system

Abstract

The system according to this embodiment aims to raise disaster prevention awareness by displaying earthquake simulations in real time. [Solution] The system according to the embodiment comprises an acquisition unit, a reception unit, a generation unit, and a display unit. The acquisition unit acquires earthquake data. The reception unit receives information about a location specified by the user. The generation unit generates an earthquake simulation based on the information obtained by the acquisition unit and the reception unit. The display unit displays the simulation generated by the generation unit in real time.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes 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

[0004] In the prior art, there is a problem that it is difficult to display earthquake simulations in real time and there are insufficient means for enhancing disaster prevention awareness.

[0005] The system according to the embodiment aims to enhance disaster prevention awareness by displaying earthquake simulations in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a reception unit, a generation unit, and a display unit. The acquisition unit acquires earthquake data. The reception unit receives information about a location specified by the user. The generation unit generates an earthquake simulation based on the information obtained by the acquisition unit and the reception unit. The display unit displays the simulation generated by the generation unit in real time. [Effects of the Invention]

[0007] The system according to this embodiment can raise disaster prevention awareness by displaying earthquake simulations in real time. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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 receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The earthquake simulation system according to an embodiment of the present invention is a system that improves disaster preparedness by allowing users to view earthquake simulations in real time in their immediate surroundings. This earthquake simulation system takes information for an earthquake simulation at a location specified by the user as input, generates an earthquake simulation based on that information, and displays it on the user's device in real time. This allows users to visually understand the impact of an earthquake in their immediate surroundings, thereby improving their disaster preparedness awareness. For example, when a user inputs the address of their home, workplace, or school, the system uses that information to perform a simulation using data such as the earthquake's origin, epicenter depth, and seismic intensity. The generated simulation is displayed in real time on the screen of a smartphone, tablet, or personal computer. This allows users to visually understand the impact of an earthquake in their immediate surroundings. This mechanism allows users to realistically feel the impact of an earthquake, thereby improving their disaster preparedness awareness. For example, experiencing earthquake shaking at home through a simulation can prompt users to consider concrete disaster preparedness measures, such as furniture placement and evacuation routes. Similarly, conducting simulations at workplaces and schools can raise disaster preparedness awareness throughout the organization. In this way, the earthquake simulation system can improve users' disaster preparedness awareness.

[0029] The earthquake simulation system according to the embodiment comprises an acquisition unit, a reception unit, a generation unit, and a display unit. The acquisition unit acquires earthquake data. Earthquake data includes, but is not limited to, the epicenter, seismic intensity, and time of occurrence. The acquisition unit can, for example, acquire earthquake data in real time from an earthquake observation agency. The reception unit receives information about a location specified by the user. Information about the specified location includes, for example, the address, latitude and longitude, and place name. The reception unit can, for example, allow the user to input the address of their home, workplace, school, etc. The generation unit generates an earthquake simulation based on the information obtained by the acquisition unit and the reception unit. The simulation includes, for example, data such as the earthquake's origin, the depth of the epicenter, and the seismic intensity. The generation unit can, for example, perform a simulation based on data such as the earthquake's origin, the depth of the epicenter, and the seismic intensity. The display unit displays the simulation generated by the generation unit in real time. The display unit can, for example, display the simulation in real time on a smartphone, tablet, or personal computer device. As a result, the earthquake simulation system according to this embodiment can improve users' awareness of disaster prevention.

[0030] The acquisition unit can acquire earthquake data from earthquake observation agencies in real time. Specific methods and criteria for real-time acquisition include, but are not limited to, data update intervals and acquisition protocols. For example, the acquisition unit can acquire earthquake data from earthquake observation agencies in real time. This allows for the provision of the latest earthquake information by acquiring data from earthquake observation agencies in real time.

[0031] The generation unit can perform simulations based on data regarding the earthquake's origin, focal depth, and seismic intensity. Specific methods for identifying the earthquake's origin and data formats include, but are not limited to, latitude, longitude, and place names. Specific methods for measuring the focal depth and data formats include, but are not limited to, meters and kilometers. Specific definitions and measurement methods for seismic intensity include, but are not limited to, seismic intensity scales and measuring instruments. The generation unit can perform simulations based on data such as the earthquake's origin, focal depth, and seismic intensity. This allows for the realistic reproduction of earthquake effects by performing simulations based on detailed earthquake data.

[0032] The display unit can show simulations in real time on smartphones, tablets, or personal computers. Specific types of devices, operating systems, and applications include, but are not limited to, iOS, Android, and Windows. This allows users to view earthquake simulations in real time on various devices.

[0033] The generation unit can perform simulations considering topographic data or building structural data. Specific types and acquisition methods of topographic data include, but are not limited to, elevation data and geological data. Specific types and acquisition methods of building structural data include, but are not limited to, building height, materials, and design drawings. The generation unit can perform simulations considering topographic data or building structural data. This allows for the provision of more realistic earthquake simulations by considering topographic data and building structural data.

[0034] The display unit can provide users with advice on disaster preparedness. Specific examples of this advice, including but not limited to, evacuation routes, emergency contact information, and lists of emergency supplies, may include: evacuation routes, emergency contact information, and emergency supplies. For example, the display unit can provide users with advice on disaster preparedness. This allows users to receive disaster preparedness advice through earthquake simulations.

