System

Abstract

An object of a system according to an embodiment is to efficiently aggregate information to be collected in a disaster area and provide the information to persons concerned.SOLUTION: A system includes a collection part, an analysis part, a provision part, a contribution part, and a notice part. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit and determines the degree of urgency. The provision unit provides the information determined by the analysis unit to the person concerned. The posting section extends the function of the disaster message board so that a familiar situation can also be posted. The posting unit posts the information posted by the posting unit on the bulletin board application.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of making it difficult to efficiently collect and provide the confusing information in disaster-stricken areas to those involved.

[0005] The system according to the embodiment aims to efficiently collect confusing information in disaster-stricken areas and provide it to those involved. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a posting unit, and a bulletin board. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit and determines the level of urgency. The provision unit provides the information determined by the analysis unit to relevant parties. The posting unit expands the functionality of the disaster message board, allowing users to post information about familiar situations. The bulletin board displays the information posted by the posting unit on a bulletin board app. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect confusing information in disaster areas and provide it to those involved. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information aggregation system according to an embodiment of the present invention aggregates, organizes, and conveys confusing or difficult-to-obtain information in disaster-stricken areas. The information aggregation system collects and analyzes information from local governments, social media, and inquiries, assesses the level of urgency, and provides the information to relevant parties. It also enhances the functionality of disaster message boards to allow users to post information about familiar situations. For example, the information aggregation system shares information such as "water is unavailable" or "food is out" with the national or local government, and notifies the national or local government of information such as "roads are cut off by landslides." Information such as "telephones are out of service in XX" or "person X cannot be contacted" is also provided via the network. During normal times, the information aggregation system provides the local government with a disaster prevention app. This allows the information aggregation system to aggregate, organize, and convey confusing or difficult-to-obtain information in disaster-stricken areas. For example, local governments can use the app during normal times to prepare for disasters, and then quickly collect and respond to disasters when they occur.

[0029] The information aggregation system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a posting unit, and a bulletin board. The collection unit collects information. For example, the collection unit collects information from official websites of local governments, posts on social media, and inquiries. The collection unit can also centrally collect data from each information source and store it in a database. For example, the collection unit collects disaster information from official websites of local governments, posts on social media, and inquiries to customer service desks. The analysis unit analyzes the collected information and determines the level of urgency. For example, the analysis unit analyzes the collected information and determines the content and level of urgency of the information. For example, the analysis unit determines information such as "no water" or "no food" as having a high level of urgency. The analysis unit can also analyze information using natural language processing technology or machine learning algorithms and score the level of urgency. The provision unit provides the determined information to relevant parties. For example, the provision unit provides the determined information to relevant parties to promote rapid response. For example, the provision unit notifies the national government or local government of information such as "a road has been cut off by a landslide." The providing unit can also provide information by methods such as email notification, app notification, and SMS. The posting unit expands the functionality of the disaster message board to allow users to post familiar situations. For example, the posting unit expands the functionality of the disaster message board to allow users to post familiar situations such as the status of evacuation shelters and shortages of supplies. The posting unit posts the posted information on a bulletin board app. For example, the posting unit can post the posted information on a bulletin board app and update it in real time. The posting unit can also organize and display information using a filtering function. As a result, the information aggregation system according to the embodiment can aggregate, organize, and convey confusing information or information that is difficult to obtain in disaster-stricken areas.

[0030] The collection unit can collect information from the official websites of local governments, posts on social media, and inquiries to the inquiry desk. The collection unit, for example, collects information from the official websites of local governments. For example, the collection unit collects disaster information and evacuation information from the official websites of local governments. The collection unit can also collect posts on social media. For example, the collection unit collects information from social media platforms such as Twitter (registered trademark), Facebook (registered trademark), and Instagram (registered trademark). The collection unit can also collect inquiries to the inquiry desk. For example, the collection unit collects inquiry contents via telephone, email, chat, etc. This allows the collection unit to collect information from a variety of information sources.

[0031] The analysis unit can analyze the collected information and determine the content and urgency of the information. The analysis unit, for example, analyzes the collected information and determines the content and urgency of the information. For example, the analysis unit determines information such as "no water" or "no food" as having a high level of urgency. The analysis unit can also analyze the information using natural language processing technology or a machine learning algorithm and score the urgency. For example, the analysis unit can analyze the content of the information using keyword extraction technology and determine the urgency. The analysis unit can also quantify the urgency of the information using a scoring algorithm. This allows the analysis unit to determine the content and urgency of the information.

[0032] The providing unit provides the determined information to the relevant parties, thereby facilitating a prompt response. The providing unit, for example, provides the determined information to the relevant parties, thereby facilitating a prompt response. For example, the providing unit notifies the national or local government of information such as "a road has been cut off by a landslide." The providing unit can also provide the information by methods such as email notification, app notification, or SMS. For example, the providing unit notifies the relevant parties of the determined information by email. The providing unit can also provide the information to the relevant parties through an app. In this way, the providing unit can quickly provide information to the relevant parties and facilitate a response.

[0033] The posting unit can expand the functionality of the disaster message board to enable posting of familiar situations as well. For example, the posting unit can expand the functionality of the disaster message board to enable posting of familiar situations such as the situation at an evacuation shelter or a shortage of supplies. For example, the posting unit posts the situation at an evacuation shelter on the disaster message board. The posting unit can also post a shortage of supplies. In this way, the posting unit can expand the functionality of the disaster message board to enable posting of familiar situations as well.

[0034] The bulletin board unit can post the posted information on a bulletin board app. For example, the bulletin board unit can post the posted information on the bulletin board app and update it in real time. For example, the bulletin board unit posts the posted information on the bulletin board app. The bulletin board unit can also organize and display information using a filtering function. For example, the bulletin board unit organizes and posts the posted information by category. This allows the bulletin board unit to post the posted information on the bulletin board app.

