System

Abstract

An object of a system according to an embodiment is to efficiently aggregate and utilize information posted by users at the time of a disaster.SOLUTION: A system according to an embodiment includes a post analysis unit, an information aggregation unit, and a portal integration unit. A contribution analysis part automatically analyzes the contribution contents of the user by using the generation AI. The information aggregation unit aggregates the information analyzed by the post analysis unit. The portal integration unit integrates the information aggregated by the information aggregation unit into the portal site.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently aggregate and utilize information posted by users during disasters.

[0005] The system according to the embodiment aims to efficiently collect and utilize information posted by users in the event of a disaster. [Means for solving the problem]

[0006] The system according to the embodiment includes a post analysis unit, an information aggregation unit, and a portal integration unit. The post analysis unit automatically analyzes user post content using a generation AI. The information aggregation unit aggregates the information analyzed by the post analysis unit. The portal integration unit integrates the information aggregated by the information aggregation unit into a portal site. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and utilize information posted by users in the event of a disaster. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The information aggregation system according to an embodiment of the present invention is a system that automatically analyzes content posted by users, aggregates the information, and integrates it on a portal site, allowing disaster victims, support groups, and local governments to utilize the information.

[0029] The information aggregation system according to the embodiment includes a post analysis unit, an information aggregation unit, and a portal integration unit. The post analysis unit automatically analyzes user posts using a generation AI. For example, the generation AI analyzes the posts using a text generation AI (e.g., GPT-3) and extracts important information. The generation AI can also extract information from posted images using image analysis technology. The generation AI can also extract information from posted audio using speech recognition technology. The information aggregation unit aggregates the information analyzed by the post analysis unit. For example, the information aggregation unit stores the information in a database and classifies it by category. The information aggregation unit can also evaluate the reliability of the information and prioritize the aggregation of highly reliable information. The information aggregation unit can also eliminate duplicate information and aggregate information efficiently. The portal integration unit integrates the information aggregated by the information aggregation unit into a portal site. For example, the portal integration unit displays the locations of evacuation shelters and the distribution status of supplies on a map. The portal integration unit can also summarize the damage situation and the need for assistance in list format. The portal integration unit can also add a voice assistant function to enable information searches by voice. This allows the information aggregation system according to the embodiment to be utilized by disaster victims, support organizations, and local governments. For example, disaster victims can obtain the information they need through the portal site. Support organizations can understand the damage situation and the need for support through the portal site and can carry out support activities efficiently. Local governments can respond quickly and appropriately based on the information aggregated through the portal site.

[0030] The post analysis unit can extract information on the location of evacuation shelters, the shortage of supplies, and the extent of damage from the posted content. For example, the generation AI performs an emotional analysis of the posted content and evaluates its importance based on the intensity and type of emotion. For example, the generation AI analyzes emotions such as "fear," "anxiety," and "relief" contained in the posted content and calculates an emotion score. The post analysis unit also automatically acquires the poster's location information along with the posted content and analyzes geographical relevance. For example, it identifies the poster's current location using GPS data. The post analysis unit also performs text analysis of the posted content and extracts information on the location of evacuation shelters, the shortage of supplies, and the extent of damage. For example, the generation AI uses text generation AI to analyze the posted content and identify the location of evacuation shelters and the shortage of supplies. This enables rapid response by extracting important information from the posted content.

[0031] The portal integration unit can display the locations of evacuation shelters and the distribution status of supplies on a map. The portal integration unit, for example, builds a system that displays the locations of evacuation shelters and the distribution status of supplies on a map. For example, the portal integration unit displays the locations of evacuation shelters on a map using the Google Maps API. The portal integration unit also obtains supply inventory data and distribution schedules and displays them on a map to display the distribution status of supplies. For example, the portal integration unit updates supply inventory data in real time and displays the distribution schedule. The portal integration unit can also display the damage status on a map. For example, the portal integration unit obtains damage report data and displays the extent of damage on a map. By displaying information on a map, victims can quickly obtain the information they need.

[0032] The portal integration unit can summarize the damage situation and the need for support in list format. The portal integration unit, for example, builds a system that summarizes the damage situation and the need for support in list format. For example, it acquires damage report data and displays the damage situation in list format. The portal integration unit also evaluates the need for support and displays it in list format. For example, it acquires support request data and evaluates the need for support. The portal integration unit can also use an emotion estimation function to analyze the emotions of posters and prioritize posts with positive emotions. For example, it can use the emotion estimation function to analyze the emotions of posters in real time and calculate an emotion score. By compiling information in list format, support organizations and local governments can efficiently grasp the information they need.

[0033] The post analysis unit can automatically acquire the poster's location information and take geographical relevance into consideration. The post analysis unit, for example, automatically acquires the poster's location information along with the post content and builds a system that analyzes geographical relevance. For example, the poster's current location is identified using GPS data. The post analysis unit also evaluates the geographical relevance of the post content based on the location information. For example, it analyzes the relevance between the poster's location information and the post content and prioritizes processing information with high geographical relevance. The post analysis unit can also identify the location of evacuation shelters and the distribution status of supplies based on the location information. For example, it uses GPS data to identify the location of evacuation shelters and evaluate the distribution status of supplies. In this way, by taking location information into consideration, it is possible to prioritize processing information with high geographical relevance.

[0034] The post analysis unit can analyze images and videos and extract important information from visual information. For example, the post analysis unit uses a generation AI to analyze posted images and videos and build a system that extracts important information from visual information. For example, it can use image recognition technology to identify the location of evacuation shelters and the extent of damage. The post analysis unit can also analyze video frames and extract important information. For example, it can detect specific objects in videos and extract that information. The post analysis unit can also evaluate the importance of posted content based on visual information. For example, it can analyze the content of images and videos and score their importance. This improves the accuracy of information by extracting important information from visual information.

[0035] The post analysis unit can automatically translate content posted in different languages ​​and perform multilingual analysis. The post analysis unit, for example, builds a system that automatically translates content posted in different languages. For example, it translates posts in English, Spanish, Chinese, etc. into Japanese. The post analysis unit also performs multilingual analysis based on the automatically translated content. For example, it analyzes the translated text and extracts important information. The post analysis unit can also use a language model to perform multilingual analysis. For example, it uses a language model such as BERT or GPT-3 to analyze post content in different languages. This allows multilingual analysis to be performed, making it possible to centrally manage post content in different languages.