[0035] The data acquisition unit can analyze past earthquake data and select the optimal data acquisition method. Specific selection criteria and methods for the optimal data acquisition method include, but are not limited to, data reliability, acquisition speed, and cost. For example, the data acquisition unit can analyze earthquake occurrence patterns in a specific region based on past earthquake data and select the optimal data acquisition method. Furthermore, for example, the data acquisition unit can analyze trends in earthquake epicenter depth and seismic intensity based on past earthquake data and select the optimal data acquisition method. Also, for example, the data acquisition unit can analyze trends in earthquake occurrence time and season based on past earthquake data and select the optimal data acquisition method. In this way, the optimal data acquisition method can be selected by analyzing past earthquake data.

[0036] The acquisition unit can filter earthquake data based on the user's current location information when acquiring it. Specific filtering methods and criteria include, but are not limited to, the accuracy of location information and filtering conditions. For example, the acquisition unit can prioritize acquiring earthquake data for nearby areas based on the user's current location information. Furthermore, the acquisition unit can filter data according to the distance from the epicenter based on the user's current location information. Additionally, the acquisition unit can acquire only earthquake data for specific areas based on the user's current location information. This allows for the provision of highly relevant information by filtering earthquake data based on the user's current location information.

[0037] The acquisition unit can prioritize the acquisition of highly relevant data when acquiring earthquake data, taking into account the user's geographical location information. Specific definitions and selection criteria for highly relevant data include, but are not limited to, geographical proximity and the degree of agreement with past data. For example, the acquisition unit can prioritize the acquisition of earthquake data for nearby areas based on the user's geographical location information. Furthermore, for example, the acquisition unit can prioritize the acquisition of data based on the user's geographical location information, according to the distance from the epicenter. Also, for example, the acquisition unit can prioritize the acquisition of earthquake data for a specific area based on the user's geographical location information. This allows for the priority acquisition of highly relevant earthquake data by considering the user's geographical location information.

[0038] The data acquisition unit can analyze users' social media activity when acquiring earthquake data and obtain relevant data. Specific methods and criteria for analyzing social media activity include, but are not limited to, post content, follower count, and likes. For example, the data acquisition unit can analyze users' social media activity and prioritize the acquisition of data from areas with many earthquake-related posts. Furthermore, the data acquisition unit can analyze users' social media activity and prioritize the acquisition of data from areas with high interest in earthquakes. It can also analyze users' social media activity and prioritize the acquisition of data from areas with a large amount of earthquake-related information. This allows for the priority acquisition of relevant earthquake data by analyzing users' social media activity.

[0039] The reception desk can analyze the user's past input history and select the optimal input method. Specific selection criteria and methods for the optimal input method include, but are not limited to, ease of input and low error rates. For example, the reception desk can automatically display locations that the user has frequently entered in the past as suggestions. Furthermore, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Additionally, the reception desk can predict and suggest locations that the user will use at specific times based on their past input history. In this way, by analyzing the user's past input history, the optimal input method can be provided.

[0040] The input system can automatically complete the user's current location information when they input location information. Specific methods and criteria for automatic completion include, but are not limited to, the method of acquiring location information and the timing of completion. For example, when a user opens the app, the input system can automatically acquire their current location and set it as the starting point. Furthermore, when a user inputs a destination, the input system can suggest the most suitable location considering its distance from the current location. Additionally, if a user uses the app while on the move, the input system can update their current location in real time and reflect it as the starting point. This streamlines the input process by automatically completing the user's current location information.

[0041] The reception system can prioritize displaying highly relevant locations when a user enters information about a specified location, taking into account the user's geographical location. Specific definitions and selection criteria for highly relevant locations include, but are not limited to, geographical proximity and past visit history. For example, the reception system can prioritize displaying nearby locations based on the user's geographical location. Furthermore, it can prioritize displaying locations within a specific region based on the user's geographical location. In this way, by considering the user's geographical location, the system can prioritize displaying highly relevant locations.

[0042] The reception desk can analyze the user's social media activity when the user inputs information about a specified location and suggest relevant locations. Specific definitions and selection criteria for relevant locations include, but are not limited to, the frequency of mentions on social media and location information. For example, the reception desk can analyze the user's social media activity and suggest locations with many posts related to earthquakes. It can also analyze the user's social media activity and suggest locations with high interest in earthquakes. Furthermore, it can analyze the user's social media activity and suggest locations with a lot of earthquake-related information. In this way, relevant locations can be suggested by analyzing the user's social media activity.

[0043] The generation unit can incorporate additional data during simulation generation to more realistically reproduce the effects of earthquakes. Specific types of additional data and methods of acquisition include, but are not limited to, geological data and meteorological data. For example, the generation unit can incorporate topographic data to realistically reproduce the effects of earthquakes. It can also incorporate structural data of buildings to realistically reproduce the effects of earthquakes. Furthermore, it can incorporate data on roads and bridges to realistically reproduce the effects of earthquakes. In this way, incorporating additional data allows for a more realistic reproduction of earthquake effects.

[0044] The generation unit can select the optimal simulation method by referring to the user's past simulation history when generating a simulation. Specific selection criteria and methods for the optimal simulation method include, but are not limited to, past simulation results and user settings. For example, the generation unit can select the optimal simulation method based on simulation methods previously used by the user. Furthermore, the generation unit can select a highly detailed simulation method from the user's past simulation history. Additionally, the generation unit can analyze the user's past simulation history to select the most effective simulation method. Thus, by referring to the user's past simulation history, the optimal simulation method can be selected.

