System

Abstract

An object of a system according to an embodiment is to quickly and accurately collect information at the time of occurrence of a disaster and provide the information to a person concerned.SOLUTION: A system includes an information collection unit, an information analysis unit, and an information provision unit. The information collection unit collects information on a site when a disaster occurs. The information analysis unit analyzes the information collected by the information collection unit and extracts important data. The information providing unit provides the information analyzed by the information analysis unit to the person concerned.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 quickly and accurately collect information and provide it to those involved when a disaster occurs.

[0005] The system according to the embodiment aims to quickly and accurately collect information when a disaster occurs and provide it to those involved. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an information analysis unit, and an information provision unit. The information collection unit collects local information when a disaster occurs. The information analysis unit analyzes the information collected by the information collection unit and extracts important data. The information provision unit provides the information analyzed by the information analysis unit to relevant parties. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately collect information when a disaster occurs and provide it to those involved. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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) A disaster information collection system according to an embodiment of the present invention uses a generation AI to quickly collect, analyze, and provide information when a disaster occurs. When a disaster occurs, the generation AI collects local information, analyzes that information, extracts important data, and provides it to relevant parties. This enables the disaster information collection system to speed up and streamline disaster response.

[0029] A disaster information collection system according to an embodiment includes an information collection unit, an information analysis unit, and an information provision unit. The information collection unit collects local information when a disaster occurs. For example, the information collection unit collects information such as "An earthquake has occurred" or "A flood is occurring" from social media posts. The information collection unit can also analyze news articles to grasp the damage situation and the progress of relief efforts. The information collection unit can also collect data from sensors on site to obtain specific information such as the earthquake's seismic intensity and flood water levels. For example, the information collection unit analyzes social media posts to collect disaster-related information. The information collection unit can also analyze news articles to grasp the damage situation and the progress of relief efforts. The information collection unit can also collect data from sensors on site to obtain specific information such as the earthquake's seismic intensity and flood water levels. The information analysis unit analyzes the information collected by the information collection unit and extracts important data. For example, the information analysis unit analyzes social media posts to identify areas with severe damage and areas requiring evacuation. The information analysis unit can also analyze news articles to grasp the extent of damage and the progress of relief efforts. Furthermore, the information analysis unit can analyze data from on-site sensors to assess the scale and scope of the disaster. For example, the information analysis unit can analyze social media posts to identify areas with severe damage and areas requiring evacuation. Furthermore, the information analysis unit can analyze news articles to grasp the extent of damage and the progress of relief efforts. Furthermore, the information analysis unit can analyze data from on-site sensors to assess the scale and scope of the disaster. The information provision unit provides the information analyzed by the information analysis unit to relevant parties. For example, the information provision unit provides information on areas with severe damage and areas requiring evacuation to local governments and relief organizations. Furthermore, the information provision unit can provide information on the extent of damage and the progress of relief efforts to residents and encourage them to take appropriate action. Furthermore, the information provision unit can provide information on the scale and scope of the disaster to the media to widely disseminate the information. For example, the information provision unit provides information on areas with severe damage and areas requiring evacuation to local governments and relief organizations. The information department can also provide residents with information on the extent of damage and the progress of relief efforts, encouraging them to take appropriate action.Furthermore, the information providing unit can provide information on the scale and scope of the disaster to the media to widely disseminate the information. This enables the disaster information collection system according to the embodiment to quickly and efficiently collect, analyze, and provide information when a disaster occurs. For example, when an earthquake occurs, the generation AI collects information from social media and news articles, identifies areas with severe damage, and provides the information to local governments. In addition, when a flood occurs, the generation AI analyzes data from on-site sensors and notifies residents of areas where evacuation is necessary. This speeds up and streamlines disaster response, potentially saving many lives.

[0030] The information collection unit can collect information such as "An earthquake has occurred" or "A flood is occurring" from social media posts. For example, the information collection unit collects information such as "An earthquake has occurred" or "A flood is occurring" from social media posts. For example, the generation AI analyzes social media posts and collects information related to disasters. The information collection unit can also analyze social media posts and collect information related to disasters. This allows disaster information to be collected quickly from social media posts.

[0031] The information analysis unit can analyze news articles to grasp the extent of the damage or the progress of relief activities. The information analysis unit, for example, analyzes news articles to grasp the extent of the damage or the progress of relief activities. For example, the generation AI analyzes news articles to grasp the extent of the damage or the progress of relief activities. The information analysis unit can also analyze news articles to grasp the extent of the damage or the progress of relief activities. This makes it possible to grasp the extent of the damage or the progress of relief activities from news articles.