[0036] The information aggregation unit can classify the aggregated information by category, making it easy for users to access. For example, the information aggregation unit builds a system that classifies the information aggregated by the generation AI by category. For example, it classifies the information into categories such as "evacuation shelter information," "supply information," and "damage information." The information aggregation unit also stores the categorized information in a database, making it easy for users to access. For example, it provides an interface that allows users to search for information by category. The information aggregation unit can also optimize the user interface to display information by category. For example, it provides a dashboard for displaying information by category. This allows users to quickly access the information they need by classifying information by category.

[0037] The information aggregating unit can evaluate the reliability of the aggregated information and preferentially display highly reliable information. The information aggregating unit, for example, builds a system for evaluating the reliability of aggregated information. For example, it develops an algorithm for evaluating the reliability of the source of information and the poster. The information aggregating unit also calculates a reliability score in order to preferentially display highly reliable information. For example, it calculates a reliability score based on the reliability of the source of information and the poster, and preferentially displays highly reliable information. The information aggregating unit can also filter information with low reliability. For example, it prevents information with a low reliability score from being displayed. This allows highly reliable information to be preferentially displayed, allowing users to quickly obtain reliable information.

[0038] The portal integration unit can add a voice assistant function to enable information search by voice. The portal integration unit, for example, adds a voice assistant function to build a system that enables information search by voice. For example, the portal integration unit analyzes a user's voice input using voice recognition technology. The portal integration unit can also use the voice assistant function to search for information based on a voice command. For example, when a user voice-inputs, "Tell me the location of an evacuation shelter," the portal integration unit searches for the location of the evacuation shelter and responds by voice. The portal integration unit can also use the voice assistant function to read out information. For example, when a user voice-inputs, "Tell me the latest damage situation," the portal integration unit searches for the latest damage situation and reads it out by voice. This enables information search by voice, allowing users to easily obtain information.

[0039] The portal integration unit can employ a responsive design that optimally displays the aggregated information on different devices. The portal integration unit, for example, builds a system that employs a responsive design that optimally displays the aggregated information on different devices. For example, it optimizes display on smartphones, tablets, and PCs. The portal integration unit can also use responsive design to adjust the display layout of information according to the screen size of the device. For example, it can use CSS media queries to apply styles according to the screen size. The portal integration unit can also optimize the user interface to improve operability on different devices. For example, it provides an interface that supports touch operations. This allows the information to be displayed optimally on different devices, allowing users to obtain information on any device.

[0040] The information aggregation unit can analyze access logs for open information and monitor usage status in real time. The information aggregation unit, for example, builds a system that analyzes access logs for open information in real time. For example, it monitors the number of accesses and viewing time. The information aggregation unit can also evaluate usage status based on the access logs. For example, it evaluates the usefulness of information based on access frequency and usage time. The information aggregation unit can also optimize the display content of information based on usage status. For example, it prioritizes displaying information with a high number of accesses and filters out information with a low number of accesses. In this way, the usefulness of information can be evaluated by monitoring usage status in real time.

[0041] The information aggregation unit can add a feedback function to the open information and collect opinions from users. The information aggregation unit, for example, adds a feedback function to the open information and builds a system for collecting opinions from users. For example, it adds a comment function or a rating function. The information aggregation unit can also use the feedback function to collect opinions from users in real time. For example, when a user posts a comment, the opinion is collected immediately. The information aggregation unit can also improve the quality of the information based on the collected opinions. For example, it updates the information to reflect the user's opinions. In this way, the quality of the information is improved by collecting opinions from users.

[0042] The information aggregation unit can link open information with social media to promote widespread information dissemination. For example, the information aggregation unit can link open information with social media to build a system that promotes widespread information dissemination. For example, it can add a function to automatically post to Twitter or Facebook. The information aggregation unit can also monitor the spread of information through social media. For example, it can evaluate the spread of information based on the number of shares and retweets. The information aggregation unit can also collect feedback from users through social media. For example, it can evaluate the usefulness of information based on the number of comments and likes. In this way, linking with social media increases the ability of information to be spread.

[0043] The information aggregating unit can provide open information in different formats, allowing users to select a data format according to their needs. The information aggregating unit, for example, builds a system that provides open information in different formats. For example, it allows users to download information in PDF, CSV, or XML format. The information aggregating unit can also optimize the user interface to allow users to select a data format according to their needs. For example, it provides a drop-down menu for selecting a data format. The information aggregating unit can also use a data conversion algorithm to maintain the consistency of information provided in different formats. For example, it develops an algorithm to convert data in CSV format into XML format. This allows users to select a data format according to their needs by providing information in different formats.

[0044] The information aggregator can provide information on the nearest evacuation shelter and relief supplies based on the location information of the disaster victim. The information aggregator, for example, builds a system that provides information on the nearest evacuation shelter and relief supplies based on the location information of the disaster victim. For example, it identifies the current location of the disaster victim using GPS data. The information aggregator can also identify the location of the nearest evacuation shelter based on the location information and display it on a map. For example, it displays the location of the evacuation shelter using a Google Maps API. The information aggregator can also obtain inventory data and distribution schedules for relief supplies and provide information on the nearest relief supplies. For example, it updates inventory data for supplies in real time and displays distribution schedules. This allows disaster victims to quickly obtain the information they need by providing information on the nearest evacuation shelter and relief supplies based on their location information.

[0045] The information aggregation unit can add a voice readout function so that disaster victims can obtain information by voice. The information aggregation unit, for example, builds a system that adds a voice readout function so that disaster victims can obtain information by voice. For example, it uses technology that converts text information into voice. The information aggregation unit also uses the voice readout function to enable disaster victims to obtain necessary information by voice. For example, it provides voice guidance on the location of evacuation shelters and the distribution status of supplies. The information aggregation unit can also use the voice readout function to provide information according to the disaster victim's emotions. For example, if the emotion score is low, it provides information to reduce stress by voice. In this way, by enabling disaster victims to obtain information by voice, they can obtain information even in situations where it is difficult to obtain information visually.