[0045] The generation unit can select the optimal simulation method when generating simulations, taking into account the user's geographical location information. Specific selection criteria and methods for the optimal simulation method include, but are not limited to, geographical conditions and historical data. For example, the generation unit can select a simulation method for a nearby area based on the user's geographical location information. Furthermore, for example, the generation unit can select a simulation method based on the user's geographical location information according to the distance from the epicenter. Also, for example, the generation unit can select a simulation method for a specific area based on the user's geographical location information. This allows for the selection of the optimal simulation method by considering the user's geographical location information.

[0046] The generation unit can analyze the user's social media activity during simulation generation and suggest relevant simulations. Specific definitions and selection criteria for relevant simulations include, but are not limited to, the frequency of mentions on social media and past simulation results. For example, the generation unit can analyze the user's social media activity and suggest simulations for areas with many earthquake-related posts. Furthermore, it can analyze the user's social media activity and suggest simulations for areas with high interest in earthquakes. In this way, relevant simulations can be suggested by analyzing the user's social media activity.

[0047] The display unit can select the optimal display method by referring to the user's past display history during simulation display. Specific selection criteria and methods for the optimal display method include, but are not limited to, past display history and user settings. For example, the display unit can select the optimal display method based on display methods previously used by the user. Furthermore, the display unit can select a highly detailed display method from the user's past display history. Additionally, the display unit can analyze the user's past display history to select the most effective display method. Thus, by referring to the user's past display history, the optimal display method can be selected.

[0048] The display unit can customize the display method based on the user's current device information when displaying a simulation. Specific types of device information and methods of obtaining them include, but are not limited to, the device type and OS version. For example, if the user is using a smartphone, the display unit can provide a display method adapted to the screen size. Similarly, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Furthermore, if the user is using a personal computer, the display unit can provide a display method that includes detailed information. This allows for optimal display by customizing the display method based on the user's device information.

[0049] The display unit can select the optimal display method when displaying simulations, taking into account the user's geographical location information. Specific selection criteria and methods for the optimal display method include, but are not limited to, geographical conditions and historical data. For example, the display unit can prioritize displaying simulations for nearby areas based on the user's geographical location information. Furthermore, the display unit can display simulations according to the distance from the epicenter, based on the user's geographical location information. Additionally, the display unit can prioritize displaying simulations for specific areas based on the user's geographical location information. This allows the optimal display method to be selected by considering the user's geographical location information.

[0050] The display unit can analyze the user's social media activity during simulation display and suggest relevant display methods. Specific definitions and selection criteria for relevant display methods include, but are not limited to, the frequency of mentions on social media and past display results. For example, the display unit can analyze the user's social media activity and display simulations of areas with many earthquake-related posts. It can also analyze the user's social media activity and display simulations of areas with high interest in earthquakes. Furthermore, it can analyze the user's social media activity and display simulations of areas with a large amount of earthquake-related information. This allows the system to suggest relevant display methods by analyzing the user's social media activity.

[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 acquisition unit can filter earthquake data based on the user's current location information when acquiring it. Specific filtering methods and criteria include, but are not limited to, the accuracy of location information and filtering conditions. For example, the acquisition unit can prioritize acquiring earthquake data for nearby areas based on the user's current location information. Furthermore, the acquisition unit can filter data according to the distance from the epicenter based on the user's current location information. Additionally, the acquisition unit can acquire only earthquake data for specific areas based on the user's current location information. This allows for the provision of highly relevant information by filtering earthquake data based on the user's current location information.

[0053] The generation unit can incorporate additional data during simulation generation to more realistically reproduce the effects of earthquakes. Specific types of additional data and methods of acquisition include, but are not limited to, geological data and meteorological data. For example, the generation unit can incorporate topographic data to realistically reproduce the effects of earthquakes. It can also incorporate structural data of buildings to realistically reproduce the effects of earthquakes. Furthermore, it can incorporate data on roads and bridges to realistically reproduce the effects of earthquakes. In this way, incorporating additional data allows for a more realistic reproduction of earthquake effects.

[0054] The generation unit can select the optimal simulation method by referring to the user's past simulation history when generating a simulation. Specific selection criteria and methods for the optimal simulation method include, but are not limited to, past simulation results and user settings. For example, the generation unit can select the optimal simulation method based on simulation methods previously used by the user. Furthermore, the generation unit can select a highly detailed simulation method from the user's past simulation history. Additionally, the generation unit can analyze the user's past simulation history to select the most effective simulation method. Thus, by referring to the user's past simulation history, the optimal simulation method can be selected.

[0055] The display unit can select the optimal display method by referring to the user's past display history during simulation display. Specific selection criteria and methods for the optimal display method include, but are not limited to, past display history and user settings. For example, the display unit can select the optimal display method based on display methods previously used by the user. Furthermore, the display unit can select a highly detailed display method from the user's past display history. Additionally, the display unit can analyze the user's past display history to select the most effective display method. Thus, by referring to the user's past display history, the optimal display method can be selected.

[0056] The reception desk can analyze the user's past input history and select the optimal input method. Specific selection criteria and methods for the optimal input method include, but are not limited to, ease of input and low error rates. For example, the reception desk can automatically display locations that the user has frequently entered in the past as suggestions. Furthermore, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Additionally, the reception desk can predict and suggest locations that the user will use at specific times based on their past input history. In this way, by analyzing the user's past input history, the optimal input method can be provided.