[0032] The information provision unit can provide information on heavily damaged areas or areas where evacuation is required to local governments or relief organizations. For example, the information provision unit provides information on heavily damaged areas or areas where evacuation is required to local governments or relief organizations. For example, the generation AI analyzes information on heavily damaged areas or areas where evacuation is required and provides it to local governments or relief organizations. The information provision unit can also provide information on heavily damaged areas or areas where evacuation is required to local governments and relief organizations. This allows information on heavily damaged areas or areas where evacuation is required to be provided quickly.

[0033] The information collection unit can control drones or robots to collect on-site video or audio in real time. For example, the information collection unit controls drones to fly over affected areas and collect video in real time. For example, a camera mounted on the drone captures images of the damage, and the video is analyzed by the generation AI. The information collection unit can also control robots to collect on-site audio in real time. For example, a microphone mounted on the robot collects on-site audio, and the generation AI analyzes that audio. This makes it possible to use drones or robots to collect on-site video and audio in real time.

[0034] The information collection unit can analyze satellite images and quickly identify topographical changes or the extent of damage in disaster-stricken areas. The information collection unit, for example, analyzes satellite images and identifies topographical changes caused by an earthquake. For example, it compares satellite images before and after an earthquake to identify locations of cracks and landslides. The information collection unit can also analyze satellite images and identify the extent of damage caused by floods. For example, it compares satellite images before and after a flood to identify the extent of flooding. This makes it possible to quickly identify topographical changes and the extent of damage in disaster-stricken areas by analyzing satellite images.

[0035] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI creates a summary, the summary generation unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The summary generation unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The summary generation unit also references related background information and topic models when the generation AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0036] The summary generation unit can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, the summary generation unit uses a generation AI to analyze the logical structure of an answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The summary generation unit also analyzes the development of arguments in an answer and builds a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The summary generation unit also develops an algorithm for the generation AI to analyze the logical structure of an answer and the development of arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of arguments in a question.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The information collection unit collects local information when a disaster occurs. For example, the information collection unit collects information such as "an earthquake has occurred" or "flooding is occurring" from social media posts. The information collection unit can also analyze news articles to grasp the damage situation and the progress of relief efforts. Furthermore, the information collection unit can collect data from sensors on site to obtain specific information such as the earthquake's seismic intensity and flood water levels. For example, the information collection unit analyzes social media posts to collect information related to the disaster. The information collection unit can also analyze news articles to grasp the damage situation and the progress of relief efforts. Furthermore, the information collection unit can collect data from sensors on site to obtain specific information such as the earthquake's seismic intensity and flood water levels. The information analysis unit analyzes the information collected by the information collection unit and extracts important data. For example, the information analysis unit analyzes social media posts to identify areas that have suffered severe damage and areas where evacuation is necessary. The information analysis unit can also analyze news articles to grasp the damage situation and the progress of relief efforts. Furthermore, the information analysis unit can analyze data from on-site sensors to assess the scale and extent of the disaster. For example, the information analysis unit can analyze social media posts to identify areas that have suffered severe damage or areas that require evacuation. The information analysis unit can also analyze news articles to grasp the damage situation and the progress of relief efforts. The information analysis unit can also analyze data from on-site sensors to assess the scale and extent of the disaster. The information provision unit provides the information analyzed by the information analysis unit to relevant parties. For example, the information provision unit provides information on areas that have suffered severe damage or areas that require evacuation to local governments and relief organizations. The information provision unit can also provide residents with the damage situation and the progress of relief efforts to encourage appropriate action. The information provision unit can also provide information on the scale and extent of the disaster to the media to widely disseminate it. For example, the information provision unit provides information on areas that have suffered severe damage or areas that require evacuation to local governments and relief organizations. The information provision unit can also provide residents with the damage situation and the progress of relief efforts to encourage appropriate action. Furthermore, the information provision department can provide information on the scale and scope of the disaster to the media to disseminate it widely.As a result, the disaster information collection system according to the embodiment enables rapid and efficient information collection, analysis, and provision when a disaster occurs. For example, when an earthquake occurs, the generation AI collects information from social media and news articles, identifies areas with severe damage, and provides the information to local governments. In addition, when a flood occurs, the generation AI analyzes data from on-site sensors and notifies residents of areas where evacuation is necessary. This speeds up and streamlines disaster response, potentially saving many lives.