[0035] The collection unit can evaluate the reliability of each information source and prioritize collecting highly reliable information. For example, the collection unit prioritizes collecting information from the official website of a local government. The collection unit can also evaluate the reliability of posts on social media and prioritize collecting information from highly reliable accounts. The collection unit can also analyze the content of inquiries to the inquiry desk and prioritize collecting highly reliable information. This allows the collection unit to prioritize collecting highly reliable information.

[0036] When collecting information, the collection unit can adjust the collection range taking into account the geographical conditions of the disaster-stricken area. For example, the collection unit sets the information collection range based on topographical data of the disaster-stricken area. The collection unit can also take into account the traffic conditions in the disaster-stricken area and collect information within an accessible range. The collection unit can also take into account the population density of the disaster-stricken area and collect information preferentially from important areas. In this way, the collection unit can optimize the collection range taking into account the geographical conditions of the disaster-stricken area.

[0037] When collecting information, the collection unit can select collection targets by referring to past disaster data. For example, the collection unit identifies risk areas in disaster-stricken areas based on past disaster data and collects information. The collection unit can also select collection targets by referring to information that was important during past disasters. The collection unit can also analyze past disaster data and collect information from areas with a high risk of recurrence. This allows the collection unit to select collection targets by referring to past disaster data.

[0038] When collecting information, the collection unit can set the collection range taking into account meteorological information in the disaster-stricken area. For example, if the weather in the disaster-stricken area is worsening, the collection unit can reduce the collection range and collect information within a safe range. Furthermore, if the weather in the disaster-stricken area is stable, the collection unit can also collect information over a wide area. Furthermore, the collection unit can adjust the collection range in advance based on the weather forecast for the disaster-stricken area. This allows the collection unit to adjust the collection range taking into account meteorological information in the disaster-stricken area.

[0039] When collecting information, the collection unit can determine the collection means taking into consideration the infrastructure situation in the disaster-stricken area. For example, if the communication infrastructure in the disaster-stricken area is unstable, the collection unit selects a means that allows offline collection. Furthermore, if the power supply in the disaster-stricken area is unstable, the collection unit can also select a battery-powered collection means. Furthermore, if the transportation infrastructure in the disaster-stricken area is cut off, the collection unit can also select a collection means such as a drone. This allows the collection unit to select the collection means taking into consideration the infrastructure situation in the disaster-stricken area.

[0040] When collecting information, the collection unit can limit the collection targets by taking into account the population density of the disaster-stricken area. For example, the collection unit prioritizes information collection from areas of the disaster-stricken area with high population density. The collection unit can also collect only important information from areas of the disaster-stricken area with low population density. The collection unit can also dynamically adjust the collection targets based on population density data of the disaster-stricken area. This allows the collection unit to narrow down the collection targets by taking into account the population density of the disaster-stricken area.

[0041] The analysis unit can determine the priority of analysis based on the urgency of information during analysis. For example, the analysis unit gives the highest priority to analyzing information with a high urgency. The analysis unit can also postpone analysis of information with a low urgency. The analysis unit can also analyze information with a medium urgency at an appropriate time. This allows the analysis unit to determine the priority of analysis based on the urgency of information.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a specific analysis algorithm to local government information. The analysis unit can also apply a different analysis algorithm to SNS information. The analysis unit can also apply yet another analysis algorithm to inquiry information. This allows the analysis unit to apply different analysis algorithms depending on the category of information.

[0043] During analysis, the analysis unit can improve the analysis accuracy by referring to past analysis results. The analysis unit can improve the current analysis accuracy, for example, based on past analysis results. The analysis unit can also correct errors by referring to past analysis results. The analysis unit can also analyze past analysis results and optimize the analysis algorithm. This allows the analysis unit to improve the analysis accuracy by referring to past analysis results.

[0044] During analysis, the analysis unit can determine the order of analysis based on the time of submission of information. For example, the analysis unit gives the most recent information the highest priority for analysis. The analysis unit can also postpone analysis of older information. The analysis unit can also analyze information that has been submitted at a medium time, at an appropriate timing. This allows the analysis unit to determine the order of analysis priority based on the time of submission of information.

[0045] During analysis, the analysis unit can set the order of analysis based on the relevance of the information. For example, the analysis unit gives top priority to analyzing highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also analyze information with a medium degree of relevance at an appropriate time. This allows the analysis unit to adjust the order of analysis based on the relevance of the information.

[0046] The analysis unit can weight the analysis based on the reliability of the information during analysis. For example, the analysis unit performs analysis by assigning a high weight to highly reliable information. The analysis unit can also analyze by assigning a low weight to low-reliability information. The analysis unit can also analyze by assigning an appropriate weight to medium-reliability information. This allows the analysis unit to weight the analysis based on the reliability of the information.

[0047] The providing unit can determine the priority of provision based on the urgency of the information at the time of provision. For example, the providing unit provides information with a high urgency as the highest priority. The providing unit can also provide information with a low urgency later. The providing unit can also provide information with a medium urgency at an appropriate time. In this way, the providing unit can determine the priority of provision based on the urgency of the information.

[0048] The providing unit can apply different providing means depending on the category of information when providing the information. For example, the providing unit applies a specific providing means to local government information. The providing unit can also apply a different providing means to SNS information. The providing unit can also apply yet another providing means to inquiry information. This allows the providing unit to apply different providing means depending on the category of information.

