can collect satellite observation data such as meteorological data, topographic data, and disaster data. The collection unit can also efficiently collect satellite observation data using AI. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using analysis methods appropriate to the type of data. The analysis unit can also improve the accuracy of data analysis using AI. The proposal unit proposes support systems and material provision based on the data analyzed by the analysis unit. The proposal unit can propose, for example, the type of support, the type of materials, and the timing of provision. The proposal unit can also propose the optimal support system and material provision method using AI. The provision unit aggregates information from various organizations in the cloud and provides the optimal route and support solution. The provision unit can aggregate information from national and local governments, NGOs, etc., in the cloud and propose the optimal support route and solution using AI. This allows the disaster response system to collect and analyze satellite observation data, propose support systems and supply arrangements, and provide optimal routes and support solutions.

[0030] The data collection unit collects satellite observation data. For example, the unit can collect satellite observation data such as meteorological data, topographic data, and disaster data. Specifically, for meteorological data, it collects information such as rainfall, wind speed, temperature, and humidity; for topographic data, it collects information on topographic elevation and geological features; and for disaster data, it can collect information such as earthquake epicenters and seismic intensity, tsunami occurrence, and volcanic eruption information. This data is acquired in real time from satellite observation data and transmitted to a central database. The data collection unit can also use AI to efficiently collect satellite observation data. The AI ​​automatically filters the large amount of data transmitted from satellites and extracts the necessary information. For example, the AI ​​can detect signs of abnormal weather in meteorological data and identify areas with a high risk of landslides from topographic data. It can also prioritize the collection of information requiring rapid response from disaster data. This allows the data collection unit to efficiently and accurately collect satellite observation data and provide the data that forms the basis of disaster response systems. Furthermore, the data collection unit can centrally manage the collected data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze data using analysis methods appropriate to the type of data. Specifically, for meteorological data, it uses meteorological models to predict rainfall and wind speed; for topographic data, it uses topographic analysis software to assess landslide risk; and for disaster data, it performs seismic wave analysis and tsunami simulations to predict the extent and impact of damage. The analysis unit can also use AI to improve the accuracy of data analysis. AI uses machine learning algorithms to compare past and current data and detect abnormal patterns and trends. For example, AI can predict the probability of extreme weather events based on past meteorological data and identify areas with a high risk of landslides based on topographic data. It can also prioritize the analysis of areas requiring rapid response based on disaster data. This allows the analysis unit to quickly and accurately analyze collected data and provide information that forms the basis of disaster response systems. Furthermore, the analysis unit can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific areas and time periods based on past disaster data and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Proposal Department proposes support systems and material provision based on data analyzed by the Analysis Department. For example, the Proposal Department can propose the type of support, the type of materials, and the timing of provision. Specifically, depending on the situation in the affected area, it can propose the provision of emergency supplies such as food, water, and medicine, and set up shelters and medical support systems. It can also propose support for the restoration of power and communications, taking into account the infrastructure situation in the affected area. The Proposal Department can also use AI to propose the optimal support system and material provision method. Based on past disaster data and support results, the AI ​​learns the optimal support method and develops a support plan that is appropriate to the situation in the affected area. For example, the AI ​​can create an optimal material distribution plan and determine the priority of support, taking into account the population density and the extent of damage in the affected area. The AI ​​can also propose the optimal timing of provision, taking into account the inventory status of support materials and means of transportation. This allows the Proposal Department to build a rapid and effective support system and provide appropriate support that meets the needs of the affected area. Furthermore, the Proposal Department can monitor the progress of support activities and revise the support plan as needed. For example, the department can grasp the arrival status of relief supplies and changes in the situation in the affected areas in real time, and flexibly adjust the support plan. Furthermore, the proposal department can evaluate the effectiveness of support activities and identify areas for improvement for future disaster response. This allows the proposal department to consistently provide the optimal support system and rapidly assist in the recovery of disaster-stricken areas.