[0039] The information collection unit can collect information such as "an earthquake has occurred" or "flooding is occurring" from social media posts. For example, the generation AI can analyze social media posts to collect disaster-related information. The information collection unit can also analyze social media posts to collect disaster-related information. This allows for rapid collection of disaster information from social media posts. The information collection unit can also control drones and robots to collect local video and audio in real time when a disaster occurs. For example, a drone can be controlled to fly over the affected area and collect video in real time. A camera mounted on the drone captures footage of the damage, and the video is analyzed by the generation AI. A robot can also be controlled to collect local audio in real time. A microphone mounted on the robot collects local audio, which is then analyzed by the generation AI. This allows drones and robots to collect local video and audio in real time.

[0040] The information analysis unit can analyze news articles to grasp the extent of the damage or the progress of relief efforts. For example, the generation AI analyzes news articles to grasp the extent of the damage or the progress of relief efforts. The information analysis unit can also analyze news articles to grasp the extent of the damage or the progress of relief efforts. This makes it possible to grasp the extent of the damage or the progress of relief efforts from news articles. The information analysis unit can also analyze satellite images to quickly identify topographical changes or the extent of damage in disaster-stricken areas. For example, it can compare satellite images before and after an earthquake to identify the locations of cracks and landslides. It can also compare satellite images before and after a flood to identify the extent of flooding. This makes it possible to quickly identify topographical changes and the extent of damage in disaster-stricken areas by analyzing satellite images.

[0041] The information provision unit can provide local governments or relief organizations with information on heavily damaged areas or areas where evacuation is required. For example, the generation AI can analyze information on heavily damaged areas or areas where evacuation is required and provide it to local governments or relief organizations. The information provision unit can also provide information on heavily damaged areas or areas where evacuation is required to local governments or relief organizations. This allows information on heavily damaged areas or areas where evacuation is required to be provided quickly. The information provision unit can also provide residents with the damage situation and the progress of relief efforts to encourage appropriate action. For example, the generation AI can analyze the damage situation and the progress of relief efforts and provide it to residents. The information provision unit can also provide residents with the damage situation and the progress of relief efforts to encourage appropriate action. This allows residents to take appropriate action.

[0042] The processing flow of the first embodiment will be briefly explained below.

[0043] Step 1: The information gathering unit collects local information when a disaster occurs. For example, it collects information such as "an earthquake has occurred" or "flooding is occurring" from social media posts. It can also analyze news articles to understand the extent of the damage and the progress of relief efforts. It can also collect data from sensors on-site to obtain specific information such as the earthquake's seismic intensity and flood water levels. Step 2: The information analysis unit analyzes the information collected by the information collection unit and extracts important data. For example, it can analyze social media posts to identify areas with severe damage or areas requiring evacuation. It can also analyze news articles to understand the extent of the damage and the progress of relief efforts. It can also analyze data from on-site sensors to assess the scale and extent of the disaster. Step 3: The information provision department provides the information analyzed by the information analysis department to relevant parties. For example, it provides information on areas with severe damage or areas requiring evacuation to local governments and relief organizations. It can also provide information on the extent of the damage and the progress of relief efforts to residents, encouraging them to take appropriate action. It can also provide information on the scale and scope of the disaster to the media, so that it can be widely disseminated.

[0044] (Example 2) A disaster information collection system according to an embodiment of the present invention uses a generation AI to quickly collect, analyze, and provide information when a disaster occurs. When a disaster occurs, the generation AI collects local information, analyzes that information, extracts important data, and provides it to relevant parties. This enables the disaster information collection system to speed up and streamline disaster response.