[0049] The providing unit can improve the accuracy of provision by referring to past provision results when providing information. The providing unit can improve the current accuracy of provision, for example, based on past provision results. The providing unit can also correct errors by referring to past provision results. The providing unit can also analyze past provision results and optimize the provision means. This allows the providing unit to improve the accuracy of provision by referring to past provision results.

[0050] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. For example, the providing unit provides the latest information with the highest priority. The providing unit can also provide older information later. The providing unit can also provide information that is due for submission at an appropriate time. This allows the providing unit to determine the priority of provision based on the time of submission of information.

[0051] The providing unit can adjust the order of providing information based on the relevance of the information when providing the information. For example, the providing unit provides highly relevant information with the highest priority. The providing unit can also provide information with low relevance later. The providing unit can also provide information with medium relevance at an appropriate time. In this way, the providing unit can adjust the order of providing information based on the relevance of the information.

[0052] The providing unit can weight the information provided based on the reliability of the information when providing the information. For example, the providing unit provides information with a high weight assigned to highly reliable information. The providing unit can also provide information with a low weight assigned to low reliable information. The providing unit can also provide information with an appropriate weight assigned to medium reliable information. This allows the providing unit to weight the information provided based on the reliability of the information.

[0053] The posting unit can determine the priority of posting based on the urgency of the information when posting. For example, the posting unit posts information with a high level of urgency as a top priority. The posting unit can also post information with a low level of urgency later. The posting unit can also post information with a medium level of urgency at an appropriate time. This allows the posting unit to determine the priority of posting based on the urgency of the information.

[0054] The posting unit can apply different posting methods depending on the category of information when posting. For example, the posting unit applies a specific posting method to local government information. The posting unit can also apply a different posting method to SNS information. The posting unit can also apply yet another posting method to inquiry information. This allows the posting unit to apply different posting methods depending on the category of information.

[0055] The posting unit can improve posting accuracy by referring to past posting results when posting. The posting unit can improve current posting accuracy, for example, based on past posting results. The posting unit can also correct errors by referring to past posting results. The posting unit can also analyze past posting results and optimize posting means. This allows the posting unit to improve posting accuracy by referring to past posting results.

[0056] The posting unit can determine the priority of posting based on the time of submission of information when posting. For example, the posting unit posts the most recent information with the highest priority. The posting unit can also postpone posting old information. The posting unit can also post information that needs to be submitted at an appropriate time. This allows the posting unit to determine the priority of posting based on the time of submission of information.

[0057] The posting unit can adjust the order of posting based on the relevance of the information when posting. For example, the posting unit posts highly relevant information with the highest priority. The posting unit can also post less relevant information later. The posting unit can also post information with medium relevance at an appropriate time. This allows the posting unit to adjust the order of posting based on the relevance of the information.

[0058] The posting unit can weight the posts based on the reliability of the information when posting. For example, the posting unit posts highly reliable information with a high weight. The posting unit can also post low reliability information with a low weight. The posting unit can also post medium reliability information with an appropriate weight. This allows the posting unit to weight the posts based on the reliability of the information.

[0059] The posting unit can determine the priority of posting based on the urgency of the information when posting. For example, the posting unit posts information with a high level of urgency with the highest priority. The posting unit can also post information with a low level of urgency later. The posting unit can also post information with a medium level of urgency at an appropriate time. This allows the posting unit to determine the priority of posting based on the urgency of the information.

[0060] The posting unit can apply different posting methods depending on the category of information when posting. For example, the posting unit applies a specific posting method to local government information. The posting unit can also apply a different posting method to SNS information. The posting unit can also apply yet another posting method to inquiry information. This allows the posting unit to apply different posting methods depending on the category of information.

[0061] The posting unit can improve posting accuracy by referring to past posting results when posting. The posting unit can improve current posting accuracy, for example, based on past posting results. The posting unit can also correct errors by referring to past posting results. The posting unit can also analyze past posting results and optimize posting means. In this way, the posting unit can improve posting accuracy by referring to past posting results.

[0062] The posting unit can adjust the posting order based on the relevance of the information when posting. For example, the posting unit posts highly relevant information with the highest priority. The posting unit can also post less relevant information later. The posting unit can also post information with medium relevance at an appropriate time. This allows the posting unit to adjust the posting order based on the relevance of the information.

[0063] The posting unit can weight the posting based on the reliability of the information when posting. For example, the posting unit posts information with a high weight to highly reliable information. The posting unit can also post information with a low weight to low reliability information. The posting unit can also post information with an appropriate weight to information with medium reliability information. This allows the posting unit to weight the posting based on the reliability of the information.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The information aggregation system can also be equipped with a voice recognition unit. The voice recognition unit can collect voice information from disaster areas and convert it into text data. For example, if a disaster victim reports over the phone that "there is no water," the voice is converted into text and the analysis unit determines the level of urgency. The voice recognition unit can also analyze local radio broadcasts and emergency announcements in real time to extract important information. This makes it possible to collect data from a variety of sources, including voice information.

[0066] The information aggregation system can also be equipped with an image analysis unit. The image analysis unit can analyze image data sent from the disaster area and automatically determine the extent of the damage. For example, it can analyze photos of roads sent by disaster victims and detect landslides and road closures. The image analysis unit can also analyze image data from drones and satellites to grasp the extent of damage over a wide area. This makes it possible to quickly grasp the extent of damage using visual information.

[0067] The information aggregation system can further include a predictive analysis unit. The predictive analysis unit can predict the risk of future damage expansion based on collected data. For example, it can analyze collected weather data and earthquake data to predict the risk of secondary disasters. The predictive analysis unit can also refer to past disaster data to detect similar patterns and improve prediction accuracy. This makes it possible to identify risks in advance and take prompt countermeasures.