[0046] The information aggregation unit can add a social media integration function that allows disaster victims to share information, thereby promoting information sharing within the community. The information aggregation unit, for example, adds a social media integration function that allows disaster victims to share information, and builds a system that promotes information sharing within the community. For example, it can integrate with Twitter or Facebook. The information aggregation unit can also use the social media integration function to enable disaster victims to share information in real time. For example, information posted by disaster victims can be instantly shared on social media. The information aggregation unit can also use the social media integration function to promote information exchange within the community. For example, disaster victims can share information with each other to understand the need for support. This allows disaster victims to share information, thereby promoting information sharing within the community.

[0047] The information aggregation unit can analyze the support needs of support organizations and prioritize displaying necessary information. For example, the information aggregation unit uses generation AI to analyze the support needs of support organizations and build a system that prioritizes displaying necessary information. For example, it analyzes the content posted by support organizations and identifies the necessary information. The information aggregation unit can also customize the information displayed based on the support needs of support organizations. For example, it can prioritize displaying the distribution status of supplies and the damage situation according to the needs of the support organization. The information aggregation unit can also evaluate the importance of information based on the support needs of the support organization. For example, it can score the importance of information according to the needs of the support organization. This makes it possible to quickly provide necessary information by analyzing the support needs of support organizations.

[0048] The information aggregating unit can propose an optimal support plan based on the activity history of the support organization. The information aggregating unit, for example, builds a system that proposes an optimal support plan based on the activity history of the support organization. For example, it analyzes past support activity data and generates an optimal support plan. The information aggregating unit can also evaluate the effectiveness of the support plan based on the activity history of the support organization. For example, it scores the effectiveness of the support plan based on the results of past support activities. The information aggregating unit can also propose an optimal allocation of resources based on the activity history of the support organization. For example, it analyzes past support activity data and calculates the optimal allocation of resources. In this way, the efficiency of support activities is improved by proposing an optimal support plan based on the activity history of the support organization.

[0049] The information aggregating unit can add a chat function that allows support organizations to share information in real time. The information aggregating unit, for example, builds a system that adds a chat function that allows support organizations to share information in real time. For example, it promotes information exchange between support organizations. The information aggregating unit also uses the chat function to enable support organizations to share information in real time. For example, it instantly shares information posted by a support organization via chat. The information aggregating unit can also use the chat function to facilitate communication between support organizations. For example, it provides a group chat function to promote information exchange between support organizations. This allows support organizations to share information in real time, thereby improving the efficiency of support activities.

[0050] The information aggregation unit can add a dashboard function that allows aid organizations to report the status of their activities, thereby making the activities more visible. The information aggregation unit, for example, builds a system that adds a dashboard function that allows aid organizations to report the status of their activities. For example, it visualizes the progress and results of aid activities. The information aggregation unit also uses the dashboard function to enable aid organizations to report the status of their activities in real time. For example, it updates the progress of aid activities in real time. The information aggregation unit can also use the dashboard function to centrally manage the activity status of aid organizations. For example, it stores the results of aid activities in a database and displays them on a dashboard. This allows aid organizations to report the status of their activities, making the activities more visible and improving the efficiency of aid activities.

[0051] The information aggregation unit can propose optimal response measures based on the local government's past response history. The information aggregation unit, for example, builds a system that proposes optimal response measures based on the local government's past response history. For example, it analyzes past disaster response data and generates optimal response measures. The information aggregation unit can also evaluate the effectiveness of response measures based on the local government's past response history. For example, it scores the effectiveness of response measures based on the results of past responses. The information aggregation unit can also propose optimal resource allocation based on the local government's past response history. For example, it analyzes past response data and calculates optimal resource allocation. In this way, the efficiency of disaster response is improved by proposing optimal response measures based on the local government's past response history.

[0052] The information aggregation unit can add an interface that allows local governments to share information in real time. The information aggregation unit, for example, builds a system that adds an interface that allows local governments to share information in real time. For example, it promotes information exchange between local governments. The information aggregation unit also uses the interface to enable local governments to share information in real time. For example, it instantly shares information posted by local governments. The information aggregation unit can also use the interface to facilitate communication between local governments. For example, it provides a real-time messaging function to promote information exchange between local governments. This allows local governments to share information in real time, thereby improving the efficiency of disaster response.

[0053] The information aggregation unit can add a dashboard function that allows local governments to report their response status, thereby visualizing the response. The information aggregation unit, for example, builds a system that adds a dashboard function that allows local governments to report their response status. For example, it visualizes the progress and results of disaster response. The information aggregation unit also uses the dashboard function to enable local governments to report their response status in real time. For example, it updates the progress of disaster response in real time. The information aggregation unit can also use the dashboard function to centrally manage the response status of local governments. For example, it stores the results of disaster response in a database and displays them on a dashboard. This allows local governments to report their response status, thereby visualizing the response and improving the efficiency of disaster response.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The information aggregation unit can provide information on the nearest evacuation shelters and relief supplies based on the location information of disaster victims. For example, it can use GPS data to identify the current location of a disaster victim and display the location of the nearest evacuation shelter on a map. It can also obtain stock data and distribution schedules for relief supplies and provide information on the nearest relief supplies. This allows disaster victims to quickly obtain the information they need.

[0056] The information aggregation unit can link open information with social media to spread information widely. For example, it can add a function to automatically post to Twitter or Facebook and monitor the spread of information. It can also collect feedback from users through social media and evaluate the usefulness of the information. This can increase the spread of information.

[0057] The information aggregation unit can automatically translate content posted in different languages ​​and perform multilingual analysis. For example, it can translate posts in English, Spanish, Chinese, etc. into Japanese, analyze the translated text, and extract important information. This allows for centralized management of posts in different languages.

[0058] The information aggregation unit can add a feedback function to the open information and collect opinions from users. For example, it can add a comment function or rating function, allowing users to post their opinions in real time. It can also improve the quality of the information based on the collected opinions. This allows the information to be updated to reflect user opinions.