[0045] A disaster information collection system according to an embodiment includes an information collection unit, an information analysis unit, and an information provision unit. The information collection unit collects local information when a disaster occurs. For example, the information collection unit collects information such as "An earthquake has occurred" or "A flood is occurring" from social media posts. The information collection unit can also analyze news articles to grasp the damage situation and the progress of relief efforts. The information collection unit can also collect data from sensors on site to obtain specific information such as the earthquake's seismic intensity and flood water levels. For example, the information collection unit analyzes social media posts to collect disaster-related information. The information collection unit can also analyze news articles to grasp the damage situation and the progress of relief efforts. The information collection unit can also collect data from sensors on site to obtain specific information such as the earthquake's seismic intensity and flood water levels. The information analysis unit analyzes the information collected by the information collection unit and extracts important data. For example, the information analysis unit analyzes social media posts to identify areas with severe damage and areas requiring evacuation. The information analysis unit can also analyze news articles to grasp the extent of damage and the progress of relief efforts. Furthermore, the information analysis unit can analyze data from on-site sensors to assess the scale and scope of the disaster. For example, the information analysis unit can analyze social media posts to identify areas with severe damage and areas requiring evacuation. Furthermore, the information analysis unit can analyze news articles to grasp the extent of damage and the progress of relief efforts. Furthermore, the information analysis unit can analyze data from on-site sensors to assess the scale and scope of the disaster. The information provision unit provides the information analyzed by the information analysis unit to relevant parties. For example, the information provision unit provides information on areas with severe damage and areas requiring evacuation to local governments and relief organizations. Furthermore, the information provision unit can provide information on the extent of damage and the progress of relief efforts to residents and encourage them to take appropriate action. Furthermore, the information provision unit can provide information on the scale and scope of the disaster to the media to widely disseminate the information. For example, the information provision unit provides information on areas with severe damage and areas requiring evacuation to local governments and relief organizations. The information department can also provide residents with information on the extent of damage and the progress of relief efforts, encouraging them to take appropriate action.Furthermore, the information providing unit can provide information on the scale and scope of the disaster to the media to widely disseminate the information. This enables the disaster information collection system according to the embodiment to quickly and efficiently collect, analyze, and provide information when a disaster occurs. For example, when an earthquake occurs, the generation AI collects information from social media and news articles, identifies areas with severe damage, and provides the information to local governments. In addition, when a flood occurs, the generation AI analyzes data from on-site sensors and notifies residents of areas where evacuation is necessary. This speeds up and streamlines disaster response, potentially saving many lives.

[0046] The information collection unit can collect information such as "An earthquake has occurred" or "A flood is occurring" from social media posts. For example, the information collection unit collects information such as "An earthquake has occurred" or "A flood is occurring" from social media posts. For example, the generation AI analyzes social media posts and collects information related to disasters. The information collection unit can also analyze social media posts and collect information related to disasters. This allows disaster information to be collected quickly from social media posts.

[0047] The information analysis unit can analyze news articles to grasp the extent of the damage or the progress of relief activities. The information analysis unit, for example, analyzes news articles to grasp the extent of the damage or the progress of relief activities. For example, the generation AI analyzes news articles to grasp the extent of the damage or the progress of relief activities. The information analysis unit can also analyze news articles to grasp the extent of the damage or the progress of relief activities. This makes it possible to grasp the extent of the damage or the progress of relief activities from news articles.

[0048] The information provision unit can provide information on heavily damaged areas or areas where evacuation is required to local governments or relief organizations. For example, the information provision unit provides information on heavily damaged areas or areas where evacuation is required to local governments or relief organizations. For example, the generation AI analyzes information on heavily damaged areas or areas where evacuation is required and provides it to local governments or relief organizations. The information provision unit can also provide information on heavily damaged areas or areas where evacuation is required to local governments and relief organizations. This allows information on heavily damaged areas or areas where evacuation is required to be provided quickly.

[0049] The information collection unit can control drones or robots to collect on-site video or audio in real time. For example, the information collection unit controls drones to fly over affected areas and collect video in real time. For example, a camera mounted on the drone captures images of the damage, and the video is analyzed by the generation AI. The information collection unit can also control robots to collect on-site audio in real time. For example, a microphone mounted on the robot collects on-site audio, and the generation AI analyzes that audio. This makes it possible to use drones or robots to collect on-site video and audio in real time.

[0050] The information collection unit can analyze satellite images and quickly identify topographical changes or the extent of damage in disaster-stricken areas. The information collection unit, for example, analyzes satellite images and identifies topographical changes caused by an earthquake. For example, it compares satellite images before and after an earthquake to identify locations of cracks and landslides. The information collection unit can also analyze satellite images and identify the extent of damage caused by floods. For example, it compares satellite images before and after a flood to identify the extent of flooding. This makes it possible to quickly identify topographical changes and the extent of damage in disaster-stricken areas by analyzing satellite images.