[0068] The information aggregation system can also be equipped with a translation unit. The translation unit can automatically translate collected multilingual information and provide it to relevant parties. For example, it can translate social media posts and inquiries from foreign disaster victims, and the analysis unit can determine the level of urgency. The translation unit can also translate information from local governments and international organizations into multiple languages ​​and provide it to disaster victims. This makes it possible to share information across language barriers.

[0069] The information aggregation system can further include an energy management unit. The energy management unit can monitor the energy supply situation in the disaster-stricken area and support efficient energy distribution. For example, it can monitor the power supply situation in the disaster-stricken area in real time and provide priority support to areas where energy shortages are predicted. The energy management unit can also monitor the usage of solar power generation and batteries and propose optimal energy usage. This will help stabilize the energy supply in the disaster-stricken area.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The collection unit collects information. For example, the collection unit collects information from the official website of a local government, posts on social media, and inquiries. The collection unit can also centrally collect data from each information source and store it in a database. For example, the collection unit collects disaster information from the official website of a local government, posts on social media, and inquiries to the inquiry desk. Step 2: The analysis unit analyzes the collected information and determines the level of urgency. For example, the analysis unit analyzes the collected information and determines the content and level of urgency of the information. For example, the analysis unit determines that information such as "no water" or "no food" has a high level of urgency. The analysis unit can also analyze information using natural language processing technology or machine learning algorithms and score the level of urgency. Step 3: The provision unit provides the determined information to relevant parties. For example, the provision unit provides the determined information to relevant parties to promote a prompt response. For example, the provision unit notifies the national or local government of information such as "a road has been cut off by a landslide." The provision unit can also provide information by email notification, app notification, SMS, or other methods. Step 4: The posting department will expand the functionality of the disaster message board to allow users to post about familiar situations. For example, the posting department will expand the functionality of the disaster message board to allow users to post about familiar situations such as the situation at evacuation centers and shortages of supplies. Step 5: The bulletin board unit posts the posted information on the bulletin board application. For example, the bulletin board unit can post the posted information on the bulletin board application and update it in real time. The bulletin board unit can also use a filtering function to organize and display the information.

[0072] (Example 2) An information aggregation system according to an embodiment of the present invention aggregates, organizes, and conveys confusing or difficult-to-obtain information in disaster-stricken areas. The information aggregation system collects and analyzes information from local governments, social media, and inquiries, assesses the level of urgency, and provides the information to relevant parties. It also enhances the functionality of disaster message boards to allow users to post information about familiar situations. For example, the information aggregation system shares information such as "water is unavailable" or "food is out" with the national or local government, and notifies the national or local government of information such as "roads are cut off by landslides." Information such as "telephones are out of service in XX" or "person X cannot be contacted" is also provided via the network. During normal times, the information aggregation system provides the local government with a disaster prevention app. This allows the information aggregation system to aggregate, organize, and convey confusing or difficult-to-obtain information in disaster-stricken areas. For example, local governments can use the app during normal times to prepare for disasters, and then quickly collect and respond to disasters when they occur.

[0073] The information aggregation system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a posting unit, and a bulletin board. The collection unit collects information. For example, the collection unit collects information from official websites of local governments, posts on social media, and inquiries. The collection unit can also centrally collect data from each information source and store it in a database. For example, the collection unit collects disaster information from official websites of local governments, posts on social media, and inquiries to customer service desks. The analysis unit analyzes the collected information and determines the level of urgency. For example, the analysis unit analyzes the collected information and determines the content and level of urgency of the information. For example, the analysis unit determines information such as "no water" or "no food" as having a high level of urgency. The analysis unit can also analyze information using natural language processing technology or machine learning algorithms and score the level of urgency. The provision unit provides the determined information to relevant parties. For example, the provision unit provides the determined information to relevant parties to promote rapid response. For example, the provision unit notifies the national government or local government of information such as "a road has been cut off by a landslide." The providing unit can also provide information by methods such as email notification, app notification, and SMS. The posting unit expands the functionality of the disaster message board to allow users to post familiar situations. For example, the posting unit expands the functionality of the disaster message board to allow users to post familiar situations such as the status of evacuation shelters and shortages of supplies. The posting unit posts the posted information on a bulletin board app. For example, the posting unit can post the posted information on a bulletin board app and update it in real time. The posting unit can also organize and display information using a filtering function. As a result, the information aggregation system according to the embodiment can aggregate, organize, and convey confusing information or information that is difficult to obtain in disaster-stricken areas.

[0074] The collection unit can collect information from the official websites of local governments, posts on social media, and inquiries to the inquiry desk. The collection unit, for example, collects information from the official websites of local governments. For example, the collection unit collects disaster information and evacuation information from the official websites of local governments. The collection unit can also collect posts on social media. For example, the collection unit collects information from social media platforms such as Twitter, Facebook, and Instagram. The collection unit can also collect inquiries to the inquiry desk. For example, the collection unit collects inquiry contents via telephone, email, chat, etc. This allows the collection unit to collect information from a variety of information sources.

[0075] The analysis unit can analyze the collected information and determine the content and urgency of the information. The analysis unit, for example, analyzes the collected information and determines the content and urgency of the information. For example, the analysis unit determines information such as "no water" or "no food" as having a high level of urgency. The analysis unit can also analyze the information using natural language processing technology or a machine learning algorithm and score the urgency. For example, the analysis unit can analyze the content of the information using keyword extraction technology and determine the urgency. The analysis unit can also quantify the urgency of the information using a scoring algorithm. This allows the analysis unit to determine the content and urgency of the information.