[0033] The support department aggregates information from various organizations in the cloud and provides optimal routes and support solutions. For example, the support department can aggregate information from national and local governments, NGOs, etc., in the cloud and use AI to propose optimal support routes and solutions. Specifically, it centrally manages information such as the progress of each organization's support activities, the inventory status of supplies, and transportation methods on the cloud and updates it in real time. Based on this information, the AI ​​calculates the optimal support route and creates an efficient distribution plan for relief supplies. For example, the AI ​​considers road conditions and traffic information in the affected area to propose the optimal transportation route and minimize the arrival time of supplies. It also coordinates support to avoid duplication of support activities among various organizations and builds an efficient support system. In this way, the support department can integrate information from each organization and provide optimal support solutions. Furthermore, the support department can monitor the progress of support activities and revise support plans as needed. For example, it can grasp the arrival status of relief supplies and changes in the situation in the affected area in real time and flexibly adjust support plans. In addition, the support department can evaluate the effectiveness of support activities and extract areas for improvement for future disaster response. This allows the service provider to consistently offer the most optimal support system and quickly assist in the recovery of disaster-stricken areas.

[0034] The data collection unit can collect data on crustal changes, water levels, and climate using IoT technology. For example, it can collect data on crustal changes, water levels, and climate using IoT technologies such as seismometers, water level gauges, and weather sensors. The data collection unit can also efficiently analyze the collected data using AI. This allows for the efficient collection of data on crustal changes, water levels, and climate by utilizing IoT technology.

[0035] The data collection unit can analyze past disaster data and select the optimal data collection method. For example, the data collection unit can analyze past earthquake data and select the most effective data collection method when an earthquake occurs. Similarly, the data collection unit can analyze past flood data and select the most effective data collection method when a flood occurs. Furthermore, the data collection unit can analyze past typhoon data and select the most effective data collection method when a typhoon occurs. This allows for efficient data collection by analyzing past disaster data and selecting the optimal data collection method. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI.

[0036] The data collection unit can filter data based on local conditions and environment during data collection. For example, the data collection unit can adjust the timing of data collection based on local weather conditions. Furthermore, the data collection unit can limit the scope of data collection based on local topographic information. In addition, the data collection unit can change the data collection method based on local infrastructure conditions. This allows for efficient data collection by filtering data collection based on local conditions and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0037] The data collection unit can prioritize the collection of highly relevant data, taking geographical location information into consideration. For example, the data collection unit can prioritize the collection of data surrounding earthquake-affected areas. It can also prioritize the collection of water level data in flood-affected areas. Furthermore, it can prioritize the collection of meteorological data along typhoon paths. This enables efficient data collection by prioritizing the collection of highly relevant data, taking geographical location information into consideration. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without using AI.

[0038] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze disaster reports on social media to understand the situation on the ground. Furthermore, the data collection unit can collect evacuation information on social media to prepare support systems. In addition, the data collection unit can collect information on material provision on social media to ensure efficient distribution of supplies. Thus, by analyzing social media activity and collecting relevant data, it is possible to understand the situation on the ground and prepare an efficient support system. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI.

[0039] 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 important data. It can also perform a standard analysis on general data. Furthermore, it can perform a simplified analysis on less important data. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0040] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an earthquake analysis algorithm to earthquake data. It can also apply a flood analysis algorithm to flood data. Furthermore, it can apply a typhoon analysis algorithm to typhoon data. This allows for efficient analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0041] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. Furthermore, the analysis unit can perform analysis while referring to past data. In addition, the analysis unit can adjust the priority of analysis according to the data collection period. This enables efficient analysis by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0043] The proposal department can adjust the level of detail in its proposals based on the importance of the support system and supplies. For example, it can provide detailed proposals for important support systems and supplies, standard proposals for general support systems and supplies, and simplified proposals for less important support systems and supplies. By adjusting the level of detail in proposals based on the importance of the support systems and supplies, efficient proposals can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI.

[0044] The proposal unit can apply different proposal algorithms depending on the support system and the category of supplies when making a proposal. For example, the proposal unit can apply a medical support algorithm for medical support, a food support algorithm for food support, and a housing support algorithm for housing support. By applying different proposal algorithms depending on the support system and the category of supplies, efficient proposals become possible. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0045] The proposal department can determine the priority of proposals based on the timing of support systems and supplies provided. For example, the proposal department can prioritize proposals for support systems and supplies that are urgent. Conversely, the proposal department can postpone proposals for support systems and supplies that have ample time to provide. Furthermore, the proposal department can adjust the priority of proposals according to the timing of support systems and supplies provided. This allows for efficient proposals by determining the priority of proposals based on the timing of support systems and supplies provided. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0046] The proposal department can adjust the order of proposals based on the relevance of support systems and supplies. For example, the proposal department can prioritize proposing highly relevant support systems and supplies. It can also postpone proposing less relevant support systems and supplies. Furthermore, the proposal department can adjust the order of proposals according to the relevance of support systems and supplies. This allows for efficient proposals by adjusting the order of proposals based on the relevance of support systems and supplies. Some or all of the above processing in the proposal department may be performed using AI, for example, or without using AI.