[0059] The information aggregation unit can analyze the support needs of support organizations and prioritize displaying necessary information. For example, it can analyze the content posted by support organizations and prioritize displaying the status of distribution of supplies and damage status. It can also evaluate the importance of information based on the support needs of support organizations and quickly provide necessary information.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The post analysis unit automatically analyzes the user's post content using the generation AI. For example, the generation AI may use a text generation AI (e.g., GPT-3) to analyze the post content and extract important information. The generation AI may also use image analysis technology to extract information from posted images. Furthermore, the generation AI may also use voice recognition technology to extract information from posted audio. Step 2: The information aggregation unit aggregates the information analyzed by the post analysis unit. For example, the information aggregation unit stores the information in a database and classifies it by category. The information aggregation unit can also evaluate the reliability of the information and prioritize aggregation of highly reliable information. Furthermore, the information aggregation unit can eliminate duplicate information and aggregate information efficiently. Step 3: The portal integration unit integrates the information collected by the information aggregation unit into the portal site. For example, the portal integration unit displays the locations of evacuation shelters and the distribution status of supplies on a map. The portal integration unit can also summarize the damage situation and the need for assistance in list format. Furthermore, the portal integration unit can add a voice assistant function to enable information searches by voice.

[0062] (Example 2) The information aggregation system according to an embodiment of the present invention is a system that automatically analyzes content posted by users, aggregates the information, and integrates it on a portal site, allowing disaster victims, support groups, and local governments to utilize the information.

[0063] The information aggregation system according to the embodiment includes a post analysis unit, an information aggregation unit, and a portal integration unit. The post analysis unit automatically analyzes user posts using a generation AI. For example, the generation AI analyzes the posts using a text generation AI (e.g., GPT-3) and extracts important information. The generation AI can also extract information from posted images using image analysis technology. The generation AI can also extract information from posted audio using speech recognition technology. The information aggregation unit aggregates the information analyzed by the post analysis unit. For example, the information aggregation unit stores the information in a database and classifies it by category. The information aggregation unit can also evaluate the reliability of the information and prioritize the aggregation of highly reliable information. The information aggregation unit can also eliminate duplicate information and aggregate information efficiently. The portal integration unit integrates the information aggregated by the information aggregation unit into a portal site. For example, the portal integration unit displays the locations of evacuation shelters and the distribution status of supplies on a map. The portal integration unit can also summarize the damage situation and the need for assistance in list format. The portal integration unit can also add a voice assistant function to enable information searches by voice. This allows the information aggregation system according to the embodiment to be utilized by disaster victims, support organizations, and local governments. For example, disaster victims can obtain the information they need through the portal site. Support organizations can understand the damage situation and the need for support through the portal site and can carry out support activities efficiently. Local governments can respond quickly and appropriately based on the information aggregated through the portal site.

[0064] The post analysis unit can extract information on the location of evacuation shelters, the shortage of supplies, and the extent of damage from the posted content. For example, the generation AI performs an emotional analysis of the posted content and evaluates its importance based on the intensity and type of emotion. For example, the generation AI analyzes emotions such as "fear," "anxiety," and "relief" contained in the posted content and calculates an emotion score. The post analysis unit also automatically acquires the poster's location information along with the posted content and analyzes geographical relevance. For example, it identifies the poster's current location using GPS data. The post analysis unit also performs text analysis of the posted content and extracts information on the location of evacuation shelters, the shortage of supplies, and the extent of damage. For example, the generation AI uses text generation AI to analyze the posted content and identify the location of evacuation shelters and the shortage of supplies. This enables rapid response by extracting important information from the posted content.

[0065] The portal integration unit can display the locations of evacuation shelters and the distribution status of supplies on a map. The portal integration unit, for example, builds a system that displays the locations of evacuation shelters and the distribution status of supplies on a map. For example, the portal integration unit displays the locations of evacuation shelters on a map using the Google Maps API. The portal integration unit also obtains supply inventory data and distribution schedules and displays them on a map to display the distribution status of supplies. For example, the portal integration unit updates supply inventory data in real time and displays the distribution schedule. The portal integration unit can also display the damage status on a map. For example, the portal integration unit obtains damage report data and displays the extent of damage on a map. By displaying information on a map, victims can quickly obtain the information they need.

[0066] The portal integration unit can summarize the damage situation and the need for support in list format. The portal integration unit, for example, builds a system that summarizes the damage situation and the need for support in list format. For example, it acquires damage report data and displays the damage situation in list format. The portal integration unit also evaluates the need for support and displays it in list format. For example, it acquires support request data and evaluates the need for support. The portal integration unit can also use an emotion estimation function to analyze the emotions of posters and prioritize posts with positive emotions. For example, it can use the emotion estimation function to analyze the emotions of posters in real time and calculate an emotion score. By compiling information in list format, support organizations and local governments can efficiently grasp the information they need.

[0067] The post analysis unit performs emotional analysis of the posted content and can evaluate the importance based on the intensity and type of emotion. In the post analysis unit, for example, the generation AI performs emotional analysis of the posted content and quantifies the intensity and type of emotion. For example, the generation AI analyzes emotions such as "fear," "anxiety," and "relief" contained in the posted content and calculates an emotion score. The post analysis unit also uses a scoring system to evaluate the intensity of emotion. For example, it evaluates the intensity of emotion with a score from 0 to 100. The post analysis unit also categorizes the type of emotion into categories such as positive, negative, and neutral. For example, it classifies the type of emotion based on the emotion score. This allows important information to be processed preferentially through emotion analysis.

[0068] The post analysis unit can automatically acquire the poster's location information and take geographical relevance into consideration. The post analysis unit, for example, automatically acquires the poster's location information along with the post content and builds a system that analyzes geographical relevance. For example, the poster's current location is identified using GPS data. The post analysis unit also evaluates the geographical relevance of the post content based on the location information. For example, it analyzes the relevance between the poster's location information and the post content and prioritizes processing information with high geographical relevance. The post analysis unit can also identify the location of evacuation shelters and the distribution status of supplies based on the location information. For example, it uses GPS data to identify the location of evacuation shelters and evaluate the distribution status of supplies. In this way, by taking location information into consideration, it is possible to prioritize processing information with high geographical relevance.