[0076] The providing unit provides the determined information to the relevant parties, thereby facilitating a prompt response. The providing unit, for example, provides the determined information to the relevant parties, thereby facilitating a prompt response. For example, the providing unit notifies the national or local government of information such as "a road has been cut off by a landslide." The providing unit can also provide the information by methods such as email notification, app notification, or SMS. For example, the providing unit notifies the relevant parties of the determined information by email. The providing unit can also provide the information to the relevant parties through an app. In this way, the providing unit can quickly provide information to the relevant parties and facilitate a response.

[0077] The posting unit can expand the functionality of the disaster message board to enable posting of familiar situations as well. For example, the posting unit can expand the functionality of the disaster message board to enable posting of familiar situations such as the situation at an evacuation shelter or a shortage of supplies. For example, the posting unit posts the situation at an evacuation shelter on the disaster message board. The posting unit can also post a shortage of supplies. In this way, the posting unit can expand the functionality of the disaster message board to enable posting of familiar situations as well.

[0078] The bulletin board unit can post the posted information on a bulletin board app. For example, the bulletin board unit can post the posted information on the bulletin board app and update it in real time. For example, the bulletin board unit posts the posted information on the bulletin board app. The bulletin board unit can also organize and display information using a filtering function. For example, the bulletin board unit organizes and posts the posted information by category. This allows the bulletin board unit to post the posted information on the bulletin board app.

[0079] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit can immediately start collecting information and quickly collect data. Furthermore, if the user is calm, the collection unit can collect information at regular intervals to avoid excessive data collection. Furthermore, if the user is facing an emergency, the collection unit can collect information in real time to enable immediate response. This allows the collection unit to adjust the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The collection unit can evaluate the reliability of each information source and prioritize collecting highly reliable information. For example, the collection unit prioritizes collecting information from the official website of a local government. The collection unit can also evaluate the reliability of posts on social media and prioritize collecting information from highly reliable accounts. The collection unit can also analyze the content of inquiries to the inquiry desk and prioritize collecting highly reliable information. This allows the collection unit to prioritize collecting highly reliable information.

[0081] When collecting information, the collection unit can adjust the collection range taking into account the geographical conditions of the disaster-stricken area. For example, the collection unit sets the information collection range based on topographical data of the disaster-stricken area. The collection unit can also take into account the traffic conditions in the disaster-stricken area and collect information within an accessible range. The collection unit can also take into account the population density of the disaster-stricken area and collect information preferentially from important areas. In this way, the collection unit can optimize the collection range taking into account the geographical conditions of the disaster-stricken area.

[0082] When collecting information, the collection unit can select collection targets by referring to past disaster data. For example, the collection unit identifies risk areas in disaster-stricken areas based on past disaster data and collects information. The collection unit can also select collection targets by referring to information that was important during past disasters. The collection unit can also analyze past disaster data and collect information from areas with a high risk of recurrence. This allows the collection unit to select collection targets by referring to past disaster data.

[0083] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit prioritizes collecting information with a high level of urgency. Furthermore, if the user is calm, the collection unit can also prioritize collecting detailed information. Furthermore, if the user is facing an emergency, the collection unit can also prioritize collecting information requiring an immediate response. This allows the collection unit to determine the priority of information to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0084] When collecting information, the collection unit can set the collection range taking into account meteorological information in the disaster-stricken area. For example, if the weather in the disaster-stricken area is worsening, the collection unit can reduce the collection range and collect information within a safe range. Furthermore, if the weather in the disaster-stricken area is stable, the collection unit can also collect information over a wide area. Furthermore, the collection unit can adjust the collection range in advance based on the weather forecast for the disaster-stricken area. This allows the collection unit to adjust the collection range taking into account meteorological information in the disaster-stricken area.

[0085] When collecting information, the collection unit can determine the collection means taking into consideration the infrastructure situation in the disaster-stricken area. For example, if the communication infrastructure in the disaster-stricken area is unstable, the collection unit selects a means that allows offline collection. Furthermore, if the power supply in the disaster-stricken area is unstable, the collection unit can also select a battery-powered collection means. Furthermore, if the transportation infrastructure in the disaster-stricken area is cut off, the collection unit can also select a collection means such as a drone. This allows the collection unit to select the collection means taking into consideration the infrastructure situation in the disaster-stricken area.

[0086] When collecting information, the collection unit can limit the collection targets by taking into account the population density of the disaster-stricken area. For example, the collection unit prioritizes information collection from areas of the disaster-stricken area with high population density. The collection unit can also collect only important information from areas of the disaster-stricken area with low population density. The collection unit can also dynamically adjust the collection targets based on population density data of the disaster-stricken area. This allows the collection unit to narrow down the collection targets by taking into account the population density of the disaster-stricken area.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method including detailed information. If the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0088] The analysis unit can determine the priority of analysis based on the urgency of information during analysis. For example, the analysis unit gives the highest priority to analyzing information with a high urgency. The analysis unit can also postpone analysis of information with a low urgency. The analysis unit can also analyze information with a medium urgency at an appropriate time. This allows the analysis unit to determine the priority of analysis based on the urgency of information.

[0089] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a specific analysis algorithm to local government information. The analysis unit can also apply a different analysis algorithm to SNS information. The analysis unit can also apply yet another analysis algorithm to inquiry information. This allows the analysis unit to apply different analysis algorithms depending on the category of information.

[0090] During analysis, the analysis unit can improve the analysis accuracy by referring to past analysis results. The analysis unit can improve the current analysis accuracy, for example, based on past analysis results. The analysis unit can also correct errors by referring to past analysis results. The analysis unit can also analyze past analysis results and optimize the analysis algorithm. This allows the analysis unit to improve the analysis accuracy by referring to past analysis results.