[0047] The provisioning department can improve the accuracy of provision by considering the interrelationships of information among the organizations at the time of provision. For example, the provisioning department can analyze the support system of each organization and provide the optimal support route. Furthermore, the provisioning department can analyze the material provision status of each organization and distribute materials efficiently. In addition, the provisioning department can integrate the information of each organization and provide a comprehensive support solution. This makes efficient support possible by improving the accuracy of provision by considering the interrelationships of information among the organizations. Some or all of the above processing in the provisioning department may be performed using AI, for example, or without using AI.

[0048] The service provider can provide services while considering the attribute information of each organization. For example, the service provider can provide the optimal support solution based on each organization's area of ​​expertise. Furthermore, the service provider can provide support solutions appropriate to the region based on the regional characteristics of each organization. In addition, the service provider can provide efficient support solutions based on each organization's resource situation. This enables efficient support by providing services while considering the attribute information of each organization. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0049] The service provider can provide services while considering geographical distribution. For example, the service provider can provide the optimal support route by considering the geographical distribution of the affected areas. Furthermore, the service provider can provide support solutions appropriate to the region based on the geographical characteristics of the affected areas. In addition, the service provider can analyze the geographical situation of the affected areas and provide efficient support solutions. This enables efficient support by providing services while considering geographical distribution. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0050] The service provider can improve the accuracy of its services by referring to relevant literature during the service provision process. For example, the service provider can provide the latest support technologies by referring to relevant literature. Furthermore, the service provider can analyze relevant literature and provide the optimal support method. In addition, the service provider can provide efficient support solutions based on relevant literature. This enables efficient support by improving the accuracy of services through the referencing of relevant literature. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The data collection unit can use drones to collect detailed images of disaster sites. For example, it can use drones to photograph the extent of building damage after an earthquake and transmit the data to the analysis unit in real time. Furthermore, the data collection unit can deploy drones during floods to assess the extent of the inundation. In addition, during wildfires, the data collection unit can use drones to monitor the progress of the fire and support firefighting efforts. This demonstrates how drones can be used to quickly collect detailed information from disaster sites and build an efficient support system.

[0053] The analysis unit can use AI to predict damage during disasters. For example, during an earthquake, the analysis unit can predict damage based on past earthquake data and propose rapid support to areas expected to be affected. Furthermore, during a flood, the analysis unit can analyze meteorological and topographical data to identify areas expected to be flooded. In addition, during a typhoon, the analysis unit can predict its path and issue evacuation advisories in advance to areas expected to be affected. Thus, by utilizing AI, it is possible to predict damage during disasters and provide rapid and appropriate support.

[0054] The distribution department can track the delivery status of relief supplies in real time. For example, it can use GPS to track the location of relief supplies during delivery and understand the progress of the delivery. The distribution department can also send notifications when relief supplies arrive at their destination and confirm receipt. Furthermore, the distribution department can provide information to respond quickly if problems occur during delivery. In this way, by tracking the delivery status of relief supplies in real time, efficient provision of supplies can be achieved.

[0055] The analysis unit can visualize the damage situation in the event of a disaster using a 3D model. For example, it can display the extent of building damage after an earthquake using a 3D model, allowing for an intuitive understanding of the damage. Furthermore, it can display the extent of flooded areas using a 3D model during a flood, clearly indicating the scope of the damage. Additionally, it can display the predicted path of a typhoon using a 3D model, visually showing areas where damage is expected. This allows for rapid and appropriate responses by visualizing the damage situation in a 3D model during a disaster.

[0056] The supply department can manage the inventory status of relief supplies in real time. For example, the supply department can constantly monitor the number of relief supplies in stock and replenish them as needed. In addition, the supply department can monitor the consumption status of relief supplies and manage inventory efficiently. Furthermore, the supply department can manage the expiration dates of relief supplies and dispose of expired supplies appropriately. As a result, by managing the inventory status of relief supplies in real time, efficient provision of supplies can be carried out.