[0069] The post analysis unit can use the emotion estimation function to analyze the emotions of posters and prioritize posts with positive emotions. The post analysis unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of posters in real time. For example, it performs text analysis of the content of posts and calculates an emotion score. The post analysis unit also prioritizes posts with positive emotions based on the emotion score. For example, it prioritizes displaying posts with high emotion scores. The post analysis unit can also use the emotion estimation function to filter posts with negative emotions. For example, it filters out posts with low emotion scores so that they are not displayed. In this way, the quality of information is improved by prioritizing processing posts with positive emotions.

[0070] The post analysis unit can analyze images and videos and extract important information from visual information. For example, the post analysis unit uses a generation AI to analyze posted images and videos and build a system that extracts important information from visual information. For example, it can use image recognition technology to identify the location of evacuation shelters and the extent of damage. The post analysis unit can also analyze video frames and extract important information. For example, it can detect specific objects in videos and extract that information. The post analysis unit can also evaluate the importance of posted content based on visual information. For example, it can analyze the content of images and videos and score their importance. This improves the accuracy of information by extracting important information from visual information.

[0071] The post analysis unit can automatically translate content posted in different languages ​​and perform multilingual analysis. The post analysis unit, for example, builds a system that automatically translates content posted in different languages. For example, it translates posts in English, Spanish, Chinese, etc. into Japanese. The post analysis unit also performs multilingual analysis based on the automatically translated content. For example, it analyzes the translated text and extracts important information. The post analysis unit can also use a language model to perform multilingual analysis. For example, it uses a language model such as BERT or GPT-3 to analyze post content in different languages. This allows multilingual analysis to be performed, making it possible to centrally manage post content in different languages.

[0072] The post analysis unit can use the emotion estimation function to analyze the emotions of the poster in real time and provide feedback based on the emotions. The post analysis unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of the poster in real time. For example, it performs text analysis of the post content and calculates an emotion score. The post analysis unit also provides emotion-based feedback based on the emotion score. For example, if the emotion score is high, it provides positive feedback, and if the emotion score is low, it provides negative feedback. The post analysis unit can also use the emotion estimation function to analyze the emotions of the poster and provide psychological support. For example, if the emotion score is low, it provides information on psychological support. In this way, by providing emotion-based feedback, it is possible to provide psychological support to the poster.

[0073] The information aggregation unit can classify the aggregated information by category, making it easy for users to access. For example, the information aggregation unit builds a system that classifies the information aggregated by the generation AI by category. For example, it classifies the information into categories such as "evacuation shelter information," "supply information," and "damage information." The information aggregation unit also stores the categorized information in a database, making it easy for users to access. For example, it provides an interface that allows users to search for information by category. The information aggregation unit can also optimize the user interface to display information by category. For example, it provides a dashboard for displaying information by category. This allows users to quickly access the information they need by classifying information by category.

[0074] The information aggregating unit can evaluate the reliability of the aggregated information and preferentially display highly reliable information. The information aggregating unit, for example, builds a system for evaluating the reliability of aggregated information. For example, it develops an algorithm for evaluating the reliability of the source of information and the poster. The information aggregating unit also calculates a reliability score in order to preferentially display highly reliable information. For example, it calculates a reliability score based on the reliability of the source of information and the poster, and preferentially displays highly reliable information. The information aggregating unit can also filter information with low reliability. For example, it prevents information with a low reliability score from being displayed. This allows highly reliable information to be preferentially displayed, allowing users to quickly obtain reliable information.

[0075] The information aggregating unit can analyze the user's emotional response to the aggregated information using the emotion estimation function and optimize the display content of the portal site. The information aggregating unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotional response to the aggregated information in real time. For example, the information display content is optimized based on the user's emotion score. The information aggregating unit can also customize the display content of the portal site based on the emotional response. For example, information with positive emotions is preferentially displayed and information with negative emotions is filtered out. The information aggregating unit can also evaluate the importance of information based on the user's emotional response. For example, the importance of information is scored based on the emotion score. In this way, the display content of the portal site can be optimized by analyzing the user's emotional response.

[0076] The portal integration unit can add a voice assistant function to enable information search by voice. The portal integration unit, for example, adds a voice assistant function to build a system that enables information search by voice. For example, the portal integration unit analyzes a user's voice input using voice recognition technology. The portal integration unit can also use the voice assistant function to search for information based on a voice command. For example, when a user voice-inputs, "Tell me the location of an evacuation shelter," the portal integration unit searches for the location of the evacuation shelter and responds by voice. The portal integration unit can also use the voice assistant function to read out information. For example, when a user voice-inputs, "Tell me the latest damage situation," the portal integration unit searches for the latest damage situation and reads it out by voice. This enables information search by voice, allowing users to easily obtain information.

[0077] The portal integration unit can employ a responsive design that optimally displays the aggregated information on different devices. The portal integration unit, for example, builds a system that employs a responsive design that optimally displays the aggregated information on different devices. For example, it optimizes display on smartphones, tablets, and PCs. The portal integration unit can also use responsive design to adjust the display layout of information according to the screen size of the device. For example, it can use CSS media queries to apply styles according to the screen size. The portal integration unit can also optimize the user interface to improve operability on different devices. For example, it provides an interface that supports touch operations. This allows the information to be displayed optimally on different devices, allowing users to obtain information on any device.

[0078] The portal integration unit can use the emotion estimation function to display information customized based on the user's emotions. The portal integration unit, for example, uses the emotion estimation function to build a system that displays information customized based on the user's emotions. For example, the portal integration unit adjusts the content of displayed information based on the user's emotion score. The portal integration unit can also use the emotion estimation function to preferentially display information according to the user's emotions. For example, information associated with positive emotions is preferentially displayed, and information associated with negative emotions is filtered out. The portal integration unit can also customize the display layout of information based on the user's emotions. For example, a layout that is easy for the user to view is provided based on the emotion score. This improves user satisfaction by displaying information based on the user's emotions.

[0079] The information aggregation unit can analyze access logs for open information and monitor usage status in real time. The information aggregation unit, for example, builds a system that analyzes access logs for open information in real time. For example, it monitors the number of accesses and viewing time. The information aggregation unit can also evaluate usage status based on the access logs. For example, it evaluates the usefulness of information based on access frequency and usage time. The information aggregation unit can also optimize the display content of information based on usage status. For example, it prioritizes displaying information with a high number of accesses and filters out information with a low number of accesses. In this way, the usefulness of information can be evaluated by monitoring usage status in real time.