[0057] The display unit can customize the display method based on the user's current device information when displaying a simulation. Specific types of device information and methods of obtaining them include, but are not limited to, the device type and OS version. For example, if the user is using a smartphone, the display unit can provide a display method adapted to the screen size. Similarly, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Furthermore, if the user is using a personal computer, the display unit can provide a display method that includes detailed information. This allows for optimal display by customizing the display method based on the user's device information.

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

[0059] Step 1: The acquisition unit acquires earthquake data. Earthquake data includes the epicenter, seismic intensity, and time of occurrence. The acquisition unit can acquire earthquake data in real time from earthquake observation agencies. Step 2: The reception desk receives information about the location specified by the user. This information includes the address, latitude and longitude, and place name. Users can enter addresses such as their home, workplace, or school. Step 3: The generation unit generates an earthquake simulation based on the information obtained by the acquisition and reception units. The simulation includes data such as the earthquake's origin, focal depth, and seismic intensity. The generation unit can perform the simulation based on this data. Step 4: The display unit displays the simulation generated by the generation unit in real time. The display unit can display the simulation in real time on a smartphone, tablet, or PC device.

[0060] (Example of form 2) The earthquake simulation system according to an embodiment of the present invention is a system that improves disaster preparedness by allowing users to view earthquake simulations in real time in their immediate surroundings. This earthquake simulation system takes information for an earthquake simulation at a location specified by the user as input, generates an earthquake simulation based on that information, and displays it on the user's device in real time. This allows users to visually understand the impact of an earthquake in their immediate surroundings, thereby improving their disaster preparedness awareness. For example, when a user inputs the address of their home, workplace, or school, the system uses that information to perform a simulation using data such as the earthquake's origin, epicenter depth, and seismic intensity. The generated simulation is displayed in real time on the screen of a smartphone, tablet, or personal computer. This allows users to visually understand the impact of an earthquake in their immediate surroundings. This mechanism allows users to realistically feel the impact of an earthquake, thereby improving their disaster preparedness awareness. For example, experiencing earthquake shaking at home through a simulation can prompt users to consider concrete disaster preparedness measures, such as furniture placement and evacuation routes. Similarly, conducting simulations at workplaces and schools can raise disaster preparedness awareness throughout the organization. In this way, the earthquake simulation system can improve users' disaster preparedness awareness.

[0061] The earthquake simulation system according to the embodiment comprises an acquisition unit, a reception unit, a generation unit, and a display unit. The acquisition unit acquires earthquake data. Earthquake data includes, but is not limited to, the epicenter, seismic intensity, and time of occurrence. The acquisition unit can, for example, acquire earthquake data in real time from an earthquake observation agency. The reception unit receives information about a location specified by the user. Information about the specified location includes, for example, the address, latitude and longitude, and place name. The reception unit can, for example, allow the user to input the address of their home, workplace, school, etc. The generation unit generates an earthquake simulation based on the information obtained by the acquisition unit and the reception unit. The simulation includes, for example, data such as the earthquake's origin, the depth of the epicenter, and the seismic intensity. The generation unit can, for example, perform a simulation based on data such as the earthquake's origin, the depth of the epicenter, and the seismic intensity. The display unit displays the simulation generated by the generation unit in real time. The display unit can, for example, display the simulation in real time on a smartphone, tablet, or personal computer device. As a result, the earthquake simulation system according to this embodiment can improve users' awareness of disaster prevention.

[0062] The acquisition unit can acquire earthquake data from earthquake observation agencies in real time. Specific methods and criteria for real-time acquisition include, but are not limited to, data update intervals and acquisition protocols. For example, the acquisition unit can acquire earthquake data from earthquake observation agencies in real time. This allows for the provision of the latest earthquake information by acquiring data from earthquake observation agencies in real time.

[0063] The generation unit can perform simulations based on data regarding the earthquake's origin, focal depth, and seismic intensity. Specific methods for identifying the earthquake's origin and data formats include, but are not limited to, latitude, longitude, and place names. Specific methods for measuring the focal depth and data formats include, but are not limited to, meters and kilometers. Specific definitions and measurement methods for seismic intensity include, but are not limited to, seismic intensity scales and measuring instruments. The generation unit can perform simulations based on data such as the earthquake's origin, focal depth, and seismic intensity. This allows for the realistic reproduction of earthquake effects by performing simulations based on detailed earthquake data.

[0064] The display unit can show simulations in real time on smartphones, tablets, or personal computers. Specific types of devices, operating systems, and applications include, but are not limited to, iOS, Android, and Windows. This allows users to view earthquake simulations in real time on various devices.

[0065] The generation unit can perform simulations considering topographic data or building structural data. Specific types and acquisition methods of topographic data include, but are not limited to, elevation data and geological data. Specific types and acquisition methods of building structural data include, but are not limited to, building height, materials, and design drawings. The generation unit can perform simulations considering topographic data or building structural data. This allows for the provision of more realistic earthquake simulations by considering topographic data and building structural data.

[0066] The display unit can provide users with advice on disaster preparedness. Specific examples of this advice, including but not limited to, evacuation routes, emergency contact information, and lists of emergency supplies, may include: evacuation routes, emergency contact information, and emergency supplies. For example, the display unit can provide users with advice on disaster preparedness. This allows users to receive disaster preparedness advice through earthquake simulations.