[0057] The proposal department can evaluate the effectiveness of the support system and provision of supplies and reflect this in future proposals. For example, the proposal department can evaluate how effective the support system was and use this to improve future proposals. They can also evaluate how quickly supplies were provided and identify areas for improvement. Furthermore, the proposal department can collect feedback from users who received support and incorporate it into future proposals. This allows for more effective future proposals by evaluating the effectiveness of the support system and provision of supplies.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The collection unit collects satellite observation data. The collection unit can collect satellite observation data such as weather data, topographic data, and disaster data. The collection unit can also use AI to efficiently collect satellite observation data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, an analysis method appropriate to the type of data. The analysis unit can also use AI to improve the accuracy of the data analysis. Step 3: The proposal department proposes support systems and material provision based on the data analyzed by the analysis department. For example, the proposal department can propose the type of support, the type of materials, and the timing of provision. The proposal department can also use AI to propose the optimal support system and material provision method. Step 4: The service provider aggregates information from each organization in the cloud and provides the optimal route and support solution. For example, the service provider can aggregate information from national and local governments, NGOs, etc., in the cloud and use AI to propose the optimal support route and solution.

[0060] (Example of form 2) The disaster response system according to an embodiment of the present invention is a system that provides tools and solutions for mitigating damage caused by natural disasters, wars, conflicts, etc., and for building a rapid support system. This disaster response system utilizes satellite observation and AI image and pattern analysis to grasp the disaster situation in real time. Next, big data analysis is used to quickly establish support systems, provide supplies, and coordinate with private companies. Furthermore, IoT technology is used to aggregate crustal change, water level, and climate data in the cloud and perform real-time analysis with AI. This allows for monitoring and prediction of earthquakes, floods, typhoons, wildfires, droughts, refugees, conflicts, etc. In addition, information from various organizations is aggregated in the cloud to build a system that provides optimal routes and support solutions. Finally, disaster-resilient infrastructure is used to provide disaster response services on a global scale. By building this common platform, new solutions that do not succumb to disasters will be provided, contributing to people's well-being. For example, rapid support when a disaster occurs will protect many lives and properties. Also, prior preparation will minimize damage caused by disasters. This will stabilize people's lives and increase their happiness. This allows disaster response systems to mitigate damage from natural disasters, wars, conflicts, and other sources, and to establish a rapid support system.

[0061] The disaster response system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects satellite observation data. The collection unit can collect satellite observation data such as meteorological data, topographic data, and disaster data. The collection unit can also efficiently collect satellite observation data using AI. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using analysis methods appropriate to the type of data. The analysis unit can also improve the accuracy of data analysis using AI. The proposal unit proposes support systems and material provision based on the data analyzed by the analysis unit. The proposal unit can propose, for example, the type of support, the type of materials, and the timing of provision. The proposal unit can also propose the optimal support system and material provision method using AI. The provision unit aggregates information from various organizations in the cloud and provides the optimal route and support solution. The provision unit can aggregate information from national and local governments, NGOs, etc., in the cloud and propose the optimal support route and solution using AI. This allows the disaster response system to collect and analyze satellite observation data, propose support systems and supply arrangements, and provide optimal routes and support solutions.