[0080] The information aggregation unit can add a feedback function to the open information and collect opinions from users. The information aggregation unit, for example, adds a feedback function to the open information and builds a system for collecting opinions from users. For example, it adds a comment function or a rating function. The information aggregation unit can also use the feedback function to collect opinions from users in real time. For example, when a user posts a comment, the opinion is collected immediately. The information aggregation unit can also improve the quality of the information based on the collected opinions. For example, it updates the information to reflect the user's opinions. In this way, the quality of the information is improved by collecting opinions from users.

[0081] The information aggregating unit can use the emotion estimation function to analyze the user's emotional response to open information and improve the quality of the information. For example, the information aggregating unit uses the emotion estimation function to build a system that analyzes the user's emotional response to open information in real time. For example, the information aggregating unit evaluates the quality of the information based on the user's emotion score. The information aggregating unit can also provide feedback to improve the quality of the information based on the emotional response. For example, it can suggest improvements for information that has a negative emotion. The information aggregating unit can also optimize the display content of the information based on the user's emotional response. For example, it can preferentially display information that has a positive emotion and filter out information that has a negative emotion. In this way, the quality of the information can be improved by analyzing the user's emotional response.

[0082] The information aggregation unit can link open information with social media to promote widespread information dissemination. For example, the information aggregation unit can link open information with social media to build a system that promotes widespread information dissemination. For example, it can add a function to automatically post to Twitter or Facebook. The information aggregation unit can also monitor the spread of information through social media. For example, it can evaluate the spread of information based on the number of shares and retweets. The information aggregation unit can also collect feedback from users through social media. For example, it can evaluate the usefulness of information based on the number of comments and likes. In this way, linking with social media increases the ability of information to be spread.

[0083] The information aggregating unit can provide open information in different formats, allowing users to select a data format according to their needs. The information aggregating unit, for example, builds a system that provides open information in different formats. For example, it allows users to download information in PDF, CSV, or XML format. The information aggregating unit can also optimize the user interface to allow users to select a data format according to their needs. For example, it provides a drop-down menu for selecting a data format. The information aggregating unit can also use a data conversion algorithm to maintain the consistency of information provided in different formats. For example, it develops an algorithm to convert data in CSV format into XML format. This allows users to select a data format according to their needs by providing information in different formats.

[0084] The information aggregating unit uses the emotion estimation function to filter information based on the user's emotions, thereby providing optimal information. The information aggregating unit, for example, uses the emotion estimation function to build a system that filters information based on the user's emotions. For example, it filters information based on the user's emotion score. The information aggregating unit can also use the emotion estimation function to preferentially display information according to the user's emotions. For example, it preferentially displays information with positive emotions and filters out information with negative emotions. The information aggregating unit can also customize the display layout of information based on the user's emotions. For example, it provides a layout that is easy for the user to view based on the emotion score. This allows optimal information to be provided by filtering information based on the user's emotions.

[0085] The information aggregator can provide information on the nearest evacuation shelter and relief supplies based on the location information of the disaster victim. The information aggregator, for example, builds a system that provides information on the nearest evacuation shelter and relief supplies based on the location information of the disaster victim. For example, it identifies the current location of the disaster victim using GPS data. The information aggregator can also identify the location of the nearest evacuation shelter based on the location information and display it on a map. For example, it displays the location of the evacuation shelter using a Google Maps API. The information aggregator can also obtain inventory data and distribution schedules for relief supplies and provide information on the nearest relief supplies. For example, it updates inventory data for supplies in real time and displays distribution schedules. This allows disaster victims to quickly obtain the information they need by providing information on the nearest evacuation shelter and relief supplies based on their location information.

[0086] The information aggregating unit can use the emotion estimation function to analyze the emotions of disaster victims and provide information for stress reduction. The information aggregating unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of disaster victims in real time. For example, it analyzes the content posted by disaster victims and calculates an emotion score. The information aggregating unit also provides information for stress reduction based on the emotion score. For example, it provides relaxation methods and psychological support information. The information aggregating unit can also use the emotion estimation function to provide information customized according to the emotions of disaster victims. For example, if the emotion score is low, it prioritizes displaying information for stress reduction. This makes it possible to provide psychological support to disaster victims by analyzing their emotions and providing information for stress reduction.

[0087] The information aggregation unit can add a voice readout function so that disaster victims can obtain information by voice. The information aggregation unit, for example, builds a system that adds a voice readout function so that disaster victims can obtain information by voice. For example, it uses technology that converts text information into voice. The information aggregation unit also uses the voice readout function to enable disaster victims to obtain necessary information by voice. For example, it provides voice guidance on the location of evacuation shelters and the distribution status of supplies. The information aggregation unit can also use the voice readout function to provide information according to the disaster victim's emotions. For example, if the emotion score is low, it provides information to reduce stress by voice. In this way, by enabling disaster victims to obtain information by voice, they can obtain information even in situations where it is difficult to obtain information visually.

[0088] The information aggregation unit can add a social media integration function that allows disaster victims to share information, thereby promoting information sharing within the community. The information aggregation unit, for example, adds a social media integration function that allows disaster victims to share information, and builds a system that promotes information sharing within the community. For example, it can integrate with Twitter or Facebook. The information aggregation unit can also use the social media integration function to enable disaster victims to share information in real time. For example, information posted by disaster victims can be instantly shared on social media. The information aggregation unit can also use the social media integration function to promote information exchange within the community. For example, disaster victims can share information with each other to understand the need for support. This allows disaster victims to share information, thereby promoting information sharing within the community.

[0089] The information aggregating unit can use the emotion estimation function to provide customized information based on the emotions of disaster victims. The information aggregating unit, for example, uses the emotion estimation function to build a system that provides customized information based on the emotions of disaster victims. For example, the information display content can be adjusted based on the emotion score of the disaster victim. The information aggregating unit can also use the emotion estimation function to preferentially display information according to the emotions of the disaster victim. For example, information with positive emotions can be preferentially displayed and information with negative emotions can be filtered. The information aggregating unit can also customize the display layout of information based on the emotions of the disaster victim. For example, a layout that is easy for disaster victims to view can be provided based on the emotion score. This makes it possible to provide psychological support to disaster victims by providing information based on their emotions.