[0067] The data acquisition unit can estimate the user's emotions and adjust the timing of earthquake data acquisition based on the estimated emotions. Specific methods and criteria for estimating user emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. Specific methods and criteria for adjusting the acquisition timing include, but are not limited to, data update intervals and timing based on the user's situation. For example, if the user is feeling anxious, the data acquisition unit can increase the frequency of earthquake data acquisition to provide the latest information in real time. Conversely, if the user is relaxed, the data acquisition unit can return the frequency of earthquake data acquisition to normal and provide only the necessary information. Furthermore, if the user is excited, the data acquisition unit can adjust the frequency of earthquake data acquisition to avoid providing excessive information. By adjusting the timing of earthquake data acquisition according to the user's emotions, more appropriate information can be provided.

[0068] The data acquisition unit can analyze past earthquake data and select the optimal data acquisition method. Specific selection criteria and methods for the optimal data acquisition method include, but are not limited to, data reliability, acquisition speed, and cost. For example, the data acquisition unit can analyze earthquake occurrence patterns in a specific region based on past earthquake data and select the optimal data acquisition method. Furthermore, for example, the data acquisition unit can analyze trends in earthquake epicenter depth and seismic intensity based on past earthquake data and select the optimal data acquisition method. Also, for example, the data acquisition unit can analyze trends in earthquake occurrence time and season based on past earthquake data and select the optimal data acquisition method. In this way, the optimal data acquisition method can be selected by analyzing past earthquake data.

[0069] The acquisition unit can filter earthquake data based on the user's current location information when acquiring it. Specific filtering methods and criteria include, but are not limited to, the accuracy of location information and filtering conditions. For example, the acquisition unit can prioritize acquiring earthquake data for nearby areas based on the user's current location information. Furthermore, the acquisition unit can filter data according to the distance from the epicenter based on the user's current location information. Additionally, the acquisition unit can acquire only earthquake data for specific areas based on the user's current location information. This allows for the provision of highly relevant information by filtering earthquake data based on the user's current location information.

[0070] The data acquisition unit can estimate the user's emotions and determine the priority of earthquake data to acquire based on the estimated emotions. Specific methods and criteria for determining priority include, but are not limited to, data importance and the user's situation. For example, if the user is feeling anxious, the data acquisition unit can prioritize acquiring data for earthquakes with high seismic intensity. Similarly, if the user is relaxed, the data acquisition unit can prioritize acquiring data for earthquakes with low seismic intensity. Furthermore, if the user is excited, the data acquisition unit can prioritize acquiring data for earthquakes with shallow epicenters. By prioritizing earthquake data according to the user's emotions, it becomes possible to provide more appropriate information.

[0071] The acquisition unit can prioritize the acquisition of highly relevant data when acquiring earthquake data, taking into account the user's geographical location information. Specific definitions and selection criteria for highly relevant data include, but are not limited to, geographical proximity and the degree of agreement with past data. For example, the acquisition unit can prioritize the acquisition of earthquake data for nearby areas based on the user's geographical location information. Furthermore, for example, the acquisition unit can prioritize the acquisition of data based on the user's geographical location information, according to the distance from the epicenter. Also, for example, the acquisition unit can prioritize the acquisition of earthquake data for a specific area based on the user's geographical location information. This allows for the priority acquisition of highly relevant earthquake data by considering the user's geographical location information.

[0072] The data acquisition unit can analyze users' social media activity when acquiring earthquake data and obtain relevant data. Specific methods and criteria for analyzing social media activity include, but are not limited to, post content, follower count, and likes. For example, the data acquisition unit can analyze users' social media activity and prioritize the acquisition of data from areas with many earthquake-related posts. Furthermore, the data acquisition unit can analyze users' social media activity and prioritize the acquisition of data from areas with high interest in earthquakes. It can also analyze users' social media activity and prioritize the acquisition of data from areas with a large amount of earthquake-related information. This allows for the priority acquisition of relevant earthquake data by analyzing users' social media activity.

[0073] The reception desk can estimate the user's emotions and adjust the display of the input interface based on those emotions. Specific methods and criteria for adjusting the input interface display include, but are not limited to, changing colors and layouts. For example, if the user is stressed, the reception desk can provide an interface with calming colors to reduce visual stress. Similarly, if the user is enjoying themselves, the reception desk can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the reception desk can provide a simple and highly visible interface to facilitate the input process. By adjusting the input interface display according to the user's emotions, a more comfortable user experience is possible.

[0074] The reception desk can analyze the user's past input history and select the optimal input method. Specific selection criteria and methods for the optimal input method include, but are not limited to, ease of input and low error rates. For example, the reception desk can automatically display locations that the user has frequently entered in the past as suggestions. Furthermore, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Additionally, the reception desk can predict and suggest locations that the user will use at specific times based on their past input history. In this way, by analyzing the user's past input history, the optimal input method can be provided.

[0075] The input system can automatically complete the user's current location information when they input location information. Specific methods and criteria for automatic completion include, but are not limited to, the method of acquiring location information and the timing of completion. For example, when a user opens the app, the input system can automatically acquire their current location and set it as the starting point. Furthermore, when a user inputs a destination, the input system can suggest the most suitable location considering its distance from the current location. Additionally, if a user uses the app while on the move, the input system can update their current location in real time and reflect it as the starting point. This streamlines the input process by automatically completing the user's current location information.