[0062] The data collection unit collects satellite observation data. For example, the unit can collect satellite observation data such as meteorological data, topographic data, and disaster data. Specifically, for meteorological data, it collects information such as rainfall, wind speed, temperature, and humidity; for topographic data, it collects information on topographic elevation and geological features; and for disaster data, it can collect information such as earthquake epicenters and seismic intensity, tsunami occurrence, and volcanic eruption information. This data is acquired in real time from satellite observation data and transmitted to a central database. The data collection unit can also use AI to efficiently collect satellite observation data. The AI ​​automatically filters the large amount of data transmitted from satellites and extracts the necessary information. For example, the AI ​​can detect signs of abnormal weather in meteorological data and identify areas with a high risk of landslides from topographic data. It can also prioritize the collection of information requiring rapid response from disaster data. This allows the data collection unit to efficiently and accurately collect satellite observation data and provide the data that forms the basis of disaster response systems. Furthermore, the data collection unit can centrally manage the collected data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0063] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze data using analysis methods appropriate to the type of data. Specifically, for meteorological data, it uses meteorological models to predict rainfall and wind speed; for topographic data, it uses topographic analysis software to assess landslide risk; and for disaster data, it performs seismic wave analysis and tsunami simulations to predict the extent and impact of damage. The analysis unit can also use AI to improve the accuracy of data analysis. AI uses machine learning algorithms to compare past and current data and detect abnormal patterns and trends. For example, AI can predict the probability of extreme weather events based on past meteorological data and identify areas with a high risk of landslides based on topographic data. It can also prioritize the analysis of areas requiring rapid response based on disaster data. This allows the analysis unit to quickly and accurately analyze collected data and provide information that forms the basis of disaster response systems. Furthermore, the analysis unit can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific areas and time periods based on past disaster data and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0064] The Proposal Department proposes support systems and material provision based on data analyzed by the Analysis Department. For example, the Proposal Department can propose the type of support, the type of materials, and the timing of provision. Specifically, depending on the situation in the affected area, it can propose the provision of emergency supplies such as food, water, and medicine, and set up shelters and medical support systems. It can also propose support for the restoration of power and communications, taking into account the infrastructure situation in the affected area. The Proposal Department can also use AI to propose the optimal support system and material provision method. Based on past disaster data and support results, the AI ​​learns the optimal support method and develops a support plan that is appropriate to the situation in the affected area. For example, the AI ​​can create an optimal material distribution plan and determine the priority of support, taking into account the population density and the extent of damage in the affected area. The AI ​​can also propose the optimal timing of provision, taking into account the inventory status of support materials and means of transportation. This allows the Proposal Department to build a rapid and effective support system and provide appropriate support that meets the needs of the affected area. Furthermore, the Proposal Department can monitor the progress of support activities and revise the support plan as needed. For example, the department can grasp the arrival status of relief supplies and changes in the situation in the affected areas in real time, and flexibly adjust the support plan. Furthermore, the proposal department can evaluate the effectiveness of support activities and identify areas for improvement for future disaster response. This allows the proposal department to consistently provide the optimal support system and rapidly assist in the recovery of disaster-stricken areas.

[0065] The support department aggregates information from various organizations in the cloud and provides optimal routes and support solutions. For example, the support department can aggregate information from national and local governments, NGOs, etc., in the cloud and use AI to propose optimal support routes and solutions. Specifically, it centrally manages information such as the progress of each organization's support activities, the inventory status of supplies, and transportation methods on the cloud and updates it in real time. Based on this information, the AI ​​calculates the optimal support route and creates an efficient distribution plan for relief supplies. For example, the AI ​​considers road conditions and traffic information in the affected area to propose the optimal transportation route and minimize the arrival time of supplies. It also coordinates support to avoid duplication of support activities among various organizations and builds an efficient support system. In this way, the support department can integrate information from each organization and provide optimal support solutions. Furthermore, the support department can monitor the progress of support activities and revise support plans as needed. For example, it can grasp the arrival status of relief supplies and changes in the situation in the affected area in real time and flexibly adjust support plans. In addition, the support department can evaluate the effectiveness of support activities and extract areas for improvement for future disaster response. This allows the service provider to consistently offer the most optimal support system and quickly assist in the recovery of disaster-stricken areas.

[0066] The data collection unit can collect data on crustal changes, water levels, and climate using IoT technology. For example, it can collect data on crustal changes, water levels, and climate using IoT technologies such as seismometers, water level gauges, and weather sensors. The data collection unit can also efficiently analyze the collected data using AI. This allows for the efficient collection of data on crustal changes, water levels, and climate by utilizing IoT technology.

[0067] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed information. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting only the most important data. By adjusting the timing of data collection based on the user's emotions, the user's burden is reduced and efficient data collection becomes possible. 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.

[0068] The data collection unit can analyze past disaster data and select the optimal data collection method. For example, the data collection unit can analyze past earthquake data and select the most effective data collection method when an earthquake occurs. Similarly, the data collection unit can analyze past flood data and select the most effective data collection method when a flood occurs. Furthermore, the data collection unit can analyze past typhoon data and select the most effective data collection method when a typhoon occurs. This allows for efficient data collection by analyzing past disaster data and selecting the optimal data collection method. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI.