[0091] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. This allows the analysis unit to adjust the level of detail of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] During analysis, the analysis unit can determine the order of analysis based on the time of submission of information. For example, the analysis unit gives the most recent information the highest priority for analysis. The analysis unit can also postpone analysis of older information. The analysis unit can also analyze information that has been submitted at a medium time, at an appropriate timing. This allows the analysis unit to determine the order of analysis priority based on the time of submission of information.

[0093] During analysis, the analysis unit can set the order of analysis based on the relevance of the information. For example, the analysis unit gives top priority to analyzing highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also analyze information with a medium degree of relevance at an appropriate time. This allows the analysis unit to adjust the order of analysis based on the relevance of the information.

[0094] The analysis unit can weight the analysis based on the reliability of the information during analysis. For example, the analysis unit performs analysis by assigning a high weight to highly reliable information. The analysis unit can also analyze by assigning a low weight to low-reliability information. The analysis unit can also analyze by assigning an appropriate weight to medium-reliability information. This allows the analysis unit to weight the analysis based on the reliability of the information.

[0095] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can also provide a display method including detailed information. If the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This allows the providing unit to adjust the method of providing information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] The providing unit can determine the priority of provision based on the urgency of the information at the time of provision. For example, the providing unit provides information with a high urgency as the highest priority. The providing unit can also provide information with a low urgency later. The providing unit can also provide information with a medium urgency at an appropriate time. In this way, the providing unit can determine the priority of provision based on the urgency of the information.

[0097] The providing unit can apply different providing means depending on the category of information when providing the information. For example, the providing unit applies a specific providing means to local government information. The providing unit can also apply a different providing means to SNS information. The providing unit can also apply yet another providing means to inquiry information. This allows the providing unit to apply different providing means depending on the category of information.

[0098] The providing unit can improve the accuracy of provision by referring to past provision results when providing information. The providing unit can improve the current accuracy of provision, for example, based on past provision results. The providing unit can also correct errors by referring to past provision results. The providing unit can also analyze past provision results and optimize the provision means. This allows the providing unit to improve the accuracy of provision by referring to past provision results.

[0099] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide information immediately. Also, if the user is relaxed, the providing unit can provide information at regular intervals. Also, if the user is in a hurry, the providing unit can provide information quickly. This allows the providing unit to adjust the timing of providing information according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0100] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. For example, the providing unit provides the latest information with the highest priority. The providing unit can also provide older information later. The providing unit can also provide information that is due for submission at an appropriate time. This allows the providing unit to determine the priority of provision based on the time of submission of information.

[0101] The providing unit can adjust the order of providing information based on the relevance of the information when providing the information. For example, the providing unit provides highly relevant information with the highest priority. The providing unit can also provide information with low relevance later. The providing unit can also provide information with medium relevance at an appropriate time. In this way, the providing unit can adjust the order of providing information based on the relevance of the information.

[0102] The providing unit can weight the information provided based on the reliability of the information when providing the information. For example, the providing unit provides information with a high weight assigned to highly reliable information. The providing unit can also provide information with a low weight assigned to low reliable information. The providing unit can also provide information with an appropriate weight assigned to medium reliable information. This allows the providing unit to weight the information provided based on the reliability of the information.

[0103] The posting unit can estimate the user's emotions and adjust the display method of the posted content based on the estimated user's emotions. For example, if the user is nervous, the posting unit can provide a simple, highly visible display method. If the user is relaxed, the posting unit can also provide a display method including detailed information. If the user is in a hurry, the posting unit can also provide a display method that focuses on the main points. This allows the posting unit to adjust the display method of the posted content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0104] The posting unit can determine the priority of posting based on the urgency of the information when posting. For example, the posting unit posts information with a high level of urgency as a top priority. The posting unit can also post information with a low level of urgency later. The posting unit can also post information with a medium level of urgency at an appropriate time. This allows the posting unit to determine the priority of posting based on the urgency of the information.

[0105] The posting unit can apply different posting methods depending on the category of information when posting. For example, the posting unit applies a specific posting method to local government information. The posting unit can also apply a different posting method to SNS information. The posting unit can also apply yet another posting method to inquiry information. This allows the posting unit to apply different posting methods depending on the category of information.

[0106] The posting unit can improve posting accuracy by referring to past posting results when posting. The posting unit can improve current posting accuracy, for example, based on past posting results. The posting unit can also correct errors by referring to past posting results. The posting unit can also analyze past posting results and optimize posting means. This allows the posting unit to improve posting accuracy by referring to past posting results.

[0107] The posting unit can estimate the user's emotions and adjust the level of detail of the posted content based on the estimated user's emotions. For example, if the user is nervous, the posting unit can provide simple, highly visible posted content. Furthermore, if the user is relaxed, the posting unit can provide detailed posted content. Furthermore, if the user is in a hurry, the posting unit can provide posted content that focuses on the main points. This allows the posting unit to adjust the level of detail of the posted content according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0108] The posting unit can determine the priority of posting based on the time of submission of information when posting. For example, the posting unit posts the most recent information with the highest priority. The posting unit can also postpone posting old information. The posting unit can also post information that needs to be submitted at an appropriate time. This allows the posting unit to determine the priority of posting based on the time of submission of information.

[0109] The posting unit can adjust the order of posting based on the relevance of the information when posting. For example, the posting unit posts highly relevant information with the highest priority. The posting unit can also post less relevant information later. The posting unit can also post information with medium relevance at an appropriate time. This allows the posting unit to adjust the order of posting based on the relevance of the information.

[0110] The posting unit can weight the posts based on the reliability of the information when posting. For example, the posting unit posts highly reliable information with a high weight. The posting unit can also post low reliability information with a low weight. The posting unit can also post medium reliability information with an appropriate weight. This allows the posting unit to weight the posts based on the reliability of the information.