[0076] The reception desk can estimate the user's emotions and adjust the operation procedures of the input interface based on those emotions. Specific methods and criteria for adjusting the operation procedures include, but are not limited to, reducing the number of steps and simplifying the operation. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. Conversely, if the user is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick information entry. By adjusting the operation procedures of the input interface according to the user's emotions, a more comfortable user experience is possible.

[0077] The reception system can prioritize displaying highly relevant locations when a user enters information about a specified location, taking into account the user's geographical location. Specific definitions and selection criteria for highly relevant locations include, but are not limited to, geographical proximity and past visit history. For example, the reception system can prioritize displaying nearby locations based on the user's geographical location. Furthermore, it can prioritize displaying locations within a specific region based on the user's geographical location. In this way, by considering the user's geographical location, the system can prioritize displaying highly relevant locations.

[0078] The reception desk can analyze the user's social media activity when the user inputs information about a specified location and suggest relevant locations. Specific definitions and selection criteria for relevant locations include, but are not limited to, the frequency of mentions on social media and location information. For example, the reception desk can analyze the user's social media activity and suggest locations with many posts related to earthquakes. It can also analyze the user's social media activity and suggest locations with high interest in earthquakes. Furthermore, it can analyze the user's social media activity and suggest locations with a lot of earthquake-related information. In this way, relevant locations can be suggested by analyzing the user's social media activity.

[0079] The generation unit can estimate the user's emotions and adjust the level of detail of the simulation based on the estimated emotions. Specific methods and criteria for adjusting the level of detail of the simulation include, but are not limited to, the amount of data displayed and the accuracy of the analysis. For example, if the user is feeling anxious, the generation unit can provide a detailed simulation to reassure them. Conversely, if the user is relaxed, the generation unit can provide a simulation with normal level of detail. Furthermore, if the user is excited, the generation unit can provide a visually stimulating simulation. By adjusting the level of detail of the simulation according to the user's emotions, a more appropriate simulation can be provided.

[0080] The generation unit can incorporate additional data during simulation generation to more realistically reproduce the effects of earthquakes. Specific types of additional data and methods of acquisition include, but are not limited to, geological data and meteorological data. For example, the generation unit can incorporate topographic data to realistically reproduce the effects of earthquakes. It can also incorporate structural data of buildings to realistically reproduce the effects of earthquakes. Furthermore, it can incorporate data on roads and bridges to realistically reproduce the effects of earthquakes. In this way, incorporating additional data allows for a more realistic reproduction of earthquake effects.

[0081] The generation unit can select the optimal simulation method by referring to the user's past simulation history when generating a simulation. Specific selection criteria and methods for the optimal simulation method include, but are not limited to, past simulation results and user settings. For example, the generation unit can select the optimal simulation method based on simulation methods previously used by the user. Furthermore, the generation unit can select a highly detailed simulation method from the user's past simulation history. Additionally, the generation unit can analyze the user's past simulation history to select the most effective simulation method. Thus, by referring to the user's past simulation history, the optimal simulation method can be selected.

[0082] The generation unit can estimate the user's emotions and adjust the simulation's display method based on those emotions. Specific methods and criteria for adjusting the simulation's display method include, but are not limited to, changes in display order and color. For example, if the user is tense, the generation unit can provide a simple and highly visible display method. Similarly, if the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise display method. By adjusting the simulation's display method according to the user's emotions, a more appropriate display becomes possible.

[0083] The generation unit can select the optimal simulation method when generating simulations, taking into account the user's geographical location information. Specific selection criteria and methods for the optimal simulation method include, but are not limited to, geographical conditions and historical data. For example, the generation unit can select a simulation method for a nearby area based on the user's geographical location information. Furthermore, for example, the generation unit can select a simulation method based on the user's geographical location information according to the distance from the epicenter. Also, for example, the generation unit can select a simulation method for a specific area based on the user's geographical location information. This allows for the selection of the optimal simulation method by considering the user's geographical location information.

[0084] The generation unit can analyze the user's social media activity during simulation generation and suggest relevant simulations. Specific definitions and selection criteria for relevant simulations include, but are not limited to, the frequency of mentions on social media and past simulation results. For example, the generation unit can analyze the user's social media activity and suggest simulations for areas with many earthquake-related posts. Furthermore, it can analyze the user's social media activity and suggest simulations for areas with high interest in earthquakes. In this way, relevant simulations can be suggested by analyzing the user's social media activity.

[0085] The display unit can estimate the user's emotions and adjust the timing of the simulation display based on the estimated emotions. Specific methods and criteria for adjusting the display timing include, but are not limited to, timing according to the user's situation and data update intervals. For example, if the user is feeling anxious, the display unit can accelerate the simulation display timing to provide information quickly. Conversely, if the user is relaxed, the display unit can display the simulation at the normal timing. Furthermore, if the user is excited, the display unit can adjust the simulation display timing to avoid providing excessive information. By adjusting the simulation display timing according to the user's emotions, more appropriate information can be provided.

[0086] The display unit can select the optimal display method by referring to the user's past display history during simulation display. Specific selection criteria and methods for the optimal display method include, but are not limited to, past display history and user settings. For example, the display unit can select the optimal display method based on display methods previously used by the user. Furthermore, the display unit can select a highly detailed display method from the user's past display history. Additionally, the display unit can analyze the user's past display history to select the most effective display method. Thus, by referring to the user's past display history, the optimal display method can be selected.