[0051] The information collection unit can use the emotion estimation function to analyze the emotions of SNS posters and prioritize the collection of information with high urgency. The information collection unit, for example, analyzes SNS posts and estimates the emotions of the posters. For example, posts with strong emotions of fear or anxiety are preferentially collected and treated as information with high urgency. The information collection unit can also use the emotion estimation function to analyze the emotions of SNS posters and prioritize the collection of information with high urgency. This makes it possible to analyze the emotions of SNS posters and prioritize the collection of information with high urgency.

[0052] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI creates a summary, the summary generation unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The summary generation unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The summary generation unit also references related background information and topic models when the generation AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0053] The summary generation unit can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, the summary generation unit uses a generation AI to analyze the logical structure of an answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The summary generation unit also analyzes the development of arguments in an answer and builds a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The summary generation unit also develops an algorithm for the generation AI to analyze the logical structure of an answer and the development of arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of arguments in a question.

[0054] The summary generation unit uses the emotion estimation function to generate a summary that captures the emotional nuances of the answer, allowing the emotional elements to be reflected in the evaluation. For example, when the generation AI summarizes, the summary generation unit uses the emotion estimation function to capture the emotional nuances of the answer. For example, it generates a summary based on an emotion score. The summary generation unit also uses the emotion estimation function to build a system in which the generation AI reflects the emotional elements of the answer in the evaluation. For example, it performs the evaluation based on the emotion score. The summary generation unit also develops an algorithm for the generation AI to use the emotion estimation function to generate a summary that captures the emotional nuances of the answer. For example, it generates a summary based on the emotion score and reflects that in the evaluation. In this way, by generating a summary that captures the emotional nuances, the emotional elements can also be reflected in the evaluation.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The information collection unit collects local information when a disaster occurs. For example, the information collection unit collects information such as "an earthquake has occurred" or "flooding is occurring" from social media posts. The information collection unit can also analyze news articles to grasp the damage situation and the progress of relief efforts. Furthermore, the information collection unit can collect data from sensors on site to obtain specific information such as the earthquake's seismic intensity and flood water levels. For example, the information collection unit analyzes social media posts to collect information related to the disaster. The information collection unit can also analyze news articles to grasp the damage situation and the progress of relief efforts. Furthermore, the information collection unit can collect data from sensors on site to obtain specific information such as the earthquake's seismic intensity and flood water levels. The information analysis unit analyzes the information collected by the information collection unit and extracts important data. For example, the information analysis unit analyzes social media posts to identify areas that have suffered severe damage and areas where evacuation is necessary. The information analysis unit can also analyze news articles to grasp the damage situation and the progress of relief efforts. Furthermore, the information analysis unit can analyze data from on-site sensors to assess the scale and extent of the disaster. For example, the information analysis unit can analyze social media posts to identify areas that have suffered severe damage or areas that require evacuation. The information analysis unit can also analyze news articles to grasp the damage situation and the progress of relief efforts. The information analysis unit can also analyze data from on-site sensors to assess the scale and extent of the disaster. The information provision unit provides the information analyzed by the information analysis unit to relevant parties. For example, the information provision unit provides information on areas that have suffered severe damage or areas that require evacuation to local governments and relief organizations. The information provision unit can also provide residents with the damage situation and the progress of relief efforts to encourage appropriate action. The information provision unit can also provide information on the scale and extent of the disaster to the media to widely disseminate it. For example, the information provision unit provides information on areas that have suffered severe damage or areas that require evacuation to local governments and relief organizations. The information provision unit can also provide residents with the damage situation and the progress of relief efforts to encourage appropriate action. Furthermore, the information provision department can provide information on the scale and scope of the disaster to the media to disseminate it widely.As a result, the disaster information collection system according to the embodiment enables rapid and efficient information collection, analysis, and provision when a disaster occurs. For example, when an earthquake occurs, the generation AI collects information from social media and news articles, identifies areas with severe damage, and provides the information to local governments. In addition, when a flood occurs, the generation AI analyzes data from on-site sensors and notifies residents of areas where evacuation is necessary. This speeds up and streamlines disaster response, potentially saving many lives.

[0057] The information collection unit can collect information such as "an earthquake has occurred" or "flooding is occurring" from social media posts. For example, the generation AI can analyze social media posts to collect disaster-related information. The information collection unit can also analyze social media posts to collect disaster-related information. This allows for rapid collection of disaster information from social media posts. The information collection unit can also control drones and robots to collect local video and audio in real time when a disaster occurs. For example, a drone can be controlled to fly over the affected area and collect video in real time. A camera mounted on the drone captures footage of the damage, and the video is analyzed by the generation AI. A robot can also be controlled to collect local audio in real time. A microphone mounted on the robot collects local audio, which is then analyzed by the generation AI. This allows drones and robots to collect local video and audio in real time.