[0069] The data collection unit can filter data based on local conditions and environment during data collection. For example, the data collection unit can adjust the timing of data collection based on local weather conditions. Furthermore, the data collection unit can limit the scope of data collection based on local topographic information. In addition, the data collection unit can change the data collection method based on local infrastructure conditions. This allows for efficient data collection by filtering data collection based on local conditions and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0070] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is stressed, the unit can prioritize collecting only the most important data. If the user is relaxed, the unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the unit can prioritize collecting data that can be retrieved quickly. This enables efficient data collection by prioritizing the data to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0071] The data collection unit can prioritize the collection of highly relevant data, taking geographical location information into consideration. For example, the data collection unit can prioritize the collection of data surrounding earthquake-affected areas. It can also prioritize the collection of water level data in flood-affected areas. Furthermore, it can prioritize the collection of meteorological data along typhoon paths. This enables efficient data collection by prioritizing the collection of highly relevant data, taking geographical location information into consideration. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without using AI.

[0072] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze disaster reports on social media to understand the situation on the ground. Furthermore, the data collection unit can collect evacuation information on social media to prepare support systems. In addition, the data collection unit can collect information on material provision on social media to ensure efficient distribution of supplies. Thus, by analyzing social media activity and collecting relevant data, it is possible to understand the situation on the ground and prepare an efficient support system. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI.

[0073] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. 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.

[0074] 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 important data. It can also perform a standard analysis on general data. Furthermore, it can perform a simplified analysis on less important data. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0075] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an earthquake analysis algorithm to earthquake data. It can also apply a flood analysis algorithm to flood data. Furthermore, it can apply a typhoon analysis algorithm to typhoon data. This allows for efficient analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis based on the user's emotions, the analysis can be made easier for the user to understand. 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.

[0077] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. Furthermore, the analysis unit can perform analysis while referring to past data. In addition, the analysis unit can adjust the priority of analysis according to the data collection period. This enables efficient analysis by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0078] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0079] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion function can provide simple and easily understandable suggestions. If the user is relaxed, it can provide detailed suggestions. Furthermore, if the user is in a hurry, it can provide concise suggestions. By adjusting the way suggestions are presented based on the user's emotions, the system can provide suggestions that are easy for the user to understand. 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.

[0080] The proposal department can adjust the level of detail in its proposals based on the importance of the support system and supplies. For example, it can provide detailed proposals for important support systems and supplies, standard proposals for general support systems and supplies, and simplified proposals for less important support systems and supplies. By adjusting the level of detail in proposals based on the importance of the support systems and supplies, efficient proposals can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI.

[0081] The proposal unit can apply different proposal algorithms depending on the support system and the category of supplies when making a proposal. For example, the proposal unit can apply a medical support algorithm for medical support, a food support algorithm for food support, and a housing support algorithm for housing support. By applying different proposal algorithms depending on the support system and the category of supplies, efficient proposals become possible. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0082] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, it can provide detailed suggestions. Furthermore, if the user is excited, it can provide visually stimulating suggestions. By adjusting the length of suggestions based on the user's emotions, the system can provide suggestions that are easy for the user to understand. 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.

[0083] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, it can provide detailed suggestions. Furthermore, if the user is excited, it can provide visually stimulating suggestions. By adjusting the length of suggestions based on the user's emotions, the system can provide suggestions that are easy for the user to understand. 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 proposal department can determine the priority of proposals based on the timing of support systems and supplies provided. For example, the proposal department can prioritize proposals for support systems and supplies that are urgent. Conversely, the proposal department can postpone proposals for support systems and supplies that have ample time to provide. Furthermore, the proposal department can adjust the priority of proposals according to the timing of support systems and supplies provided. This allows for efficient proposals by determining the priority of proposals based on the timing of support systems and supplies provided. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0085] The proposal department can adjust the order of proposals based on the relevance of support systems and supplies. For example, the proposal department can prioritize proposing highly relevant support systems and supplies. It can also postpone proposing less relevant support systems and supplies. Furthermore, the proposal department can adjust the order of proposals according to the relevance of support systems and supplies. This allows for efficient proposals by adjusting the order of proposals based on the relevance of support systems and supplies. Some or all of the above processing in the proposal department may be performed using AI, for example, or without using AI.

[0086] The service provider can estimate the user's emotions and prioritize the solutions offered based on those emotions. For example, if the user is stressed, the service provider can prioritize providing important solutions. If the user is relaxed, the service provider can provide more detailed solutions. Furthermore, if the user is in a hurry, the service provider can prioritize providing solutions that can be delivered quickly. This enables efficient solution delivery by prioritizing solutions based on the user's 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.