[0090] The information aggregation unit can analyze the support needs of support organizations and prioritize displaying necessary information. For example, the information aggregation unit uses generation AI to analyze the support needs of support organizations and build a system that prioritizes displaying necessary information. For example, it analyzes the content posted by support organizations and identifies the necessary information. The information aggregation unit can also customize the information displayed based on the support needs of support organizations. For example, it can prioritize displaying the distribution status of supplies and the damage situation according to the needs of the support organization. The information aggregation unit can also evaluate the importance of information based on the support needs of the support organization. For example, it can score the importance of information according to the needs of the support organization. This makes it possible to quickly provide necessary information by analyzing the support needs of support organizations.

[0091] The information aggregating unit can propose an optimal support plan based on the activity history of the support organization. The information aggregating unit, for example, builds a system that proposes an optimal support plan based on the activity history of the support organization. For example, it analyzes past support activity data and generates an optimal support plan. The information aggregating unit can also evaluate the effectiveness of the support plan based on the activity history of the support organization. For example, it scores the effectiveness of the support plan based on the results of past support activities. The information aggregating unit can also propose an optimal allocation of resources based on the activity history of the support organization. For example, it analyzes past support activity data and calculates the optimal allocation of resources. In this way, the efficiency of support activities is improved by proposing an optimal support plan based on the activity history of the support organization.

[0092] The information aggregating unit can use the emotion estimation function to analyze the emotions of support organizations and provide information that will increase motivation. The information aggregating unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of support organizations in real time. For example, it analyzes the content posted by the support organization and calculates an emotion score. The information aggregating unit also provides information that will increase motivation based on the emotion score. For example, it provides sharing of success stories and messages of gratitude. The information aggregating unit can also use the emotion estimation function to provide information customized according to the emotions of the support organization. For example, if the emotion score is low, it prioritizes displaying information that will increase motivation. In this way, by analyzing the emotions of the support organization and providing information that will increase motivation, the efficiency of support activities is improved.

[0093] The information aggregating unit can add a chat function that allows support organizations to share information in real time. The information aggregating unit, for example, builds a system that adds a chat function that allows support organizations to share information in real time. For example, it promotes information exchange between support organizations. The information aggregating unit also uses the chat function to enable support organizations to share information in real time. For example, it instantly shares information posted by a support organization via chat. The information aggregating unit can also use the chat function to facilitate communication between support organizations. For example, it provides a group chat function to promote information exchange between support organizations. This allows support organizations to share information in real time, thereby improving the efficiency of support activities.

[0094] The information aggregation unit can add a dashboard function that allows aid organizations to report the status of their activities, thereby making the activities more visible. The information aggregation unit, for example, builds a system that adds a dashboard function that allows aid organizations to report the status of their activities. For example, it visualizes the progress and results of aid activities. The information aggregation unit also uses the dashboard function to enable aid organizations to report the status of their activities in real time. For example, it updates the progress of aid activities in real time. The information aggregation unit can also use the dashboard function to centrally manage the activity status of aid organizations. For example, it stores the results of aid activities in a database and displays them on a dashboard. This allows aid organizations to report the status of their activities, making the activities more visible and improving the efficiency of aid activities.

[0095] The information aggregating unit can use the emotion estimation function to provide customized information based on the emotions of the support organization. The information aggregating unit, for example, uses the emotion estimation function to build a system that provides customized information based on the emotions of the support organization. For example, the information display content is adjusted based on the emotion score of the support organization. The information aggregating unit can also use the emotion estimation function to preferentially display information according to the emotions of the support organization. For example, information with positive emotions is preferentially displayed and information with negative emotions is filtered out. The information aggregating unit can also customize the display layout of the information based on the emotions of the support organization. For example, a layout that is easy for the support organization to view is provided based on the emotion score. In this way, the efficiency of support activities is improved by providing information based on the emotions of the support organization.

[0096] The information aggregation unit can propose optimal response measures based on the local government's past response history. The information aggregation unit, for example, builds a system that proposes optimal response measures based on the local government's past response history. For example, it analyzes past disaster response data and generates optimal response measures. The information aggregation unit can also evaluate the effectiveness of response measures based on the local government's past response history. For example, it scores the effectiveness of response measures based on the results of past responses. The information aggregation unit can also propose optimal resource allocation based on the local government's past response history. For example, it analyzes past response data and calculates optimal resource allocation. In this way, the efficiency of disaster response is improved by proposing optimal response measures based on the local government's past response history.

[0097] The information aggregation unit can use the emotion estimation function to analyze the emotions of local government employees and provide information for stress reduction. The information aggregation unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of local government employees in real time. For example, it analyzes the content posted by employees and calculates an emotion score. The information aggregation unit also provides information for stress reduction based on the emotion score. For example, it provides relaxation methods and psychological support information. The information aggregation unit can also use the emotion estimation function to provide information customized according to the emotions of local government employees. For example, if the emotion score is low, it prioritizes displaying information for stress reduction. This makes it possible to analyze the emotions of local government employees and provide information for stress reduction, thereby providing psychological support to employees.

[0098] The information aggregation unit can add an interface that allows local governments to share information in real time. The information aggregation unit, for example, builds a system that adds an interface that allows local governments to share information in real time. For example, it promotes information exchange between local governments. The information aggregation unit also uses the interface to enable local governments to share information in real time. For example, it instantly shares information posted by local governments. The information aggregation unit can also use the interface to facilitate communication between local governments. For example, it provides a real-time messaging function to promote information exchange between local governments. This allows local governments to share information in real time, thereby improving the efficiency of disaster response.