[0058] The information analysis unit can analyze news articles to grasp the extent of the damage or the progress of relief efforts. For example, the generation AI analyzes news articles to grasp the extent of the damage or the progress of relief efforts. The information analysis unit can also analyze news articles to grasp the extent of the damage or the progress of relief efforts. This makes it possible to grasp the extent of the damage or the progress of relief efforts from news articles. The information analysis unit can also analyze satellite images to quickly identify topographical changes or the extent of damage in disaster-stricken areas. For example, it can compare satellite images before and after an earthquake to identify the locations of cracks and landslides. It can also compare satellite images before and after a flood to identify the extent of flooding. This makes it possible to quickly identify topographical changes and the extent of damage in disaster-stricken areas by analyzing satellite images.

[0059] The information provision unit can provide local governments or relief organizations with information on heavily damaged areas or areas where evacuation is required. For example, the generation AI can analyze information on heavily damaged areas or areas where evacuation is required and provide it to local governments or relief organizations. The information provision unit can also provide information on heavily damaged areas or areas where evacuation is required to local governments or relief organizations. This allows information on heavily damaged areas or areas where evacuation is required to be provided quickly. The information provision unit can also provide residents with the damage situation and the progress of relief efforts to encourage appropriate action. For example, the generation AI can analyze the damage situation and the progress of relief efforts and provide it to residents. The information provision unit can also provide residents with the damage situation and the progress of relief efforts to encourage appropriate action. This allows residents to take appropriate action.

[0060] The information collection unit can control a drone or robot to collect video or audio from the affected area in real time. For example, a drone can be controlled to fly over the affected area and collect video in real time. For example, a camera mounted on the drone can capture images of the damage, and the video is analyzed by a generation AI. The information collection unit can also control a robot to collect audio from the affected area in real time. For example, a microphone mounted on the robot can collect audio from the affected area, and the audio can be analyzed by a generation AI. This allows for real-time collection of video and audio from the affected area using a drone or robot. The information collection unit can also use an emotion estimation function to analyze the emotions of social media posters and prioritize the collection of information with high urgency. For example, it can analyze social media posts and estimate the poster's emotions. Posts with strong emotions of fear or anxiety can be prioritized and collected, and treated as high-urgency information. This allows for the analysis of the emotions of social media posters and the priority collection of information with high urgency.

[0061] The information collection unit can analyze satellite images to quickly identify topographical changes or the extent of damage in affected areas. For example, it can compare satellite images before and after an earthquake to identify the locations of cracks and landslides. It can also compare satellite images before and after a flood to identify the extent of flooding. This makes it possible to analyze satellite images to quickly identify topographical changes and the extent of damage in affected areas. The information collection unit can also use an emotion estimation function to analyze the emotions of SNS posters and prioritize the collection of information with a high level of urgency. For example, it can analyze SNS posts and estimate the poster's emotions. Posts with strong emotions of fear or anxiety can be prioritized and collected, and treated as information with a high level of urgency. This makes it possible to analyze the emotions of SNS posters and prioritize the collection of information with a high level of urgency.

[0062] The information collection unit can use the emotion estimation function to analyze the emotions of SNS posters and prioritize the collection of information with high urgency. For example, the information collection unit analyzes SNS posts and estimates the poster's emotions. For example, posts with strong emotions of fear or anxiety can be preferentially collected and treated as information with high urgency. The information collection unit can also use the emotion estimation function to analyze the emotions of SNS posters and prioritize the collection of information with high urgency. This allows the emotions of SNS posters to be analyzed and information with high urgency to be preferentially collected. The information analysis unit can further use the emotion estimation function to capture the emotional nuances of news articles and more accurately grasp the damage situation and the progress of relief efforts. For example, the information analysis unit analyzes news articles and reflects emotional elements in the evaluation. This allows the emotional nuances of news articles to be captured and the damage situation and the progress of relief efforts to be more accurately grasped.

[0063] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI generates a summary, it automatically collects relevant background information and references it to understand the context. For example, it collects related news articles and academic papers. The summary generation unit also uses topic models to understand the context when the generation AI generates a summary. For example, it extracts related keywords and phrases based on the topic model. The summary generation unit also references relevant background information and topic models when the generation AI generates a summary, building a system to understand the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context. The summary generation unit can also use emotion estimation functions to generate summaries that capture the emotional nuances of the summary. For example, it generates summaries based on emotion scores. This allows for generating summaries that capture emotional nuances, thereby reflecting emotional elements in the evaluation.