[0087] The provisioning department can improve the accuracy of provision by considering the interrelationships of information among the organizations at the time of provision. For example, the provisioning department can analyze the support system of each organization and provide the optimal support route. Furthermore, the provisioning department can analyze the material provision status of each organization and distribute materials efficiently. In addition, the provisioning department can integrate the information of each organization and provide a comprehensive support solution. This makes efficient support possible by improving the accuracy of provision by considering the interrelationships of information among the organizations. Some or all of the above processing in the provisioning department may be performed using AI, for example, or without using AI.

[0088] The service provider can provide services while considering the attribute information of each organization. For example, the service provider can provide the optimal support solution based on each organization's area of ​​expertise. Furthermore, the service provider can provide support solutions appropriate to the region based on the regional characteristics of each organization. In addition, the service provider can provide efficient support solutions based on each organization's resource situation. This enables efficient support by providing services while considering the attribute information of each organization. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0089] The service provider can estimate the user's emotions and adjust how the solutions are displayed based on those emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display. If the user is relaxed, the service provider can provide a display that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display. By adjusting how the solutions are displayed based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. 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.

[0090] The service provider can provide services while considering geographical distribution. For example, the service provider can provide the optimal support route by considering the geographical distribution of the affected areas. Furthermore, the service provider can provide support solutions appropriate to the region based on the geographical characteristics of the affected areas. In addition, the service provider can analyze the geographical situation of the affected areas and provide efficient support solutions. This enables efficient support by providing services while considering geographical distribution. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0091] The service provider can improve the accuracy of its services by referring to relevant literature during the service provision process. For example, the service provider can provide the latest support technologies by referring to relevant literature. Furthermore, the service provider can analyze relevant literature and provide the optimal support method. In addition, the service provider can provide efficient support solutions based on relevant literature. This enables efficient support by improving the accuracy of services through the referencing of relevant literature. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The data collection unit can use drones to collect detailed images of disaster sites. For example, it can use drones to photograph the extent of building damage after an earthquake and transmit the data to the analysis unit in real time. Furthermore, the data collection unit can deploy drones during floods to assess the extent of the inundation. In addition, during wildfires, the data collection unit can use drones to monitor the progress of the fire and support firefighting efforts. This demonstrates how drones can be used to quickly collect detailed information from disaster sites and build an efficient support system.

[0094] The analysis unit can use AI to predict damage during disasters. For example, during an earthquake, the analysis unit can predict damage based on past earthquake data and propose rapid support to areas expected to be affected. Furthermore, during a flood, the analysis unit can analyze meteorological and topographical data to identify areas expected to be flooded. In addition, during a typhoon, the analysis unit can predict its path and issue evacuation advisories in advance to areas expected to be affected. Thus, by utilizing AI, it is possible to predict damage during disasters and provide rapid and appropriate support.

[0095] The suggestion function can estimate the user's emotions and customize the support content based on those emotions. For example, if the user is feeling anxious, the suggestion function can suggest support content that provides a sense of security. If the user is tired, the suggestion function can suggest support content that encourages rest. Furthermore, if the user is confused, the suggestion function can suggest simple and easy-to-understand support content. In this way, by customizing support content based on the user's emotions, the system can provide the most appropriate support for the user.

[0096] The distribution department can track the delivery status of relief supplies in real time. For example, it can use GPS to track the location of relief supplies during delivery and understand the progress of the delivery. The distribution department can also send notifications when relief supplies arrive at their destination and confirm receipt. Furthermore, the distribution department can provide information to respond quickly if problems occur during delivery. In this way, by tracking the delivery status of relief supplies in real time, efficient provision of supplies can be achieved.

[0097] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting only the most important data. This allows for efficient data collection by adjusting the data collection method based on the user's emotions.

[0098] The analysis unit can visualize the damage situation in the event of a disaster using a 3D model. For example, it can display the extent of building damage after an earthquake using a 3D model, allowing for an intuitive understanding of the damage. Furthermore, it can display the extent of flooded areas using a 3D model during a flood, clearly indicating the scope of the damage. Additionally, it can display the predicted path of a typhoon using a 3D model, visually showing areas where damage is expected. This allows for rapid and appropriate responses by visualizing the damage situation in a 3D model during a disaster.

[0099] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion function can delay the timing of suggestions to reduce the user's burden. Conversely, if the user is relaxed, the suggestion function can speed up the timing of suggestions to provide more detailed information. Furthermore, if the user is in a hurry, the suggestion function can quickly provide suggestions and necessary information. In this way, by adjusting the timing of suggestions based on the user's emotions, suggestions can be delivered at the optimal time for the user.