[0099] The information aggregation unit can add a dashboard function that allows local governments to report their response status, thereby visualizing the response. The information aggregation unit, for example, builds a system that adds a dashboard function that allows local governments to report their response status. For example, it visualizes the progress and results of disaster response. The information aggregation unit also uses the dashboard function to enable local governments to report their response status in real time. For example, it updates the progress of disaster response in real time. The information aggregation unit can also use the dashboard function to centrally manage the response status of local governments. For example, it stores the results of disaster response in a database and displays them on a dashboard. This allows local governments to report their response status, thereby visualizing the response and improving the efficiency of disaster response.

[0100] The information aggregation unit can use the emotion estimation function to provide customized information based on the emotions of local government employees. The information aggregation unit, for example, uses the emotion estimation function to build a system that provides customized information based on the emotions of local government employees. For example, the information display content can be adjusted based on the emotion score of the employee. The information aggregation unit can also use the emotion estimation function to preferentially display information according to the emotions of the local government employees. For example, information with positive emotions can be preferentially displayed and information with negative emotions can be filtered. The information aggregation unit can also customize the display layout of information based on the emotions of the local government employees. For example, a layout that is easy for employees to view can be provided based on the emotion score. This makes it possible to provide psychological support to local government employees by providing information based on their emotions.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The information aggregation unit can provide information on the nearest evacuation shelters and relief supplies based on the location information of disaster victims. For example, it can use GPS data to identify the current location of a disaster victim and display the location of the nearest evacuation shelter on a map. It can also obtain stock data and distribution schedules for relief supplies and provide information on the nearest relief supplies. This allows disaster victims to quickly obtain the information they need.

[0103] The information aggregation unit can link open information with social media to spread information widely. For example, it can add a function to automatically post to Twitter or Facebook and monitor the spread of information. It can also collect feedback from users through social media and evaluate the usefulness of the information. This can increase the spread of information.

[0104] The information aggregation unit can automatically translate content posted in different languages ​​and perform multilingual analysis. For example, it can translate posts in English, Spanish, Chinese, etc. into Japanese, analyze the translated text, and extract important information. This allows for centralized management of posts in different languages.

[0105] The information aggregation unit can add a feedback function to the open information and collect opinions from users. For example, it can add a comment function or rating function, allowing users to post their opinions in real time. It can also improve the quality of the information based on the collected opinions. This allows the information to be updated to reflect user opinions.

[0106] The information aggregation unit can analyze the support needs of support organizations and prioritize displaying necessary information. For example, it can analyze the content posted by support organizations and prioritize displaying the status of distribution of supplies and damage status. It can also evaluate the importance of information based on the support needs of support organizations and quickly provide necessary information.

[0107] The information aggregation unit can use the emotion estimation function to analyze the emotions of disaster victims and provide information to reduce stress. For example, it can analyze the content posted by disaster victims, calculate an emotion score, and provide relaxation techniques and psychological support information. In addition, if the emotion score is low, it can prioritize the display of information to reduce stress. This makes it possible to provide psychological support to disaster victims.

[0108] The information aggregation unit can use the emotion estimation function to analyze the emotions of support groups and provide information that will increase their motivation. For example, it can analyze the content posted by support groups, calculate an emotion score, and provide sharing of success stories and messages of gratitude. In addition, if the emotion score is low, it can prioritize the display of information that will increase motivation. This can improve the efficiency of support activities.

[0109] The information aggregation unit can use the emotion estimation function to analyze the emotions of local government employees and provide information to reduce stress. For example, it can analyze the content posted by employees, calculate an emotion score, and provide relaxation methods and psychological support information. In addition, if the emotion score is low, it can prioritize the display of information to reduce stress. This makes it possible to provide psychological support to employees.

[0110] The information aggregation unit can use the emotion estimation function to analyze the user's emotional response to the open information and improve the quality of the information. For example, it can evaluate the quality of the information based on the user's emotion score and suggest improvements for information that has a negative emotion. It can also prioritize the display of information that has a positive emotion and filter out information that has a negative emotion. This can improve the quality of the information.

[0111] The information aggregation unit can use the emotion estimation function to display customized information based on the user's emotions. For example, it can adjust the content of the displayed information based on the user's emotion score, and prioritize displaying information associated with positive emotions. It can also filter out information associated with negative emotions and provide a layout that is easy for the user to view. This can improve user satisfaction.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The post analysis unit automatically analyzes the user's post content using the generation AI. For example, the generation AI may use a text generation AI (e.g., GPT-3) to analyze the post content and extract important information. The generation AI may also use image analysis technology to extract information from posted images. Furthermore, the generation AI may also use voice recognition technology to extract information from posted audio. Step 2: The information aggregation unit aggregates the information analyzed by the post analysis unit. For example, the information aggregation unit stores the information in a database and classifies it by category. The information aggregation unit can also evaluate the reliability of the information and prioritize aggregation of highly reliable information. Furthermore, the information aggregation unit can eliminate duplicate information and aggregate information efficiently. Step 3: The portal integration unit integrates the information collected by the information aggregation unit into the portal site. For example, the portal integration unit displays the locations of evacuation shelters and the distribution status of supplies on a map. The portal integration unit can also summarize the damage situation and the need for assistance in list format. Furthermore, the portal integration unit can add a voice assistant function to enable information searches by voice.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0143] 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0158] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0159] 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.

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0181] 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 post analysis unit that automatically analyzes user posts using AI generation, an information aggregation unit that aggregates the information analyzed by the post analysis unit; a portal integration unit that integrates the information aggregated by the information aggregation unit into a portal site. A system characterized by:

2. The post analysis unit Extract information from posts about the location of evacuation shelters, shortages of supplies, and the extent of damage 2. The system of claim 1.

3. The portal integration unit Displaying evacuation shelter locations and supply distribution status on a map 2. The system of claim 1.

4. The information aggregation unit Categorizing the aggregated information for easy access by the user 2. The system of claim 1.

5. The information aggregation unit Analyze access logs for open information and monitor usage in real time 2. The system of claim 1.

6. The information aggregation unit Analyzing the individual needs of disaster victims and displaying necessary information in a prioritized manner 2. The system of claim 1.

7. The information aggregation unit Analyze the support needs of support organizations and prioritize displaying necessary information 2. The system of claim 1.

8. The information aggregation unit Analyzing the emotions of local government employees using emotion estimation functionality and providing information to reduce stress 2. The system of claim 1.

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