[0064] The summary generation unit can analyze the logical structure and development of the arguments in an answer to generate a logical summary. For example, the generation AI analyzes the logical structure of an answer to generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects these in the summary. The summary generation unit also analyzes the development of arguments in an answer to build a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of the arguments. The summary generation unit also develops an algorithm for the generation AI to analyze the logical structure and development of arguments in an answer to generate a logical summary. For example, it evaluates the logical consistency and importance of the arguments. This makes it possible to analyze the logical structure and development of arguments in an answer to generate a logical summary. The summary generation unit can also use an emotion estimation function to generate a summary that captures the emotional nuances of the answer, thereby reflecting emotional elements in the evaluation. For example, it generates a summary based on an emotion score and reflects these in the evaluation. This allows emotional elements to be reflected in the evaluation by generating a summary that captures emotional nuances.

[0065] The summary generation unit uses the emotion estimation function to generate summaries that capture the emotional nuances of answers, allowing emotional elements to be reflected in the evaluation. For example, when the generation AI generates a summary, it uses the emotion estimation function to capture the emotional nuances of answers. For example, it generates a summary based on an emotion score. The summary generation unit also uses the emotion estimation function to build a system in which the generation AI reflects the emotional elements of answers in the evaluation. For example, it performs evaluation based on the emotion score. The summary generation unit also develops an algorithm for the generation AI to use the emotion estimation function to generate summaries that capture the emotional nuances of answers. For example, it generates a summary based on the emotion score and reflects this in the evaluation. In this way, by generating summaries that capture emotional nuances, emotional elements can also be reflected in the evaluation. The summary generation unit can also refer to background information and topic models to understand the context. For example, when the generation AI generates a summary, it automatically collects relevant background information and references it to understand the context. For example, it collects related news articles and academic papers. The summary generation unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. This allows for more accurate summaries by referring to background information and topic models to understand the context.

[0066] The processing flow of the second embodiment will be briefly explained below.

[0067] Step 1: The information gathering unit collects local information when a disaster occurs. For example, it collects information such as "an earthquake has occurred" or "flooding is occurring" from social media posts. It can also analyze news articles to understand the extent of the damage and the progress of relief efforts. It can also collect data from sensors on-site to obtain specific information such as the earthquake's seismic intensity and flood water levels. Step 2: The information analysis unit analyzes the information collected by the information collection unit and extracts important data. For example, it can analyze social media posts to identify areas with severe damage or areas requiring evacuation. It can also analyze news articles to understand the extent of the damage and the progress of relief efforts. It can also analyze data from on-site sensors to assess the scale and extent of the disaster. Step 3: The information provision department provides the information analyzed by the information analysis department to relevant parties. For example, it provides information on areas with severe damage or areas requiring evacuation to local governments and relief organizations. It can also provide information on the extent of the damage and the progress of relief efforts to residents, encouraging them to take appropriate action. It can also provide information on the scale and scope of the disaster to the media, so that it can be widely disseminated.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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).

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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."

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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]

[0135] 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. An information gathering department that collects local information when a disaster occurs; an information analysis unit that analyzes the information collected by the information collection unit and extracts important data; an information providing unit that provides the information analyzed by the information analysis unit to a related party; A system characterized by:

2. The information collecting unit Controlling a drone or robot to collect video or audio of the location in real time The system of claim 1 .

3. The information analysis unit Analyze news articles to understand the extent of damage or the progress of relief efforts The system of claim 1 .

4. The information providing unit Providing information about hard-hit areas or areas requiring evacuation to local governments or relief organizations The system of claim 1 .

5. The information collecting unit A chatbot equipped with emotion estimation functionality is used to encourage information provision from residents and feed the collected information back to the generation AI. The system of claim 1 .

6. The information analysis unit Using emotion estimation function, the emotional aspects of the collected information are analyzed to understand the psychological state of the victims. The system of claim 1 .

7. The information providing unit Using an emotion estimation function, customized information is provided according to the emotion of the recipient of the provided information. The system of claim 1 .

8. The information providing unit Using emotion estimation functionality, support messages are generated based on the emotions of disaster victims, providing psychological support. The system of claim 1 .

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