[0111] The message board unit can estimate the user's emotions and adjust the display method of the message content based on the estimated user's emotions. For example, if the user is nervous, the message board unit can provide a simple, highly visible display method. If the user is relaxed, the message board unit can also provide a display method including detailed information. If the user is in a hurry, the message board unit can also provide a display method that focuses on the main points. This allows the message board unit to adjust the display method of the message content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0112] The posting unit can determine the priority of posting based on the urgency of the information when posting. For example, the posting unit posts information with a high level of urgency with the highest priority. The posting unit can also post information with a low level of urgency later. The posting unit can also post information with a medium level of urgency at an appropriate time. This allows the posting unit to determine the priority of posting based on the urgency of the information.

[0113] The posting unit can apply different posting methods depending on the category of information when posting. For example, the posting unit applies a specific posting method to local government information. The posting unit can also apply a different posting method to SNS information. The posting unit can also apply yet another posting method to inquiry information. This allows the posting unit to apply different posting methods depending on the category of information.

[0114] The posting unit can improve posting accuracy by referring to past posting results when posting. The posting unit can improve current posting accuracy, for example, based on past posting results. The posting unit can also correct errors by referring to past posting results. The posting unit can also analyze past posting results and optimize posting means. In this way, the posting unit can improve posting accuracy by referring to past posting results.

[0115] The message board unit can estimate the user's emotions and adjust the level of detail of the message content based on the estimated user's emotions. For example, if the user is nervous, the message board unit can provide simple, highly visible message content. If the user is relaxed, the message board unit can also provide detailed message content. If the user is in a hurry, the message board unit can also provide message content that focuses on the main points. This allows the message board unit to adjust the level of detail of the message content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0116] The posting unit can adjust the posting order based on the relevance of the information when posting. For example, the posting unit posts highly relevant information with the highest priority. The posting unit can also post less relevant information later. The posting unit can also post information with medium relevance at an appropriate time. This allows the posting unit to adjust the posting order based on the relevance of the information.

[0117] The posting unit can weight the posting based on the reliability of the information when posting. For example, the posting unit posts information with a high weight to highly reliable information. The posting unit can also post information with a low weight to low reliability information. The posting unit can also post information with an appropriate weight to information with medium reliability information. This allows the posting unit to weight the posting based on the reliability of the information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, posting unit, and bulletin board unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect posts and inquiries from local government official websites and social networking sites using the camera 42 and microphone 38B of the smart device 14. The collection unit can also centrally collect data from various information sources using the specific processing unit 290 of the data processing device 12 and store the data in the database 24. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and determines the level of urgency. For example, the provision unit provides information determined by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 to relevant parties to promote a rapid response. For example, the posting unit expands the functionality of the disaster message board using the control unit 46A of the smart device 14, allowing users to post information about familiar situations, such as the status of evacuation shelters and shortages of supplies. The bulletin board unit can, for example, post information posted by the control unit 46A of the smart device 14 on a bulletin board application and update the information in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, posting unit, and bulletin board unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect posts and inquiries from local government official websites and social networking sites using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also centrally collect data from various information sources using the specific processing unit 290 of the data processing device 12 and store the data in the database 24. The analysis unit, for example, analyzes the information collected by the specific processing unit 290 of the data processing device 12 and determines the level of urgency. The provision unit, for example, provides information determined by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 to relevant parties to promote a rapid response. The posting unit, for example, expands the functionality of the disaster message board using the control unit 46A of the smart glasses 214, allowing users to post information about familiar situations, such as the status of evacuation shelters and shortages of supplies. The bulletin board unit can, for example, post information posted by the control unit 46A of the smart glasses 214 on a bulletin board application and update the information in real time. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, posting unit, and bulletin board unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect posts and inquiries from local government official websites and social media sites using the camera 42 and microphone 238 of the headset terminal 314. The collection unit can also centrally collect data from various information sources using the specific processing unit 290 of the data processing device 12 and store the data in the database 24. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and determines the level of urgency. For example, the provision unit provides information determined by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 to relevant parties, promoting a rapid response. For example, the posting unit expands the functionality of the disaster message board using the control unit 46A of the headset terminal 314, allowing users to post information about familiar situations, such as the status of evacuation shelters and shortages of supplies. The bulletin board unit can, for example, post information posted by the control unit 46A of the headset type terminal 314 on a bulletin board application and update the information in real time. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, posting unit, and bulletin board unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect posts and inquiries from local government official websites and social media sites using the camera 42 and microphone 238 of the robot 414. The collection unit can also centrally collect data from various information sources using the specific processing unit 290 of the data processing device 12 and store the data in the database 24. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and determines the level of urgency. For example, the provision unit provides information determined by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 to relevant parties, promoting a rapid response. For example, the posting unit expands the functionality of the disaster message board using the control unit 46A of the robot 414, allowing users to post information about familiar situations, such as the status of evacuation shelters and shortages of supplies. The bulletin board unit can, for example, post information posted by the control unit 46A of the robot 414 on a bulletin board application and update the information in real time.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The information aggregation system can also be equipped with a voice recognition unit. The voice recognition unit can collect voice information from disaster areas and convert it into text data. For example, if a disaster victim reports over the phone that "there is no water," the voice is converted into text and the analysis unit determines the level of urgency. The voice recognition unit can also analyze local radio broadcasts and emergency announcements in real time to extract important information. This makes it possible to collect data from a variety of sources, including voice information.

[0120] The information aggregation system can also be equipped with an image analysis unit. The image analysis unit can analyze image data sent from the disaster area and automatically determine the extent of the damage. For example, it can analyze photos of roads sent by disaster victims and detect landslides and road closures. The image analysis unit can also analyze image data from drones and satellites to grasp the extent of damage over a wide area. This makes it possible to quickly grasp the extent of damage using visual information.