[0100] The supply department can manage the inventory status of relief supplies in real time. For example, the supply department can constantly monitor the number of relief supplies in stock and replenish them as needed. In addition, the supply department can monitor the consumption status of relief supplies and manage inventory efficiently. Furthermore, the supply department can manage the expiration dates of relief supplies and dispose of expired supplies appropriately. As a result, by managing the inventory status of relief supplies in real time, efficient provision of supplies can be carried out.

[0101] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the display method of the analysis results based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0102] The proposal department can evaluate the effectiveness of the support system and provision of supplies and reflect this in future proposals. For example, the proposal department can evaluate how effective the support system was and use this to improve future proposals. They can also evaluate how quickly supplies were provided and identify areas for improvement. Furthermore, the proposal department can collect feedback from users who received support and incorporate it into future proposals. This allows for more effective future proposals by evaluating the effectiveness of the support system and provision of supplies.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The collection unit collects satellite observation data. The collection unit can collect satellite observation data such as weather data, topographic data, and disaster data. The collection unit can also use AI to efficiently collect satellite observation data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, an analysis method appropriate to the type of data. The analysis unit can also use AI to improve the accuracy of the data analysis. Step 3: The proposal department proposes support systems and material provision based on the data analyzed by the analysis department. For example, the proposal department can propose the type of support, the type of materials, and the timing of provision. The proposal department can also use AI to propose the optimal support system and material provision method. Step 4: The service provider aggregates information from each organization in the cloud and provides the optimal route and support solution. For example, the service provider can aggregate information from national and local governments, NGOs, etc., in the cloud and use AI to propose the optimal support route and solution.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects satellite observation data and crustal change, sea level, and climate data using IoT technology with the camera 42 and sensors of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes support systems and material provision based on the analysis results. The provision unit is implemented in the control unit 46A of the smart device 14 and provides the optimal route and support solution based on information aggregated in the cloud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects satellite observation data and crustal change, sea level, and climate data using IoT technology with the camera 42 and sensors of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes support systems and material provision based on the analysis results. The provision unit is implemented in the control unit 46A of the smart glasses 214 and provides the optimal route and support solution based on information aggregated in the cloud. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and sensors of the headset terminal 314 to collect satellite observation data and crustal change, sea level, and climate data using IoT technology. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes support systems and material provision based on the analysis results. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides optimal routes and support solutions based on information aggregated in the cloud. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects satellite observation data and crustal change, sea level, and climate data using the camera 42 and sensors of the robot 414, as well as IoT technology. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes support systems and material provision based on the analysis results. The provision unit is implemented in the control unit 46A of the robot 414 and provides optimal routes and support solutions based on information aggregated in the cloud. 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] (Note 1) A data collection unit that collects satellite observation data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the data analyzed by the aforementioned analysis unit, the proposal unit proposes support systems and provision of supplies. It includes a service department that aggregates information from each organization in the cloud and provides optimal routes and support solutions. A system characterized by the following features. (Note 2) It is equipped with a data collection unit that utilizes IoT technology to collect data on crustal changes, water levels, and climate. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Analyze past disaster data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is When collecting data, filtering is performed based on local conditions and the environment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) 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 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) 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 15) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the support system and supplies. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the support system and the category of supplies. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When submitting proposals, prioritize them based on the support system and the timing of supply provision. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of support systems and supplies. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the solutions to provide based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, we will improve the accuracy of the information provided by considering the interrelationships between the information of each organization. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, we will take into account the attribute information of each organization. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how solutions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, geographical distribution will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing information, we will refer to relevant literature to improve the accuracy of the information provided. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0177] 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 satellite observation data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the data analyzed by the aforementioned analysis unit, the proposal unit proposes support systems and provision of supplies. It includes a service department that aggregates information from each organization in the cloud and provides optimal routes and support solutions. A system characterized by the following features.

2. It is equipped with a data collection unit that utilizes IoT technology to collect data on crustal changes, water levels, and climate. The system according to feature 1.

3. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

4. The aforementioned collection unit is Analyze past disaster data and select the optimal data collection method. The system according to feature 1.

5. The aforementioned collection unit is When collecting data, filtering is performed based on local conditions and the environment. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system according to feature 1.

8. The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. The system according to feature 1.

9. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system according to feature 1.

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