[0087] The display unit can customize the display method based on the user's current device information when displaying a simulation. Specific types of device information and methods of obtaining them include, but are not limited to, the device type and OS version. For example, if the user is using a smartphone, the display unit can provide a display method adapted to the screen size. Similarly, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Furthermore, if the user is using a personal computer, the display unit can provide a display method that includes detailed information. This allows for optimal display by customizing the display method based on the user's device information.

[0088] The display unit can estimate the user's emotions and adjust the display order of the simulation based on those emotions. Specific methods and criteria for adjusting the display order include, but are not limited to, importance and the user's situation. For example, if the user is feeling anxious, the display unit can prioritize displaying important information. Conversely, if the user is relaxed, the display unit can display the simulation in the normal order. Furthermore, if the user is excited, the display unit can prioritize displaying visually stimulating information. This allows for more appropriate information to be provided by adjusting the display order of the simulation according to the user's emotions.

[0089] The display unit can select the optimal display method when displaying simulations, taking into account the user's geographical location information. Specific selection criteria and methods for the optimal display method include, but are not limited to, geographical conditions and historical data. For example, the display unit can prioritize displaying simulations for nearby areas based on the user's geographical location information. Furthermore, the display unit can display simulations according to the distance from the epicenter, based on the user's geographical location information. Additionally, the display unit can prioritize displaying simulations for specific areas based on the user's geographical location information. This allows the optimal display method to be selected by considering the user's geographical location information.

[0090] The display unit can analyze the user's social media activity during simulation display and suggest relevant display methods. Specific definitions and selection criteria for relevant display methods include, but are not limited to, the frequency of mentions on social media and past display results. For example, the display unit can analyze the user's social media activity and display simulations of areas with many earthquake-related posts. It can also analyze the user's social media activity and display simulations of areas with high interest in earthquakes. Furthermore, it can analyze the user's social media activity and display simulations of areas with a large amount of earthquake-related information. This allows the system to suggest relevant display methods by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements described above, including the acquisition unit, reception unit, generation unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit can acquire earthquake data in real time from an earthquake observation agency via the communication I / F 44 of the smart device 14. The reception unit receives information about a location specified by the user using the reception device 38 of the smart device 14. The generation unit generates an earthquake simulation using the identification processing unit 290 of the data processing unit 12. The display unit displays the generated simulation in real time using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the acquisition unit, reception unit, generation unit, and display unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit can acquire earthquake data in real time from an earthquake observation agency via the communication I / F 44 of the smart glasses 214. The reception unit receives information about a location specified by the user using the microphone 238 of the smart glasses 214. The generation unit generates an earthquake simulation using the identification processing unit 290 of the data processing unit 12. The display unit displays the generated simulation in real time using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the acquisition unit, reception unit, generation unit, and display unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit can acquire earthquake data in real time from an earthquake observation agency via the communication I / F 44 of the headset terminal 314. The reception unit receives information about a location specified by the user using the microphone 238 of the headset terminal 314. The generation unit generates an earthquake simulation using the identification processing unit 290 of the data processing unit 12. The display unit displays the generated simulation in real time using the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the acquisition unit, reception unit, generation unit, and display unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit can acquire earthquake data in real time from an earthquake observation agency via the robot 414's communication I / F 44. The reception unit receives information about a location specified by the user using the robot 414's microphone 238. The generation unit generates an earthquake simulation using the identification processing unit 290 of the data processing unit 12. The display unit displays the generated simulation in real time using the robot 414's speaker 240.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The acquisition unit can filter earthquake data based on the user's current location information when acquiring it. Specific filtering methods and criteria include, but are not limited to, the accuracy of location information and filtering conditions. For example, the acquisition unit can prioritize acquiring earthquake data for nearby areas based on the user's current location information. Furthermore, the acquisition unit can filter data according to the distance from the epicenter based on the user's current location information. Additionally, the acquisition unit can acquire only earthquake data for specific areas based on the user's current location information. This allows for the provision of highly relevant information by filtering earthquake data based on the user's current location information.

[0093] The generation unit can incorporate additional data during simulation generation to more realistically reproduce the effects of earthquakes. Specific types of additional data and methods of acquisition include, but are not limited to, geological data and meteorological data. For example, the generation unit can incorporate topographic data to realistically reproduce the effects of earthquakes. It can also incorporate structural data of buildings to realistically reproduce the effects of earthquakes. Furthermore, it can incorporate data on roads and bridges to realistically reproduce the effects of earthquakes. In this way, incorporating additional data allows for a more realistic reproduction of earthquake effects.

[0094] The display unit can estimate the user's emotions and adjust the timing of the simulation display based on the estimated emotions. Specific methods and criteria for adjusting the display timing include, but are not limited to, timing according to the user's situation and data update intervals. For example, if the user is feeling anxious, the display unit can accelerate the simulation display timing to provide information quickly. Conversely, if the user is relaxed, the display unit can display the simulation at the normal timing. Furthermore, if the user is excited, the display unit can adjust the simulation display timing to avoid providing excessive information. By adjusting the simulation display timing according to the user's emotions, more appropriate information can be provided.