[0121] The information aggregation system can further include a predictive analysis unit. The predictive analysis unit can predict the risk of future damage expansion based on collected data. For example, it can analyze collected weather data and earthquake data to predict the risk of secondary disasters. The predictive analysis unit can also refer to past disaster data to detect similar patterns and improve prediction accuracy. This makes it possible to identify risks in advance and take prompt countermeasures.

[0122] The information aggregation system can also be equipped with a translation unit. The translation unit can automatically translate collected multilingual information and provide it to relevant parties. For example, it can translate social media posts and inquiries from foreign disaster victims, and the analysis unit can determine the level of urgency. The translation unit can also translate information from local governments and international organizations into multiple languages ​​and provide it to disaster victims. This makes it possible to share information across language barriers.

[0123] The information aggregation system can further include an energy management unit. The energy management unit can monitor the energy supply situation in the disaster-stricken area and support efficient energy distribution. For example, it can monitor the power supply situation in the disaster-stricken area in real time and provide priority support to areas where energy shortages are predicted. The energy management unit can also monitor the usage of solar power generation and batteries and propose optimal energy usage. This will help stabilize the energy supply in the disaster-stricken area.

[0124] The information aggregation system can further be equipped with an emotion analysis unit. The emotion analysis unit can analyze the emotions of disaster victims from the collected text and voice data and determine the priority of assistance. For example, if the content posted on social media by a disaster victim indicates anxiety or fear, assistance can be provided promptly. The emotion analysis unit can also infer emotions from the tone of voice and choice of words of the disaster victim and suggest appropriate responses. This makes it possible to provide assistance according to the psychological state of the disaster victim.

[0125] The information aggregation system can also be equipped with a health management unit. This unit can monitor the health status of disaster victims and provide necessary medical support. For example, if a disaster victim reports feeling unwell through the app, it can quickly notify the medical team. The health management unit can also monitor the risk of infectious diseases in the disaster area and suggest preventive measures. This makes it possible to grasp the health status of disaster victims in real time and provide appropriate medical support.

[0126] The information aggregation system can further include a communication support unit. The communication support unit provides functions to facilitate communication between disaster victims and between disaster victims and supporters. For example, it provides a chat function that allows disaster victims to contact their family and friends. The communication support unit can also provide a function that allows disaster victims to send messages directly to supporters. This allows for smooth information sharing between disaster victims and supporters.

[0127] The information aggregation system can also be equipped with a psychological support unit. This unit can monitor the psychological state of disaster victims and provide necessary psychological support. For example, if a disaster victim reports stress or anxiety through the app, it can notify a professional counselor. The psychological support unit can also provide disaster victims with advice on relaxation methods and stress management. This allows for psychological care for disaster victims.

[0128] The information aggregation system can further include an education support unit. The education support unit has the function of providing educational opportunities to children in the disaster-stricken areas. For example, if schools in the disaster-stricken areas are closed, online classes can be provided. The education support unit can also provide learning materials and educational content to children in the disaster-stricken areas. This allows children in the disaster-stricken areas to continue their education.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The collection unit collects information. For example, the collection unit collects information from the official website of a local government, posts on social media, and inquiries. The collection unit can also centrally collect data from each information source and store it in a database. For example, the collection unit collects disaster information from the official website of a local government, posts on social media, and inquiries to the inquiry desk. Step 2: The analysis unit analyzes the collected information and determines the level of urgency. For example, the analysis unit analyzes the collected information and determines the content and level of urgency of the information. For example, the analysis unit determines that information such as "no water" or "no food" has a high level of urgency. The analysis unit can also analyze information using natural language processing technology or machine learning algorithms and score the level of urgency. Step 3: The provision unit provides the determined information to relevant parties. For example, the provision unit provides the determined information to relevant parties to promote a prompt response. For example, the provision unit notifies the national or local government of information such as "a road has been cut off by a landslide." The provision unit can also provide information by email notification, app notification, SMS, or other methods. Step 4: The posting department will expand the functionality of the disaster message board to allow users to post about familiar situations. For example, the posting department will expand the functionality of the disaster message board to allow users to post about familiar situations such as the situation at evacuation centers and shortages of supplies. Step 5: The bulletin board unit posts the posted information on the bulletin board application. For example, the bulletin board unit can post the posted information on the bulletin board application and update it in real time. The bulletin board unit can also use a filtering function to organize and display the information.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0170] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0173] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0174] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0175] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0176] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0177] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0178] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0179] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0180] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0181] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0182] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0185] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0186] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0187] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0188] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0189] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0190] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0191] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0192] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0193] 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.

[0194] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0195] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0196] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0197] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0198] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0199] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0200] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0201] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0202] [Explanation of symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information; an analysis unit that analyzes the information collected by the collection unit and determines the degree of urgency; a providing unit that provides the information determined by the analysis unit to a related person; A posting section that expands the functionality of the disaster message board and allows users to post about situations close to home, a posting unit that posts the information posted by the posting unit on a bulletin board application. A system characterized by:

2. The collecting unit Collect information from local government official websites, posts on social media, and inquiries to customer service centers 2. The system of claim 1.

3. The analysis unit Analyze the collected information and determine the content and urgency of the information 2. The system of claim 1.

4. The providing unit Provide the determined information to the relevant parties to facilitate a prompt response.

2. The system of claim 1.

5. The posting unit: Expanding the functionality of the disaster message board to enable posting of situations close to home 2. The system of claim 1.

6. The bulletin board unit Post the posted information on a bulletin board app 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Evaluate the credibility of each source and prioritize collecting reliable information 2. The system of claim 1.

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