[0095] The reception desk can estimate the user's emotions and adjust the display of the input interface based on those emotions. Specific methods and criteria for adjusting the input interface display include, but are not limited to, changing colors and layouts. For example, if the user is stressed, the reception desk can provide an interface with calming colors to reduce visual stress. Similarly, if the user is enjoying themselves, the reception desk can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the reception desk can provide a simple and highly visible interface to facilitate the input process. By adjusting the input interface display according to the user's emotions, a more comfortable user experience is possible.

[0096] The generation unit can select the optimal simulation method by referring to the user's past simulation history when generating a simulation. Specific selection criteria and methods for the optimal simulation method include, but are not limited to, past simulation results and user settings. For example, the generation unit can select the optimal simulation method based on simulation methods previously used by the user. Furthermore, the generation unit can select a highly detailed simulation method from the user's past simulation history. Additionally, the generation unit can analyze the user's past simulation history to select the most effective simulation method. Thus, by referring to the user's past simulation history, the optimal simulation method can be selected.

[0097] The data acquisition unit can estimate the user's emotions and determine the priority of earthquake data to acquire based on the estimated emotions. Specific methods and criteria for determining priority include, but are not limited to, data importance and the user's situation. For example, if the user is feeling anxious, the data acquisition unit can prioritize acquiring data for earthquakes with high seismic intensity. Similarly, if the user is relaxed, the data acquisition unit can prioritize acquiring data for earthquakes with low seismic intensity. Furthermore, if the user is excited, the data acquisition unit can prioritize acquiring data for earthquakes with shallow epicenters. By prioritizing earthquake data according to the user's emotions, it becomes possible to provide more appropriate information.

[0098] The display unit can select the optimal display method by referring to the user's past display history during simulation display. Specific selection criteria and methods for the optimal display method include, but are not limited to, past display history and user settings. For example, the display unit can select the optimal display method based on display methods previously used by the user. Furthermore, the display unit can select a highly detailed display method from the user's past display history. Additionally, the display unit can analyze the user's past display history to select the most effective display method. Thus, by referring to the user's past display history, the optimal display method can be selected.

[0099] The reception desk can analyze the user's past input history and select the optimal input method. Specific selection criteria and methods for the optimal input method include, but are not limited to, ease of input and low error rates. For example, the reception desk can automatically display locations that the user has frequently entered in the past as suggestions. Furthermore, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Additionally, the reception desk can predict and suggest locations that the user will use at specific times based on their past input history. In this way, by analyzing the user's past input history, the optimal input method can be provided.

[0100] The generation unit can estimate the user's emotions and adjust the level of detail of the simulation based on the estimated emotions. Specific methods and criteria for adjusting the level of detail of the simulation include, but are not limited to, the amount of data displayed and the accuracy of the analysis. For example, if the user is feeling anxious, the generation unit can provide a detailed simulation to reassure them. Conversely, if the user is relaxed, the generation unit can provide a simulation with normal level of detail. Furthermore, if the user is excited, the generation unit can provide a visually stimulating simulation. By adjusting the level of detail of the simulation according to the user's emotions, a more appropriate simulation can be provided.

[0101] The display unit can customize the display method based on the user's current device information when displaying a simulation. Specific types of device information and methods of obtaining them include, but are not limited to, the device type and OS version. For example, if the user is using a smartphone, the display unit can provide a display method adapted to the screen size. Similarly, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Furthermore, if the user is using a personal computer, the display unit can provide a display method that includes detailed information. This allows for optimal display by customizing the display method based on the user's device information.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The acquisition unit acquires earthquake data. Earthquake data includes the epicenter, seismic intensity, and time of occurrence. The acquisition unit can acquire earthquake data in real time from earthquake observation agencies. Step 2: The reception desk receives information about the location specified by the user. This information includes the address, latitude and longitude, and place name. Users can enter addresses such as their home, workplace, or school. Step 3: The generation unit generates an earthquake simulation based on the information obtained by the acquisition and reception units. The simulation includes data such as the earthquake's origin, focal depth, and seismic intensity. The generation unit can perform the simulation based on this data. Step 4: The display unit displays the simulation generated by the generation unit in real time. The display unit can display the simulation in real time on a smartphone, tablet, or PC device.

[0104] 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.

[0105] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.).

[0120] 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.

[0121] 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. 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.

[0122] 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.

[0123] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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. 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.

[0138] 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.

[0139] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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. 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.

[0155] 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.

[0156] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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. The acquisition unit acquires earthquake data, A reception desk that accepts information about the location specified by the user, A generation unit that generates an earthquake simulation based on the information obtained by the acquisition unit and the reception unit, The system includes a display unit that displays the simulation generated by the generation unit in real time. A system characterized by the following features.

2. The acquisition unit is, Obtain earthquake data in real time from earthquake observation agencies. The system according to feature 1.

3. The generating unit is The simulation is performed based on data on the earthquake's origin, epicenter depth, and seismic intensity. The system according to feature 1.

4. The aforementioned display unit is Display the simulation in real time on your smartphone, tablet, or PC device. The system according to feature 1.

5. The generating unit is Perform simulations considering terrain data or building structure data. The system according to feature 1.

6. The aforementioned display unit is Provide users with advice on disaster prevention measures. The system according to feature 1.

7. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of earthquake data acquisition based on those estimated emotions. The system according to feature 1.

8. The acquisition unit is, Analyze past earthquake data and select the optimal acquisition method. The system according to feature 1.

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